Palestine Perception Study

Q1 survey frequencies - Awareness and perception of USAID

Q1 Exposure to donor logos

Overall

Code

#args(ov_tab)

q1a <- ov_tab(svyrdat, q1a, "q1a", "Seen logo") %>%
  mutate(Donor="UNRWA") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

q1b <- ov_tab(svyrdat, q1b, "q1b", "Seen logo") %>%
  mutate(Donor="UNDP") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

q1c <- ov_tab(svyrdat, q1c, "q1c", "Seen logo") %>%
  mutate(Donor="UNICEF") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

q1d <- ov_tab(svyrdat, q1d, "q1d", "Seen logo") %>%
  mutate(Donor="Red Crescent") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

q1e <- ov_tab(svyrdat, q1e, "q1e", "Seen logo") %>%
  mutate(Donor="WFP") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

q1f <- ov_tab(svyrdat, q1f, "q1f", "Seen logo") %>%
  mutate(Donor="EU") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

q1g <- ov_tab(svyrdat, q1g, "q1g", "Seen logo") %>%
  mutate(Donor="USAID") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

q1h <- ov_tab(svyrdat, q1h, "q1h", "Seen logo") %>%
  mutate(Donor="ActionAid") %>%
  select(Item, Label, Donor, Disaggregation, Number, Percent:ci)

#q1h

q1 <- bind_rows(q1a, q1b, q1c, q1d, q1e, q1f, q1g, q1h) %>%
  arrange(desc(Percent))

write_csv(q1, here("output/tables/eval Q1/q1 logos.csv"))

q1_flx <- q1 %>%
  select(Donor, Number, Percent, `Confidence interval`=ci) %>%
  flextable() %>%
  colformat_double(j=2,
                   digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=4, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q1. Seen donor logo in past year")

save_as_docx(q1_flx, path=here("output/tables/eval Q1/q1 logos.docx"))

q1_flx

Donor	Number	Percent	Confidence interval
UNRWA	2,291	91.8%	90.2% - 93.4%
Red Crescent	2,009	80.5%	77.8% - 83.2%
UNICEF	1,909	76.5%	74% - 79%
EU	1,444	57.9%	55% - 60.7%
UNDP	993	39.8%	36.7% - 42.9%
WFP	957	38.4%	35.5% - 41.2%
USAID	816	32.7%	29.9% - 35.5%
ActionAid	164	6.6%	5.2% - 7.9%
Q1. Seen donor logo in past year

USAID logo, disaggregated

Code

q1g_ov <- ov_tab(svyrdat, q1g, 
                "q1g", "Seen USAID logo")

q1g_sex <- disag_tab(svyrdat, q1g, sex, sex_key, 
                    "q1g", "Seen USAID logo", "Sex")

q1g_age <- disag_tab(svyrdat, q1g, age_grp,age_grp_key,  
                    "q1g", "Seen USAID logo", "Age group")

q1g_ed <- disag_tab(svyrdat, q1g, educ_cat, educ_key,
                   "q1g", "Seen USAID logo", "Education")

q1g_mad <- disag_tab(svyrdat, q1g, madrassa,mad_key,
                   "q1g", "Seen USAID logo", "Madrassa education")

q1g_area <- disag_tab(svyrdat, q1g, area,area_key,
                     "q1g","Seen USAID logo","Area")

q1g_gov <- disag_tab(svyrdat, q1g, gov,gov_key,
                     "q1g","Seen USAID logo","Governorate")

q1g_subreg <- disag_tab(svyrdat, q1g, subregion,subreg_key,
                     "q1g","Seen USAID logo","Subregion")

q1g_reg <- disag_tab(svyrdat, q1g, region,reg_key,
                     "q1g","Seen USAID logo","Region")

q1g_disag <- bind_rows(q1g_ov,
                      q1g_reg,
                      q1g_subreg,
                      q1g_gov,
                      q1g_area,
                      q1g_sex,
                      q1g_age,
                      q1g_ed,
                      q1g_mad)

#q1g_disag

write_csv(q1g_disag, here("output/tables/eval Q1/q1g disag.csv"))

q1g_disag_flx <- q1g_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3,
                   digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q1. Seen USAID logo in past year") %>%
  border_inner_h()

save_as_docx(q1g_disag_flx, path=here("output/tables/eval Q1/q1g_disag.docx"))

q1g_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Seen USAID logo	816	32.7%	29.9% - 35.5%
Region	West Bank	495	33.8%	29.9% - 37.8%
	Gaza	321	31.1%	27.3% - 34.9%
Subregion	Northern West Bank	214	37.6%	32.3% - 43%
	Central West Bank	161	41.2%	31.4% - 51%
	Southern West Bank	120	23.8%	18.2% - 29.4%
	Gaza Strip	321	31.1%	27.3% - 34.9%
Governorate	Jenin	75	49.9%	37.7% - 62.2%
	Tubas	14	40.7%	32.6% - 48.7%
	Tulkarm	17	21.2%	9.2% - 33.2%
	Nablus	71	33.1%	23.1% - 43.2%
	Qalqiliya	21	40.9%	30.7% - 51.1%
	Salfit	17	41.4%	30.6% - 52.1%
	Ramallah	94	54.0%	38.2% - 69.7%
	Jericho	9	42.6%	17.8% - 67.3%
	Jerusalem	58	29.8%	17.4% - 42.2%
	Bethlehem	30	24.9%	17.2% - 32.6%
	Hebron	90	23.5%	16.5% - 30.4%
	North Gaza	74	34.7%	26.4% - 42.9%
	Gaza	125	33.6%	27.7% - 39.4%
	Dier al-Balah	35	24.9%	13.3% - 36.6%
	Khan Yunis	52	29.0%	19% - 39.1%
	Rafah	35	27.7%	16.9% - 38.5%
Area	Urban	608	31.6%	28.4% - 34.8%
	Village	151	41.0%	32.3% - 49.8%
	Refugee camp	57	28.3%	21.7% - 34.9%
Sex	Male	457	36.8%	33.2% - 40.4%
	Female	359	28.7%	25.5% - 31.8%
Age group	Youth (18-29)	333	31.8%	27.2% - 36.4%
	Adult (30-54)	381	35.4%	32.1% - 38.7%
	Mature (55+)	102	27.6%	23% - 32.2%
Education	Elementary school education	107	19.9%	15.9% - 23.9%
	Secondary education	356	33.2%	28.9% - 37.6%
	Post-secondary education	353	39.8%	36% - 43.7%
Madrassa education	No madrassa education	694	32.2%	29.1% - 35.3%
	Madrassa education	123	35.8%	30.1% - 41.5%
Q1. Seen USAID logo in past year

Q2 Familiar with USAID

Overall

Code

q2 <- fac_tab(svyrdat, q2) %>%
  mutate(#item = "q2",
         #varlab="Familiar with USAID", 
         lab=fam_key$familiar_lab[1:4]) %>%
  select(Response=q2, Label=lab, Percent:Number, margin, Lower, Upper, ci) %>%
  mutate(Ind="Familiar with USAID")

#q2
q2 <- write_csv(q2, here("output/tables/eval Q1/q2 familiar.csv"))

q2_flx <- q2 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3,
                   digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q2. Familiar with USAID")

save_as_docx(q2_flx, path=here("output/tables/eval Q1/q2 familiar.docx"))

q2_flx

Response	Label	Number	Percent	Confidence interval
0	Not at all familiar/unsure	1,493	58.6%	55.5% - 61.7%
1	Somewhat familiar	873	34.2%	31.4% - 37%
2	Very familiar	181	7.1%	5.7% - 8.5%
98	Refused	1	0.1%	0% - 0.1%
Q2. Familiar with USAID

Disaggregated

Code

q2_ov <- ov_tab(svyrdat, usaid_famil_bin, 
                "q2", "Familiar with USAID") # 3.1 margin, 2.6 deff

q2_sex <- disag_tab(svyrdat, usaid_famil_bin, sex, sex_key, 
                    "q2", "Familiar with USAID", "Sex")

q2_age <- disag_tab(svyrdat, usaid_famil_bin, age_grp,age_grp_key,  

                    "q2", "Familiar with USAID", "Age group")


q2_ed <- disag_tab(svyrdat, usaid_famil_bin, educ_cat, educ_key,
                   "q2", "Familiar with USAID", "Education")

q2_mad <- disag_tab(svyrdat, usaid_famil_bin, madrassa,mad_key,
                   "q2", "Familiar with USAID", "Madrassa education")

q2_area <- disag_tab(svyrdat, usaid_famil_bin, area,area_key,
                     "q2","Familiar with USAID","Area")

q2_subreg <- disag_tab(svyrdat, usaid_famil_bin, subregion,subreg_key,
                     "q2","Familiar with USAID","Subregion")

q2_gov <- disag_tab(svyrdat, usaid_famil_bin, gov,gov_key,
                     "q2","Seen USAID logo","Governorate")

q2_reg <- disag_tab(svyrdat, usaid_famil_bin, region,reg_key,
                     "q2","Familiar with USAID","Region") # 4.7 marg

q2_disag <- bind_rows(q2_ov,
                      q2_area,
                      q2_reg,
                      q2_subreg,
                      q2_gov,
                      q2_sex,
                      q2_age,
                      q2_ed,
                      q2_mad)

q2_disag_flx <- q2_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q2. Familiar with USAID") %>%
  border_inner_h()

save_as_docx(q2_disag_flx, path=here("output/tables/eval Q1/q2_disag.docx"))

q2_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Familiar with USAID	1,054	41.3%	38.3% - 44.4%
Area	Urban	837	42.5%	38.9% - 46%
	Village	129	34.3%	26.8% - 41.8%
	Refugee camp	88	43.6%	30.9% - 56.2%
Region	West Bank	543	36.4%	32.4% - 40.5%
	Gaza	511	48.3%	43.5% - 53%
Subregion	Northern West Bank	244	42.0%	36.9% - 47.2%
	Central West Bank	135	33.6%	24.7% - 42.5%
	Southern West Bank	164	32.3%	24.8% - 39.8%
	Gaza Strip	511	48.3%	43.5% - 53%
Governorate	Jenin	67	44.2%	33.1% - 55.3%
	Tubas	17	50.6%	42.3% - 58.8%
	Tulkarm	39	49.3%	35.1% - 63.6%
	Nablus	78	35.6%	26.4% - 44.9%
	Qalqiliya	27	48.4%	32.6% - 64.2%
	Salfit	16	38.2%	25.7% - 50.6%
	Ramallah	62	35.9%	21.4% - 50.4%
	Jericho	7	32.5%	15.4% - 49.6%
	Jerusalem	65	31.7%	19.5% - 44%
	Bethlehem	50	40.9%	28.6% - 53.2%
	Hebron	115	29.6%	20.5% - 38.7%
	North Gaza	101	47.0%	38.8% - 55.2%
	Gaza	234	61.7%	51.4% - 72.1%
	Dier al-Balah	55	37.4%	26% - 48.8%
	Khan Yunis	69	36.0%	27% - 44.9%
	Rafah	52	41.4%	30.4% - 52.4%
Sex	Male	544	42.8%	39.1% - 46.6%
	Female	509	39.9%	36.1% - 43.7%
Age group	Youth (18-29)	404	37.7%	32.8% - 42.7%
	Adult (30-54)	514	46.7%	43% - 50.4%
	Mature (55+)	136	36.0%	31.2% - 40.8%
Education	Elementary school education	149	27.6%	22.9% - 32.3%
	Secondary education	439	39.9%	36% - 43.9%
	Post-secondary education	466	51.3%	46.4% - 56.2%
Madrassa education	No madrassa education	852	38.7%	35.6% - 41.8%
	Madrassa education	202	57.8%	48.8% - 66.9%
Q2. Familiar with USAID

Q3 USAID activities familiar with, ranked

Code

q3_1 <- ov_tab(svyrdat, q3_1, "q3_1", "Food security/nutrition") 

q3_2 <- ov_tab(svyrdat, q3_2, "q3_2", "Job training") 

q3_3 <- ov_tab(svyrdat, q3_3, "q3_3", "Support for community groups") 

q3_4 <- ov_tab(svyrdat, q3_4, "q3_4", "Economic growth / entrepreneurship") 

q3_5 <- ov_tab(svyrdat, q3_5, "q3_5", "Water/wastewater") 

q3_6 <- ov_tab(svyrdat, q3_6, "q3_6", "Roads/bridges") 

q3_7 <- ov_tab(svyrdat, q3_7, "q3_7", "Transportation") 

q3_8 <- ov_tab(svyrdat, q3_8, "q3_8", "Youth") 

q3_9 <- ov_tab(svyrdat, q3_9, "q3_9", "Education") 

q3_10 <- ov_tab(svyrdat, q3_10, "q3_10", "Health") 

q3_11 <- ov_tab(svyrdat, q3_11, "q3_11", "Peacebuilding") 

q3_12 <- ov_tab(svyrdat, q3_12, "q3_12", "Tourism") 

q3_13 <- ov_tab(svyrdat, q3_13, "q3_13", "Agriculture") 

q3 <- bind_rows(q3_1, q3_2, q3_3, q3_4, q3_5, q3_6, q3_7, q3_8, q3_9, q3_10, q3_11, q3_12, q3_13) %>%
  arrange(desc(Percent))

q3_flx <- q3 %>%
  select(Activity=Label, Number, Percent, `Confidence interval`=ci) %>%
  flextable() %>%
  colformat_double(j=2, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=4, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q3. USAID activities familiar with")

save_as_docx(q3_flx, path=here("output/tables/eval Q1/q3.docx"))

q3_flx

Activity	Number	Percent	Confidence interval
Food security/nutrition	716	68.0%	64.1% - 71.9%
Education	469	44.5%	40.5% - 48.6%
Water/wastewater	418	39.6%	35.8% - 43.4%
Health	376	35.7%	31.5% - 39.9%
Economic growth / entrepreneurship	318	30.2%	26.3% - 34.1%
Support for community groups	271	25.7%	22.3% - 29.1%
Job training	257	24.4%	20.8% - 28%
Roads/bridges	233	22.1%	19% - 25.2%
Youth	224	21.3%	17.8% - 24.7%
Agriculture	144	13.7%	11.1% - 16.2%
Tourism	125	11.8%	8.8% - 14.9%
Transportation	86	8.2%	5.8% - 10.5%
Peacebuilding	77	7.3%	5.1% - 9.5%
Q3. USAID activities familiar with

Dissaggregated

Code

q3_ov <- ov_tab(svyrdat, q3_1, 
                "q3_1", "Food security and nutritional support") 

q3_sex <- disag_tab(svyrdat, q3_1, sex, sex_key, 
                    "q3_1", "Familiar with USAID", "Sex")

q3_age <- disag_tab(svyrdat, q3_1, age_grp,age_grp_key,  

                    "q3_1", "Familiar with USAID", "Age group")


q3_ed <- disag_tab(svyrdat, q3_1, educ_cat, educ_key,
                   "q3_1", "Familiar with USAID", "Education")

q3_mad <- disag_tab(svyrdat, q3_1, madrassa,mad_key,
                   "q3_1", "Familiar with USAID", "Madrassa education")

q3_area <- disag_tab(svyrdat, q3_1, area,area_key,
                     "q3_1","Familiar with USAID","Area")

q3_gov <- disag_tab(svyrdat, q3_1, gov,gov_key,
                     "q3_1","Seen USAID logo","Governorate")

q3_subreg <- disag_tab(svyrdat, q3_1, subregion,subreg_key,
                     "q3_1","Familiar with USAID","Subregion")


q3_reg <- disag_tab(svyrdat, q3_1, region,reg_key,
                     "q3_1","Familiar with USAID","Region") # 4.7 marg

q3_disag <- bind_rows(q3_ov,
                      q3_area,
                      q3_reg,
                      q3_subreg,
                      q3_gov,
                      q3_sex,
                      q3_age,
                      q3_ed,
                      q3_mad)

q3_disag_flx <- q3_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q3. Food security and nutritional support") %>%
  border_inner_h

save_as_docx(q3_disag_flx, path=here("output/tables/eval Q1/q3_disag.docx"))

q3_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Food security and nutritional support	716	68.0%	64.1% - 71.9%
Area	Urban	568	67.8%	63.4% - 72.3%
	Village	89	69.1%	58.1% - 80%
	Refugee camp	59	67.7%	55.5% - 80%
Region	West Bank	347	63.9%	58.5% - 69.3%
	Gaza	370	72.3%	66.6% - 78%
Subregion	Northern West Bank	155	63.7%	56% - 71.3%
	Central West Bank	90	66.3%	56.2% - 76.5%
	Southern West Bank	102	62.3%	51.3% - 73.2%
	Gaza Strip	370	72.3%	66.6% - 78%
Governorate	Jenin	39	57.8%	40.8% - 74.7%
	Tubas	7	38.0%	12.1% - 63.8%
	Tulkarm	30	76.2%	64.7% - 87.6%
	Nablus	63	80.9%	70.2% - 91.7%
	Qalqiliya	7	27.5%	8.5% - 46.4%
	Salfit	10	61.7%	30.6% - 92.9%
	Ramallah	39	63.0%	53% - 73%
	Jericho	5	67.5%	44.8% - 90.2%
	Jerusalem	45	69.4%	51.8% - 87%
	Bethlehem	22	45.2%	29.1% - 61.3%
	Hebron	80	69.7%	55.5% - 83.8%
	North Gaza	64	63.8%	51.1% - 76.5%
	Gaza	182	77.8%	70.5% - 85.1%
	Dier al-Balah	40	72.0%	55.6% - 88.3%
	Khan Yunis	46	67.4%	48.8% - 86%
	Rafah	37	70.9%	48.6% - 93.3%
Sex	Male	346	63.5%	58.3% - 68.6%
	Female	371	72.8%	67.6% - 78%
Age group	Youth (18-29)	277	68.6%	61.7% - 75.5%
	Adult (30-54)	345	67.2%	62.7% - 71.6%
	Mature (55+)	95	69.3%	62.3% - 76.4%
Education	Elementary school education	112	75.1%	67.5% - 82.6%
	Secondary education	313	71.4%	66.1% - 76.7%
	Post-secondary education	291	62.5%	57.2% - 67.8%
Madrassa education	No madrassa education	572	67.1%	62.8% - 71.3%
	Madrassa education	145	71.8%	63.8% - 79.7%
Q3. Food security and nutritional support

Ranked by region

Code

# q3_1reg <- disag_tab(svyrdat, q3_1, region,reg_key, "q3_1", "Food security/nutrition","Region") 
# 
# q3_2reg <- disag_tab(svyrdat, q3_2, region,reg_key, " q3_2", "Job training","Region") 
# 
# q3_3reg <- disag_tab(svyrdat, q3_3, region,reg_key, " q3_3", "Support for community groups","Region") 
# 
# q3_4reg <- disag_tab(svyrdat, q3_4, region,reg_key, " q3_4", " Economic growth / entrepreneurship","Region") 
# 
# q3_5reg <- disag_tab(svyrdat, q3_5, region,reg_key, " q3_5", " Water/wastewater","Region") 
# 
# q3_6reg <- disag_tab(svyrdat, q3_6, region,reg_key, " q3_6", "Roads/bridges","Region") 
# 
# q3_7reg <- disag_tab(svyrdat, q3_7, region,reg_key, " q3_7", "Transportation","Region") 
# 
# q3_8reg <- disag_tab(svyrdat, q3_8, region,reg_key, " q3_8", "Youth","Region") 
# 
# q3_9reg <- disag_tab(svyrdat, q3_9, region,reg_key, " q3_9", "Education","Region") 
# 
# q3_10reg <- disag_tab(svyrdat, q3_10, region,reg_key, " q3_10", "Health","Region") 
# 
# q3_11reg <- disag_tab(svyrdat, q3_11, region,reg_key, " q3_11", "Peacebuilding","Region") 
# 
# q3_12reg <- disag_tab(svyrdat, q3_12, region,reg_key, " q3_12", "Tourism","Region") 
# q3_13reg <- disag_tab(svyrdat, q3_13, region,reg_key, " q3_13", "Agriculture","Region")

Code

# q3_reg <- bind_rows(q3_1reg, q3_13reg, q3_12reg, q3_11reg, q3_10reg, q3_9reg, q3_8reg, q3_7reg, q3_6reg, q3_5reg, q3_4reg, q3_3reg, q3_2reg ) 
# 
# q3_reg <- q3_reg %>%
#   group_by(`Disaggregation type`) %>%
#   mutate(region_rank = rank(-Percent)) %>% 
#   arrange(`Disaggregation type`, desc(Percent)) 

#q3_reg

Code

q3_reg_flx <-q3_reg  %>%
  select( Activity=Label, Number, Percent, `Confidence interval`=ci) %>%
  flextable() %>%
  colformat_double(j=2, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=4, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q3.Familiar with USAID activities across region")


q3_1reg <- disag_tab(svyrdat, q3_1, region,reg_key, "q3_1", "Food security/nutrition","Region")
q3_2reg <- disag_tab(svyrdat, q3_2, region,reg_key, " q3_2", "Job training","Region")
q3_3reg <- disag_tab(svyrdat, q3_3, region,reg_key, " q3_3", "Support for community groups","Region") 
q3_4reg <- disag_tab(svyrdat, q3_4, region,reg_key, " q3_4", " Economic growth / entrepreneurship","Region")
q3_5reg <- disag_tab(svyrdat, q3_5, region,reg_key, " q3_5", " Water/wastewater","Region")
q3_6reg <- disag_tab(svyrdat, q3_6, region,reg_key, " q3_6", "Roads/bridges","Region") 
q3_7reg <- disag_tab(svyrdat, q3_7, region,reg_key, " q3_7", "Transportation","Region")
q3_8reg <- disag_tab(svyrdat, q3_8, region,reg_key, " q3_8", "Youth","Region")

q3_9reg <- disag_tab(svyrdat, q3_9, region,reg_key, " q3_9", "Education","Region") 
q3_10reg <- disag_tab(svyrdat, q3_10, region,reg_key, " q3_10", "Health","Region") 
q3_11reg <- disag_tab(svyrdat, q3_11, region,reg_key, " q3_11", "Peacebuilding","Region")
q3_12reg <- disag_tab(svyrdat, q3_12, region,reg_key, " q3_12", "Tourism","Region")
q3_13reg <- disag_tab(svyrdat, q3_13, region,reg_key, " q3_13", "Agriculture","Region")

q3_reg <- bind_rows(q3_1reg, q3_13reg, q3_12reg, q3_11reg, q3_10reg, q3_9reg, q3_8reg, q3_7reg, q3_6reg, q3_5reg, q3_4reg, q3_3reg, q3_2reg ) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent))%>% 
arrange(`Disaggregation type`, desc(Percent))

q3_reg_flx <- q3_reg %>% select(`USAID activities`=Label, Number, Percent, `Confidence interval`=ci) %>%
 flextable() %>% set_formatter(Percent=function(x) sprintf("%.1f%%", x*100))%>%
  align(j=4, align="center") %>% 
  colformat_double(j=3, digits=0)%>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q3. Familiar with USAID activities across region")

q3_reg_flx

Disaggregation type	USAID activities	Number	Percent	Confidence interval
Gaza	Food security/nutrition	370	72.3%	66.6% - 78%
Gaza	Education	207	40.5%	34% - 47%
Gaza	Economic growth / entrepreneurship	192	37.6%	31.8% - 43.5%
Gaza	Water/wastewater	187	36.5%	30.6% - 42.4%
Gaza	Job training	176	34.4%	28.1% - 40.6%
Gaza	Health	168	32.9%	27% - 38.9%
Gaza	Support for community groups	162	31.7%	26.3% - 37.1%
Gaza	Youth	134	26.2%	21.1% - 31.3%
Gaza	Roads/bridges	111	21.7%	17% - 26.4%
Gaza	Tourism	102	20.0%	14.9% - 25.2%
Gaza	Agriculture	68	13.2%	9.5% - 17%
Gaza	Peacebuilding	66	12.9%	9% - 16.8%
Gaza	Transportation	57	11.2%	7.1% - 15.4%
West Bank	Food security/nutrition	347	63.9%	58.5% - 69.3%
West Bank	Education	262	48.3%	43.3% - 53.3%
West Bank	Water/wastewater	231	42.6%	37.8% - 47.3%
West Bank	Health	208	38.3%	32.4% - 44.2%
West Bank	Economic growth / entrepreneurship	126	23.2%	18.4% - 28%
West Bank	Roads/bridges	122	22.5%	18.4% - 26.5%
West Bank	Support for community groups	109	20.1%	15.8% - 24.3%
West Bank	Youth	90	16.6%	12% - 21.3%
West Bank	Job training	81	14.9%	11.5% - 18.3%
West Bank	Agriculture	76	14.1%	10.6% - 17.6%
West Bank	Transportation	28	5.2%	2.9% - 7.6%
West Bank	Tourism	22	4.1%	1.1% - 7.1%
West Bank	Peacebuilding	11	2.1%	0.1% - 4%
Q3. Familiar with USAID activities across region

Code

save_as_docx(q3_reg_flx, path=here("output/tables/eval Q1/q3 ranked by region.docx"))

Ranked by subregion

Code

q3_1subreg <- disag_tab(svyrdat, q3_1, subregion,subreg_key, "q3_1", "Food security/nutrition"," Sub Region")
q3_2subreg <- disag_tab(svyrdat, q3_2, subregion,subreg_key, " q3_2", "Job training"," Sub Region")
q3_3subreg <- disag_tab(svyrdat, q3_3, subregion,subreg_key, " q3_3", "Support for community groups"," Sub Region")
q3_4subreg <- disag_tab(svyrdat, q3_4, subregion,subreg_key, " q3_4", " Economic growth / entrepreneurship"," Sub Region")
q3_5subreg <- disag_tab(svyrdat, q3_5, subregion,subreg_key, " q3_5", " Water/wastewater"," Sub Region")
q3_6subreg <- disag_tab(svyrdat, q3_6, subregion,subreg_key, " q3_6", "Roads/bridges"," Sub Region")
q3_7subreg <- disag_tab(svyrdat, q3_7, subregion,subreg_key, " q3_7", "Transportation"," Sub Region")
q3_8subreg <- disag_tab(svyrdat, q3_8, subregion,subreg_key, " q3_8", "Youth"," Sub Region")
q3_9subreg <- disag_tab(svyrdat, q3_9, subregion,subreg_key, " q3_9", "Education"," Sub Region")
q3_10subreg <- disag_tab(svyrdat, q3_10, subregion,subreg_key, " q3_10", "Health"," Sub Region")
q3_11subreg <- disag_tab(svyrdat, q3_11, subregion,subreg_key, " q3_11", "Peacebuilding"," Sub Region")
q3_12subreg <- disag_tab(svyrdat, q3_12, subregion,subreg_key, " q3_12", "Tourism"," Sub Region")
q3_13subreg <- disag_tab(svyrdat, q3_13, subregion,subreg_key, " q3_13", "Agriculture"," Sub Region")

q3_subreg <- bind_rows(q3_1subreg, q3_13subreg, q3_12subreg, q3_11subreg, q3_10subreg, q3_9subreg, q3_8subreg, q3_7subreg, q3_6subreg, q3_5subreg, q3_4subreg, q3_3subreg, q3_2subreg ) %>% 
group_by(`Disaggregation type`) %>%
mutate(region_rank = rank(-Percent)) %>%
arrange(`Disaggregation type`, desc(Percent))

q3sub_flx <- q3_subreg %>%
  select(`USAID activities`=Label, Number, Percent, `Confidence interval`=ci) %>% flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100))%>% 
align(j=4, align="center") %>%
  colformat_double(j=3, digits=0)%>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q3. Familiar with USAID activities across sub region")

q3sub_flx

Disaggregation type	USAID activities	Number	Percent	Confidence interval
Central West Bank	Food security/nutrition	90	66.3%	56.2% - 76.5%
Central West Bank	Education	79	58.5%	49.8% - 67.1%
Central West Bank	Health	66	48.9%	37.4% - 60.5%
Central West Bank	Water/wastewater	59	43.9%	33.9% - 53.8%
Central West Bank	Youth	33	24.3%	11.8% - 36.8%
Central West Bank	Support for community groups	26	19.4%	11.5% - 27.2%
Central West Bank	Roads/bridges	20	14.8%	9.3% - 20.4%
Central West Bank	Economic growth / entrepreneurship	17	12.9%	4.8% - 21%
Central West Bank	Job training	14	10.3%	5.8% - 14.7%
Central West Bank	Agriculture	8	6.0%	1.5% - 10.6%
Central West Bank	Tourism	3	2.1%	0.3% - 4%
Central West Bank	Transportation	3	2.0%	-0.1% - 4.1%
Central West Bank	Peacebuilding	1	0.7%	-0.6% - 2%
Gaza Strip	Food security/nutrition	370	72.3%	66.6% - 78%
Gaza Strip	Education	207	40.5%	34% - 47%
Gaza Strip	Economic growth / entrepreneurship	192	37.6%	31.8% - 43.5%
Gaza Strip	Water/wastewater	187	36.5%	30.6% - 42.4%
Gaza Strip	Job training	176	34.4%	28.1% - 40.6%
Gaza Strip	Health	168	32.9%	27% - 38.9%
Gaza Strip	Support for community groups	162	31.7%	26.3% - 37.1%
Gaza Strip	Youth	134	26.2%	21.1% - 31.3%
Gaza Strip	Roads/bridges	111	21.7%	17% - 26.4%
Gaza Strip	Tourism	102	20.0%	14.9% - 25.2%
Gaza Strip	Agriculture	68	13.2%	9.5% - 17%
Gaza Strip	Peacebuilding	66	12.9%	9% - 16.8%
Gaza Strip	Transportation	57	11.2%	7.1% - 15.4%
Northern West Bank	Food security/nutrition	155	63.7%	56% - 71.3%
Northern West Bank	Education	113	46.4%	39.7% - 53.1%
Northern West Bank	Water/wastewater	95	38.8%	32.7% - 44.9%
Northern West Bank	Health	79	32.5%	24.6% - 40.4%
Northern West Bank	Roads/bridges	62	25.5%	18.7% - 32.3%
Northern West Bank	Economic growth / entrepreneurship	62	25.4%	19% - 31.7%
Northern West Bank	Agriculture	43	17.6%	11.8% - 23.4%
Northern West Bank	Job training	40	16.5%	11.1% - 21.9%
Northern West Bank	Support for community groups	36	14.8%	9.8% - 19.9%
Northern West Bank	Youth	29	11.7%	6.1% - 17.3%
Northern West Bank	Tourism	16	6.4%	0% - 12.8%
Northern West Bank	Transportation	12	5.0%	2.1% - 8%
Northern West Bank	Peacebuilding	9	3.7%	-0.5% - 7.8%
Southern West Bank	Food security/nutrition	102	62.3%	51.3% - 73.2%
Southern West Bank	Water/wastewater	77	47.1%	37.2% - 57%
Southern West Bank	Education	70	42.7%	32.2% - 53.2%
Southern West Bank	Health	63	38.2%	27% - 49.4%
Southern West Bank	Economic growth / entrepreneurship	47	28.5%	18.2% - 38.8%
Southern West Bank	Support for community groups	47	28.4%	18.3% - 38.5%
Southern West Bank	Roads/bridges	40	24.3%	17.2% - 31.4%
Southern West Bank	Youth	29	17.5%	10.2% - 24.8%
Southern West Bank	Job training	27	16.4%	9.5% - 23.3%
Southern West Bank	Agriculture	25	15.4%	9.4% - 21.4%
Southern West Bank	Transportation	14	8.3%	2.3% - 14.2%
Southern West Bank	Tourism	4	2.3%	0.3% - 4.3%
Southern West Bank	Peacebuilding	1	0.8%	-0.5% - 2.1%
Q3. Familiar with USAID activities across sub region

Code

save_as_docx(q3_reg_flx, path=here("output/tables/eval Q1/q3 ranked by region.docx"))

Familiar with food security, disaggregated

Code

q3_1_ov <- ov_tab(svyrdat, q3_1, 
                "q3_1", "Familiar with food security activity") # 3.1 margin, 2.6 deff

q3_1_sex <- disag_tab(svyrdat, q3_1, sex, sex_key, 
                    "q3_1", "Familiar with food security activity", "Sex")

q3_1_age <- disag_tab(svyrdat, q3_1, age_grp,age_grp_key,  

                    "q3_1", "Familiar with food security activity", "Age group")


q3_1_ed <- disag_tab(svyrdat, q3_1, educ_cat, educ_key,
                   "q3_1", "Familiar with food security activity", "Education")

q3_1_mad <- disag_tab(svyrdat, q3_1, madrassa,mad_key,
                   "q3_1", "Familiar with food security activity", "Madrassa education")

q3_1_area <- disag_tab(svyrdat, q3_1, area,area_key, 
                       "q3_1","Familiar with food security activity","Area")

q3_1_gov <- disag_tab(svyrdat, q3_1, gov,gov_key,
                     "q3_1","Familiar with food security activity","Governorate")

q3_1_subreg <- disag_tab(svyrdat, q3_1, subregion,subreg_key,
                     "q3_1","Familiar with food security activity","Subregion")


q3_1_reg <- disag_tab(svyrdat, q3_1, region,reg_key,
                     "q3_1","Familiar with food security activity","Region") # 4.7 marg

q3_1_disag <- bind_rows(q3_1_ov,
                        q3_1_area,
                      q3_1_reg,
                      q3_1_subreg,
                      q3_1_gov,
                      q3_1_sex,
                      q3_1_age,
                      q3_1_ed,
                      q3_1_mad)

q3_1_disag_flx <- q3_1_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  #fix_border_issues() %>%
  #hline(i=c(1,4,7,10,12,15,18), border=smlbrdr) %>%
  border_inner_h() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q3_1. Familiar with food security activity")

save_as_docx(q3_1_disag_flx, path=here("output/tables/eval Q1/q3_1_disag.docx"))

Error in print.rdocx(z, target = path): C:/Users/dan.killian/Documents/Palestinian Perception Study/output/tables/eval Q1/q3_1_disag.docx is already edited. You must close the document in order to be able to write the file.

Code

q3_1_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Familiar with food security activity	716	68.0%	64.1% - 71.9%
Area	Urban	568	67.8%	63.4% - 72.3%
	Village	89	69.1%	58.1% - 80%
	Refugee camp	59	67.7%	55.5% - 80%
Region	West Bank	347	63.9%	58.5% - 69.3%
	Gaza	370	72.3%	66.6% - 78%
Subregion	Northern West Bank	155	63.7%	56% - 71.3%
	Central West Bank	90	66.3%	56.2% - 76.5%
	Southern West Bank	102	62.3%	51.3% - 73.2%
	Gaza Strip	370	72.3%	66.6% - 78%
Governorate	Jenin	39	57.8%	40.8% - 74.7%
	Tubas	7	38.0%	12.1% - 63.8%
	Tulkarm	30	76.2%	64.7% - 87.6%
	Nablus	63	80.9%	70.2% - 91.7%
	Qalqiliya	7	27.5%	8.5% - 46.4%
	Salfit	10	61.7%	30.6% - 92.9%
	Ramallah	39	63.0%	53% - 73%
	Jericho	5	67.5%	44.8% - 90.2%
	Jerusalem	45	69.4%	51.8% - 87%
	Bethlehem	22	45.2%	29.1% - 61.3%
	Hebron	80	69.7%	55.5% - 83.8%
	North Gaza	64	63.8%	51.1% - 76.5%
	Gaza	182	77.8%	70.5% - 85.1%
	Dier al-Balah	40	72.0%	55.6% - 88.3%
	Khan Yunis	46	67.4%	48.8% - 86%
	Rafah	37	70.9%	48.6% - 93.3%
Sex	Male	346	63.5%	58.3% - 68.6%
	Female	371	72.8%	67.6% - 78%
Age group	Youth (18-29)	277	68.6%	61.7% - 75.5%
	Adult (30-54)	345	67.2%	62.7% - 71.6%
	Mature (55+)	95	69.3%	62.3% - 76.4%
Education	Elementary school education	112	75.1%	67.5% - 82.6%
	Secondary education	313	71.4%	66.1% - 76.7%
	Post-secondary education	291	62.5%	57.2% - 67.8%
Madrassa education	No madrassa education	572	67.1%	62.8% - 71.3%
	Madrassa education	145	71.8%	63.8% - 79.7%
Q3_1. Familiar with food security activity

Q4 Perception of USAID

Overall

Code

q4_tot <- svytotal(~factor(q4),
         na.rm=T,
         design=svydat) %>%
  as.data.frame()

q4 <- svymean(~factor(q4),
        na.rm=T,
        deff="replace",
        design=svydat) %>%
  as.data.frame() %>%
  rownames_to_column() %>%
  mutate(#item="q4",
         Ind="Positive perception of USAID",
         Response = str_sub(rowname, 11, nchar(rowname)),
         Label=percept_key$perception_lab,
         Number=q4_tot$total,
          margin = 1.96*SE,
           Lower=mean - margin,
           Upper=mean + margin,
         ci=paste(round(Lower*100,1), "%", " - ", round(Upper*100,1), "%", sep="")) %>%
  select(Response, Label, Number, Percent=mean, SE, deff, margin:ci)

write_csv(q4, here("output/tables/eval Q1/q4.csv"))
#q4

q4_flx <- q4 %>%
  select(1:4,`Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q4. Perception of USAID")

save_as_docx(q4_flx, path=here("output/tables/eval Q1/q4.docx"))

q4_flx

Response	Label	Number	Percent	Confidence interval
1	Very negative	41	3.8%	2.8% - 4.9%
2	Somewhat negative	77	7.3%	5.7% - 9%
3	Neither negative nor positive	269	25.6%	22.4% - 28.7%
4	Somewhat positive	492	46.7%	43% - 50.3%
5	Very positive	154	14.6%	11.6% - 17.6%
98	Refused	0	0.0%	0% - 0.1%
99	Don't know	20	1.9%	1.2% - 2.7%
Q4. Perception of USAID

Disaggregated

Code

percep_ov <- ov_tab(svyrdat, usaid_percep_bin, "q4", "Positive perception of USAID") 

percep_sex <- disag_tab(svyrdat, usaid_percep_bin, sex, sex_key, 
                    "q4", "Positive perception of USAID", "Sex")

percep_age <- disag_tab(svyrdat, usaid_percep_bin, age_grp, age_grp_key, 
                    "q4", "Perception of USAID", "Age group")

percep_ed <- disag_tab(svyrdat, usaid_percep_bin, educ_cat, educ_key, 
                    "q4", "Positive perception of USAID", "Education")

percep_mad <- disag_tab(svyrdat, usaid_percep_bin, madrassa, mad_key, 
                    "q4", "Positive perception of USAID", "Madrassa education")

percep_area <- disag_tab(svyrdat, usaid_percep_bin, area, area_key, 
                    "q4", "Positive perception of USAID", "Area")

percep_gov <- disag_tab(svyrdat, usaid_percep_bin, gov,gov_key,
                     "q4","Positive perception of USAID", "Governorate")

percep_subreg <- disag_tab(svyrdat, usaid_percep_bin, subregion, subreg_key, 
                    "q4", "Positive perception of USAID", "Subregion")

percep_reg <- disag_tab(svyrdat, usaid_percep_bin, region, reg_key, 
                    "q4", "Positive perception of USAID", "Region") # 5.6 margin

percep_disag <- bind_rows(percep_ov,
                          percep_area,
                      percep_reg,
                      percep_subreg,
                      percep_gov,
                      percep_sex,
                      percep_age,
                      percep_ed,
                      percep_mad)

percep_disag_flx <- percep_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q4. Positive perception of USAID") %>%
  border_inner_h()

save_as_docx(percep_disag_flx, path=here("output/tables/eval Q1/q4 percep_disag.docx"))

percep_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Positive perception of USAID	646	61.3%	57.6% - 64.9%
Area	Urban	503	60.0%	55.9% - 64.2%
	Village	84	65.3%	55.3% - 75.2%
	Refugee camp	59	67.2%	56.4% - 77.9%
Region	West Bank	311	57.4%	52.5% - 62.3%
	Gaza	334	65.4%	59.8% - 71%
Subregion	Northern West Bank	141	57.7%	49.9% - 65.5%
	Central West Bank	67	49.7%	39.8% - 59.6%
	Southern West Bank	104	63.2%	55% - 71.4%
	Gaza Strip	334	65.4%	59.8% - 71%
Governorate	Jenin	39	57.6%	38.6% - 76.6%
	Tubas	13	76.5%	60.2% - 92.9%
	Tulkarm	18	46.0%	31.1% - 61%
	Nablus	48	61.5%	47.9% - 75.2%
	Qalqiliya	14	52.7%	38% - 67.5%
	Salfit	9	56.7%	39.1% - 74.4%
	Ramallah	31	48.8%	33.3% - 64.4%
	Jericho	4	56.5%	43.2% - 69.9%
	Jerusalem	33	49.8%	35.6% - 63.9%
	Bethlehem	29	58.3%	41% - 75.6%
	Hebron	75	65.3%	56.1% - 74.6%
	North Gaza	82	81.2%	67.5% - 94.8%
	Gaza	137	58.5%	50.9% - 66.1%
	Dier al-Balah	30	54.6%	28.9% - 80.2%
	Khan Yunis	43	62.7%	47.9% - 77.5%
	Rafah	42	80.8%	74.2% - 87.4%
Sex	Male	319	58.6%	54% - 63.2%
	Female	327	64.1%	59% - 69.3%
Age group	Youth (18-29)	269	66.7%	60.2% - 73.1%
	Adult (30-54)	300	58.4%	54.4% - 62.3%
	Mature (55+)	76	56.1%	48.9% - 63.2%
Education	Elementary school education	83	55.9%	46.3% - 65.4%
	Secondary education	266	60.7%	55% - 66.4%
	Post-secondary education	296	63.6%	58.9% - 68.2%
Madrassa education	No madrassa education	518	60.8%	56.9% - 64.8%
	Madrassa education	127	63.0%	55.1% - 70.9%
Q4. Positive perception of USAID

Q5 Negative impressions, ranked

Code

q5a <- ov_tab(svyrdat, q5a_bin, " q5a", " I haven’t seen any projects in my community") 
q5b <- ov_tab(svyrdat, q5b_bin, " q5b ", " I haven’t seen any results in my community ") 
q5c <- ov_tab(svyrdat, q5c_bin, " q5c ", " The US doesn’t support Palestinians ") 
q5d <- ov_tab(svyrdat, q5d_bin, " q5d ", " Foreign interference is always bad ") 
q5e <- ov_tab(svyrdat, q5e_bin, " q5e ", " The projects are not relevant ") 
q5f <- ov_tab(svyrdat, q5f_bin, " q5f ", " The projects are harmful to my community ") 
q5g <- ov_tab(svyrdat, q5g_bin, " q5g ", " The projects are unfair in who gets to participate") 
q5 <- bind_rows(q5a, q5b, q5c, q5d, q5e, q5f, q5g) %>%
      arrange(desc(Percent))

#q5
write_csv(q5, here("output/tables/eval Q1/q5.csv"))

q5_flx <- q5 %>% select(`Negative Impression`=Label, Number, Percent, `Confidence interval`=ci) %>% 
flextable() %>%
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
align(j=4, align="center") %>% 
colformat_double(j=2, digits=0) %>%
set_table_properties(layout="autofit") %>%
 add_footer_lines(values="Q5. Negative Impression  Ranked")
q5_flx

Negative Impression	Number	Percent	Confidence interval
I haven’t seen any projects in my community	51	43.4%	34.7% - 52.1%
I haven’t seen any results in my community	51	43.2%	33.2% - 53.1%
The projects are unfair in who gets to participate	48	40.6%	31.3% - 50%
The US doesn’t support Palestinians	48	40.4%	31.1% - 49.8%
Foreign interference is always bad	39	32.7%	24.2% - 41.2%
The projects are not relevant	29	24.5%	16.7% - 32.3%
The projects are harmful to my community	22	18.4%	10.3% - 26.5%
Q5. Negative Impression Ranked

Code

save_as_docx(q5_flx, path=here("output/tables/eval Q1/q5 percep_disag.docx"))

Ranked by region

Code

q5a_reg <- disag_tab(svyrdat, q5a_bin, region,reg_key, "q5a", " I haven’t seen any projects in my community","Region")
q5b_reg <- disag_tab(svyrdat, q5b_bin, region,reg_key, "q5b", " I haven’t seen any results in my community ","Region")
q5c_reg <- disag_tab(svyrdat, q5c_bin, region,reg_key, "q5c", " The US doesn’t support Palestinians ","Region")
q5d_reg <- disag_tab(svyrdat, q5d_bin, region,reg_key, "q5d", " Foreign interference is always bad ","Region")
q5e_reg <- disag_tab(svyrdat, q5e_bin, region,reg_key, "q5e", " The projects are not relevant ","Region")
q5f_reg <- disag_tab(svyrdat, q5f_bin, region,reg_key, "q5f", " The projects are harmful to my community ","Region")
q5g_reg <- disag_tab(svyrdat, q5g_bin, region,reg_key, "q5g", " The projects are unfair in who gets to participate ","Region")

q5_reg <- bind_rows(q5a_reg, q5b_reg, q5c_reg,
                     q5d_reg,
                     q5e_reg,
                     q5f_reg,
                     q5g_reg,
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))
#q5_reg
write_csv(q5_reg, here("output/tables/eval Q1/q5_reg.csv"))

q5_flx <- q5_reg %>% select(`Negative Impression By Region`=Label, Number, Percent, `Confidence interval`=ci) %>%
  flextable() %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
  align(j=5, align="center") %>%
  colformat_double(j=3, digits=0) %>%
  set_table_properties(layout="autofit") %>% 
  hline(i=c(7), border=smlbrdr) %>%

  add_footer_lines(values="Q5. Negative Impression Ranked by Region")
q5_flx

Disaggregation type	Negative Impression By Region	Number	Percent	Confidence interval
Gaza	I haven’t seen any results in my community	26	58.1%	42.8% - 73.5%
Gaza	I haven’t seen any projects in my community	24	52.4%	38.3% - 66.5%
Gaza	The US doesn’t support Palestinians	22	48.6%	34.6% - 62.5%
Gaza	The projects are unfair in who gets to participate	18	39.5%	24.1% - 54.9%
Gaza	Foreign interference is always bad	14	31.9%	15.8% - 47.9%
Gaza	The projects are not relevant	13	29.3%	16.3% - 42.2%
Gaza	The projects are harmful to my community	13	28.4%	11.6% - 45.2%
West Bank	The projects are unfair in who gets to participate	30	41.4%	29.6% - 53.1%
West Bank	I haven’t seen any projects in my community	27	37.8%	27% - 48.6%
West Bank	The US doesn’t support Palestinians	26	35.4%	23% - 47.7%
West Bank	I haven’t seen any results in my community	25	33.8%	21.3% - 46.4%
West Bank	Foreign interference is always bad	24	33.2%	23.7% - 42.7%
West Bank	The projects are not relevant	16	21.5%	11.8% - 31.2%
West Bank	The projects are harmful to my community	9	12.2%	5.2% - 19.1%
Q5. Negative Impression Ranked by Region

Code

save_as_docx(q5_flx, path=here("output/tables/eval Q1/q5_percep_disag_region.docx"))

Ranked by subregion

Code

q5a_subreg <- disag_tab(svyrdat, q5a_bin, subregion,subreg_key, "q5a", " I haven’t seen any projects in my community "," Sub region ")
q5b_subreg <- disag_tab(svyrdat, q5b_bin, subregion,subreg_key, "q5b", " I haven’t seen any results in my community "," Sub region ")
q5c_subreg <- disag_tab(svyrdat, q5c_bin, subregion,subreg_key, "q5c", " The US doesn’t support Palestinians "," Sub region ")
q5d_subreg <- disag_tab(svyrdat, q5d_bin, subregion,subreg_key, "q5d", " Foreign interference is always bad "," Sub region ")
q5e_subreg <- disag_tab(svyrdat, q5e_bin, subregion,subreg_key, "q5e", " The projects are not relevant "," Sub region ")
q5f_subreg <- disag_tab(svyrdat, q5f_bin, subregion,subreg_key, "q5f", " The projects are harmful to my community "," Sub region ")
q5g_subreg <- disag_tab(svyrdat, q5g_bin, subregion,subreg_key, "q5g", " The projects are unfair in who gets to participate"," Sub region ")
q5_subreg <- bind_rows(q5a_subreg, q5b_subreg, q5c_subreg,
                     q5d_subreg,
                     q5e_subreg,
                     q5f_subreg,
                     q5g_subreg,
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))
write_csv(q5_subreg, here("output/tables/eval Q1/q5_subreg.csv"))

q5sub_flx <- q5_subreg %>%
select(`Negative Impression By Sub Region`=Label, Number, Percent, `Confidence interval`=ci) %>% 
  flextable() %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=4, align="center") %>%
  colformat_double(j=3, digits=0) %>%
    hline(i=c(7,14,21), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q5. Negative Impression Ranked Subregion")

q5sub_flx

Disaggregation type	Negative Impression By Sub Region	Number	Percent	Confidence interval
Central West Bank	The US doesn’t support Palestinians	13	58.0%	38.6% - 77.3%
Central West Bank	Foreign interference is always bad	9	39.7%	23.1% - 56.2%
Central West Bank	I haven’t seen any results in my community	8	36.0%	12.8% - 59.3%
Central West Bank	The projects are unfair in who gets to participate	7	30.7%	13.1% - 48.4%
Central West Bank	The projects are harmful to my community	6	29.2%	9.5% - 48.8%
Central West Bank	I haven’t seen any projects in my community	6	25.2%	11.9% - 38.5%
Central West Bank	The projects are not relevant	3	15.3%	5.3% - 25.3%
Gaza Strip	I haven’t seen any results in my community	26	58.1%	42.8% - 73.5%
Gaza Strip	I haven’t seen any projects in my community	24	52.4%	38.3% - 66.5%
Gaza Strip	The US doesn’t support Palestinians	22	48.6%	34.6% - 62.5%
Gaza Strip	The projects are unfair in who gets to participate	18	39.5%	24.1% - 54.9%
Gaza Strip	Foreign interference is always bad	14	31.9%	15.8% - 47.9%
Gaza Strip	The projects are not relevant	13	29.3%	16.3% - 42.2%
Gaza Strip	The projects are harmful to my community	13	28.4%	11.6% - 45.2%
Northern West Bank	The projects are unfair in who gets to participate	17	48.0%	29.7% - 66.3%
Northern West Bank	I haven’t seen any projects in my community	17	47.4%	29.9% - 64.9%
Northern West Bank	I haven’t seen any results in my community	12	33.8%	16.7% - 50.9%
Northern West Bank	Foreign interference is always bad	10	27.9%	13.2% - 42.6%
Northern West Bank	The US doesn’t support Palestinians	9	26.5%	10.6% - 42.4%
Northern West Bank	The projects are not relevant	7	18.4%	5.2% - 31.6%
Northern West Bank	The projects are harmful to my community	1	4.1%	-1% - 9.1%
Southern West Bank	The projects are unfair in who gets to participate	6	41.4%	17.3% - 65.5%
Southern West Bank	The projects are not relevant	6	38.1%	8.8% - 67.3%
Southern West Bank	Foreign interference is always bad	5	36.1%	17.3% - 54.9%
Southern West Bank	I haven’t seen any projects in my community	5	33.6%	9.4% - 57.8%
Southern West Bank	I haven’t seen any results in my community	5	30.8%	2.3% - 59.3%
Southern West Bank	The US doesn’t support Palestinians	3	23.0%	-5.3% - 51.2%
Southern West Bank	The projects are harmful to my community	1	6.3%	-2% - 14.6%
Q5. Negative Impression Ranked Subregion

Code

save_as_docx(q5sub_flx, path=here("output/tables/eval Q1/q5_percep_disag_subregion.docx"))

Q6 Positive impressions, ranked

Code

q6a <- ov_tab(svyrdat, q6a_bin, " q6a", " They help the needy/poor/vulnerable") 
q6b <- ov_tab(svyrdat, q6b_bin, " q6b ", " They build infrastructure") 
q6c <- ov_tab(svyrdat, q6c_bin, " q6c ", " They provide hope") 
q6d <- ov_tab(svyrdat, q6d_bin, " q6d ", " Because of what I’ve seen or read") 
q6e <- ov_tab(svyrdat, q6e_bin, " q6e ", " Because they are from the US") 
q6f <- ov_tab(svyrdat, q6f_bin, " q6f ", " The projects are fair") 
q6g <- ov_tab(svyrdat, q6g_bin, " q6g ", " They care about Palestinians") 

q6  <- bind_rows(q6a, q6b, q6c, q6d, q6e, q6f, q6g) %>%
      arrange(desc(Percent))
write_csv(q6, here("output/tables/eval Q1/q6.csv"))

#q6 
q6_flx <- q6 %>% 
select(`Postive Impression`=Label, Number, Percent, `Confidence interval`=ci) %>% flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
align(j=4, align="center") %>% 
colformat_double(j=2, digits=0) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q6. Postive Impression  Ranked")

q6_flx

Postive Impression	Number	Percent	Confidence interval
They help the needy/poor/vulnerable	555	85.9%	82.1% - 89.7%
They care about Palestinians	378	58.6%	53.9% - 63.3%
They build infrastructure	306	47.4%	42.3% - 52.5%
The projects are fair	124	19.2%	15.4% - 23.1%
Because of what I’ve seen or read	111	17.1%	12.9% - 21.4%
They provide hope	109	16.9%	12.8% - 21.1%
Because they are from the US	72	11.1%	7.7% - 14.5%
Q6. Postive Impression Ranked

Code

save_as_docx(q6_flx, path=here("output/tables/eval Q1/q6_percep_disag.docx"))

Ranked by region

Code

q6a_reg <- disag_tab(svyrdat, q6a_bin, region,reg_key, "q6a", " They help the needy/poor/vulnerable ","Region")
q6b_reg <- disag_tab(svyrdat, q6b_bin, region,reg_key, "q6b", " They build infrastructure ","Region")
q6c_reg <- disag_tab(svyrdat, q6c_bin, region,reg_key, "q6c", " They provide hope ","Region")
q6d_reg <- disag_tab(svyrdat, q6d_bin, region,reg_key, "q6d", " Because of what I’ve seen or read ","Region")
q6e_reg <- disag_tab(svyrdat, q6e_bin, region,reg_key, "q6e", " Because they are from the US ","Region")
q6f_reg <- disag_tab(svyrdat, q6f_bin, region,reg_key, "q6f", " The projects are fair ","Region")
q6g_reg <- disag_tab(svyrdat, q6g_bin, region,reg_key, "q6g", " They care about Palestinians ","Region")

q6_reg <- bind_rows(q6a_reg, q6b_reg, q6c_reg,
                     q6d_reg,
                     q6e_reg,
                     q6f_reg,
                     q6g_reg,
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))
#q6_reg
q6_flx <- q6_reg %>% select(`Postive Impression By Region`=Label, Number, Percent, `Confidence interval`=ci) %>% 
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
align(j=4, align="center") %>%
colformat_double(j=3, digits=0) %>%
   hline(i=c(7), border=smlbrdr) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q6. Postive Impression Ranked")
q6_flx

Disaggregation type	Postive Impression By Region	Number	Percent	Confidence interval
Gaza	They help the needy/poor/vulnerable	297	88.8%	83.4% - 94.1%
Gaza	They care about Palestinians	204	61.2%	54.2% - 68.1%
Gaza	They build infrastructure	135	40.4%	33.7% - 47.2%
Gaza	They provide hope	80	23.9%	17.1% - 30.6%
Gaza	Because of what I’ve seen or read	78	23.3%	16.5% - 30.1%
Gaza	The projects are fair	68	20.4%	15.1% - 25.7%
Gaza	Because they are from the US	64	19.3%	13.3% - 25.2%
West Bank	They help the needy/poor/vulnerable	258	82.9%	77.4% - 88.4%
West Bank	They care about Palestinians	174	55.9%	49.6% - 62.2%
West Bank	They build infrastructure	171	54.9%	47.4% - 62.4%
West Bank	The projects are fair	56	18.0%	12.4% - 23.6%
West Bank	Because of what I’ve seen or read	33	10.5%	6.2% - 14.9%
West Bank	They provide hope	30	9.5%	5.3% - 13.7%
West Bank	Because they are from the US	7	2.4%	0.6% - 4.1%
Q6. Postive Impression Ranked

Code

save_as_docx(q6_flx, path=here("output/tables/eval Q1/q6_percep_region.docx"))

Ranked by subregion

Code

q6a_subreg <- disag_tab(svyrdat, q6a_bin, subregion,subreg_key, "q6a", " They help the needy/poor/vulnerable "," Sub region ")
q6b_subreg <- disag_tab(svyrdat, q6b_bin, subregion,subreg_key, "q6b", " They build infrastructure "," Sub region ")
q6c_subreg <- disag_tab(svyrdat, q6c_bin, subregion,subreg_key, "q6c", " They provide hope "," Sub region ")
q6d_subreg <- disag_tab(svyrdat, q6d_bin, subregion,subreg_key, "q6d", " Because of what I’ve seen or read "," Sub region ")
q6e_subreg <- disag_tab(svyrdat, q6e_bin, subregion,subreg_key, "q6e", " Because they are from the US "," Sub region ")
q6f_subreg <- disag_tab(svyrdat, q6f_bin, subregion,subreg_key, "q6f", " The projects are fair "," Sub region ")
q6g_subreg <- disag_tab(svyrdat, q6g_bin, subregion,subreg_key, "q6g", " They care about Palestinians "," Sub region ")
q6_subreg <- bind_rows(q6a_subreg, q6b_subreg, q6c_subreg,
                     q6d_subreg,
                     q6e_subreg,
                     q6f_subreg,
                     q6g_subreg,
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))

q6sub_flx <- q6_subreg %>%
select(`Postive Impression By Sub Region`=Label, Number, Percent, `Confidence interval`=ci) %>% flextable() %>% set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
align(j=4, align="center") %>%
hline(i=c(7,14,21), border=smlbrdr) %>%
colformat_double(j=3, digits=0) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q6. Postive Impression Ranked")

q6sub_flx

Disaggregation type	Postive Impression By Sub Region	Number	Percent	Confidence interval
Central West Bank	They help the needy/poor/vulnerable	64	95.2%	90.8% - 99.6%
Central West Bank	They care about Palestinians	42	62.1%	50.4% - 73.8%
Central West Bank	They build infrastructure	28	41.0%	25.4% - 56.6%
Central West Bank	The projects are fair	13	19.7%	4.8% - 34.7%
Central West Bank	They provide hope	8	11.2%	1.9% - 20.5%
Central West Bank	Because of what I’ve seen or read	3	5.0%	0.8% - 9.2%
Central West Bank	Because they are from the US	0	0.2%	-0.2% - 0.7%
Gaza Strip	They help the needy/poor/vulnerable	297	88.8%	83.4% - 94.1%
Gaza Strip	They care about Palestinians	204	61.2%	54.2% - 68.1%
Gaza Strip	They build infrastructure	135	40.4%	33.7% - 47.2%
Gaza Strip	They provide hope	80	23.9%	17.1% - 30.6%
Gaza Strip	Because of what I’ve seen or read	78	23.3%	16.5% - 30.1%
Gaza Strip	The projects are fair	68	20.4%	15.1% - 25.7%
Gaza Strip	Because they are from the US	64	19.3%	13.3% - 25.2%
Northern West Bank	They help the needy/poor/vulnerable	105	74.7%	65% - 84.5%
Northern West Bank	They build infrastructure	85	60.3%	50.5% - 70%
Northern West Bank	They care about Palestinians	83	58.8%	50% - 67.6%
Northern West Bank	Because of what I’ve seen or read	23	16.6%	8.1% - 25%
Northern West Bank	The projects are fair	20	14.1%	7% - 21.1%
Northern West Bank	They provide hope	14	10.2%	4.3% - 16%
Northern West Bank	Because they are from the US	6	4.6%	1% - 8.3%
Southern West Bank	They help the needy/poor/vulnerable	89	85.9%	78.1% - 93.7%
Southern West Bank	They build infrastructure	59	56.6%	42.6% - 70.6%
Southern West Bank	They care about Palestinians	50	48.0%	36.3% - 59.6%
Southern West Bank	The projects are fair	23	22.3%	12.5% - 32.1%
Southern West Bank	They provide hope	8	7.4%	-0.2% - 15.1%
Southern West Bank	Because of what I’ve seen or read	6	5.9%	1.6% - 10.2%
Southern West Bank	Because they are from the US	1	0.7%	-0.7% - 2%
Q6. Postive Impression Ranked

Code

save_as_docx(q6sub_flx, path=here("output/tables/eval Q1/q6_percep_subregion.docx"))

Q7 Perception of USG

Overall

Code

#frq(dat$q7)

q7 <- fac_tab(svyrdat, q7) %>%
  mutate(lab=percept_key$perception_lab) %>%
  select(Label=q7, Response=lab, Percent:Number, margin, Lower, Upper, ci) %>%
  mutate(Ind="Positive perception of United States Government")

write_csv(q7, here("output/tables/eval Q1/q7.csv"))
#q7

q7_flx <- q7 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q7. Perception of USG")

save_as_docx(q7_flx, path=here("output/tables/eval Q1/q7.docx"))

q7_flx

Label	Response	Number	Percent	Confidence interval
1	Very negative	833	32.7%	29.8% - 35.6%
2	Somewhat negative	474	18.6%	16.6% - 20.6%
3	Neither negative nor positive	520	20.4%	18.1% - 22.7%
4	Somewhat positive	474	18.6%	16.4% - 20.8%
5	Very positive	104	4.1%	2.9% - 5.2%
98	Refused	11	0.4%	0.1% - 0.8%
99	Don't know	132	5.2%	4% - 6.4%
Q7. Perception of USG

Disaggregated

Code

usg_ov <- ov_tab(svyrdat, usg_percep_bin, "q7", "Positive perception of USG") 

usg_sex <- disag_tab(svyrdat, usg_percep_bin, sex, sex_key, 
                    "q7", "Positive perception of USG", "Sex")

usg_age <- disag_tab(svyrdat, usg_percep_bin, age_grp, age_grp_key, 
                    "q7", "Positive perception of USG", "Age group")

usg_ed <- disag_tab(svyrdat, usg_percep_bin, educ_cat, educ_key, 
                    "q7", "Positive perception of USG", "Education")

usg_mad <- disag_tab(svyrdat, usg_percep_bin, madrassa, mad_key, 
                    "q7", "Positive perception of USG", "Madrassa education")

usg_area <- disag_tab(svyrdat, usg_percep_bin, area, area_key, 
                    "q7", "Positive perception of USG", "Area")

usg_gov <- disag_tab(svyrdat, usg_percep_bin, gov,gov_key,
                     "q7","Positive perception of USG", "Governorate")

usg_subreg <- disag_tab(svyrdat, usg_percep_bin, subregion, subreg_key, 
                    "q7", "Positive perception of USG", "Subregion")

usg_reg <- disag_tab(svyrdat, usg_percep_bin, region, reg_key, 
                    "q7", "Positive perception of USG", "Region")

usg_disag <- bind_rows(usg_ov,
                       usg_area,
                      usg_reg,
                      usg_subreg,
                      usg_gov,
                      usg_sex,
                      usg_age,
                      usg_ed,
                      usg_mad)

write_csv(usg_disag, here("output/tables/eval Q1/q7 usg disag.csv"))

usg_disag_flx <- usg_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  #hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  border_inner_h() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q7. Positive perception of USG")

save_as_docx(usg_disag_flx, path=here("output/tables/eval Q1/q7 usg disag.docx"))

usg_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Positive perception of USG	578	22.7%	20.3% - 25.1%
Area	Urban	473	24.0%	21.2% - 26.8%
	Village	68	18.2%	11.2% - 25.1%
	Refugee camp	37	18.3%	10.5% - 26%
Region	West Bank	233	15.7%	12.8% - 18.5%
	Gaza	345	32.6%	28.3% - 36.8%
Subregion	Northern West Bank	89	15.3%	10.9% - 19.7%
	Central West Bank	60	15.0%	10.1% - 19.9%
	Southern West Bank	84	16.6%	11.2% - 22%
	Gaza Strip	345	32.6%	28.3% - 36.8%
Governorate	Jenin	22	14.6%	5.3% - 24%
	Tubas	5	14.5%	7.3% - 21.8%
	Tulkarm	14	17.0%	5.6% - 28.4%
	Nablus	41	18.7%	10.5% - 27%
	Qalqiliya	0	0.5%	-0.6% - 1.5%
	Salfit	7	17.5%	12.1% - 23%
	Ramallah	32	18.5%	10.5% - 26.6%
	Jericho	4	18.0%	7.4% - 28.5%
	Jerusalem	24	11.7%	5.2% - 18.2%
	Bethlehem	27	22.1%	11% - 33.3%
	Hebron	57	14.8%	8.8% - 20.9%
	North Gaza	62	29.0%	19.5% - 38.6%
	Gaza	156	41.3%	33.9% - 48.6%
	Dier al-Balah	19	12.8%	3.3% - 22.4%
	Khan Yunis	50	26.3%	17.4% - 35.2%
	Rafah	57	45.2%	29.7% - 60.7%
Sex	Male	266	20.9%	17.9% - 23.9%
	Female	312	24.4%	21.1% - 27.8%
Age group	Youth (18-29)	283	26.5%	22.5% - 30.5%
	Adult (30-54)	246	22.3%	19.4% - 25.3%
	Mature (55+)	49	12.9%	9.5% - 16.3%
Education	Elementary school education	98	18.0%	13.5% - 22.6%
	Secondary education	277	25.2%	21.4% - 29%
	Post-secondary education	204	22.4%	19.2% - 25.6%
Madrassa education	No madrassa education	456	20.7%	18.2% - 23.3%
	Madrassa education	122	35.0%	29.5% - 40.5%
Q7. Positive perception of USG

Q7a Perception of US people

Overall

Code

#frq(dat$q7a)

q7a <- fac_tab(svyrdat, q7a) %>%
  mutate(lab=percept_key$perception_lab) %>%
  select(Label=q7a, Response=lab, Percent:Number, margin, Lower, Upper, ci) %>%
  mutate(Ind="Positive perception of United States people")

write_csv(q7a, here("output/tables/eval Q1/q7a.csv"))
#q7a

q7a_flx <- q7a %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q7a. Perception of United States people")

save_as_docx(q7a_flx, path=here("output/tables/eval Q1/q7a.docx"))

q7a_flx

Label	Response	Number	Percent	Confidence interval
1	Very negative	371	14.6%	12.6% - 16.5%
2	Somewhat negative	429	16.8%	14.9% - 18.8%
3	Neither negative nor positive	386	15.1%	13.3% - 17%
4	Somewhat positive	899	35.3%	32.8% - 37.7%
5	Very positive	267	10.5%	8.7% - 12.2%
98	Refused	11	0.4%	0.1% - 0.7%
99	Don't know	186	7.3%	6% - 8.6%
Q7a. Perception of United States people

Disaggregated

Code

usp_ov <- ov_tab(svyrdat, usp_bin, "q7a", "Positive perception of American people") 

usp_sex <- disag_tab(svyrdat, usp_bin, sex, sex_key, 
                    "q7a", "Positive perception of American people", "Sex")

usp_age <- disag_tab(svyrdat, usp_bin, age_grp, age_grp_key, 
                    "q7a", "Positive perception of American people", "Age group")

usp_ed <- disag_tab(svyrdat, usp_bin, educ_cat, educ_key, 
                    "q7a", "Positive perception of American people", "Education")

usp_mad <- disag_tab(svyrdat, usp_bin, madrassa, mad_key, 
                    "q7a", "Positive perception of American people", "Madrassa")

usp_area <- disag_tab(svyrdat, usp_bin, area, area_key, 
                    "q7a", "Positive perception of American people", "Area")

usp_gov <- disag_tab(svyrdat, usp_bin, gov,gov_key,
                     "q7a","Positive perception of American people", "Governorate")

usp_subreg <- disag_tab(svyrdat, usp_bin, subregion, subreg_key, 
                    "q7a", "Positive perception of American people", "Subregion")

usp_reg <- disag_tab(svyrdat, usp_bin, region, reg_key, 
                    "q7a", "Positive perception of American people", "Region")

usp_disag <- bind_rows(usp_ov,
                       usp_area,
                      usp_reg,
                      usp_subreg,
                      usp_gov,
                      usp_sex,
                      usp_age,
                      usp_ed,
                      usp_mad)

write_csv(usp_disag, here("output/tables/eval Q1/q7a usp disag.csv"))

usp_disag_flx <- usp_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q7a. Positive perception of United States people") %>%
  border_inner_h()

save_as_docx(usp_disag_flx, path=here("output/tables/eval Q1/q7a usp disag.docx"))

usp_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Positive perception of American people	1,166	45.7%	43% - 48.5%
Area	Urban	893	45.3%	42.1% - 48.5%
	Village	191	50.7%	45% - 56.4%
	Refugee camp	82	40.6%	28.8% - 52.4%
Region	West Bank	751	50.4%	47% - 53.8%
	Gaza	415	39.2%	34.7% - 43.6%
Subregion	Northern West Bank	272	47.0%	41.8% - 52.1%
	Central West Bank	200	49.8%	43.9% - 55.7%
	Southern West Bank	279	54.8%	48.1% - 61.5%
	Gaza Strip	415	39.2%	34.7% - 43.6%
Governorate	Jenin	62	41.1%	34% - 48.1%
	Tubas	15	43.6%	33.9% - 53.3%
	Tulkarm	35	44.2%	26.4% - 62%
	Nablus	113	51.7%	42.4% - 60.9%
	Qalqiliya	24	44.4%	22.9% - 65.9%
	Salfit	23	55.7%	45.4% - 66%
	Ramallah	88	50.6%	40.6% - 60.6%
	Jericho	9	41.9%	26.9% - 56.9%
	Jerusalem	103	49.9%	42.3% - 57.5%
	Bethlehem	66	54.1%	42.9% - 65.3%
	Hebron	213	55.0%	47% - 63.1%
	North Gaza	131	60.8%	52.1% - 69.5%
	Gaza	81	21.5%	15.6% - 27.3%
	Dier al-Balah	66	44.6%	28.6% - 60.6%
	Khan Yunis	73	38.2%	27.5% - 49%
	Rafah	63	50.6%	36.4% - 64.8%
Sex	Male	587	46.2%	42.7% - 49.7%
	Female	578	45.3%	41.7% - 48.8%
Age group	Youth (18-29)	481	45.0%	40.9% - 49.1%
	Adult (30-54)	527	47.9%	44.3% - 51.5%
	Mature (55+)	157	41.4%	36.5% - 46.3%
Education	Elementary school education	230	42.4%	37% - 47.8%
	Secondary education	526	47.9%	44.2% - 51.5%
	Post-secondary education	410	45.1%	40.9% - 49.4%
Madrassa	No madrassa education	1,049	47.7%	44.8% - 50.6%
	Madrassa education	117	33.4%	27.1% - 39.7%
Q7a. Positive perception of United States people

Q8 USAID performance

Overall

Code

#frq(dat$q8)

q8 <- fac_tab(svyrdat, q8) %>%
  mutate(lab=percept_key$perception_lab) %>%
  select(Label=q8, Response=lab, Percent:Number, margin, Lower, Upper, ci) %>%
  mutate(Ind="Positive perception of USAID performance")

write_csv(q8, here("output/tables/eval Q1/q8.csv"))
#q8

q8_flx <- q8 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q8. Perception of USAID performance")

save_as_docx(q8_flx, path=here("output/tables/eval Q1/q8.docx"))

q8_flx

Label	Response	Number	Percent	Confidence interval
1	Very negative	162	6.4%	5.1% - 7.7%
2	Somewhat negative	271	10.6%	8.9% - 12.3%
3	Neither negative nor positive	523	20.5%	18.2% - 22.8%
4	Somewhat positive	1,009	39.6%	36.8% - 42.4%
5	Very positive	410	16.1%	13.7% - 18.5%
98	Refused	8	0.3%	0% - 0.6%
99	Don't know	165	6.5%	5.1% - 7.9%
Q8. Perception of USAID performance

Disaggregated

Code

#frq(dat$usaid_perf_bin)

perf_ov <- ov_tab(svyrdat, usaid_perf_bin, "q8", "Positive perception of USAID performance") 
# 3 margin, 2.5 deff

perf_sex <- disag_tab(svyrdat, usaid_perf_bin, sex, sex_key, 
                    "q8", "Positive perception of USAID performance", "Sex")

perf_age <- disag_tab(svyrdat, usaid_perf_bin, age_grp, age_grp_key, 
                    "q8", "Positive perception of USAID performance", "Age group")

perf_ed <- disag_tab(svyrdat, usaid_perf_bin, educ_cat, educ_key, 
                    "q8", "Positive perception of USAID performance", "Education")

perf_mad <- disag_tab(svyrdat, usaid_perf_bin, madrassa, mad_key, 
                    "q8", "Positive perception of USAID performance", "Madrassa education")

perf_area <- disag_tab(svyrdat, usaid_perf_bin, area, area_key, 
                    "q8", "Positive perception of USAID performance", "Area")

perf_gov <- disag_tab(svyrdat, usaid_perf_bin, gov,gov_key,
                     "q8","Positive perception of USAID performance", "Governorate")

perf_subreg <- disag_tab(svyrdat, usaid_perf_bin, subregion, subreg_key, 
                    "q8", "Positive perception of USAID performance", "Subregion")

perf_reg <- disag_tab(svyrdat, usaid_perf_bin, region, reg_key, 
                    "q8", "Positive perception of USAID performance", "Region") # 5.3 margin

perf_disag <- bind_rows(perf_ov,
                        perf_area,
                      perf_reg,
                      perf_subreg,
                      perf_gov,
                      perf_sex,
                      perf_age,
                      perf_ed,
                      perf_mad)

write_csv(perf_disag, here("output/tables/eval Q1/q8 perf disag.csv"))

perf_disag_flx <- perf_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  #hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q8. Positive perception of USAID performance") %>%
  border_inner_h()

save_as_docx(perf_disag_flx, path=here("output/tables/eval Q1/q8 perf disag.docx"))

perf_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Positive perception of USAID performance	1,419	55.7%	52.6% - 58.7%
Area	Urban	1,072	54.4%	50.9% - 57.8%
	Village	239	63.5%	55.8% - 71.2%
	Refugee camp	109	54.0%	42.8% - 65.3%
Region	West Bank	824	55.3%	51.8% - 58.9%
	Gaza	595	56.2%	50.8% - 61.5%
Subregion	Northern West Bank	313	54.0%	47.6% - 60.4%
	Central West Bank	217	54.0%	48.8% - 59.2%
	Southern West Bank	294	57.8%	51.5% - 64.1%
	Gaza Strip	595	56.2%	50.8% - 61.5%
Governorate	Jenin	84	55.1%	43.3% - 67%
	Tubas	22	65.6%	42.8% - 88.5%
	Tulkarm	26	32.6%	19.3% - 45.9%
	Nablus	124	56.7%	45.4% - 68.1%
	Qalqiliya	35	64.1%	46.6% - 81.7%
	Salfit	22	54.5%	31.8% - 77.1%
	Ramallah	99	56.7%	49.2% - 64.2%
	Jericho	13	60.1%	44.7% - 75.6%
	Jerusalem	105	51.1%	43.7% - 58.6%
	Bethlehem	62	50.9%	37.9% - 63.8%
	Hebron	232	60.0%	52.7% - 67.3%
	North Gaza	155	72.3%	63.5% - 81%
	Gaza	204	54.0%	47% - 61%
	Dier al-Balah	58	39.2%	19.9% - 58.6%
	Khan Yunis	104	54.3%	41.5% - 67.2%
	Rafah	73	58.0%	38.1% - 77.9%
Sex	Male	702	55.2%	51.3% - 59.1%
	Female	717	56.1%	52.3% - 60%
Age group	Youth (18-29)	617	57.7%	53.2% - 62.1%
	Adult (30-54)	635	57.7%	54.3% - 61.2%
	Mature (55+)	167	44.1%	39.2% - 49.1%
Education	Elementary school education	272	50.2%	44.6% - 55.8%
	Secondary education	609	55.4%	51.3% - 59.5%
	Post-secondary education	539	59.3%	55% - 63.6%
Madrassa education	No madrassa education	1,212	55.1%	51.9% - 58.3%
	Madrassa education	207	59.4%	52.7% - 66.1%
Q8. Positive perception of USAID performance

Q9 USAID activities

Overall

Code

#frq(dat$q8)

q9 <- fac_tab(svyrdat, q9) %>%
  #mutate(lab=percept_key$perception_lab) %>%
  left_join(yes_key,
            by=c("q9"="yes_no")) %>%
  select(Label=q9, Response=yes_no_lab, Percent:Number, margin, Lower, Upper, ci)

write_csv(q9, here("output/tables/eval Q1/q9.csv"))
#q9

q9_flx <- q9 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q9. Noticed USAID activities")

save_as_docx(q9_flx, path=here("output/tables/eval Q1/q9.docx"))

q9_flx

Label	Response	Number	Percent	Confidence interval
0	No	1,850	72.6%	70% - 75.2%
1	Yes	616	24.2%	21.8% - 26.6%
98	Refused	27	1.1%	0.5% - 1.6%
99	Don't know	56	2.2%	1.4% - 3%
Q9. Noticed USAID activities

Disaggregated

Code

q9_ov <- ov_tab(svyrdat, us_act_exposure, "q9", "Exposed to USAID activity") 

q9_sex <- disag_tab(svyrdat, us_act_exposure, sex, sex_key, 
                    "q9", "Exposed to USAID activity", "Sex")

q9_age <- disag_tab(svyrdat, us_act_exposure, age_grp, age_grp_key, 
                    "q9", "Exposed to USAID activity", "Age group")

q9_ed <- disag_tab(svyrdat, us_act_exposure, educ_cat, educ_key, 
                    "q9", "Exposed to USAID activity", "Education")

q9_mad <- disag_tab(svyrdat, us_act_exposure, madrassa, mad_key, 
                    "q9", "Exposed to USAID activity", "Madrassa education")

q9_area <- disag_tab(svyrdat, us_act_exposure, area, area_key, 
                    "q9", "Exposed to USAID activity", "Area")

q9_gov <- disag_tab(svyrdat, us_act_exposure, gov,gov_key,
                     "q9","Exposed to USAID activity", "Governorate")

q9_subreg <- disag_tab(svyrdat, us_act_exposure, subregion, subreg_key, 
                    "q9", "Exposed to USAID activity", "Subregion")

q9_reg <- disag_tab(svyrdat, us_act_exposure, region, reg_key, 
                    "q9", "Exposed to USAID activity", "Region")

q9_disag <- bind_rows(q9_ov,
                      q9_reg,
                      q9_subreg,
                      q9_area,
                      q9_sex,
                      q9_age,
                      q9_ed,
                      q9_mad)

q9_disag_flx <- q9_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q9. Noticed implementation of USAID activities") %>%
  border_inner_h()

save_as_docx(q9_disag_flx, path=here("output/tables/eval Q1/q9 usaid acts disag.docx"))

q9_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Exposed to USAID activity	616	24.2%	21.8% - 26.6%
Region	West Bank	349	23.4%	20.5% - 26.3%
	Gaza	267	25.3%	21.1% - 29.4%
Subregion	Northern West Bank	150	25.8%	21.9% - 29.7%
	Central West Bank	84	21.0%	16.3% - 25.6%
	Southern West Bank	115	22.5%	16.3% - 28.8%
	Gaza Strip	267	25.3%	21.1% - 29.4%
Area	Urban	456	23.1%	20.3% - 26%
	Village	108	28.7%	23.7% - 33.7%
	Refugee camp	52	25.8%	18.4% - 33.2%
Sex	Male	351	27.6%	24.3% - 30.9%
	Female	265	20.8%	17.8% - 23.7%
Age group	Youth (18-29)	262	24.5%	20.5% - 28.6%
	Adult (30-54)	280	25.4%	22.5% - 28.4%
	Mature (55+)	74	19.4%	15.8% - 23.1%
Education	Elementary school education	91	16.7%	13.2% - 20.3%
	Secondary education	247	22.5%	19.1% - 26%
	Post-secondary education	278	30.6%	26.8% - 34.4%
Madrassa education	No madrassa education	499	22.7%	20.2% - 25.2%
	Madrassa education	117	33.6%	27.4% - 39.8%
Q9. Noticed implementation of USAID activities

Q10 USAID activities implemented, ranked

Code

q10a <- ov_tab(svyrdat, q10a_bin, " q10a", " Food security and nutritional support") 
q10b <- ov_tab(svyrdat, q10b_bin, " q10b ", " Job training and support") 
q10c <- ov_tab(svyrdat, q10c_bin, " q10c ", " Support for local community groups") 
q10d <- ov_tab(svyrdat, q10d_bin, " q10d ", " Economic growth activities for small businesses") 
q10e <- ov_tab(svyrdat, q10e_bin, " q10e ", " Water and wastewater activities") 
q10f <- ov_tab(svyrdat, q10f_bin, " q10f ", " Transportation activities") 
q10g <- ov_tab(svyrdat, q10g_bin, " q10g ", " Youth focused activities") 
q10h <- ov_tab(svyrdat, q10g_bin, " q10h ", " Health activities") 
q10i <- ov_tab(svyrdat, q10g_bin, " q10i ", " Cross border peacebuilding") 
q10j <- ov_tab(svyrdat, q10g_bin, " q10j ", " Tourism") 
q10ja <- ov_tab(svyrdat, q10ja_bin, " q10j_a", " Educational and school activities") 

q10k <- ov_tab(svyrdat, q10g_bin, " q10k ", " Agriculture") 


q10  <- bind_rows(q10a, q10b, q10c, q10d, q10e, q10f, q10g, q10h, q10i, q10j, q10ja ,q10k) %>%
     arrange(desc(Percent))

#q10 
write_csv(q10, here("output/tables/eval Q1/q10.csv"))

q10_flx <- q10 %>%
select(`Activity`=Label, Number, Percent, `Confidence interval`=ci) %>% 
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>%
colformat_double(j=2, digits=0) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q10. Part of USAID Actvitiy Ranked")
q10_flx

Activity	Number	Percent	Confidence interval
Food security and nutritional support	330	53.5%	48.1% - 58.9%
Educational and school activities	257	41.8%	36.5% - 47.1%
Water and wastewater activities	241	39.2%	33.4% - 44.9%
Support for local community groups	159	25.8%	21.4% - 30.2%
Economic growth activities for small businesses	139	22.5%	17.7% - 27.3%
Youth focused activities	119	19.3%	15% - 23.6%
Health activities	119	19.3%	15% - 23.6%
Cross border peacebuilding	119	19.3%	15% - 23.6%
Tourism	119	19.3%	15% - 23.6%
Agriculture	119	19.3%	15% - 23.6%
Job training and support	105	17.0%	13.2% - 20.8%
Transportation activities	59	9.6%	6.7% - 12.4%
Q10. Part of USAID Actvitiy Ranked

Code

save_as_docx(q10_flx, path=here("output/tables/eval Q1/q10.docx"))

Ranked by region

Code

q10a_reg <- disag_tab(svyrdat, q10a_bin, region,reg_key, "q10a", " Food security and nutritional support ","Region")
q10b_reg <- disag_tab(svyrdat, q10b_bin, region,reg_key, "q10b", " Job training and support ","Region")
q10c_reg <- disag_tab(svyrdat, q10c_bin, region,reg_key, "q10c", " Support for local community groups ","Region")
q10d_reg <- disag_tab(svyrdat, q10d_bin, region,reg_key, "q10d", " Economic growth activities for small businesses ","Region")
q10e_reg <- disag_tab(svyrdat, q10e_bin, region,reg_key, "q10e", " Water and wastewater activities ","Region")
q10f_reg <- disag_tab(svyrdat, q10f_bin, region,reg_key, "q10f", " Transportation activities ","Region")
q10g_reg <- disag_tab(svyrdat, q10g_bin, region,reg_key, "q10g", " Youth focused activities ","Region")
q10h_reg <- disag_tab(svyrdat, q10h_bin, region,reg_key, "q10h", " Health activities ","Region")
q10i_reg <- disag_tab(svyrdat, q10i_bin, region,reg_key, "q10i", " Cross border peacebuilding","Region")
q10j_reg <- disag_tab(svyrdat, q10j_bin, region,reg_key, "q10j", " Tourism","Region")
q10ja_reg <- disag_tab(svyrdat, q10ja_bin, region,reg_key, " q10j_a", " Educational and school activities ","Region")

q10k_reg <- disag_tab(svyrdat, q10k_bin, region,reg_key, "q10k", " Agriculture","Region")


q10_reg <- bind_rows(q10a_reg, q10b_reg, q10c_reg,
                     q10d_reg,
                     q10e_reg,
                     q10f_reg,
                     q10g_reg, q10h_reg, q10i_reg ,q10j_reg, q10k_reg, q10ja_reg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))

#q10_reg
write_csv(q10_reg, here("output/tables/eval Q1/q10reg.csv"))

q10_flx <- q10_reg %>% select(`Activity By Region `=Label, Number, Percent, `Confidence interval`=ci) %>%
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>%
colformat_double(j=3, digits=0) %>%
hline(i=c(12), border=smlbrdr) %>%
set_table_properties(layout="autofit") %>% 
add_footer_lines(values="Q10. Part of USAID Actvitiy By Region")
q10_flx

Disaggregation type	Activity By Region	Number	Percent	Confidence interval
Gaza	Food security and nutritional support	189	70.5%	63% - 78.1%
Gaza	Educational and school activities	100	37.3%	28.8% - 45.9%
Gaza	Economic growth activities for small businesses	96	36.1%	28.9% - 43.2%
Gaza	Health activities	91	33.9%	27.3% - 40.4%
Gaza	Water and wastewater activities	83	30.9%	22.5% - 39.2%
Gaza	Support for local community groups	73	27.4%	21.3% - 33.6%
Gaza	Youth focused activities	70	26.2%	18.3% - 34%
Gaza	Job training and support	67	24.9%	18% - 31.7%
Gaza	Agriculture	33	12.5%	7.6% - 17.4%
Gaza	Cross border peacebuilding	31	11.6%	6.3% - 16.8%
Gaza	Transportation activities	18	6.7%	2.9% - 10.5%
Gaza	Tourism	14	5.2%	2% - 8.4%
West Bank	Water and wastewater activities	159	45.5%	37.8% - 53.2%
West Bank	Educational and school activities	158	45.2%	38.7% - 51.7%
West Bank	Food security and nutritional support	141	40.5%	33.2% - 47.7%
West Bank	Health activities	133	38.1%	31.1% - 45.1%
West Bank	Support for local community groups	86	24.6%	18.3% - 30.9%
West Bank	Agriculture	50	14.2%	9.7% - 18.8%
West Bank	Youth focused activities	49	14.0%	9.4% - 18.7%
West Bank	Economic growth activities for small businesses	42	12.1%	6.4% - 17.8%
West Bank	Transportation activities	41	11.7%	7.6% - 15.9%
West Bank	Job training and support	38	10.9%	6.8% - 15.1%
West Bank	Cross border peacebuilding	7	2.0%	0.1% - 3.9%
West Bank	Tourism	5	1.4%	-0.2% - 3.1%
Q10. Part of USAID Actvitiy By Region

Code

save_as_docx(q10_flx, path=here("output/tables/eval Q1/q10region.docx"))

Ranked by subregion

Code

q10a_subreg <- disag_tab(svyrdat, q10a_bin, subregion,subreg_key, "q10a", " Food security and nutritional support "," Sub region ")
q10b_subreg <- disag_tab(svyrdat, q10b_bin, subregion,subreg_key, "q10b", " Job training and support "," Sub region ")
q10c_subreg <- disag_tab(svyrdat, q10c_bin, subregion,subreg_key, "q10c", " Support for local community groups "," Sub region ")
q10d_subreg <- disag_tab(svyrdat, q10d_bin, subregion,subreg_key, "q10d", " Economic growth activities for small businesses "," Sub region ")
q10e_subreg <- disag_tab(svyrdat, q10e_bin, subregion,subreg_key, "q10e", " Water and wastewater activities "," Sub region ")
q10f_subreg <- disag_tab(svyrdat, q10f_bin, subregion,subreg_key, "q10f", " Transportation activities "," Sub region ")
q10g_subreg <- disag_tab(svyrdat, q10g_bin, subregion,subreg_key, "q10g", " Youth focused activities "," Sub region ")
q10h_subreg <- disag_tab(svyrdat, q10h_bin, subregion,subreg_key, "q10h", " Health activities "," Sub region ")
q10i_subreg <- disag_tab(svyrdat, q10i_bin, subregion,subreg_key, "q10i", " Cross border peacebuilding "," Sub region ")
q10j_subreg <- disag_tab(svyrdat, q10j_bin, subregion,subreg_key, "q10j", " Tourism "," Sub region ")
q10ja_subreg <- disag_tab(svyrdat, q10ja_bin, subregion,subreg_key, " q10j_a", " Educational and school activities "," Sub region ")

q10k_subreg <- disag_tab(svyrdat, q10k_bin, subregion,subreg_key, "q10k", " Agriculture "," Sub region ")


q10_subreg <- bind_rows(q10a_subreg, q10b_subreg, q10c_subreg,
                     q10d_subreg,
                     q10e_subreg,
                     q10f_subreg,
                     q10g_subreg, q10h_subreg, q10i_subreg, q10j_subreg, q10k_subreg, q10ja_subreg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))


#q10_subreg

write_csv(q10_subreg, here("output/tables/eval Q1/q10subreg.csv"))


q10sub_flx <- q10_subreg %>% select(`Part of USAID Actvitiy By SubRegion`=Label, Number, Percent, `Confidence interval`=ci) %>%
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>%
colformat_double(j=3, digits=0) %>%
set_table_properties(layout="autofit") %>% 
hline(i=c(12,24,36), border=smlbrdr) %>%
add_footer_lines(values="Q10. Part of USAID Actvitiy By SubRegion")

q10sub_flx 

Disaggregation type	Part of USAID Actvitiy By SubRegion	Number	Percent	Confidence interval
Central West Bank	Educational and school activities	46	54.2%	40.1% - 68.2%
Central West Bank	Health activities	35	41.5%	27.3% - 55.8%
Central West Bank	Water and wastewater activities	31	37.2%	22.7% - 51.6%
Central West Bank	Food security and nutritional support	28	33.6%	18.7% - 48.5%
Central West Bank	Support for local community groups	11	13.3%	5.5% - 21%
Central West Bank	Youth focused activities	11	12.6%	3.7% - 21.4%
Central West Bank	Economic growth activities for small businesses	8	9.7%	-1% - 20.3%
Central West Bank	Agriculture	8	9.1%	0.1% - 18.1%
Central West Bank	Transportation activities	8	9.0%	1% - 17.1%
Central West Bank	Job training and support	6	7.5%	2.2% - 12.7%
Central West Bank	Cross border peacebuilding	0	0.0%	0% - 0%
Central West Bank	Tourism	0	0.0%	0% - 0%
Gaza Strip	Food security and nutritional support	189	70.5%	63% - 78.1%
Gaza Strip	Educational and school activities	100	37.3%	28.8% - 45.9%
Gaza Strip	Economic growth activities for small businesses	96	36.1%	28.9% - 43.2%
Gaza Strip	Health activities	91	33.9%	27.3% - 40.4%
Gaza Strip	Water and wastewater activities	83	30.9%	22.5% - 39.2%
Gaza Strip	Support for local community groups	73	27.4%	21.3% - 33.6%
Gaza Strip	Youth focused activities	70	26.2%	18.3% - 34%
Gaza Strip	Job training and support	67	24.9%	18% - 31.7%
Gaza Strip	Agriculture	33	12.5%	7.6% - 17.4%
Gaza Strip	Cross border peacebuilding	31	11.6%	6.3% - 16.8%
Gaza Strip	Transportation activities	18	6.7%	2.9% - 10.5%
Gaza Strip	Tourism	14	5.2%	2% - 8.4%
Northern West Bank	Water and wastewater activities	75	49.9%	38.3% - 61.6%
Northern West Bank	Educational and school activities	70	46.8%	37.7% - 55.8%
Northern West Bank	Food security and nutritional support	58	38.5%	29.7% - 47.3%
Northern West Bank	Health activities	49	32.5%	23.7% - 41.3%
Northern West Bank	Support for local community groups	39	25.8%	17.1% - 34.4%
Northern West Bank	Transportation activities	23	15.4%	8.8% - 22%
Northern West Bank	Agriculture	22	14.7%	8.3% - 21.1%
Northern West Bank	Economic growth activities for small businesses	15	10.2%	4.7% - 15.7%
Northern West Bank	Job training and support	14	9.5%	3.9% - 15.1%
Northern West Bank	Youth focused activities	12	7.8%	2.9% - 12.7%
Northern West Bank	Tourism	5	3.4%	-0.6% - 7.3%
Northern West Bank	Cross border peacebuilding	4	2.5%	-0.6% - 5.7%
Southern West Bank	Food security and nutritional support	55	48.1%	33.4% - 62.8%
Southern West Bank	Water and wastewater activities	53	45.9%	31.8% - 59.9%
Southern West Bank	Health activities	49	42.8%	28% - 57.6%
Southern West Bank	Educational and school activities	42	36.6%	24.4% - 48.8%
Southern West Bank	Support for local community groups	36	31.4%	17.5% - 45.2%
Southern West Bank	Youth focused activities	27	23.2%	13.5% - 32.9%
Southern West Bank	Agriculture	20	17.3%	8.8% - 25.8%
Southern West Bank	Economic growth activities for small businesses	19	16.4%	3% - 29.8%
Southern West Bank	Job training and support	18	15.4%	6.3% - 24.5%
Southern West Bank	Transportation activities	10	8.9%	2.2% - 15.6%
Southern West Bank	Cross border peacebuilding	3	2.8%	-1.1% - 6.7%
Southern West Bank	Tourism	0	0.0%	0% - 0%
Q10. Part of USAID Actvitiy By SubRegion

Code

save_as_docx(q10sub_flx, path=here("output/tables/eval Q1/q10subreg.docx"))

Q11 Participation in USAID activity

Overall

Code

q11 <- fac_tab(svyrdat, q11) %>%
  #mutate(lab=percept_key$perception_lab) %>%
  left_join(yes_key,
            by=c("q11"="yes_no")) %>%
  select(Label=q11, Response =yes_no_lab, Percent:Number, margin, Lower, Upper, ci)
write_csv(q11, here("output/tables/eval Q1/q11.csv"))
#q11

q11_flx <- q11 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q11. Participated in USAID activity")

save_as_docx(q11_flx, path=here("output/tables/eval Q1/q11.docx"))

q11_flx

Label	Response	Number	Percent	Confidence interval
0	No	2,396	94.0%	92.8% - 95.3%
1	Yes	129	5.1%	4% - 6.2%
98	Refused	12	0.5%	0.1% - 0.8%
99	Don't know	11	0.4%	0.1% - 0.7%
Q11. Participated in USAID activity

Disaggregated

Code

q11_ov <- ov_tab(svyrdat, usaid_training, "q11", "Participated in USAID activity") 

q11_sex <- disag_tab(svyrdat, usaid_training, sex, sex_key, 
                    "q11", "Participated in USAID activity", "Sex")

q11_age <- disag_tab(svyrdat, usaid_training, age_grp, age_grp_key, 
                    "q11", "Participated in USAID activity", "Age group")

q11_ed <- disag_tab(svyrdat, usaid_training, educ_cat, educ_key, 
                    "q11", "Participated in USAID activity", "Education")

q11_mad <- disag_tab(svyrdat, usaid_training, madrassa, mad_key, 
                    "q11", "Participated in USAID activity", "Madrassa education")

q11_area <- disag_tab(svyrdat, usaid_training, area, area_key, 
                    "q11", "Participated in USAID activity", "Area")

q11_gov <- disag_tab(svyrdat, usaid_training, gov,gov_key,
                     "q11","Participated in USAID activity", "Governorate")

q11_subreg <- disag_tab(svyrdat, usaid_training, subregion, subreg_key, 
                    "q11", "Participated in USAID activity", "Subregion")

q11_reg <- disag_tab(svyrdat, usaid_training, region, reg_key, 
                    "q11", "Participated in USAID activity", "Region")


q11_disag <- bind_rows(q11_ov,
                       q11_area,
                      q11_reg,
                      q11_subreg,
                      q11_gov,
                      q11_sex,
                      q11_age,
                      q11_ed,
                      q11_mad)

write_csv(q11_disag, here("output/tables/eval Q1/q11 usaid participation disag.csv"))

q11_disag_flx <- q11_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q11. Participated in USAID USAID activity") %>%
  border_inner_h()

save_as_docx(q11_disag_flx, path=here("output/tables/eval Q1/q11 usaid acts disag.docx"))

q11_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Participated in USAID activity	129	5.1%	4% - 6.2%
Area	Urban	109	5.5%	4.1% - 6.9%
	Village	5	1.5%	0.4% - 2.5%
	Refugee camp	15	7.4%	3.1% - 11.6%
Region	West Bank	31	2.1%	1.1% - 3.1%
	Gaza	98	9.3%	6.9% - 11.6%
Subregion	Northern West Bank	10	1.8%	0.4% - 3.1%
	Central West Bank	9	2.1%	0.9% - 3.4%
	Southern West Bank	12	2.4%	0.2% - 4.7%
	Gaza Strip	98	9.3%	6.9% - 11.6%
Governorate	Jenin	5	3.2%	-0.3% - 6.7%
	Tubas	0	0.8%	-1% - 2.6%
	Tulkarm	1	1.0%	-1% - 3.1%
	Nablus	2	1.0%	-1% - 3%
	Qalqiliya	0	0.8%	-0.8% - 2.4%
	Salfit	2	4.0%	-1.9% - 9.8%
	Ramallah	6	3.2%	0.8% - 5.6%
	Jericho	0	1.3%	-0.3% - 3%
	Jerusalem	3	1.3%	0.1% - 2.5%
	Bethlehem	3	2.1%	-0.1% - 4.4%
	Hebron	10	2.5%	-0.3% - 5.4%
	North Gaza	24	11.2%	6.6% - 15.8%
	Gaza	48	12.6%	7.7% - 17.6%
	Dier al-Balah	2	1.3%	-1.3% - 4%
	Khan Yunis	14	7.3%	2% - 12.6%
	Rafah	10	8.2%	1.9% - 14.6%
Sex	Male	68	5.4%	4% - 6.8%
	Female	61	4.8%	3.3% - 6.3%
Age group	Youth (18-29)	51	4.7%	2.9% - 6.6%
	Adult (30-54)	70	6.3%	4.8% - 7.9%
	Mature (55+)	9	2.3%	1.1% - 3.6%
Education	Elementary school education	15	2.8%	1.3% - 4.3%
	Secondary education	55	5.0%	3.4% - 6.7%
	Post-secondary education	59	6.5%	4.5% - 8.4%
Madrassa education	No madrassa education	91	4.2%	3.1% - 5.2%
	Madrassa education	38	10.9%	7% - 14.8%
Q11. Participated in USAID USAID activity

Q12 USAID activities participated in, ranked

Code

q12a <- ov_tab(svyrdat, q12a_bin, " q12a", " Food security and nutritional support") 
q12b <- ov_tab(svyrdat, q12b_bin, " q12b ", " Job training and support") 
q12d <- ov_tab(svyrdat, q12d_bin, " q12d ", " Support for local community groups") 
q12h <- ov_tab(svyrdat, q12h_bin, " q12h ", " Economic growth activities for small businesses") 
q12i <- ov_tab(svyrdat, q12i_bin, " q12i ", " Water and wastewater activities") 
q12c <- ov_tab(svyrdat, q12c_bin, " q12c ", " Youth focused activities") 
q12e <- ov_tab(svyrdat, q12e_bin, " q12e ", " Health activities") 
q12j <- ov_tab(svyrdat, q12j_bin, " q12j ", " Cross border peacebuilding") 
q12k <- ov_tab(svyrdat, q12k_bin, " q12k ", " Tourism") 
q12f <- ov_tab(svyrdat, q12f_bin, " q12f", " Educational and school activities")
q12g <- ov_tab(svyrdat, q12g_bin, " q12g ", " Agriculture") 

q12  <- bind_rows(q12a, q12b, q12c, q12d, q12e, q12f, q12g, q12h, q12i, q12j, q12k) %>%
      arrange(desc(Percent))

#q12 
write_csv(q12, here("output/tables/eval Q1/q12.csv"))

q12_flx <- q12 %>% select(` USAID activities implemented  `=Label, Number, Percent, `Confidence interval`=ci) %>%
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>%
colformat_double(j=2, digits=0) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q12. USAID activities implemented anked")


q12_flx

USAID activities implemented	Number	Percent	Confidence interval
Food security and nutritional support	74	57.3%	46% - 68.7%
Health activities	59	45.6%	35.3% - 55.9%
Educational and school activities	48	36.8%	28.1% - 45.6%
Youth focused activities	47	36.2%	26.6% - 45.7%
Job training and support	41	31.7%	22.5% - 40.8%
Support for local community groups	36	27.7%	18% - 37.5%
Agriculture	30	23.2%	13.1% - 33.3%
Water and wastewater activities	28	22.0%	14% - 30%
Tourism	21	16.4%	9.4% - 23.4%
Cross border peacebuilding	20	15.4%	8.2% - 22.6%
Economic growth activities for small businesses	18	14.1%	7.7% - 20.6%
Q12. USAID activities implemented anked

Code

save_as_docx(q12_flx, path=here("output/tables/eval Q1/q12.docx"))

Disaggregated

Code

q12_ov <- ov_tab(svyrdat, q12a_bin, 
                "q12a_bin", "Food security and nutritional support") # 3.1 margin, 2.6 deff

q12_sex <- disag_tab(svyrdat, q12a_bin, sex, sex_key, 
                    "q12a_bin", "Familiar with USAID", "Sex")

q12_age <- disag_tab(svyrdat, q12a_bin, age_grp,age_grp_key,  

                    "q12a_bin", "Familiar with USAID", "Age group")


q12_ed <- disag_tab(svyrdat, q12a_bin, educ_cat, educ_key,
                   " q12a_bin", "Familiar with USAID", "Education")

q12_mad <- disag_tab(svyrdat, q12a_bin, madrassa,mad_key,
                   "q12a_bin", "Familiar with USAID", "Madrassa education")

q12_area <- disag_tab(svyrdat, q12a_bin, area,area_key,
                     "q12a_bin","Familiar with USAID","Area")

q12_gov <- disag_tab(svyrdat, q12a_bin, gov,gov_key,
                     "q12a_bin","Familiar with USAID", "Governorate")

q12_subreg <- disag_tab(svyrdat, q12a_bin, subregion,subreg_key,
                     "q12a_bin","Familiar with USAID","Subregion")


q12_reg <- disag_tab(svyrdat, q12a_bin, region,reg_key,
                     "q12a_bin","Familiar with USAID","Region") # 4.7 marg

q12_disag <- bind_rows(q12_ov,
                       q12_area,
                      q12_reg,
                      q12_subreg,
                      q12_gov,
                      q12_sex,
                      q12_age,
                      q12_ed,
                      q12_mad)

q12_disag_flx <- q12_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
#hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q12.Food security and nutritional support") %>%
  border_inner_h()

save_as_docx(q12_disag_flx, path=here("output/tables/eval Q1/q12_disag.docx"))

q12_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Food security and nutritional support	74	57.3%	46% - 68.7%
Area	Urban	66	60.9%	48.7% - 73.1%
	Village	0	0.0%	0% - 0%
	Refugee camp	8	52.6%	17.4% - 87.7%
Region	West Bank	9	30.4%	3.5% - 57.3%
	Gaza	65	65.9%	53.4% - 78.4%
Subregion	Northern West Bank	3	29.3%	-7.2% - 65.8%
	Central West Bank	1	17.2%	-5.2% - 39.6%
	Southern West Bank	5	40.4%	-12.5% - 93.4%
	Gaza Strip	65	65.9%	53.4% - 78.4%
Governorate	Jenin	0	0.0%	0% - 0%
	Tubas	0	0.0%	0% - 0%
	Tulkarm	1	100.0%	100% - 100%
	Nablus	2	100.0%	100% - 100%
	Qalqiliya	0	0.0%	0% - 0%
	Salfit	0	0.0%	0% - 0%
	Ramallah	0	7.0%	-7.6% - 21.5%
	Jericho	0	50.0%	-24.8% - 124.8%
	Jerusalem	1	34.9%	-16.8% - 86.6%
	Bethlehem	0	0.0%	0% - 0%
	Hebron	5	51.2%	-8.5% - 110.9%
	North Gaza	15	64.5%	40.6% - 88.4%
	Gaza	35	73.4%	57.2% - 89.7%
	Dier al-Balah	1	28.3%	28.3% - 28.3%
	Khan Yunis	5	38.5%	-6.8% - 83.8%
	Rafah	8	78.9%	39.9% - 117.9%
Sex	Male	31	45.8%	31.4% - 60.1%
	Female	43	70.2%	55.7% - 84.8%
Age group	Youth (18-29)	26	50.8%	29.3% - 72.3%
	Adult (30-54)	45	65.0%	54.5% - 75.6%
	Mature (55+)	3	34.3%	11% - 57.7%
Education	Elementary school education	7	46.9%	17.8% - 75.9%
	Secondary education	31	55.7%	39.3% - 72.1%
	Post-secondary education	36	61.6%	46.4% - 76.8%
Madrassa education	No madrassa education	45	49.1%	36.2% - 62%
	Madrassa education	29	77.2%	59.8% - 94.5%
Q12.Food security and nutritional support

Ranked by region

Code

q12a_reg <- disag_tab(svyrdat, q12a_bin, region,reg_key, "q12a", " Food security and nutritional support ","Region")
q12b_reg <- disag_tab(svyrdat, q12b_bin, region,reg_key, "q12b", " Job training and support ","Region")
q12c_reg <- disag_tab(svyrdat, q12c_bin, region,reg_key, "q12c", "Youth focused activities ","Region")
q12d_reg <- disag_tab(svyrdat, q12d_bin, region,reg_key, "q12d", " Support for local community groups ","Region")
q12e_reg <- disag_tab(svyrdat, q12e_bin, region,reg_key, "q12e",  " Health activities ","Region")
q12f_reg <- disag_tab(svyrdat, q12f_bin, region,reg_key, "q12f", " Educational and school activities ","Region")
q12g_reg <- disag_tab(svyrdat, q12g_bin, region,reg_key, "q12g", " Agriculture ","Region")
q12h_reg <- disag_tab(svyrdat, q12h_bin, region,reg_key, "q12h", " Economic growth activities for small businesses ","Region")
q12i_reg <- disag_tab(svyrdat, q12i_bin, region,reg_key, "q12i", " Cross border peacebuilding","Region")
q12j_reg <- disag_tab(svyrdat, q12j_bin, region,reg_key, "q12j", " Water and wastewater activities ","Region")
q12k_reg <- disag_tab(svyrdat, q12k_bin, region,reg_key, "q12k", " Tourism ","Region")


q12_reg <- bind_rows(q12a_reg, q12b_reg, q12c_reg,
                     q12d_reg,
                     q12e_reg,
                     q12f_reg,
                     q12g_reg, q12h_reg, q12i_reg ,q12j_reg, q12k_reg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))


#q12_reg
write_csv(q12_reg, here("output/tables/eval Q1/q12reg.csv"))

q12_flx <- q12_reg %>%
select(` USAID activities participated by Region`=Label, Number, Percent, `Confidence interval`=ci) %>% 
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>% 
hline(i=c(11), border=smlbrdr) %>%
colformat_double(j=3, digits=0) %>%
set_table_properties(layout="autofit") %>% 
add_footer_lines(values="Q12. USAID activities participated by Region Ranked")

q12_flx

Disaggregation type	USAID activities participated by Region	Number	Percent	Confidence interval
Gaza	Food security and nutritional support	65	65.9%	53.4% - 78.4%
Gaza	Health activities	44	44.7%	33.7% - 55.7%
Gaza	Educational and school activities	41	41.7%	31.4% - 51.9%
Gaza	Youth focused activities	40	40.5%	29% - 52%
Gaza	Job training and support	29	29.2%	18.5% - 39.9%
Gaza	Agriculture	27	27.3%	14.8% - 39.8%
Gaza	Support for local community groups	26	26.3%	15.1% - 37.4%
Gaza	Cross border peacebuilding	22	22.1%	12.4% - 31.7%
Gaza	Tourism	21	21.6%	12.9% - 30.3%
Gaza	Water and wastewater activities	19	19.3%	10.3% - 28.3%
Gaza	Economic growth activities for small businesses	16	16.0%	8.4% - 23.6%
West Bank	Health activities	15	48.4%	23.6% - 73.1%
West Bank	Job training and support	12	39.4%	22.7% - 56.2%
West Bank	Support for local community groups	10	32.4%	13.2% - 51.6%
West Bank	Food security and nutritional support	9	30.4%	3.5% - 57.3%
West Bank	Youth focused activities	7	22.7%	7.5% - 37.9%
West Bank	Educational and school activities	7	21.7%	6.9% - 36.5%
West Bank	Cross border peacebuilding	7	21.7%	8% - 35.3%
West Bank	Agriculture	3	10.4%	0.7% - 20%
West Bank	Economic growth activities for small businesses	3	8.2%	-2.6% - 19%
West Bank	Water and wastewater activities	1	3.2%	-3.1% - 9.5%
West Bank	Tourism	0	0.0%	0% - 0%
Q12. USAID activities participated by Region Ranked

Code

save_as_docx(q12_flx, path=here("output/tables/eval Q1/q12reg.docx"))

Ranked by subregion

Code

q12a_subreg <- disag_tab(svyrdat, q12a_bin, subregion,subreg_key, "q12a", " Food security and nutritional support "," Sub region ")
q12b_subreg <- disag_tab(svyrdat, q12b_bin, subregion,subreg_key, "q12b", " Job training and support "," Sub region ")
q12c_subreg <- disag_tab(svyrdat, q12c_bin, subregion,subreg_key, "q12c", " Youth focused activities "," Sub region ")
q12d_subreg <- disag_tab(svyrdat, q12d_bin, subregion,subreg_key, "q12d", " Support for local community groups "," Sub region ")
q12e_subreg <- disag_tab(svyrdat, q12e_bin, subregion,subreg_key, "q12e", " Health activities "," Sub region ")
q12f_subreg <- disag_tab(svyrdat, q12f_bin, subregion,subreg_key, "q12f", " Educational and school activities "," Sub region ")
q12g_subreg <- disag_tab(svyrdat, q12g_bin, subregion,subreg_key, "q12g", " Agriculture "," Sub region ")
q12h_subreg <- disag_tab(svyrdat, q12h_bin, subregion,subreg_key, "q12h", " Economic growth activities for small businesses "," Sub region ")
q12i_subreg <- disag_tab(svyrdat, q12i_bin, subregion,subreg_key, "q12i", " Cross border peacebuilding "," Sub region ")
q12j_subreg <- disag_tab(svyrdat, q12j_bin, subregion,subreg_key, "q12j", " Water and wastewater activities "," Sub region ")

q12k_subreg <- disag_tab(svyrdat, q12k_bin, subregion,subreg_key, "q12k", " Tourism "," Sub region ")
q12_subreg <- bind_rows(q12a_subreg, q12b_subreg, q12c_subreg,
                     q12d_subreg,
                     q12e_subreg,
                     q12f_subreg,
                     q12g_subreg, q12h_subreg, q12i_subreg, q12j_subreg, q12k_subreg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))

#q12_subreg
write_csv(q12_subreg, here("output/tables/eval Q1/q12subreg.csv"))

q12sub_flx <- q12_subreg %>%
select(` USAID activities participated  by SubRegion`=Label, Number, Percent, `Confidence interval`=ci) %>%
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>%
colformat_double(j=3, digits=0) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q12. USAID activities participated by SubRegion Ranked")

q12sub_flx 

Disaggregation type	USAID activities participated by SubRegion	Number	Percent	Confidence interval
Central West Bank	Job training and support	3	39.4%	6.2% - 72.6%
Central West Bank	Health activities	3	36.8%	2.6% - 71%
Central West Bank	Educational and school activities	2	26.0%	0.6% - 51.4%
Central West Bank	Youth focused activities	2	25.0%	-2.4% - 52.4%
Central West Bank	Support for local community groups	2	22.5%	-11.7% - 56.6%
Central West Bank	Cross border peacebuilding	2	22.1%	-1.3% - 45.5%
Central West Bank	Food security and nutritional support	1	17.2%	-5.2% - 39.6%
Central West Bank	Economic growth activities for small businesses	1	17.1%	-13.1% - 47.3%
Central West Bank	Agriculture	0	5.4%	-5.3% - 16.1%
Central West Bank	Water and wastewater activities	0	0.0%	0% - 0%
Central West Bank	Tourism	0	0.0%	0% - 0%
Gaza Strip	Food security and nutritional support	65	65.9%	53.4% - 78.4%
Gaza Strip	Health activities	44	44.7%	33.7% - 55.7%
Gaza Strip	Educational and school activities	41	41.7%	31.4% - 51.9%
Gaza Strip	Youth focused activities	40	40.5%	29% - 52%
Gaza Strip	Job training and support	29	29.2%	18.5% - 39.9%
Gaza Strip	Agriculture	27	27.3%	14.8% - 39.8%
Gaza Strip	Support for local community groups	26	26.3%	15.1% - 37.4%
Gaza Strip	Cross border peacebuilding	22	22.1%	12.4% - 31.7%
Gaza Strip	Tourism	21	21.6%	12.9% - 30.3%
Gaza Strip	Water and wastewater activities	19	19.3%	10.3% - 28.3%
Gaza Strip	Economic growth activities for small businesses	16	16.0%	8.4% - 23.6%
Northern West Bank	Health activities	5	46.7%	11.2% - 82.1%
Northern West Bank	Food security and nutritional support	3	29.3%	-7.2% - 65.8%
Northern West Bank	Job training and support	2	23.3%	-3.9% - 50.4%
Northern West Bank	Educational and school activities	2	20.0%	-4.6% - 44.6%
Northern West Bank	Support for local community groups	2	17.9%	-5.5% - 41.3%
Northern West Bank	Agriculture	1	13.4%	-5.4% - 32.2%
Northern West Bank	Water and wastewater activities	1	9.8%	-9.5% - 29%
Northern West Bank	Cross border peacebuilding	1	8.1%	-7.7% - 24%
Northern West Bank	Youth focused activities	1	5.4%	-3% - 13.7%
Northern West Bank	Economic growth activities for small businesses	0	0.0%	0% - 0%
Northern West Bank	Tourism	0	0.0%	0% - 0%
Southern West Bank	Health activities	7	57.8%	11.5% - 104%
Southern West Bank	Job training and support	7	52.8%	36.8% - 68.8%
Southern West Bank	Support for local community groups	6	51.3%	23.1% - 79.5%
Southern West Bank	Food security and nutritional support	5	40.4%	-12.5% - 93.4%
Southern West Bank	Youth focused activities	4	35.5%	15.1% - 55.9%
Southern West Bank	Cross border peacebuilding	4	32.6%	14.7% - 50.6%
Southern West Bank	Educational and school activities	3	20.2%	-6.3% - 46.7%
Southern West Bank	Agriculture	1	11.3%	-6.5% - 29.1%
Southern West Bank	Economic growth activities for small businesses	1	8.7%	-6.4% - 23.9%
Southern West Bank	Water and wastewater activities	0	0.0%	0% - 0%
Southern West Bank	Tourism	0	0.0%	0% - 0%
Q12. USAID activities participated by SubRegion Ranked

Code

save_as_docx(q12sub_flx, path=here("output/tables/eval Q1/q12subreg.docx"))

Q13 Familiar with other donors

Overall

Code

q13 <- fac_tab(svyrdat, q13) %>%
  #mutate(lab=percept_key$perception_lab) %>%
  left_join(fam_key,
            by=c("q13"="familiar")) %>%
  select(Label=q13, Response =familiar_lab, Percent:Number, margin, Lower, Upper, ci)

#q13
write_csv(q13, here("output/tables/eval Q1/q13.csv"))


q13_flx <- q13 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q13. Familiarity with other donors")

q13_flx

Label	Response	Number	Percent	Confidence interval
0	Not at all familiar/unsure	1,069	42.0%	38.8% - 45.1%
1	Somewhat familiar	1,169	45.9%	43% - 48.7%
2	Very familiar	295	11.6%	9.3% - 13.8%
98	Refused	15	0.6%	0.2% - 1%
Q13. Familiarity with other donors

Code

save_as_docx(q13_flx, path=here("output/tables/eval Q1/q13.docx"))

Disaggregated

Code

q13_ov <- ov_tab(svyrdat, donor_famil_bin, "q13", "Familiar with other donors") 

q13_sex <- disag_tab(svyrdat, donor_famil_bin, sex, sex_key, 
                    "q13", "Familiar with other donors", "Sex")

q13_age <- disag_tab(svyrdat, donor_famil_bin, age_grp,age_grp_key, 
                    "q13", "Familiar with other donors", "Sex")

q13_ed <- disag_tab(svyrdat, donor_famil_bin, educ_cat, educ_key, 
                    "q13", "Familiar with other donors", "Education")

q13_mad <- disag_tab(svyrdat, donor_famil_bin, madrassa, mad_key, 
                    "q13", "Familiar with other donors", "Madrassa education")

q13_area <- disag_tab(svyrdat, donor_famil_bin, area, area_key, 
                    "q13", "Familiar with other donors", "Area")

q13_gov <- disag_tab(svyrdat, donor_famil_bin, gov,gov_key,
                     "q13","Familiar with other donors", "Governorate")

q13_subreg <- disag_tab(svyrdat, donor_famil_bin, subregion, subreg_key, 
                    "q13", "Familiar with other donors", "Subregion")

q13_reg <- disag_tab(svyrdat, donor_famil_bin, region, reg_key, 
                    "q13", "Familiar with other donors", "Region")


q13_disag <- bind_rows(q13_ov,
                       q13_area,
                      q13_reg,
                      q13_subreg,
                      q13_gov,
                      q13_sex,
                      q13_age,
                      q13_ed,
                      q13_mad)

write_csv(q13_disag, here("output/tables/eval Q1/q13 familiar other donors disag.csv"))
#q13_disag

q13_disag_flx <- q13_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  #hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q13. Familiar with other donors") %>%
  border_inner_h()

save_as_docx(q13_disag_flx, path=here("output/tables/eval Q1/q13 familiar other donors disag.docx"))

q13_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Familiar with other donors	1,464	57.4%	54.3% - 60.6%
Area	Urban	1,143	58.0%	54.4% - 61.6%
	Village	196	52.0%	43% - 61.1%
	Refugee camp	125	62.1%	48.3% - 75.9%
Region	West Bank	758	50.9%	47.1% - 54.7%
	Gaza	706	66.7%	61.3% - 72.1%
Subregion	Northern West Bank	325	56.0%	50.4% - 61.7%
	Central West Bank	148	36.8%	30.4% - 43.2%
	Southern West Bank	285	56.1%	48.7% - 63.5%
	Gaza Strip	706	66.7%	61.3% - 72.1%
Governorate	Jenin	75	49.2%	37.4% - 61.1%
	Tubas	13	37.2%	19.7% - 54.7%
	Tulkarm	36	45.5%	32.2% - 58.9%
	Nablus	137	63.0%	53.5% - 72.6%
	Qalqiliya	38	68.6%	57.8% - 79.4%
	Salfit	26	63.1%	49.8% - 76.5%
	Ramallah	71	40.8%	27.7% - 53.8%
	Jericho	9	42.6%	28.2% - 57.1%
	Jerusalem	68	32.8%	27% - 38.5%
	Bethlehem	60	49.4%	33.8% - 65.1%
	Hebron	225	58.2%	49.7% - 66.8%
	North Gaza	174	80.8%	73% - 88.7%
	Gaza	270	71.4%	62.5% - 80.3%
	Dier al-Balah	82	55.3%	37% - 73.7%
	Khan Yunis	115	59.9%	48.1% - 71.7%
	Rafah	65	52.0%	34.8% - 69.3%
Sex	Male	770	60.6%	56.6% - 64.6%
	Female	694	54.3%	50.2% - 58.5%
	Youth (18-29)	612	57.2%	52.5% - 62%
	Adult (30-54)	668	60.7%	57.1% - 64.3%
	Mature (55+)	184	48.5%	43.5% - 53.5%
Education	Elementary school education	241	44.5%	39% - 50%
	Secondary education	642	58.4%	54.3% - 62.5%
	Post-secondary education	581	64.0%	59.8% - 68.3%
Madrassa education	No madrassa education	1,218	55.4%	52.1% - 58.7%
	Madrassa education	246	70.5%	63.3% - 77.7%
Q13. Familiar with other donors

Q14 Donors familiar with, ranked

Code

q14a <- ov_tab(svyrdat, q14a_bin, " q14a", " United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)") 
q14b <- ov_tab(svyrdat, q14b_bin, " q14b ", " United Nations Development Programme (UNDP)") 
q14c <- ov_tab(svyrdat, q14c_bin, " q14c ", " United Nations Children’s Fund (UNICEF)") 
q14d <- ov_tab(svyrdat, q14d_bin, " q14d ", " Red Crescent/Red Cross") 
q14e <- ov_tab(svyrdat, q14e_bin, " q14e ", " German Society for International Cooperation (GIZ)") 
q14f <- ov_tab(svyrdat, q14f_bin, " q14f", " UKAid/Foreign, Commonwealth & Development Office (FCDO)") 
q14g <- ov_tab(svyrdat, q14g_bin, " q14g ", " Norwegian Refugee Council (NRC)") 
q14h <- ov_tab(svyrdat, q14h_bin, " q14h ", " Japan International Cooperation Agency (JICA)") 
q14i <- ov_tab(svyrdat, q14i_bin, " q14i ", " Belgium Development Agency (ENABEL)") 
q14j <- ov_tab(svyrdat, q14j_bin, " q14j ", " Sweden International Development Agency (Sida)") 
q14k <- ov_tab(svyrdat, q14k_bin, " q14k ", " Turkish Cooperation and Coordination Agency (TiKA)") 
q14l <- ov_tab(svyrdat, q14l_bin, " q14l ", " European Union (EU)") 
q14m <- ov_tab(svyrdat, q14m_bin, " q14m ", " World Food Programme (WFP)") 
q14n <- ov_tab(svyrdat, q14n_bin, " q14n ", " World Bank") 

q14  <- bind_rows(q14a, q14b, q14c, q14d, q14e, q14f, q14g, q14h, q14i, q14j, q14k, q14l, q14m, q14n) %>%
      arrange(desc(Percent))

#q14 
write_csv(q14, here("output/tables/eval Q1/q14.csv"))


q14_flx <- q14 %>% 
select(`Familiar with foreign donors  `=Label, Number, Percent, `Confidence interval`=ci) %>% 
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
align(j=4, align="center") %>%
colformat_double(j=2, digits=0) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q14. Familiar with foreign donors Ranked")

q14_flx

Familiar with foreign donors	Number	Percent	Confidence interval
United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	1,207	82.5%	79.5% - 85.5%
Red Crescent/Red Cross	1,066	72.8%	69.1% - 76.6%
United Nations Children’s Fund (UNICEF)	997	68.1%	64.3% - 71.9%
European Union (EU)	641	43.8%	40% - 47.6%
United Nations Development Programme (UNDP)	455	31.0%	27.3% - 34.8%
World Food Programme (WFP)	346	23.6%	20.4% - 26.9%
World Bank	260	17.7%	14.3% - 21.2%
Norwegian Refugee Council (NRC)	231	15.8%	13.1% - 18.5%
Japan International Cooperation Agency (JICA)	226	15.4%	12.8% - 18%
German Society for International Cooperation (GIZ)	175	11.9%	9.7% - 14.2%
Belgium Development Agency (ENABEL)	146	10.0%	7.9% - 12.1%
Turkish Cooperation and Coordination Agency (TiKA)	132	9.0%	7% - 11.1%
Sweden International Development Agency (Sida)	101	6.9%	5.1% - 8.7%
UKAid/Foreign, Commonwealth & Development Office (FCDO)	97	6.7%	4.8% - 8.6%
Q14. Familiar with foreign donors Ranked

Code

save_as_docx(q14_flx, path=here("output/tables/eval Q1/q14.docx"))

Disaggregated q14a

Code

q14_ov <- ov_tab(svyrdat, q14a_bin, 
                " q14a_bin ", " United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) ") 

q14_sex <- disag_tab(svyrdat, q14a_bin, sex, sex_key, 
                    " q14a_bin ", "Familiar with USAID", "Sex")

q14_age <- disag_tab(svyrdat, q14a_bin, age_grp,age_grp_key,  

                    " q14a_bin ", "Familiar with USAID", "Age group")


q14_ed <- disag_tab(svyrdat, q14a_bin, educ_cat, educ_key,
                   " q14a_bin ", "Familiar with USAID", "Education")

q14_mad <- disag_tab(svyrdat, q14a_bin, madrassa,mad_key,
                   " q14a_bin ", "Familiar with USAID", "Madrassa education")

q14_area <- disag_tab(svyrdat, q14a_bin, area,area_key,
                     " q14a_bin ","Familiar with USAID","Area")

q14_gov <- disag_tab(svyrdat, q14a_bin, gov,gov_key,
                     "q14a_bin","Familiar with USAID", "Governorate")

q14_subreg <- disag_tab(svyrdat, q14a_bin, subregion,subreg_key,
                     " q14a_bin ","Familiar with USAID","Subregion")


q14_reg <- disag_tab(svyrdat, q14a_bin, region,reg_key,
                     " q14a_bin ","Familiar with USAID","Region") # 4.7 marg

q14_disag <- bind_rows(q14_ov,
                       q14_area,
                      q14_reg,
                      q14_subreg,
                      q14_gov,
                      q14_sex,
                      q14_age,
                      q14_ed,
                      q14_mad)

q14_disag_flx <- q14_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
#hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q14a. Familiar with UNRWA") %>%
  border_inner_h()

save_as_docx(q14_disag_flx, path=here("output/tables/eval Q1/q14a_disag.docx"))

q14_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	1,207	82.5%	79.5% - 85.5%
Area	Urban	946	82.8%	79.2% - 86.3%
	Village	149	76.0%	67.4% - 84.6%
	Refugee camp	112	89.7%	83.7% - 95.7%
Region	West Bank	578	76.3%	72.2% - 80.3%
	Gaza	629	89.1%	84.8% - 93.4%
Subregion	Northern West Bank	273	84.0%	79.7% - 88.4%
	Central West Bank	84	56.6%	47.6% - 65.7%
	Southern West Bank	221	77.6%	69.6% - 85.6%
	Gaza Strip	629	89.1%	84.8% - 93.4%
Governorate	Jenin	72	96.4%	92.8% - 99.9%
	Tubas	12	91.8%	82.7% - 100.9%
	Tulkarm	24	66.8%	51.4% - 82.3%
	Nablus	110	79.9%	71.5% - 88.2%
	Qalqiliya	35	93.7%	87.1% - 100.3%
	Salfit	20	76.8%	66.5% - 87.1%
	Ramallah	36	50.7%	40.2% - 61.1%
	Jericho	4	47.3%	21.1% - 73.5%
	Jerusalem	43	64.1%	48.2% - 80%
	Bethlehem	39	65.4%	42.7% - 88.1%
	Hebron	182	80.8%	72.8% - 88.9%
	North Gaza	149	85.7%	73.1% - 98.2%
	Gaza	245	90.8%	86.5% - 95%
	Dier al-Balah	78	94.6%	88.9% - 100.3%
	Khan Yunis	100	87.4%	75.1% - 99.7%
	Rafah	57	87.7%	73.1% - 102.2%
Sex	Male	630	81.9%	77.8% - 86%
	Female	577	83.1%	79.3% - 86.9%
Age group	Youth (18-29)	500	81.6%	76.6% - 86.6%
	Adult (30-54)	567	84.9%	81.8% - 88%
	Mature (55+)	141	76.5%	70% - 83%
Education	Elementary school education	200	82.9%	77.1% - 88.8%
	Secondary education	532	82.9%	78.8% - 87%
	Post-secondary education	476	81.8%	77.8% - 85.9%
Madrassa education	No madrassa education	991	81.4%	78.1% - 84.7%
	Madrassa education	216	87.8%	82.2% - 93.4%
Q14a. Familiar with UNRWA

Disaggregated 14d

Code

q14d_ov <- ov_tab(svyrdat, q14d_bin, 
                "q14d_bin", " Red Crescent/Red Cross ") 

q14d_sex <- disag_tab(svyrdat, q14d_bin, sex, sex_key, 
                    "q14d_bin", "Familiar with USAID", "Sex")

q14d_age <- disag_tab(svyrdat, q14d_bin, age_grp,age_grp_key,  
                    "q14d_bin", "Familiar with USAID", "Age group")


q14d_ed <- disag_tab(svyrdat, q14d_bin, educ_cat, educ_key,
                   "q14d_bin", "Familiar with USAID", "Education")

q14d_mad <- disag_tab(svyrdat, q14d_bin, madrassa,mad_key,
                   "q14d_bin", "Familiar with USAID", "Madrassa education")

q14d_area <- disag_tab(svyrdat, q14d_bin, area,area_key,
                     "q14d_bin","Familiar with USAID","Area")

q14d_gov <- disag_tab(svyrdat, q14d_bin, gov,gov_key,
                     "q14d_bin","Familiar with USAID", "Governorate")

q14d_subreg <- disag_tab(svyrdat, q14d_bin, subregion,subreg_key,
                     "q14d_bin","Familiar with USAID","Subregion")


q14d_reg <- disag_tab(svyrdat, q14d_bin, region,reg_key,
                     "q14d_bin","Familiar with USAID","Region") # 4.7 marg

q14d_disag <- bind_rows(q14d_ov,
                        q14d_area,
                      q14d_reg,
                      q14d_subreg,
                      q14d_gov,
                      q14d_sex,
                      q14d_age,
                      q14d_ed,
                      q14d_mad)

q14d_disag_flx <- q14d_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
#hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q14d. Familiar with Red Crescent/Red Cross") %>%
  border_inner_h()

save_as_docx(q14_disag_flx_d, path=here("output/tables/eval Q1/q14d_disag.docx"))

Error in save_as_docx(q14_disag_flx_d, path = here("output/tables/eval Q1/q14d_disag.docx")): object 'q14_disag_flx_d' not found

Code

q14d_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Red Crescent/Red Cross	1,066	72.8%	69.1% - 76.6%
Area	Urban	842	73.6%	69.2% - 78%
	Village	126	64.3%	54.9% - 73.8%
	Refugee camp	99	79.0%	70.6% - 87.4%
Region	West Bank	505	66.6%	61.2% - 72%
	Gaza	562	79.5%	74.5% - 84.6%
Subregion	Northern West Bank	229	70.7%	62.9% - 78.4%
	Central West Bank	86	58.1%	46.4% - 69.8%
	Southern West Bank	189	66.3%	56.5% - 76.2%
	Gaza Strip	562	79.5%	74.5% - 84.6%
Governorate	Jenin	57	75.5%	58.9% - 92.2%
	Tubas	12	94.3%	86.6% - 102%
	Tulkarm	26	71.9%	55.2% - 88.5%
	Nablus	99	72.1%	60.7% - 83.5%
	Qalqiliya	20	53.0%	29.8% - 76.3%
	Salfit	16	61.6%	38.8% - 84.4%
	Ramallah	38	54.1%	35.1% - 73.1%
	Jericho	2	19.1%	7.2% - 30.9%
	Jerusalem	46	67.7%	51.4% - 84%
	Bethlehem	33	55.6%	29.3% - 82%
	Hebron	156	69.2%	58.4% - 80%
	North Gaza	146	84.1%	71.1% - 97%
	Gaza	206	76.2%	69.1% - 83.2%
	Dier al-Balah	68	83.2%	74.5% - 91.8%
	Khan Yunis	93	81.1%	68.9% - 93.3%
	Rafah	48	74.0%	54.5% - 93.6%
Sex	Male	574	74.6%	70% - 79.1%
	Female	492	70.9%	65.8% - 76.1%
Age group	Youth (18-29)	451	73.7%	68.2% - 79.2%
	Adult (30-54)	494	74.0%	69.8% - 78.1%
	Mature (55+)	121	65.9%	59% - 72.8%
Education	Elementary school education	169	70.3%	62.6% - 78.1%
	Secondary education	468	72.9%	68% - 77.9%
	Post-secondary education	429	73.8%	69.3% - 78.2%
Madrassa education	No madrassa education	872	71.6%	67.4% - 75.8%
	Madrassa education	194	79.0%	73.4% - 84.5%
Q14d. Familiar with Red Crescent/Red Cross

Ranked by region

Code

q14a_reg <- disag_tab(svyrdat, q14a_bin, region,reg_key, "q14a", " United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) ","Region")
q14b_reg <- disag_tab(svyrdat, q14b_bin, region,reg_key, "q14b", " United Nations Development Programme (UNDP)","Region")
q14c_reg <- disag_tab(svyrdat, q14c_bin, region,reg_key, "q14c", "United Nations Children’s Fund (UNICEF)","Region")
q14d_reg <- disag_tab(svyrdat, q14d_bin, region,reg_key, "q14d", " Red Crescent/Red Cross","Region")
q14e_reg <- disag_tab(svyrdat, q14e_bin, region,reg_key, "q14e",  " German Society for International Cooperation (GIZ)","Region")
q14f_reg <- disag_tab(svyrdat, q14f_bin, region,reg_key, "q14f", " UKAid/Foreign, Commonwealth & Development Office (FCDO) ","Region")
q14g_reg <- disag_tab(svyrdat, q14g_bin, region,reg_key, "q14g", " Norwegian Refugee Council (NRC) ","Region")
q14h_reg <- disag_tab(svyrdat, q14h_bin, region,reg_key, "q14h", " Japan International Cooperation Agency (JICA)","Region")
q14i_reg <- disag_tab(svyrdat, q14i_bin, region,reg_key, "q14i", " Belgium Development Agency (ENABEL)","Region")
q14j_reg <- disag_tab(svyrdat, q14j_bin, region,reg_key, "q14j", " Sweden International Development Agency (Sida) ","Region")
q14k_reg <- disag_tab(svyrdat, q14k_bin, region,reg_key, "q14k", " Turkish Cooperation and Coordination Agency (TiKA)","Region")
q14l_reg <- disag_tab(svyrdat, q14k_bin, region,reg_key, "q14k", " European Union (EU)","Region")
q14m_reg <- disag_tab(svyrdat, q14m_bin, region,reg_key, "q14m", " World Food Programme (WFP)","Region")
q14n_reg <- disag_tab(svyrdat, q14n_bin, region,reg_key, "q14n", " World Bank","Region")
q14_reg <- bind_rows(q14a_reg, q14b_reg, q14c_reg,
                     q14d_reg,
                     q14e_reg,
                     q14f_reg,
                     q14g_reg, q14h_reg, q14i_reg ,q14j_reg, q14k_reg, q14l_reg, q14m_reg, q14n_reg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))

#q14_reg
write_csv(q14_reg, here("output/tables/eval Q1/q14reg.csv"))

q14_flx <- q14_reg %>%
select(`Familiar with foreign donors by Region`=Label, Number, Percent, `Confidence interval`=ci) %>% 
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>%
colformat_double(j=3, digits=0) %>%
hline(i=c(14), border=smlbrdr) %>%
set_table_properties(layout="autofit") %>% 
add_footer_lines(values="Q14. Familiar with foreign donors by Region Ranked")

q14_flx

Disaggregation type	Familiar with foreign donors by Region	Number	Percent	Confidence interval
Gaza	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	629	89.1%	84.8% - 93.4%
Gaza	Red Crescent/Red Cross	562	79.5%	74.5% - 84.6%
Gaza	United Nations Children’s Fund (UNICEF)	553	78.3%	72.9% - 83.7%
Gaza	United Nations Development Programme (UNDP)	264	37.4%	31.5% - 43.3%
Gaza	World Food Programme (WFP)	181	25.7%	20.8% - 30.6%
Gaza	Norwegian Refugee Council (NRC)	165	23.3%	18.5% - 28.1%
Gaza	World Bank	139	19.7%	14% - 25.3%
Gaza	Japan International Cooperation Agency (JICA)	128	18.1%	14.1% - 22.2%
Gaza	Belgium Development Agency (ENABEL)	114	16.1%	12.1% - 20.1%
Gaza	Turkish Cooperation and Coordination Agency (TiKA)	99	14.0%	10.2% - 17.9%
Gaza	European Union (EU)	99	14.0%	10.2% - 17.9%
Gaza	German Society for International Cooperation (GIZ)	95	13.4%	10.1% - 16.7%
Gaza	UKAid/Foreign, Commonwealth & Development Office (FCDO)	77	10.9%	7.3% - 14.4%
Gaza	Sweden International Development Agency (Sida)	57	8.0%	5.3% - 10.8%
West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	578	76.3%	72.2% - 80.3%
West Bank	Red Crescent/Red Cross	505	66.6%	61.2% - 72%
West Bank	United Nations Children’s Fund (UNICEF)	444	58.6%	53.4% - 63.8%
West Bank	United Nations Development Programme (UNDP)	190	25.1%	20.6% - 29.7%
West Bank	World Food Programme (WFP)	164	21.7%	17.4% - 26%
West Bank	World Bank	120	15.9%	11.8% - 20%
West Bank	Japan International Cooperation Agency (JICA)	98	12.9%	9.6% - 16.2%
West Bank	German Society for International Cooperation (GIZ)	80	10.6%	7.6% - 13.6%
West Bank	Norwegian Refugee Council (NRC)	67	8.8%	6.1% - 11.5%
West Bank	Sweden International Development Agency (Sida)	45	5.9%	3.5% - 8.3%
West Bank	Turkish Cooperation and Coordination Agency (TiKA)	33	4.3%	2.6% - 6.1%
West Bank	European Union (EU)	33	4.3%	2.6% - 6.1%
West Bank	Belgium Development Agency (ENABEL)	33	4.3%	2.8% - 5.8%
West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	21	2.8%	1.1% - 4.4%
Q14. Familiar with foreign donors by Region Ranked

Code

save_as_docx(q14_flx, path=here("output/tables/eval Q1/q14reg.docx"))

Ranked by subregion

Code

q14a_subreg <- disag_tab(svyrdat, q14a_bin, subregion,subreg_key, "q14a", " United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) "," Sub region ")
q14b_subreg <- disag_tab(svyrdat, q14b_bin, subregion,subreg_key, "q14b", " United Nations Development Programme (UNDP)"," Sub region ")
q14c_subreg <- disag_tab(svyrdat, q14c_bin, subregion,subreg_key, "q14c", " United Nations Children’s Fund (UNICEF)"," Sub region ")
q14d_subreg <- disag_tab(svyrdat, q14d_bin, subregion,subreg_key, "q14d", " Red Crescent/Red Cross"," Sub region ")
q14e_subreg <- disag_tab(svyrdat, q14e_bin, subregion,subreg_key, "q14e", " German Society for International Cooperation (GIZ)"," Sub region ")
q14f_subreg <- disag_tab(svyrdat, q14f_bin, subregion,subreg_key, "q14f", " UKAid/Foreign, Commonwealth & Development Office (FCDO) "," Sub region ")
q14g_subreg <- disag_tab(svyrdat, q14g_bin, subregion,subreg_key, "q14g", " Norwegian Refugee Council (NRC) "," Sub region ")
q14h_subreg <- disag_tab(svyrdat, q14h_bin, subregion,subreg_key, "q14h", " Japan International Cooperation Agency (JICA)"," Sub region ")
q14i_subreg <- disag_tab(svyrdat, q14i_bin, subregion,subreg_key, "q14i", " Belgium Development Agency (ENABEL) "," Sub region ")
q14j_subreg <- disag_tab(svyrdat, q14j_bin, subregion,subreg_key, "q14j", " Sweden International Development Agency (Sida) "," Sub region ")

q14k_subreg <- disag_tab(svyrdat, q14k_bin, subregion,subreg_key, "q14k", " Turkish Cooperation and Coordination Agency (TiKA)"," Sub region ")
q14l_subreg <- disag_tab(svyrdat, q14l_bin, subregion,subreg_key, "q14l", " European Union (EU)"," Sub region ")
q14m_subreg <- disag_tab(svyrdat, q14m_bin, subregion,subreg_key, "q14m", " World Food Programme (WFP)"," Sub region ")
q14n_subreg <- disag_tab(svyrdat, q14n_bin, subregion,subreg_key, "q14n", " World Bank"," Sub region ")
q14_subreg <- bind_rows(q14a_subreg, q14b_subreg, q14c_subreg,
                     q14d_subreg,
                     q14e_subreg,
                     q14f_subreg,
                     q14g_subreg, q14h_subreg, q14i_subreg, q14j_subreg, q14k_subreg, q14l_subreg, q14m_subreg, q14n_subreg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))

#q14_subreg
write_csv(q14_subreg, here("output/tables/eval Q1/q14subreg.csv"))

q14sub_flx <- q14_subreg %>% 
select(`Familiar with foreign donors by SubRegion`=Label, Number, Percent, `Confidence interval`=ci) %>%
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
align(j=4, align="center") %>% 
colformat_double(j=3, digits=0) %>%
hline(i=c(14,28,42), border=smlbrdr) %>%
set_table_properties(layout="autofit") %>% 
add_footer_lines(values="Q14. Familiar with foreign donors by SubRegion Ranked")

q14sub_flx 

Disaggregation type	Familiar with foreign donors by SubRegion	Number	Percent	Confidence interval
Central West Bank	Red Crescent/Red Cross	86	58.1%	46.4% - 69.8%
Central West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	84	56.6%	47.6% - 65.7%
Central West Bank	European Union (EU)	73	49.4%	38.6% - 60.2%
Central West Bank	United Nations Children’s Fund (UNICEF)	61	41.3%	31.8% - 50.8%
Central West Bank	United Nations Development Programme (UNDP)	55	37.3%	27.6% - 47%
Central West Bank	World Food Programme (WFP)	28	19.0%	10.5% - 27.5%
Central West Bank	Japan International Cooperation Agency (JICA)	27	18.6%	11.2% - 26%
Central West Bank	World Bank	25	16.7%	8.3% - 25.1%
Central West Bank	Norwegian Refugee Council (NRC)	22	15.0%	9.1% - 20.9%
Central West Bank	German Society for International Cooperation (GIZ)	17	11.7%	5.3% - 18.1%
Central West Bank	Sweden International Development Agency (Sida)	13	8.6%	1% - 16.1%
Central West Bank	Belgium Development Agency (ENABEL)	12	8.4%	2.7% - 14%
Central West Bank	Turkish Cooperation and Coordination Agency (TiKA)	10	6.7%	2.5% - 10.9%
Central West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	5	3.5%	0% - 7.1%
Gaza Strip	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	629	89.1%	84.8% - 93.4%
Gaza Strip	Red Crescent/Red Cross	562	79.5%	74.5% - 84.6%
Gaza Strip	United Nations Children’s Fund (UNICEF)	553	78.3%	72.9% - 83.7%
Gaza Strip	European Union (EU)	317	45.0%	39% - 50.9%
Gaza Strip	United Nations Development Programme (UNDP)	264	37.4%	31.5% - 43.3%
Gaza Strip	World Food Programme (WFP)	181	25.7%	20.8% - 30.6%
Gaza Strip	Norwegian Refugee Council (NRC)	165	23.3%	18.5% - 28.1%
Gaza Strip	World Bank	139	19.7%	14% - 25.3%
Gaza Strip	Japan International Cooperation Agency (JICA)	128	18.1%	14.1% - 22.2%
Gaza Strip	Belgium Development Agency (ENABEL)	114	16.1%	12.1% - 20.1%
Gaza Strip	Turkish Cooperation and Coordination Agency (TiKA)	99	14.0%	10.2% - 17.9%
Gaza Strip	German Society for International Cooperation (GIZ)	95	13.4%	10.1% - 16.7%
Gaza Strip	UKAid/Foreign, Commonwealth & Development Office (FCDO)	77	10.9%	7.3% - 14.4%
Gaza Strip	Sweden International Development Agency (Sida)	57	8.0%	5.3% - 10.8%
Northern West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	273	84.0%	79.7% - 88.4%
Northern West Bank	Red Crescent/Red Cross	229	70.7%	62.9% - 78.4%
Northern West Bank	United Nations Children’s Fund (UNICEF)	217	66.9%	59.4% - 74.4%
Northern West Bank	European Union (EU)	157	48.2%	41.7% - 54.8%
Northern West Bank	World Food Programme (WFP)	77	23.8%	18% - 29.6%
Northern West Bank	United Nations Development Programme (UNDP)	71	21.9%	15.3% - 28.6%
Northern West Bank	Japan International Cooperation Agency (JICA)	62	19.0%	12.8% - 25.3%
Northern West Bank	World Bank	57	17.6%	12.3% - 22.9%
Northern West Bank	German Society for International Cooperation (GIZ)	47	14.6%	9.3% - 20%
Northern West Bank	Norwegian Refugee Council (NRC)	33	10.2%	5.2% - 15.2%
Northern West Bank	Sweden International Development Agency (Sida)	24	7.5%	4% - 11.1%
Northern West Bank	Turkish Cooperation and Coordination Agency (TiKA)	18	5.7%	2.2% - 9.1%
Northern West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	13	3.9%	0.7% - 7%
Northern West Bank	Belgium Development Agency (ENABEL)	10	3.0%	1.4% - 4.7%
Southern West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	221	77.6%	69.6% - 85.6%
Southern West Bank	Red Crescent/Red Cross	189	66.3%	56.5% - 76.2%
Southern West Bank	United Nations Children’s Fund (UNICEF)	166	58.2%	49.3% - 67%
Southern West Bank	European Union (EU)	94	33.0%	24% - 42.1%
Southern West Bank	United Nations Development Programme (UNDP)	64	22.5%	14.8% - 30.2%
Southern West Bank	World Food Programme (WFP)	59	20.7%	12.3% - 29%
Southern West Bank	World Bank	39	13.5%	5.4% - 21.6%
Southern West Bank	German Society for International Cooperation (GIZ)	15	5.4%	1.7% - 9.2%
Southern West Bank	Norwegian Refugee Council (NRC)	11	4.0%	1.4% - 6.5%
Southern West Bank	Belgium Development Agency (ENABEL)	10	3.7%	1.8% - 5.6%
Southern West Bank	Japan International Cooperation Agency (JICA)	9	3.0%	0% - 6%
Southern West Bank	Sweden International Development Agency (Sida)	8	2.7%	-0.2% - 5.5%
Southern West Bank	Turkish Cooperation and Coordination Agency (TiKA)	5	1.6%	0.2% - 3%
Southern West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	3	1.1%	-0.7% - 2.8%
Q14. Familiar with foreign donors by SubRegion Ranked

Code

save_as_docx(q14sub_flx, path=here("output/tables/eval Q1/q14subreg.docx"))

Q15 Participated in other donor activity

Overall

Code

#frq(dat$q15)

q15 <- fac_tab(svyrdat, q15) %>%
  #mutate(lab=percept_key$perception_lab) %>%
  left_join(yes_key,
            by=c("q15"="yes_no")) %>%
  select(Label=q15, Response =yes_no_lab, Percent:Number, margin, Lower, Upper, ci)
write_csv(q15, here("output/tables/eval Q1/q15.csv"))
#q15

q15_flx <- q15 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q15. Participated in other donor activity")

save_as_docx(q15_flx, path=here("output/tables/eval Q1/q15.docx"))

q15_flx

Label	Response	Number	Percent	Confidence interval
0	No	2,292	89.9%	87.9% - 91.9%
1	Yes	236	9.3%	7.3% - 11.2%
98	Refused	14	0.6%	0.2% - 0.9%
99	Don't know	6	0.2%	0.1% - 0.4%
Q15. Participated in other donor activity

Disaggregated

Code

#frq(dat$dnr_act_exposure)

q15_ov <- ov_tab(svyrdat, dnr_act_exposure, "q15", "Participated in other donor activity") 

q15_sex <- disag_tab(svyrdat, dnr_act_exposure, sex, sex_key, 
                    "q15", "Participated in other donor activity", "Sex")

q15_age <- disag_tab(svyrdat, dnr_act_exposure, age_grp, age_grp_key, 
                    "q15", "Participated in other donor activity", "Age group")


q15_ed <- disag_tab(svyrdat, dnr_act_exposure, educ_cat, educ_key, 
                    "q15", "Participated in other donor activity", "Education")


q15_mad <- disag_tab(svyrdat, dnr_act_exposure, madrassa, mad_key, 
                    "q15", "Participated in other donor activity", "Madrassa education")


q15_area <- disag_tab(svyrdat, dnr_act_exposure, area, area_key, 
                    "q15", "Participated in other donor activity", "Area")

q15_gov <- disag_tab(svyrdat, dnr_act_exposure, gov,gov_key,
                     "q15","Participated in other donor activity", "Governorate")

q15_subreg <- disag_tab(svyrdat, dnr_act_exposure, subregion, subreg_key, 
                    "q15", "Participated in other donor activity", "Subregion")


q15_reg <- disag_tab(svyrdat, dnr_act_exposure, region, reg_key, 
                    "q15", "Participated in other donor activity", "Region")

q15_disag <- bind_rows(q15_ov,
                       q15_area,
                      q15_reg,
                      q15_subreg,
                      q15_gov,
                      q15_sex,
                      q15_age,
                      q15_ed,
                      q15_mad)

write_csv(q15_disag, here("output/tables/eval Q1/q15 participated other donor disag.csv"))

#q15_disag

q15_disag_flx <- q15_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q15. Participated in other donor activity") %>%
  border_inner_h()

save_as_docx(q15_disag_flx, path=here("output/tables/eval Q1/q15 participated other donor disag.docx"))

q15_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Participated in other donor activity	236	9.3%	7.3% - 11.2%
Area	Urban	185	9.4%	7.2% - 11.5%
	Village	15	4.0%	2% - 6.1%
	Refugee camp	37	18.1%	5.5% - 30.7%
Region	West Bank	65	4.4%	3.1% - 5.7%
	Gaza	171	16.1%	11.9% - 20.4%
Subregion	Northern West Bank	15	2.6%	1.1% - 4.1%
	Central West Bank	22	5.4%	3.2% - 7.6%
	Southern West Bank	29	5.6%	2.8% - 8.5%
	Gaza Strip	171	16.1%	11.9% - 20.4%
Governorate	Jenin	3	1.8%	0.2% - 3.4%
	Tubas	0	0.0%	0% - 0%
	Tulkarm	1	1.3%	-0.2% - 2.8%
	Nablus	9	4.1%	0.5% - 7.8%
	Qalqiliya	1	1.7%	-0.8% - 4.3%
	Salfit	1	3.0%	-1.4% - 7.4%
	Ramallah	8	4.3%	1.6% - 7.1%
	Jericho	1	6.2%	0.4% - 12%
	Jerusalem	13	6.2%	2.6% - 9.8%
	Bethlehem	9	7.2%	1.8% - 12.5%
	Hebron	20	5.2%	1.8% - 8.6%
	North Gaza	53	24.5%	14.5% - 34.5%
	Gaza	67	17.7%	11.8% - 23.7%
	Dier al-Balah	35	23.3%	2.3% - 44.4%
	Khan Yunis	10	5.4%	1.3% - 9.4%
	Rafah	6	5.0%	1.1% - 8.9%
Sex	Male	133	10.5%	7.7% - 13.2%
	Female	103	8.1%	6% - 10.2%
Age group	Youth (18-29)	106	10.0%	6.8% - 13.1%
	Adult (30-54)	110	10.0%	7.8% - 12.1%
	Mature (55+)	20	5.3%	3.2% - 7.5%
Education	Elementary school education	30	5.6%	2.8% - 8.5%
	Secondary education	102	9.3%	6.7% - 11.8%
	Post-secondary education	104	11.4%	8.6% - 14.3%
Madrassa education	No madrassa education	173	7.9%	5.9% - 9.8%
	Madrassa education	63	18.0%	13% - 23%
Q15. Participated in other donor activity

Q16 Donor activities participated in, ranked

Code

q16a <- ov_tab(svyrdat, q16a_bin, " q16a", " United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)") 
q16b <- ov_tab(svyrdat, q16b_bin, " q16b ", " United Nations Development Programme (UNDP)") 
q16c <- ov_tab(svyrdat, q16c_bin, " q16c ", " United Nations Children’s Fund (UNICEF)") 
q16d <- ov_tab(svyrdat, q16d_bin, " q16d ", " Red Crescent/Red Cross") 
q16e <- ov_tab(svyrdat, q16e_bin, " q16e ", " German Society for International Cooperation (GIZ)") 
q16f <- ov_tab(svyrdat, q16f_bin, " q16f", " UKAid/Foreign, Commonwealth & Development Office (FCDO)") 
q16g <- ov_tab(svyrdat, q16g_bin, " q16g ", " Norwegian Refugee Council (NRC)") 
q16h <- ov_tab(svyrdat, q16h_bin, " q16h ", " Japan International Cooperation Agency (JICA)") 
q16i <- ov_tab(svyrdat, q16i_bin, " q16i ", " Belgium Development Agency (ENABEL)") 
q16j <- ov_tab(svyrdat, q16j_bin, " q16j ", " Sweden International Development Agency (Sida)") 
q16k <- ov_tab(svyrdat, q16k_bin, " q16k ", " Turkish Cooperation and Coordination Agency (TiKA)") 
q16l <- ov_tab(svyrdat, q16l_bin, " q16l ", " European Union (EU)") 
q16m <- ov_tab(svyrdat, q16m_bin, " q16m ", " World Food Programme (WFP)") 
q16n <- ov_tab(svyrdat, q16n_bin, " q16n ", " World Bank") 
q16  <- bind_rows(q16a, q16b, q16c, q16d, q16e, q16f, q16g, q16h, q16i, q16j, q16k, q16l, q16m, q16n) %>%
      arrange(desc(Percent))

#q16 
write_csv(q16, here("output/tables/eval Q1/q16.csv"))

q16_flx <- q16 %>% select(`Donor activities participated in  `=Label, Number, Percent, `Confidence interval`=ci) %>%
flextable() %>%
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% 
align(j=4, align="center") %>% 
colformat_double(j=2, digits=0) %>%
set_table_properties(layout="autofit") %>% add_footer_lines(values="Q16. Donor activities Participated in Ranked")

q16_flx

Donor activities participated in	Number	Percent	Confidence interval
United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	149	62.9%	53.5% - 72.2%
Red Crescent/Red Cross	109	46.0%	36.4% - 55.5%
United Nations Children’s Fund (UNICEF)	103	43.7%	33.7% - 53.7%
United Nations Development Programme (UNDP)	65	27.7%	18.8% - 36.6%
European Union (EU)	63	26.8%	18.8% - 34.9%
World Food Programme (WFP)	46	19.6%	13.9% - 25.3%
World Bank	39	16.6%	10.6% - 22.6%
German Society for International Cooperation (GIZ)	39	16.4%	10.4% - 22.4%
Norwegian Refugee Council (NRC)	30	12.7%	6.7% - 18.7%
Sweden International Development Agency (Sida)	23	9.8%	4.4% - 15.3%
Turkish Cooperation and Coordination Agency (TiKA)	23	9.7%	4.7% - 14.7%
Belgium Development Agency (ENABEL)	22	9.5%	4% - 15%
UKAid/Foreign, Commonwealth & Development Office (FCDO)	17	7.3%	2.7% - 11.9%
Japan International Cooperation Agency (JICA)	13	5.4%	2% - 8.8%
Q16. Donor activities Participated in Ranked

Code

save_as_docx(q16_flx, path=here("output/tables/eval Q1/q16.docx"))

Ranked by region

Code

q16a_reg <- disag_tab(svyrdat, q16a_bin, region,reg_key, "q16a", " United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) ","Region")
q16b_reg <- disag_tab(svyrdat, q16b_bin, region,reg_key, "q16b", " United Nations Development Programme (UNDP)","Region")
q16c_reg <- disag_tab(svyrdat, q16c_bin, region,reg_key, "q16c", "United Nations Children’s Fund (UNICEF)","Region")
q16d_reg <- disag_tab(svyrdat, q16d_bin, region,reg_key, "q16d", " Red Crescent/Red Cross","Region")
q16e_reg <- disag_tab(svyrdat, q16e_bin, region,reg_key, "q16e",  " German Society for International Cooperation (GIZ)","Region")
q16f_reg <- disag_tab(svyrdat, q16f_bin, region,reg_key, "q16f", " UKAid/Foreign, Commonwealth & Development Office (FCDO) ","Region")
q16g_reg <- disag_tab(svyrdat, q16g_bin, region,reg_key, "q16g", " Norwegian Refugee Council (NRC) ","Region")
q16h_reg <- disag_tab(svyrdat, q16h_bin, region,reg_key, "q16h", " Japan International Cooperation Agency (JICA)","Region")
q16i_reg <- disag_tab(svyrdat, q16i_bin, region,reg_key, "q16i", " Belgium Development Agency (ENABEL)","Region")
q16j_reg <- disag_tab(svyrdat, q16j_bin, region,reg_key, "q16j", " Sweden International Development Agency (Sida) ","Region")
q16k_reg <- disag_tab(svyrdat, q16k_bin, region,reg_key, "q16k", " Turkish Cooperation and Coordination Agency (TiKA)","Region")
q16l_reg <- disag_tab(svyrdat, q16k_bin, region,reg_key, "q16k", " European Union (EU)","Region")
q16m_reg <- disag_tab(svyrdat, q16m_bin, region,reg_key, "q16m", " World Food Programme (WFP)","Region")
q16n_reg <- disag_tab(svyrdat, q16n_bin, region,reg_key, "q16n", " World Bank","Region")
q16_reg <- bind_rows(q16a_reg, q16b_reg, q16c_reg,
                     q16d_reg,
                     q16e_reg,
                     q16f_reg,
                     q16g_reg, q16h_reg, q16i_reg ,q16j_reg, q16k_reg, q16l_reg, q16m_reg, q16n_reg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))

#q16_reg

write_csv(q16_reg, here("output/tables/eval Q1/q16reg.csv"))

q16_flx <- q16_reg %>% 
select(`Donor activities participated in,by Region`=Label, Number, Percent, `Confidence interval`=ci) %>% 
flextable() %>% set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>% align(j=4, align="center") %>%
colformat_double(j=3, digits=0) %>%
set_table_properties(layout="autofit") %>%
add_footer_lines(values="Q16. Donor activities participated in by Region ")

q16_flx

Disaggregation type	Donor activities participated in,by Region	Number	Percent	Confidence interval
Gaza	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	126	73.8%	63.9% - 83.8%
Gaza	United Nations Children’s Fund (UNICEF)	85	49.5%	36.5% - 62.5%
Gaza	Red Crescent/Red Cross	81	47.3%	35.3% - 59.4%
Gaza	United Nations Development Programme (UNDP)	55	32.2%	20.5% - 43.9%
Gaza	World Food Programme (WFP)	41	23.7%	16.4% - 31.1%
Gaza	World Bank	35	20.6%	12.5% - 28.7%
Gaza	Norwegian Refugee Council (NRC)	29	17.2%	9% - 25.5%
Gaza	German Society for International Cooperation (GIZ)	29	17.0%	9.7% - 24.3%
Gaza	Turkish Cooperation and Coordination Agency (TiKA)	22	13.1%	6.2% - 20.1%
Gaza	European Union (EU)	22	13.1%	6.2% - 20.1%
Gaza	Belgium Development Agency (ENABEL)	22	13.1%	5.4% - 20.8%
Gaza	Sweden International Development Agency (Sida)	18	10.2%	3.6% - 16.9%
Gaza	UKAid/Foreign, Commonwealth & Development Office (FCDO)	17	10.1%	3.7% - 16.4%
Gaza	Japan International Cooperation Agency (JICA)	8	4.8%	1% - 8.6%
West Bank	Red Crescent/Red Cross	28	42.3%	27.9% - 56.8%
West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	22	34.1%	19.3% - 48.9%
West Bank	United Nations Children’s Fund (UNICEF)	19	28.5%	13.7% - 43.3%
West Bank	United Nations Development Programme (UNDP)	10	16.0%	6% - 26%
West Bank	German Society for International Cooperation (GIZ)	10	14.8%	4.3% - 25.3%
West Bank	Sweden International Development Agency (Sida)	6	8.8%	-0.1% - 17.7%
West Bank	World Food Programme (WFP)	6	8.6%	0.9% - 16.4%
West Bank	Japan International Cooperation Agency (JICA)	5	6.9%	0.1% - 13.8%
West Bank	World Bank	4	6.2%	-1.7% - 14.1%
West Bank	Norwegian Refugee Council (NRC)	0	0.7%	-0.7% - 2.2%
West Bank	Turkish Cooperation and Coordination Agency (TiKA)	0	0.7%	-0.6% - 2.1%
West Bank	European Union (EU)	0	0.7%	-0.6% - 2.1%
West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	0	0.0%	0% - 0%
West Bank	Belgium Development Agency (ENABEL)	0	0.0%	0% - 0%
Q16. Donor activities participated in by Region

Code

save_as_docx(q16_flx, path=here("output/tables/eval Q1/q16reg.docx"))

Ranked by subregion

Code

q16a_subreg <- disag_tab(svyrdat, q16a_bin, subregion,subreg_key, "q16a", " United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) "," Sub region ")
q16b_subreg <- disag_tab(svyrdat, q16b_bin, subregion,subreg_key, "q16b", " United Nations Development Programme (UNDP)"," Sub region ")
q16c_subreg <- disag_tab(svyrdat, q16c_bin, subregion,subreg_key, "q16c", " United Nations Children’s Fund (UNICEF)"," Sub region ")
q16d_subreg <- disag_tab(svyrdat, q16d_bin, subregion,subreg_key, "q16d", " Red Crescent/Red Cross"," Sub region ")
q16e_subreg <- disag_tab(svyrdat, q16e_bin, subregion,subreg_key, "q16e", " German Society for International Cooperation (GIZ)"," Sub region ")
q16f_subreg <- disag_tab(svyrdat, q16f_bin, subregion,subreg_key, "q16f", " UKAid/Foreign, Commonwealth & Development Office (FCDO) "," Sub region ")
q16g_subreg <- disag_tab(svyrdat, q16g_bin, subregion,subreg_key, "q16g", " Norwegian Refugee Council (NRC) "," Sub region ")
q16h_subreg <- disag_tab(svyrdat, q16h_bin, subregion,subreg_key, "q16h", " Japan International Cooperation Agency (JICA)"," Sub region ")
q16i_subreg <- disag_tab(svyrdat, q16i_bin, subregion,subreg_key, "q16i", " Belgium Development Agency (ENABEL) "," Sub region ")
q16j_subreg <- disag_tab(svyrdat, q16j_bin, subregion,subreg_key, "q16j", " Sweden International Development Agency (Sida) "," Sub region ")

q16k_subreg <- disag_tab(svyrdat, q16k_bin, subregion,subreg_key, "q16k", " Turkish Cooperation and Coordination Agency (TiKA)"," Sub region ")
q16l_subreg <- disag_tab(svyrdat, q16l_bin, subregion,subreg_key, "q16l", " European Union (EU)"," Sub region ")
q16m_subreg <- disag_tab(svyrdat, q16m_bin, subregion,subreg_key, "q16m", " World Food Programme (WFP)"," Sub region ")
q16n_subreg <- disag_tab(svyrdat, q16n_bin, subregion,subreg_key, "q16n", " World Bank"," Sub region ")
q16_subreg <- bind_rows(q16a_subreg, q16b_subreg, q16c_subreg,
                     q16d_subreg,
                     q16e_subreg,
                     q16f_subreg,
                     q16g_subreg, q16h_subreg, q16i_subreg, q16j_subreg, q16k_subreg, q16l_subreg, q16m_subreg, q16n_subreg
) %>%
  group_by(`Disaggregation type`) %>%
  mutate(region_rank = rank(-Percent)) %>%
  arrange(`Disaggregation type`, desc(Percent))

#q16_subreg
write_csv(q16_subreg, here("output/tables/eval Q1/q16subreg.csv"))


q16sub_flx <- q16_subreg %>% 
select(`Donor activities participated in,by SubRegion`=Label, Number, Percent, `Confidence interval`=ci) %>%
flextable() %>% 
set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
align(j=4, align="center") %>% 
colformat_double(j=3, digits=0) %>%
set_table_properties(layout="autofit") %>% 
add_footer_lines(values="Q16. Donor activities participated in,by SubRegion  Ranked")

q16sub_flx 

Disaggregation type	Donor activities participated in,by SubRegion	Number	Percent	Confidence interval
Central West Bank	Red Crescent/Red Cross	14	64.2%	40.7% - 87.7%
Central West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	7	33.6%	9.3% - 58%
Central West Bank	United Nations Children’s Fund (UNICEF)	5	24.8%	1.3% - 48.3%
Central West Bank	European Union (EU)	3	15.4%	1.6% - 29.2%
Central West Bank	Japan International Cooperation Agency (JICA)	3	14.9%	-3.7% - 33.6%
Central West Bank	German Society for International Cooperation (GIZ)	2	11.3%	0% - 22.6%
Central West Bank	Sweden International Development Agency (Sida)	2	8.5%	-7.4% - 24.4%
Central West Bank	United Nations Development Programme (UNDP)	2	7.5%	-2.9% - 17.9%
Central West Bank	World Food Programme (WFP)	1	5.3%	-2.3% - 12.8%
Central West Bank	Norwegian Refugee Council (NRC)	0	2.3%	-2.3% - 6.8%
Central West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	0	0.0%	0% - 0%
Central West Bank	Belgium Development Agency (ENABEL)	0	0.0%	0% - 0%
Central West Bank	Turkish Cooperation and Coordination Agency (TiKA)	0	0.0%	0% - 0%
Central West Bank	World Bank	0	0.0%	0% - 0%
Gaza Strip	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	126	73.8%	63.9% - 83.8%
Gaza Strip	United Nations Children’s Fund (UNICEF)	85	49.5%	36.5% - 62.5%
Gaza Strip	Red Crescent/Red Cross	81	47.3%	35.3% - 59.4%
Gaza Strip	United Nations Development Programme (UNDP)	55	32.2%	20.5% - 43.9%
Gaza Strip	European Union (EU)	45	26.6%	16.6% - 36.6%
Gaza Strip	World Food Programme (WFP)	41	23.7%	16.4% - 31.1%
Gaza Strip	World Bank	35	20.6%	12.5% - 28.7%
Gaza Strip	Norwegian Refugee Council (NRC)	29	17.2%	9% - 25.5%
Gaza Strip	German Society for International Cooperation (GIZ)	29	17.0%	9.7% - 24.3%
Gaza Strip	Turkish Cooperation and Coordination Agency (TiKA)	22	13.1%	6.2% - 20.1%
Gaza Strip	Belgium Development Agency (ENABEL)	22	13.1%	5.4% - 20.8%
Gaza Strip	Sweden International Development Agency (Sida)	18	10.2%	3.6% - 16.9%
Gaza Strip	UKAid/Foreign, Commonwealth & Development Office (FCDO)	17	10.1%	3.7% - 16.4%
Gaza Strip	Japan International Cooperation Agency (JICA)	8	4.8%	1% - 8.6%
Northern West Bank	European Union (EU)	7	44.2%	13.3% - 75.1%
Northern West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	5	31.6%	4% - 59.2%
Northern West Bank	German Society for International Cooperation (GIZ)	2	15.1%	-11.9% - 42.2%
Northern West Bank	United Nations Development Programme (UNDP)	2	10.9%	-3.1% - 24.9%
Northern West Bank	Japan International Cooperation Agency (JICA)	1	8.6%	-0.2% - 17.5%
Northern West Bank	World Food Programme (WFP)	1	7.5%	-1.3% - 16.3%
Northern West Bank	Red Crescent/Red Cross	1	6.9%	-1.6% - 15.5%
Northern West Bank	Turkish Cooperation and Coordination Agency (TiKA)	0	3.1%	-2.1% - 8.3%
Northern West Bank	World Bank	0	2.0%	-2% - 6%
Northern West Bank	United Nations Children’s Fund (UNICEF)	0	0.0%	0% - 0%
Northern West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	0	0.0%	0% - 0%
Northern West Bank	Norwegian Refugee Council (NRC)	0	0.0%	0% - 0%
Northern West Bank	Belgium Development Agency (ENABEL)	0	0.0%	0% - 0%
Northern West Bank	Sweden International Development Agency (Sida)	0	0.0%	0% - 0%
Southern West Bank	United Nations Children’s Fund (UNICEF)	13	46.1%	21.8% - 70.4%
Southern West Bank	Red Crescent/Red Cross	13	44.3%	21.7% - 66.9%
Southern West Bank	United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)	10	35.8%	11.4% - 60.1%
Southern West Bank	European Union (EU)	8	27.9%	8.4% - 47.4%
Southern West Bank	United Nations Development Programme (UNDP)	7	25.1%	6% - 44.2%
Southern West Bank	German Society for International Cooperation (GIZ)	5	17.3%	0.6% - 33.9%
Southern West Bank	Sweden International Development Agency (Sida)	4	13.6%	-1.8% - 29%
Southern West Bank	World Bank	4	13.1%	-3.4% - 29.6%
Southern West Bank	World Food Programme (WFP)	3	11.8%	-3.4% - 27%
Southern West Bank	UKAid/Foreign, Commonwealth & Development Office (FCDO)	0	0.0%	0% - 0%
Southern West Bank	Norwegian Refugee Council (NRC)	0	0.0%	0% - 0%
Southern West Bank	Japan International Cooperation Agency (JICA)	0	0.0%	0% - 0%
Southern West Bank	Belgium Development Agency (ENABEL)	0	0.0%	0% - 0%
Southern West Bank	Turkish Cooperation and Coordination Agency (TiKA)	0	0.0%	0% - 0%
Q16. Donor activities participated in,by SubRegion Ranked

Code

save_as_docx(q16sub_flx, path=here("output/tables/eval Q1/q16subreg.docx"))

Q17 Other donor performance

Overall

Code

#frq(dat$q17)

q17 <- fac_tab(svyrdat, q17) %>%
  #mutate(lab=percept_key$perception_lab) %>%
  left_join(percept_key,
            by=c("q17"="perception")) %>%
  select(Label=q17, Response =perception_lab, Percent:Number, margin, Lower, Upper, ci) %>%
mutate(Ind="Positive perception of all donors")

write_csv(q17, here("output/tables/eval Q1/q17.csv"))
#q17

q17_flx <- q17 %>%
  select(1, 2, 6, 3, `Confidence interval`=10) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q17. Performance of other donors")

save_as_docx(q17_flx, path=here("output/tables/eval Q1/q17.docx"))

q17_flx

Label	Response	Number	Percent	Confidence interval
1	Very negative	113	4.4%	3.2% - 5.7%
2	Somewhat negative	221	8.7%	7% - 10.3%
3	Neither negative nor positive	511	20.0%	17.7% - 22.3%
4	Somewhat positive	1,087	42.6%	39.7% - 45.6%
5	Very positive	533	20.9%	18.2% - 23.7%
98	Refused	7	0.3%	0% - 0.5%
99	Don't know	78	3.1%	2.2% - 3.9%
Q17. Performance of other donors

Disaggregated

Code

#frq(dat$donor_perf_bin)

q17_ov <- ov_tab(svyrdat, donor_perf_bin, "q17", "Performance of other donors") 

q17_sex <- disag_tab(svyrdat, donor_perf_bin, sex, sex_key, 
                    "q17", "Performance of other donors", "Sex")

q17_age <- disag_tab(svyrdat, donor_perf_bin, age_grp, age_grp_key, 
                    "q17", "Performance of other donors", "Age group")

q17_ed <- disag_tab(svyrdat, donor_perf_bin, educ_cat, educ_key, 
                    "q17", "Performance of other donors", "Education")

q17_mad <- disag_tab(svyrdat, donor_perf_bin, madrassa, mad_key, 
                    "q17", "Performance of other donors", "Madrassa education")

q17_area <- disag_tab(svyrdat, donor_perf_bin, area, area_key, 
                    "q17", "Performance of other donors", "Area")

q17_gov <- disag_tab(svyrdat, donor_perf_bin, gov,gov_key,
                     "q17","Performance of other donors", "Governorate")

q17_subreg <- disag_tab(svyrdat, donor_perf_bin, subregion, subreg_key, 
                    "q17", "Performance of other donors", "Subregion")

q17_reg <- disag_tab(svyrdat, donor_perf_bin, region, reg_key, 
                    "q17", "Performance of other donors", "Region")

q17_disag <- bind_rows(q17_ov,
                       q17_area,
                      q17_reg,
                      q17_subreg,
                      q17_gov,
                      q17_sex,
                      q17_age,
                      q17_ed,
                      q17_mad)

write_csv(q17_disag, here("output/tables/eval Q1/q17 other donor performance disag.csv"))

#q17_disag

q17_disag_flx <- q17_disag %>% 
  select(3,5:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=3, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=5, align="center") %>%
  merge_v(j="Disaggregation") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Q17. Other donor performance") %>%
  border_inner_h()

save_as_docx(q17_disag_flx, path=here("output/tables/eval Q1/q17 other donor performance disag.docx"))

q17_disag_flx

Disaggregation	Disaggregation type	Number	Percent	Confidence interval
Overall	Performance of other donors	1,620	63.6%	60.5% - 66.7%
Area	Urban	1,215	61.6%	58.1% - 65.2%
	Village	281	74.7%	67.8% - 81.6%
	Refugee camp	124	61.6%	51.1% - 72.1%
Region	West Bank	993	66.6%	62.9% - 70.3%
	Gaza	627	59.3%	54% - 64.6%
Subregion	Northern West Bank	364	62.8%	56.8% - 68.8%
	Central West Bank	253	63.0%	56.8% - 69.2%
	Southern West Bank	376	73.9%	67.3% - 80.6%
	Gaza Strip	627	59.3%	54% - 64.6%
Governorate	Jenin	103	67.7%	58.9% - 76.5%
	Tubas	22	64.6%	50.8% - 78.4%
	Tulkarm	28	35.0%	22% - 48%
	Nablus	141	64.7%	52.8% - 76.6%
	Qalqiliya	42	76.1%	57.7% - 94.6%
	Salfit	28	69.4%	52.2% - 86.5%
	Ramallah	110	63.3%	52.8% - 73.8%
	Jericho	14	65.3%	43.5% - 87.2%
	Jerusalem	129	62.4%	54.5% - 70.4%
	Bethlehem	89	73.4%	60.5% - 86.2%
	Hebron	287	74.1%	66.3% - 81.8%
	North Gaza	160	74.5%	61.1% - 87.9%
	Gaza	197	52.0%	44.2% - 59.9%
	Dier al-Balah	87	58.5%	42% - 75.1%
	Khan Yunis	107	55.6%	43.5% - 67.7%
	Rafah	77	61.5%	47.1% - 75.9%
Sex	Male	800	63.0%	59.2% - 66.8%
	Female	820	64.2%	60.4% - 67.9%
Age group	Youth (18-29)	691	64.6%	60.1% - 69.1%
	Adult (30-54)	728	66.2%	62.6% - 69.7%
	Mature (55+)	201	53.1%	48.1% - 58.2%
Education	Elementary school education	320	59.0%	53.1% - 65%
	Secondary education	700	63.7%	59.7% - 67.8%
	Post-secondary education	600	66.1%	61.9% - 70.2%
Madrassa education	No madrassa education	1,427	64.9%	61.6% - 68.2%
	Madrassa education	193	55.2%	48.4% - 62%
Q17. Other donor performance

Key indicator table

Overall

Code

q1_key_ov <- bind_rows(q2_ov, q1g_ov, percep_ov, perf_ov, usg_ov, usp_ov, q9_ov, q11_ov, q13_ov, q15_ov, q17_ov)

#q1_key
write_csv(q1_key_ov, here("output/tables/eval Q1/Eval Q1 Key Indicator Table - overall.csv"))

q1_key_ov_flx <- q1_key_ov %>%
  select(Indicator=Label, 6:7, `Confidence interval`=13) %>%
  flextable() %>%
  colformat_double(j=2, digits=0) %>%
  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(j=4, align="center") %>%
  fix_border_issues() %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Eval Q1 Key Indicator Table")

save_as_docx(q1_key_ov_flx, path=here("output/tables/eval Q1/Eval Q1 Key Indicator Table - overall.docx"))

q1_key_ov_flx

Indicator	Number	Percent	Confidence interval
Familiar with USAID	1,054	41.3%	38.3% - 44.4%
Seen USAID logo	816	32.7%	29.9% - 35.5%
Positive perception of USAID	646	61.3%	57.6% - 64.9%
Positive perception of USAID performance	1,419	55.7%	52.6% - 58.7%
Positive perception of USG	578	22.7%	20.3% - 25.1%
Positive perception of American people	1,166	45.7%	43% - 48.5%
Exposed to USAID activity	616	24.2%	21.8% - 26.6%
Participated in USAID activity	129	5.1%	4% - 6.2%
Familiar with other donors	1,464	57.4%	54.3% - 60.6%
Participated in other donor activity	236	9.3%	7.3% - 11.2%
Performance of other donors	1,620	63.6%	60.5% - 66.7%
Eval Q1 Key Indicator Table

Region

Code

q1_key_reg <- bind_rows(q2_reg, 
                        q1g_reg, 
                        percep_reg, 
                        perf_reg, 
                        usg_reg, 
                        usp_reg,
                        q9_reg,
                        q11_reg,
                        q13_reg,
                        q15_reg,
                        q17_reg) %>% 
  bind_rows(q1_key_ov) %>%
  mutate(disag=ifelse(`Disaggregation type`==Label, "Overall", `Disaggregation type`))

sum(names(q1_key_ov)!=names(q1_key_reg))

[1] 1

Code

q1_key_regW <- q1_key_reg %>%
  select(Indicator=Label, disag, Percent) %>%
  pivot_wider(values_from = Percent,
              names_from=disag) %>%
  mutate(`West Bank`=`West Bank`*100,
         Gaza = Gaza*100,
         Overall=Overall*100,
         Category=c(rep("Familiarity with USAID", 2), 
                    rep("Perception of USAID", 4),
                    rep("Exposure to USAID",2),
                    rep("Familiarity with other donors", 3))) %>%
  select(Category, everything())

#q1_key_regW

write_csv(q1_key_regW, here("output/tables/eval Q1/Eval Q1 Key Indicator Table - region.csv"))

q1_key_regW_flx <- q1_key_regW %>%
  #select(1,5:7) %>%
  flextable() %>%
  #height(height=15) %>%
  line_spacing(space=1, part="all") %>%
  colformat_double(j=3:5, digits=1, suffix = "%") %>%
#  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(part="header", align="center") %>%
  fix_border_issues() %>%
#  hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Eval Q1 Key Indicator Table, with region") %>%
  merge_v(1) %>%
  fix_border_issues(part="all") %>%
  hline(i=c(2, 6, 8))
  #border_inner_h()

save_as_docx(q1_key_regW_flx, path=here("output/tables/eval Q1/Eval Q1 Key Indicator Table - region.docx"))

q1_key_regW_flx

Category	Indicator	West Bank	Gaza	Overall
Familiarity with USAID	Familiar with USAID	36.4%	48.3%	41.3%
	Seen USAID logo	33.8%	31.1%	32.7%
Perception of USAID	Positive perception of USAID	57.4%	65.4%	61.3%
	Positive perception of USAID performance	55.3%	56.2%	55.7%
	Positive perception of USG	15.7%	32.6%	22.7%
	Positive perception of American people	50.4%	39.2%	45.7%
Exposure to USAID	Exposed to USAID activity	23.4%	25.3%	24.2%
	Participated in USAID activity	2.1%	9.3%	5.1%
Familiarity with other donors	Familiar with other donors	50.9%	66.7%	57.4%
	Participated in other donor activity	4.4%	16.1%	9.3%
	Performance of other donors	66.6%	59.3%	63.6%
Eval Q1 Key Indicator Table, with region

Subregion

Code

q1_key_subreg <- bind_rows(q2_subreg, 
                        q1g_subreg, 
                        percep_subreg, 
                        perf_subreg, 
                        usg_subreg, 
                        usp_subreg,
                        q9_subreg,
                        q11_subreg,
                        q13_subreg,
                        q15_subreg,
                        q17_subreg) %>% 
  bind_rows(q1_key_ov) %>%
  mutate(disag=ifelse(`Disaggregation type`==Label, "Overall", `Disaggregation type`))

sum(names(q1_key_ov)!=names(q1_key_reg))

[1] 1

Code

q1_key_subregW <- q1_key_subreg %>%
  select(Indicator=Label, disag, Percent) %>%
  pivot_wider(values_from = Percent,
              names_from=disag) %>%
  mutate(`Northern West Bank`=`Northern West Bank`*100,
         `Central West Bank`=`Central West Bank`*100,
         `Southern West Bank`=`Southern West Bank`*100,
         `Gaza Strip`=`Gaza Strip`*100,
         Overall=Overall*100,
         Category=c(rep("Familiarity with USAID", 2), 
                    rep("Perception of USAID", 4),
                    rep("Exposure to USAID",2),
                    rep("Familiarity with other donors", 3))) %>%
  select(Category, everything())

#q1_key_subregW

write_csv(q1_key_subregW, here("output/tables/eval Q1/Eval Q1 Key Indicator Table - subregion.csv"))

q1_key_subregW_flx <- q1_key_subregW %>%
  #select(1,5:7) %>%
  flextable() %>%
  #height(height=15) %>%
  line_spacing(space=1, part="all") %>%
  colformat_double(j=3:7, digits=1, suffix = "%") %>%
#  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
  align(part="header", align="center") %>%
  fix_border_issues() %>%
#  hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
  set_table_properties(layout="autofit") %>%
  add_footer_lines(values="Eval Q1 Key Indicator Table, with region") %>%
  merge_v(1) %>%
  fix_border_issues(part="all") %>%
  hline(i=c(2, 6, 8))
  #border_inner_h()

save_as_docx(q1_key_subregW_flx, path=here("output/tables/eval Q1/Eval Q1 Key Indicator Table - subregion.docx"))

q1_key_subregW_flx

Category	Indicator	Northern West Bank	Central West Bank	Southern West Bank	Gaza Strip	Overall
Familiarity with USAID	Familiar with USAID	42.0%	33.6%	32.3%	48.3%	41.3%
	Seen USAID logo	37.6%	41.2%	23.8%	31.1%	32.7%
Perception of USAID	Positive perception of USAID	57.7%	49.7%	63.2%	65.4%	61.3%
	Positive perception of USAID performance	54.0%	54.0%	57.8%	56.2%	55.7%
	Positive perception of USG	15.3%	15.0%	16.6%	32.6%	22.7%
	Positive perception of American people	47.0%	49.8%	54.8%	39.2%	45.7%
Exposure to USAID	Exposed to USAID activity	25.8%	21.0%	22.5%	25.3%	24.2%
	Participated in USAID activity	1.8%	2.1%	2.4%	9.3%	5.1%
Familiarity with other donors	Familiar with other donors	56.0%	36.8%	56.1%	66.7%	57.4%
	Participated in other donor activity	2.6%	5.4%	5.6%	16.1%	9.3%
	Performance of other donors	62.8%	63.0%	73.9%	59.3%	63.6%
Eval Q1 Key Indicator Table, with region

Code

# q1_key_gov <- bind_rows(q2_gov,
#                         q1g_gov,
#                         percep_gov,
#                         perf_gov,
#                         usg_gov,
#                         usp_gov,
#                         q9_gov,
#                         q11_gov,
#                         q13_gov,
#                         q15_gov,
#                         q17_gov) %>%
#   #bind_rows(q1_key_gov) %>%
#   mutate(disag=ifelse(`Disaggregation type`==Label, "Overall", `Disaggregation type`))
# 
# sum(names(q1_key_ov)!=names(q1_key_reg))
# 
# q1_key_govW <- q1_key_gov %>%
#   select(Indicator=Label, disag, Percent) %>%
#   pivot_wider(values_from = Percent,
#               names_from=disag)
# %>%
#   mutate(`West Bank`=`West Bank`*100,
#          Gaza = Gaza*100,
#          Overall=Overall*100,
#          Category=c(rep("Familiarity with USAID", 2),
#                     rep("Perception of USAID", 4),
#                     rep("Exposure to USAID",2),
#                     rep("Familiarity with other donors", 3))) %>%
#   select(Category, everything())
# 
# q1_key_regW
# 
# write_csv(q1_key_regW, here("output/tables/eval Q1/Eval Q1 Key Indicator Table - region.csv"))
# 
# q1_key_regW_flx <- q1_key_regW %>%
#   #select(1,5:7) %>%
#   flextable() %>%
#   #height(height=15) %>%
#   line_spacing(space=1, part="all") %>%
#   colformat_double(j=3:5, digits=1, suffix = "%") %>%
# #  set_formatter(Percent=function(x) sprintf("%.1f%%", x*100)) %>%
#   align(part="header", align="center") %>%
#   fix_border_issues() %>%
# #  hline(i=c(1,3,7,10,12,15,18), border=smlbrdr) %>%
#   set_table_properties(layout="autofit") %>%
#   add_footer_lines(values="Eval Q1 Key Indicator Table, with region") %>%
#   merge_v(1) %>%
#   fix_border_issues(part="all") %>%
#   hline(i=c(2, 6, 8))
#   #border_inner_h()
# 
# save_as_docx(q1_key_regW_flx, path=here("output/tables/eval Q1/Eval Q1 Key Indicator Table - region.docx"))
# 
# q1_key_regW_flx

Key indicators - ordinal

Code

q4_tot <- svytotal(~factor(q4),
         na.rm=T,
         design=svydat) %>%
  as.data.frame()

usaid_percep_ord <- svymean(~factor(usaid_percep_ord3),
        na.rm=T,
        deff="replace",
        design=svydat) %>%
  as.data.frame() %>%
  mutate(Indicator="USAID",
         Response=c(-1,0,1),
         Label=c("Negative","No opinion","Positive"),
         Percent=round(mean*100,0),
         lab_pos = cumsum(Percent) - .1*Percent) %>%
  select(Indicator, Response, Label, Percent, lab_pos) 


#write_csv(q4, here("output/tables/eval Q1/q4.csv"))
#q4

usaid_percep_ord_flx <- usaid_percep_ord %>%
  select(-2, -5) %>%
  pivot_wider(values_from=Percent,
              names_from=Label) %>%
  flextable() %>%
  set_table_properties(layout="autofit") %>%
  colformat_double(j=2:4, digits=0, suffix="%")

#usaid_percep_ord_flx

save_as_docx(usaid_percep_ord_flx, path=here("output/tables/eval Q1/usaid percep ord.docx"))

Code

#args(ov_tab)

perford <- fac_tab(svyrdat, usaid_perf_ord3) %>%
  mutate(Indicator="Positive perception of USAID performance",
         Label=c("Negative","No opinion","Positive"),
         Percent=round(Percent*100,0),
         lab_pos = cumsum(Percent) - .1*Percent) %>%
  select(Indicator, Response=1, Label, Percent, lab_pos) 

#str(perford)

# ggplot(perford, aes(x=Indicator, y=reorder(Percent, Response), fill=Label)) + 
#   geom_col(width=.2) +
# #  geom_text(aes(label=paste(Percent, "%", sep=""), group=Label)) +
# #  geom_text(aes(label=paste(round(Percent*100,3), "%", sep=""))) +
# #  coord_flip() +
#   scale_fill_viridis_d() +
#   theme(legend.position="none",
#         axis.title.y=element_blank(),
#         axis.text.y=element_blank(),
#         axis.ticks.y=element_blank())

Code

#perford

perford_flx <- perford %>%
  select(-2, -5) %>%
  pivot_wider(values_from=Percent,
              names_from=Label) %>%
  flextable() %>%
  set_table_properties(layout="autofit") %>%
  colformat_double(j=2:4, digits=0, suffix="%")

#labs$perception2

#perford
#perford_flx

save_as_docx(perford_flx, path=here("output/tables/eval Q1/usaid perf ord.docx"))


#ggplot(perford, aes())

Code

usg_ord <- fac_tab(svyrdat, usg_percep_ord3) %>%
  mutate(Indicator="Positive perception of United States Government",
         Label=c("Negative","No opinion","Positive"),
         Percent=round(Percent*100,0),
         lab_pos = cumsum(Percent) - .1*Percent) %>%
  select(Indicator, Response=1, Label, Percent, lab_pos) 

#usg_ord

usg_ord_flx <- usg_ord %>%
  select(Indicator, Label, Percent) %>%
  pivot_wider(values_from=Percent,
              names_from=Label) %>%
  flextable() %>%
  set_table_properties(layout="autofit") %>%
  colformat_double(j=2:4, digits=0, suffix="%")

#usg_ord_flx

save_as_docx(usg_ord_flx, path=here("output/tables/eval Q1/usg percep ord.docx"))

Code

usp_ord <- fac_tab(svyrdat, usp_ord3) %>%
  mutate(Indicator="United States people",
         Label=c("Negative","No opinion","Positive"),
         Percent=round(Percent*100,0),
         lab_pos = cumsum(Percent) - .1*Percent) %>%
  select(Indicator, Response=1, Label, Percent, lab_pos) 

#usp_ord

usp_ord_flx <- usp_ord %>%
  select(Indicator, Label, Percent) %>%
  pivot_wider(values_from=Percent,
              names_from=Label) %>%
  flextable() %>%
  set_table_properties(layout="autofit") %>%
  colformat_double(j=2:4, digits=0, suffix="%")

#usp_ord_flx

save_as_docx(usg_ord_flx, path=here("output/tables/eval Q1/usp percep ord.docx"))

Code

donor_perf_ord <- fac_tab(svyrdat, donor_perf_ord3) %>%
  mutate(Indicator="All donors",
         Label=c("Negative","No opinion","Positive"),
         Percent=round(Percent*100,0),
         lab_pos = cumsum(Percent) - .1*Percent) %>%
  select(Indicator, Response=1, Label, Percent, lab_pos) 

#donor_perf_ord

donor_perf_ord_flx <- donor_perf_ord %>%
  select(Indicator, Label, Percent) %>%
  pivot_wider(values_from=Percent,
              names_from=Label) %>%
  flextable() %>%
  set_table_properties(layout="autofit") %>%
  colformat_double(j=2:4, digits=0, suffix="%")

#donor_perf_ord_flx

save_as_docx(donor_perf_ord_flx, path=here("output/tables/eval Q1/donor percep ord.docx"))

Code

q1ord <- bind_rows(usaid_percep_ord,
                   perford,
                   usg_ord,
                   usp_ord,
                   donor_perf_ord) 

q1ord <- q1ord %>%
  `rownames<-` (NULL) %>%
  mutate(ind2=rep(c("USAID",
                    "USAID performance",
                    "United States Government",
                    "United States people",
                    "All donors in Palestine"), each=3),
         num=rep(1:5, each=3)) 


q1ord <- q1ord %>%
  group_by(ind2) %>%
  mutate(lab_pos2=cumsum(Percent-.2*Percent)) %>%
  ungroup()

#str(q1ord)
#q1ord

write_csv(q1ord, here("output/tables/eval Q1/q1 ord.csv"))
                 
q1ordL <- q1ord %>%
  select(Indicator, Label, Percent) %>%
  pivot_wider(values_from=Percent,
              names_from=Label) 

#q1ordL


q1ord_flx <- q1ord %>%
  select(Indicator, Label, Percent) %>%
  pivot_wider(values_from=Percent,
              names_from=Label) %>%
  flextable() %>%
  set_table_properties(layout="autofit") %>%
  colformat_double(j=2:4, digits=0, suffix="%")

save_as_docx(q1ord_flx, path=here("output/tables/eval Q1/q1 ord.docx"))

q1ord_flx

Indicator	Negative	No opinion	Positive
USAID	11%	28%	61%
Positive perception of USAID performance	17%	27%	56%
Positive perception of United States Government	51%	26%	23%
United States people	31%	23%	46%
All donors	13%	23%	64%