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library(dplyr)
library(tidyr)
KeepObjectsAcrossAnalysisStrings <- get0("KeepObjectsAcrossAnalysisStrings", ifnotfound = character())
rm(list=ls()[!ls() %in% (Keeps <- c("t0",KeepObjectsAcrossAnalysisStrings))] )
setwd(getOption("replication.root", default = getwd()))
#############################################################
#### Projects by funder
#############################################################
oda_sect_group_df <- read.csv("./data/interim/africa_oda_sector_group.csv") %>%
filter(transactions_start_year >= 2002 &
transactions_start_year <= 2013)
donor_precision_count <- oda_sect_group_df %>%
group_by(funder, precision_code) %>%
summarize(n = n_distinct(project_location_id)) %>%
pivot_wider(names_from = funder, values_from = n, values_fill = 0) %>%
mutate(precision_code = paste("project_precision",precision_code)) %>%
rename(description = precision_code)
donor_vars_df <- oda_sect_group_df %>%
dplyr::select(funder, project_id, project_location_id, site_iso3, ad_sector_codes) %>%
group_by(funder) %>%
summarize(across(everything(), ~n_distinct(.))) %>%
pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>%
pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0)
# donor_regional_unspecified_df <- oda_sect_group_df %>%
# select(funder, project_location_id, recipients_iso3) %>%
# group_by(funder) %>%
# filter(grepl("regional|Unspecified",recipients_iso3)) %>%
# summarize(regional_count= n_distinct(project_location_id)) %>%
# pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>%
# pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0)
#
# donor_recipient_site_mismatch_df <- oda_sect_group_df %>%
# select(funder, project_location_id, recipients_iso3, site_iso3) %>%
# group_by(funder) %>%
# filter(!grepl("regional|Unspecified",recipients_iso3) &
# recipients_iso3 != site_iso3) %>%
# summarize(mismatch_count= n_distinct(project_location_id)) %>%
# pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>%
# pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0)
no_end_date_df <- oda_sect_group_df %>%
dplyr::select(funder,end_actual_isodate) %>%
group_by(funder) %>%
summarize(portion_no_end_date = mean(end_actual_isodate=="")) %>%
mutate(portion_no_end_date = round(portion_no_end_date,2)) %>%
pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>%
pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0)
no_funding_df <- oda_sect_group_df %>%
dplyr::select(funder,total_disbursements) %>%
group_by(funder) %>%
summarize(portion_no_funding = mean(is.na(total_disbursements))) %>%
mutate(portion_no_funding = round(portion_no_funding,2)) %>%
pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>%
pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0)
multisector_df <- oda_sect_group_df %>%
dplyr::select(funder, geoname_id, transactions_start_year, ad_sector_codes) %>%
group_by(funder, geoname_id, transactions_start_year, ad_sector_codes) %>%
count() %>%
group_by(funder) %>%
summarize(portion_multisector = mean(n > 1)) %>%
mutate(portion_multisector = round(portion_multisector,2)) %>%
pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>%
pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0)
desired_order <- c(3, 4, 1, 2, 5, 6, 7, 8, 9, 10)
donor_comparison_df <- rbind(donor_vars_df,donor_precision_count, no_end_date_df,
no_funding_df, multisector_df) %>%
slice(match(desired_order, row_number())) %>%
mutate(description = case_match(description,
"site_iso3" ~ "Countries hosting projects count",
"ad_sector_codes" ~ "Sectors funded",
"project_id" ~ "Aid project count",
"project_location_id" ~ "Aid project location count",
"project_precision 1" ~ "Exact locations available (precision 1)",
"project_precision 2" ~ "Near (<25km) locations available (precision 2)",
"project_precision 3" ~ "ADM2 locations available (precision 3)",
"portion_no_end_date" ~ "Portion lacking end date",
"portion_no_funding" ~ "Portion lacking funding information",
"portion_multisector" ~ "Portion with concurrent, co-located, multi-sector projects",
.default = description))
# Table 1: Funder Comparison: China and World Bank
# 1 Countries hosting projects count 50 44
# 2 Sectors funded 22 13
# 3 Aid project count 722 513
# 4 Aid project location count 1373 7115
# 5 Exact locations available (precision 1) 987 4149
# 6 Near (<25km) locations available (precision 2) 169 258
# 7 ADM2 locations available (precision 3) 217 2708
# 8 Portion lacking end date 0.68 0.15
# 9 Portion lacking funding information 1 0.28
# 10 Portion with concurrent, co-located, multi-sector projects 0.03 0.02
#higher than Gehring et al, because they exclude countries with less than 1 million people
write.csv(donor_comparison_df,"./tables/funder_comparison.csv",row.names = FALSE)
#to do: make a sector level table similar to this, but divided by funders
#consider limiting to sectors actually included in analysis?
oda_sect_group_df %>%
filter(precision_code < 4) %>%
group_by(funder, ad_sector_codes, ad_sector_names) %>%
count()
#############################################################
#### DHS Units of Analysis
#############################################################
#read file with all DHS points for one survey round per country
dhs_est_iwi_df <- read.csv("./data/interim/dhs_est_iwi.csv")
#read file that contains confounder data (lower n due to missing confounders)
dhs_confounders_df <- read.csv("./data/interim/dhs_5k_confounders.csv")
#identify locations excluded due to missing confounders, group by country
excluded_dhs_df <- anti_join(dhs_est_iwi_df,dhs_confounders_df,by="dhs_id") %>%
group_by(iso3) %>% count() %>% ungroup() %>%
rename(n_excluded=n)
#get formatted country names
africa_isos_df <- read.csv("./data/interim/africa_isos.csv")
#get treated information
dhs_t_df <- read.csv("./data/interim/dhs_treated_sector_3yr.csv") %>%
filter(year_group!="2014:2016") %>%
#exclude DHS points where confounder data not available
inner_join(dhs_confounders_df %>%
dplyr::select(dhs_id, ID_adm2), by = join_by(dhs_id))
#identify locations that were never treated, group by country
dhs_never_treated_df <- anti_join(dhs_confounders_df,dhs_t_df,by="dhs_id") %>%
group_by(iso3) %>% count() %>% rename(n_never_treated=n) %>% ungroup()
#create display version of neighborhood descriptive stats
neighborhoods_df <- dhs_confounders_df %>%
dplyr::select(iso3,survey_start_year,households,rural.x,dhs_id) %>%
group_by(iso3,survey_start_year) %>%
summarize(n_cluster_locations=n_distinct(dhs_id),
n_households=sum(households),
portion_rural=round(mean(rural.x),2)) %>%
ungroup() %>%
#join to get locations that were never treated
left_join(dhs_never_treated_df, by="iso3") %>%
mutate(n_never_treated=ifelse(is.na(n_never_treated),0,n_never_treated)) %>%
mutate(portion_never_treated=round(n_never_treated/n_cluster_locations,2)) %>%
#join to get excluded count
left_join(excluded_dhs_df,by="iso3") %>%
mutate(n_excluded=ifelse(is.na(n_excluded),0,n_excluded)) %>%
#join to get country names
left_join(africa_isos_df %>% dplyr::select(-iso2), by="iso3") %>%
rename(country=name) %>%
dplyr::select(country,survey_start_year,n_cluster_locations,n_households,
portion_rural,portion_never_treated,n_excluded) %>%
arrange(country)
# country survey_start_year n_cluster_l…¹ n_hou…² porti…³ porti…⁴ n_exc…⁵
# <chr> <int> <int> <int> <dbl> <dbl> <dbl>
# 1 Angola 2006 62 1377 0.35 0.4 0
# 2 Benin 1996 190 3152 0.53 0.03 0
# 3 Burkina Faso 1998 81 754 0.62 0.25 0
# 4 Burundi 2010 307 7032 0.88 0.49 0
# 5 Cameroon 2004 464 9254 0.48 0.22 0
# 6 Central African Republic 1994 66 1142 0.65 0.86 0
# 7 Chad 2014 235 6499 0.59 0.26 0
# 8 Comoros 2012 242 4286 0.56 0 0
# 9 Congo, Democratic Republic of the 2007 286 7947 0.57 0 5
# 10 Côte d'Ivoire 1998 63 833 0.49 0.1 0
# 11 Egypt 1995 7 111 0.14 1 0
# 12 Eswatini 2006 210 3689 0.64 0.52 0
# 13 Ethiopia 2000 506 8623 0.73 0 0
# 14 Gabon 2012 332 11151 0.45 0.46 0
# 15 Ghana 1998 144 1373 0.65 0 0
# 16 Guinea 1999 290 3567 0.6 0.06 0
# 17 Kenya 2003 389 7640 0.67 0.45 0
# 18 Lesotho 2004 344 5350 0.72 0.1 0
# 19 Liberia 2009 30 855 0.1 0 0
# 20 Madagascar 1997 260 5610 0.59 0.02 0
# 21 Malawi 2000 554 14057 0.8 0.6 1
# 22 Mali 1995 167 2238 0.57 0.01 0
# 23 Morocco 2003 343 7530 0.42 0.95 1
# 24 Mozambique 2011 609 13303 0.58 0.12 0
# 25 Namibia 2000 258 6236 0.6 0.88 2
# 26 Niger 1998 184 2189 0.51 0.12 0
# 27 Nigeria 2003 356 5599 0.54 0.42 0
# 28 Rwanda 2005 447 8504 0.76 0 0
# 29 Senegal 1997 270 2518 0.63 0.04 0
# 30 Sierra Leone 2008 349 7138 0.59 0 0
# 31 South Africa 2016 745 11035 0.38 0.85 1
# 32 Tanzania, United Republic of 1999 171 2831 0.66 0.05 0
# 33 Togo 1998 271 5332 0.52 0.03 0
# 34 Uganda 2000 137 2192 0.55 0.19 0
# 35 Zambia 2007 318 7552 0.64 0.08 1
# 36 Zimbabwe 1999 212 3469 0.64 0.5 0
write.csv(neighborhoods_df,"./tables/neighborhoods.csv",row.names = FALSE)
#get total counts for use in Pipeline Figure
total_dhs_n_df <- dhs_confounders_df %>%
dplyr::select(iso3,survey_start_year,households,dhs_id) %>%
summarize(n_cluster_locations=n_distinct(dhs_id),
n_households=sum(households),
n_countries=n_distinct(iso3),
n_start_year=n_distinct(survey_start_year)
)
write.csv(total_dhs_n_df,"./tables/total_dhs_n.csv",row.names = FALSE)
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