data-raw/df_who_sites.R
In afrimapr/afrihealthsites: Geographic locations of African health facilities from different sources
## code to prepare `df_who_sites` dataset goes here
#use_data_raw('df_who_sites') #to create this script
address <- "https://www.who.int/malaria/areas/surveillance/who-cds-gmp-2019-01-eng.xlsx"
#mode='wb' needed to force binary transfer on windows apparently
download_failed <- tryCatch( utils::download.file(file.path(address), tempfilesites <- tempfile(), mode = "wb"),
error = function(e) {
message(paste('download failed'))
return(TRUE)
})
#return from this function if download error was caught by tryCatch
if (download_failed) return()
df_who_sites <- readxl::read_excel(tempfilesites)
# identify rows with no coords
indices_na_coords <- which(is.na(df_who_sites$Long) | is.na(df_who_sites$Lat))
#length(indices_na_coords)
#[1] 2350 #records that have no coords
#I used to remove rows with no coords here
#df_who_sites <- df_who_sites[-indices_na_coords,]
#move WHO Zanzibar data into Tanzania (consistent with healthsites that has it in Tanzania)
#TODO BUT think about this, Maina(2019) paper says Zanzibar data kept separate because has it's own MoH
#if do keep it separate will need to put healthsites data into Zanzibar and add Zanazibar as a country to the UI
df_who_sites$Country[df_who_sites$Country=='Zanzibar'] <- 'Tanzania'
# add iso3c helps with country ids later
df_who_sites$iso3c <- country2iso(df_who_sites$Country)
# conversion to sf now happens elsewhere in afrihealthsites.r
#sf_who_sites <- sf::st_as_sf(df_who_sites, coords = c("Long", "Lat"), crs = 4326)
#nrow(df_who_sites)
#[1] 96395
#countries_who <- unique(df_who_sites$Country)
# add a column with broad 9 category type consistent across countries
#df_who_sites <- afrihealthsites::afrihealthsites('all', datasource = 'who', plot=FALSE)
df_who_sites$facility_type_9 <- who_type_lookup$facility_type_9[ match(df_who_sites$`Facility type`,who_type_lookup$type_who) ]
# > unique(df_who_sites$`Facility type`)
# [1] "Hospital" "Municipal Hospital"
# [3] "Provincial Hospital" "Centro de Saúde"
# [5] "Posto de Saúde" "Centro Materno Infantil"
# [7] "Central Hospital" "Centro Sanatorio Materno Infantil"
# [9] "Regional Hospital" "General Hospital"
# [11] "Health Centre" "Community Health Centre"
# [13] "Hôpital de Zone" "Centre de Santé d’Arrondissement"
# [15] "Dispensaire" "Centre de Santé de Sous-Prefecture"
# [17] "Centre Médical" "Unites de Santé de Village"
# [19] "Centre Hospitalier Départemental" "Centro de Santé de Circonscription Urbaine"
# [21] "Hôpital" "Clinic"
# [23] "Unites de Santé de village" "Centre de Santé Central"
# [25] "Centre National Hospitalier Universitaire" "Centre Médico-social"
# [27] "Primary Hospital" "Health Post"
# [29] "District Hospital" "Referral Hospital"
# [31] "Centre de Santé et de Promotion Sociale" "Centre Médical Avec Antenne Chirurgicale"
# [33] "Centre Hospitalier Régional" "Centre Hospitalier National"
# [35] "Centre Hospitalier Universitaire National" "Hôpital de District"
# [37] "Hôpital Tertiaire" "Centre de Santé Intégré"
# [39] "Centre Medical d’Arrondissement" "Hôpital Régional"
# [41] "Hôpital Centraux" "Hôpital Général"
# [43] "Postos Sanitários" "Hospitais Regionais"
# [45] "Hospitais Centrais" "Centre de Santé \"C\""
# [47] "Centre de Santé \"E\"" "Centre de Santé \"B\""
# [49] "Centre de Santé \"A\"" "Poste de santé"
# [51] "Centre de Sante" "Hôpital Régional Universitaire"
# [53] "Hôpital Préfectoraux" "Centre de Santé \"D\""
# [55] "Hôpital de Regional" "Hôpital de Nationaux"
# [57] "Regional hospital" "Poste de Santé"
# [59] "Centre Médico-Chirurgical" "Centre Médico-Urbain"
# [61] "Hôpital Comboutique" "l’Hôpital de Base"
# [63] "University Hospital" "Centre de Santé Rural"
# [65] "Centre de Santé Urbain" "Hospitalier Universitaire"
# [67] "Hospitalier Régional" "Centre de Santé"
# [69] "Hôpital Général de Référence" "Centre Hôpital"
# [71] "Centre de Santé de Référence" "Polyclinique"
# [73] "Clinique" "Centre de Santé Municipal"
# [75] "Centre Medico-Chirurgical" "Poste De Santé"
# [77] "Hospital Medical Center" "Hospital Medical Centre"
# [79] "Tertiary Hospital" "Health Station"
# [81] "Mini Hospital" "Mini Clinic"
# [83] "National Referral Hospital" "Clinic without Maternity"
# [85] "Public Health Unit" "Mission Hospital"
# [87] "Clinic with Maternity" "Nucleas Health Centre"
# [89] "National Hospital" "Zonal Hospital"
# [91] "Health post" "Medical Centre"
# [93] "Centre Hospitalier Urbain" "Hôpital Coopération"
# [95] "Health Centre (minor)" "Health Centre (major)"
# [97] "Teaching Hospital" "General Hospital Hospital"
# [99] "Community-based Health Planning and Services" "Polyclinic"
# [101] "Hôpital Prefectoral" "Dispensary"
# [103] "Sub-District Hospital" "County Referral Hospital"
# [105] "Provincial General Hospital" "Filter Clinic"
# [107] "National Referral Centre" "Community Hospital"
# [109] "Health Post/Dispensary" "Rural Hospital"
# [111] "Referral Health Centre" "Area Health Centre"
# [113] "Family Health Clinic" "Medi-Clinic"
# [115] "Centro de Saúde Rural I" "Centro de Saúde Rural II"
# [117] "Centro de Saúde Urbano C" "Hospital Rural"
# [119] "Centro de Saúde Urbano B" "Hospital Provincial"
# [121] "Hospital Distrital" "Centro de Saúde Urbano A"
# [123] "Hospital Geral" "Hospital Central"
# [125] "Intermediate Hospital" "Integrated Health Centre"
# [127] "Health Hut" "Centre Hospitalier Universitaire"
# [129] "Primary Health Centre" "Model Health Centre"
# [131] "Basic Health Centre" "Comprehensive Health Centre"
# [133] "Federal Medical Centre" "Cottage Hospital"
# [135] "University Teaching Hospital" "Model Primary Health Centre"
# [137] "Natonal Hospital" "State Hospital"
# [139] "DISPENSARY" "Secondary Health Post"
# [141] "Hospitais" "Postos de Saúde Comunitária"
# [143] "Community Health Post" "Maternal & Child Health Post"
# [145] "Refferal Hospital" "Maternal & Child Health Centre"
# [147] "Satellite Clinic" "Provincial Tertiary Hospital"
# [149] "National Central Hospital" "Community Health Centre/Clinic"
# [151] "Community Health Centre (After hours)" "Primary Health Care Unit"
# [153] "Primary Health Care Centre" "County Hospital"
# [155] "Type D Hospital" "Type C Hospital"
# [157] "Type B Hospital" "Type A Hospital"
# [159] "Designated District Hospital" "Regional Referral Hospital"
# [161] "Unité de Soins Périphérique" "Centre Hospitalier Préfectoral"
# [163] "Health Centre III" "Health Centre II"
# [165] "Health Centre IV" "Level 1 Hospital"
# [167] "Level 2 Hospital" "Rural Health Centre"
# [169] "Level 3 Hospital" "Primary Health Care Unit +"
# [171] "Rural Health Clinic" "District/Provincial Hospital"
# add columns with 4 tier classication from Falchetta 2020
# see also afrimapr_dev/falchetta2021-explore.Rmd
# to explore data from https://www.pnas.org/content/117/50/31760
# Falchetta2021 Planning universal accessibility to public health care in sub-Saharan Africa
# data & analyses downloaded from https://zenodo.org/record/3757084#.YDD3euj7SUk
datafolder <- 'C:\\Dropbox\\_afrimapr\\health-facilities-africa\\falchetta2020-code\\'
# from data_preparation_and_figures.R
# Import classified version
parser_healthcare_types <- readxl::read_excel(paste0(datafolder, "parser_healthcare_types.xlsx")) %>% dplyr::select(-...1)
# names(parser_healthcare_types)
# [1] "ft" "Country" "Tier"
# 385 rows including country
parser_healthcare_types %>% dplyr::group_by(ft) %>% unique()
df_who_sites <- dplyr::left_join(df_who_sites, parser_healthcare_types,
by=c("Country", "Facility type" = "ft"))
df_who_sites$Tier_name <- ifelse(df_who_sites$Tier==1,"Tier1 health post",
ifelse(df_who_sites$Tier==2,"Tier2 health centre",
ifelse(df_who_sites$Tier==3,"Tier3 provincial hospital",
ifelse(df_who_sites$Tier==4,"Tier4 central hospital", NA))))
# change to a factor with levels in ascending order partly so it displays well in UI
levels(df_who_sites$Tier_name) <- sort(unique(df_who_sites$Tier_name))
usethis::use_data(df_who_sites, overwrite = TRUE)
afrimapr/afrihealthsites documentation built on June 12, 2021, 11:46 a.m.
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## code to prepare `df_who_sites` dataset goes here
#use_data_raw('df_who_sites') #to create this script
address <- "https://www.who.int/malaria/areas/surveillance/who-cds-gmp-2019-01-eng.xlsx"
#mode='wb' needed to force binary transfer on windows apparently
download_failed <- tryCatch( utils::download.file(file.path(address), tempfilesites <- tempfile(), mode = "wb"),
error = function(e) {
message(paste('download failed'))
return(TRUE)
})
#return from this function if download error was caught by tryCatch
if (download_failed) return()
df_who_sites <- readxl::read_excel(tempfilesites)
# identify rows with no coords
indices_na_coords <- which(is.na(df_who_sites$Long) | is.na(df_who_sites$Lat))
#length(indices_na_coords)
#[1] 2350 #records that have no coords
#I used to remove rows with no coords here
#df_who_sites <- df_who_sites[-indices_na_coords,]
#move WHO Zanzibar data into Tanzania (consistent with healthsites that has it in Tanzania)
#TODO BUT think about this, Maina(2019) paper says Zanzibar data kept separate because has it's own MoH
#if do keep it separate will need to put healthsites data into Zanzibar and add Zanazibar as a country to the UI
df_who_sites$Country[df_who_sites$Country=='Zanzibar'] <- 'Tanzania'
# add iso3c helps with country ids later
df_who_sites$iso3c <- country2iso(df_who_sites$Country)
# conversion to sf now happens elsewhere in afrihealthsites.r
#sf_who_sites <- sf::st_as_sf(df_who_sites, coords = c("Long", "Lat"), crs = 4326)
#nrow(df_who_sites)
#[1] 96395
#countries_who <- unique(df_who_sites$Country)
# add a column with broad 9 category type consistent across countries
#df_who_sites <- afrihealthsites::afrihealthsites('all', datasource = 'who', plot=FALSE)
df_who_sites$facility_type_9 <- who_type_lookup$facility_type_9[ match(df_who_sites$`Facility type`,who_type_lookup$type_who) ]
# > unique(df_who_sites$`Facility type`)
# [1] "Hospital" "Municipal Hospital"
# [3] "Provincial Hospital" "Centro de Saúde"
# [5] "Posto de Saúde" "Centro Materno Infantil"
# [7] "Central Hospital" "Centro Sanatorio Materno Infantil"
# [9] "Regional Hospital" "General Hospital"
# [11] "Health Centre" "Community Health Centre"
# [13] "Hôpital de Zone" "Centre de Santé d’Arrondissement"
# [15] "Dispensaire" "Centre de Santé de Sous-Prefecture"
# [17] "Centre Médical" "Unites de Santé de Village"
# [19] "Centre Hospitalier Départemental" "Centro de Santé de Circonscription Urbaine"
# [21] "Hôpital" "Clinic"
# [23] "Unites de Santé de village" "Centre de Santé Central"
# [25] "Centre National Hospitalier Universitaire" "Centre Médico-social"
# [27] "Primary Hospital" "Health Post"
# [29] "District Hospital" "Referral Hospital"
# [31] "Centre de Santé et de Promotion Sociale" "Centre Médical Avec Antenne Chirurgicale"
# [33] "Centre Hospitalier Régional" "Centre Hospitalier National"
# [35] "Centre Hospitalier Universitaire National" "Hôpital de District"
# [37] "Hôpital Tertiaire" "Centre de Santé Intégré"
# [39] "Centre Medical d’Arrondissement" "Hôpital Régional"
# [41] "Hôpital Centraux" "Hôpital Général"
# [43] "Postos Sanitários" "Hospitais Regionais"
# [45] "Hospitais Centrais" "Centre de Santé \"C\""
# [47] "Centre de Santé \"E\"" "Centre de Santé \"B\""
# [49] "Centre de Santé \"A\"" "Poste de santé"
# [51] "Centre de Sante" "Hôpital Régional Universitaire"
# [53] "Hôpital Préfectoraux" "Centre de Santé \"D\""
# [55] "Hôpital de Regional" "Hôpital de Nationaux"
# [57] "Regional hospital" "Poste de Santé"
# [59] "Centre Médico-Chirurgical" "Centre Médico-Urbain"
# [61] "Hôpital Comboutique" "l’Hôpital de Base"
# [63] "University Hospital" "Centre de Santé Rural"
# [65] "Centre de Santé Urbain" "Hospitalier Universitaire"
# [67] "Hospitalier Régional" "Centre de Santé"
# [69] "Hôpital Général de Référence" "Centre Hôpital"
# [71] "Centre de Santé de Référence" "Polyclinique"
# [73] "Clinique" "Centre de Santé Municipal"
# [75] "Centre Medico-Chirurgical" "Poste De Santé"
# [77] "Hospital Medical Center" "Hospital Medical Centre"
# [79] "Tertiary Hospital" "Health Station"
# [81] "Mini Hospital" "Mini Clinic"
# [83] "National Referral Hospital" "Clinic without Maternity"
# [85] "Public Health Unit" "Mission Hospital"
# [87] "Clinic with Maternity" "Nucleas Health Centre"
# [89] "National Hospital" "Zonal Hospital"
# [91] "Health post" "Medical Centre"
# [93] "Centre Hospitalier Urbain" "Hôpital Coopération"
# [95] "Health Centre (minor)" "Health Centre (major)"
# [97] "Teaching Hospital" "General Hospital Hospital"
# [99] "Community-based Health Planning and Services" "Polyclinic"
# [101] "Hôpital Prefectoral" "Dispensary"
# [103] "Sub-District Hospital" "County Referral Hospital"
# [105] "Provincial General Hospital" "Filter Clinic"
# [107] "National Referral Centre" "Community Hospital"
# [109] "Health Post/Dispensary" "Rural Hospital"
# [111] "Referral Health Centre" "Area Health Centre"
# [113] "Family Health Clinic" "Medi-Clinic"
# [115] "Centro de Saúde Rural I" "Centro de Saúde Rural II"
# [117] "Centro de Saúde Urbano C" "Hospital Rural"
# [119] "Centro de Saúde Urbano B" "Hospital Provincial"
# [121] "Hospital Distrital" "Centro de Saúde Urbano A"
# [123] "Hospital Geral" "Hospital Central"
# [125] "Intermediate Hospital" "Integrated Health Centre"
# [127] "Health Hut" "Centre Hospitalier Universitaire"
# [129] "Primary Health Centre" "Model Health Centre"
# [131] "Basic Health Centre" "Comprehensive Health Centre"
# [133] "Federal Medical Centre" "Cottage Hospital"
# [135] "University Teaching Hospital" "Model Primary Health Centre"
# [137] "Natonal Hospital" "State Hospital"
# [139] "DISPENSARY" "Secondary Health Post"
# [141] "Hospitais" "Postos de Saúde Comunitária"
# [143] "Community Health Post" "Maternal & Child Health Post"
# [145] "Refferal Hospital" "Maternal & Child Health Centre"
# [147] "Satellite Clinic" "Provincial Tertiary Hospital"
# [149] "National Central Hospital" "Community Health Centre/Clinic"
# [151] "Community Health Centre (After hours)" "Primary Health Care Unit"
# [153] "Primary Health Care Centre" "County Hospital"
# [155] "Type D Hospital" "Type C Hospital"
# [157] "Type B Hospital" "Type A Hospital"
# [159] "Designated District Hospital" "Regional Referral Hospital"
# [161] "Unité de Soins Périphérique" "Centre Hospitalier Préfectoral"
# [163] "Health Centre III" "Health Centre II"
# [165] "Health Centre IV" "Level 1 Hospital"
# [167] "Level 2 Hospital" "Rural Health Centre"
# [169] "Level 3 Hospital" "Primary Health Care Unit +"
# [171] "Rural Health Clinic" "District/Provincial Hospital"
# add columns with 4 tier classication from Falchetta 2020
# see also afrimapr_dev/falchetta2021-explore.Rmd
# to explore data from https://www.pnas.org/content/117/50/31760
# Falchetta2021 Planning universal accessibility to public health care in sub-Saharan Africa
# data & analyses downloaded from https://zenodo.org/record/3757084#.YDD3euj7SUk
datafolder <- 'C:\\Dropbox\\_afrimapr\\health-facilities-africa\\falchetta2020-code\\'
# from data_preparation_and_figures.R
# Import classified version
parser_healthcare_types <- readxl::read_excel(paste0(datafolder, "parser_healthcare_types.xlsx")) %>% dplyr::select(-...1)
# names(parser_healthcare_types)
# [1] "ft" "Country" "Tier"
# 385 rows including country
parser_healthcare_types %>% dplyr::group_by(ft) %>% unique()
df_who_sites <- dplyr::left_join(df_who_sites, parser_healthcare_types,
by=c("Country", "Facility type" = "ft"))
df_who_sites$Tier_name <- ifelse(df_who_sites$Tier==1,"Tier1 health post",
ifelse(df_who_sites$Tier==2,"Tier2 health centre",
ifelse(df_who_sites$Tier==3,"Tier3 provincial hospital",
ifelse(df_who_sites$Tier==4,"Tier4 central hospital", NA))))
# change to a factor with levels in ascending order partly so it displays well in UI
levels(df_who_sites$Tier_name) <- sort(unique(df_who_sites$Tier_name))
usethis::use_data(df_who_sites, overwrite = TRUE)
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