README.md
In validmeasures/wsup: An Implementation of the WSUP Urban Water and Sanitation Survey in R
wsup: An Implementation of the Water and Sanitation for the Urban Poor (WSUP) Urban Water and Sanitation Survey in R
Project on developing sampling and survey methods to assess water,
sanitation and hygiene (WASH) indicators in urban areas. Project
commissioned by Water and Sanitation for the Urban Poor (WSUP).
This package contains functions for producing WASH indicator results
from datasets collected in the various WSUP focus cities i.e., Dhaka,
Bangladesh; Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar;
Maputo, Mozambique; and, Lusaka, Zambia. These functions can be used by
a competent useR in producing indicators results from datasets
collected using the specific Urban Water and Sanitation Survey design.
This package also implements a Shiny application that provides a
graphical user interface to performing the data processing, analysis and
visualisation using the various functions contained in this package.
This application can be used by anyone wanting to produce indicator
results from datasets collected using the specific Urban Water and
Sanitation Survey design without having to know how to use R.
This package supersedes the RAnalyticFlow-based implementation of the
Urban Water and Sanitation Survey found in this
repository.
Installation
This latest wsup version is in active development in preparation for
submission to CRAN. You can install this development version of wsup
from GitHub with:
if(!require(remotes)) install.packages("remotes")
remotes::install_github("validmeasures/wsup")
Usage
The wsup package includes four main families of functions: 1) recode
functions; 2) data processing and handling functions; 3) data analysis
functions; and 4) Shiny web application function.
Recode functions
All recode functions in wsup package start with the verb get_ then
followed by the indicator set to be recoded. All recode functions take a
survey dataset as its one and only argument. The survey dataset is
expected to be collected using the specific Urban Water and Sanitation
Survey survey questionnaire developed by WSUP. Survey dataset/s based on
WSUP’s survey questionnaire is available via the washdata package
which is available via CRAN and is installed as a dependency when the
wsup package is installed.
There are eight recode functions which recodes the following indicator
sets:
- Administrative indicators
Administrative data is obtained as
follows:
get_admin_vars(washdata::surveyDataBGD)
#> country ccode uniqueID psu zone type quadrat hhid month year longitude
#> 1 Bangladesh BGD 120101 1201 1 2 1 1 Mar 2017 90.42397
#> 2 Bangladesh BGD 120102 1201 1 2 1 2 Mar 2017 90.42417
#> 3 Bangladesh BGD 120201 1202 1 2 2 1 Mar 2017 90.42329
#> 4 Bangladesh BGD 120202 1202 1 2 2 2 Mar 2017 90.42329
#> 5 Bangladesh BGD 120301 1203 1 2 3 1 Mar 2017 90.43500
#> 6 Bangladesh BGD 120302 1203 1 2 3 2 Mar 2017 90.43477
#> 7 Bangladesh BGD 120401 1204 1 2 4 1 Mar 2017 90.42366
#> 8 Bangladesh BGD 120402 1204 1 2 4 2 Mar 2017 90.42353
#> 9 Bangladesh BGD 120501 1205 1 2 5 1 Mar 2017 90.43361
#> 10 Bangladesh BGD 120502 1205 1 2 5 2 Mar 2017 90.43337
#> latitude
#> 1 23.80840
#> 2 23.80886
#> 3 23.82047
#> 4 23.82047
#> 5 23.81953
#> 6 23.81957
#> 7 23.83110
#> 8 23.83080
#> 9 23.83472
#> 10 23.83457
- Demographic indicators
Demographic data is obtained as
follows:
get_demo_vars(washdata::surveyDataBGD)
#> uniqueID gender landOwnStatus nWomen nMen nOldWomen nOldMen nGirls nBoys
#> 1 120101 Female Owner-occupier 1 1 0 0 2 2
#> 2 120102 Female Tenant 2 2 0 0 1 0
#> 3 120201 Female Tenant 1 1 0 0 1 0
#> 4 120202 Female Tenant 2 2 0 0 0 0
#> 5 120301 Female Tenant 1 1 0 0 0 0
#> 6 120302 Female Owner-occupier 2 1 0 0 0 0
#> 7 120401 Female Tenant 1 1 0 0 0 0
#> 8 120402 Female Tenant 1 1 0 0 0 1
#> 9 120501 Female Tenant 2 1 0 0 0 1
#> 10 120502 Female Tenant 3 2 1 0 0 0
#> nInfants nMobility nMembers
#> 1 0 0 6
#> 2 0 0 5
#> 3 1 0 4
#> 4 0 0 4
#> 5 1 0 3
#> 6 0 0 3
#> 7 1 0 3
#> 8 0 0 3
#> 9 1 0 5
#> 10 1 0 7
- Handwashing indicators
Handwashing data is obtained and recoded as follows:
get_handwashing_vars(washdata::surveyDataBGD)
#> uniqueID jmpHand1 jmpHand2 jmpHand3
#> 1 120101 NA NA NA
#> 2 120102 NA NA NA
#> 3 120201 NA NA NA
#> 4 120202 NA NA NA
#> 5 120301 NA NA NA
#> 6 120302 NA NA NA
#> 7 120401 NA NA NA
#> 8 120402 NA NA NA
#> 9 120501 NA NA NA
#> 10 120502 NA NA NA
- Hygiene indicators
Hygiene data is obtained and recoded as
follows:
get_hygiene_vars(washdata::surveyDataBGD)
#> uniqueID san26 san27 san28 san28a san29 san30 jmpWoman jmpWomenHygiene
#> 1 120101 1 1 0 2 0 0 1 0
#> 2 120102 1 1 0 2 0 0 1 0
#> 3 120201 1 1 0 2 0 0 1 0
#> 4 120202 1 1 0 3 0 0 1 0
#> 5 120301 1 1 0 2 0 0 1 0
#> 6 120302 1 1 0 3 0 0 1 0
#> 7 120401 1 1 0 2 0 0 1 0
#> 8 120402 1 1 0 2 0 0 1 0
#> 9 120501 1 1 0 3 0 0 1 0
#> 10 120502 1 1 0 3 0 0 1 0
- Sanitation indicators
Sanitation data is obtained and recoded as
follows:
get_sanitation_vars(washdata::surveyDataBGD)
#> uniqueID san1 san1a san2 san2a san3 san4 san5 san6 san7 san8a san8b san8c
#> 1 120101 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 2 120102 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 3 120201 1 1 26 1 1 2 NA 0 0 <NA> <NA> <NA>
#> 4 120202 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 5 120301 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 6 120302 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 7 120401 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 8 120402 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 9 120501 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 10 120502 1 1 24 1 1 4 NA 0 0 <NA> <NA> <NA>
#> san8d san8e san8f san8g san8h san8i san8j san8k san8l san8m san8n san8o
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san8p san8q san8r san8s san8t san8u san8v san8w san8x san8y san8z san8NA
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> 25 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 33
#> san8NA.1 san8NA.2 san8NA.3 san8NA.4 san8NA.5 san8NA.6 san8NA.7 san8NA.8
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san8NA.9 san9 san10 san11 san12 san13 san13a san14 san15 san16 san17 san18
#> 1 <NA> 1 1 0 0 0 1 80 1 0 0 0
#> 2 <NA> 1 1 0 0 0 1 70 1 0 0 0
#> 3 <NA> 1 1 0 1 0 1 0 NA 0 0 0
#> 4 <NA> 1 1 0 0 0 1 70 1 0 0 0
#> 5 <NA> 1 1 0 1 0 1 30 1 0 0 0
#> 6 <NA> 1 1 1 0 0 1 100 1 0 0 0
#> 7 <NA> 1 1 0 1 0 1 20 1 0 0 0
#> 8 <NA> 1 1 0 0 0 1 40 1 0 0 0
#> 9 <NA> 1 1 0 1 0 1 35 1 0 0 0
#> 10 <NA> 1 1 0 0 2 1 20 1 0 0 0
#> san19 san20a san20b san20c san20d san20e san20f san20g san20h san20i san20j
#> 1 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san20k san20l san20m san20n san20o san20p san20q san20r san20s san20t san20u
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> 21 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 27 <NA>
#> san20v san20w san20x san20y san20z san20NA san20NA.1 san20NA.2 san20NA.3
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san20NA.4 san20NA.5 san21 san22a san22b san22c san23 san24a san24b san24c
#> 1 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 2 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 3 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 4 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 5 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 6 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 7 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 8 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 9 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 10 <NA> <NA> 0 2 <NA> <NA> NA 2 <NA> <NA>
#> san24d san25 san32 san33 san34 san34a san35 san35a
#> 1 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 2 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 3 <NA> 0 0 NA NA NA 0 Don't know/not applicable
#> 4 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 5 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 6 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 7 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 8 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 9 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 10 <NA> 0 0 NA NA NA 0 Don't know/not applicable
#> san36 san37 jmpSan1 jmpSan2 jmpSan3 jmpSan4 jmpSan5
#> 1 Don't know/not applicable NA 0 0 0 0 NA
#> 2 Don't know/not applicable 1 0 0 0 0 NA
#> 3 Don't know/not applicable NA 0 0 0 0 NA
#> 4 Don't know/not applicable NA 0 0 0 0 NA
#> 5 Don't know/not applicable NA 0 0 0 0 NA
#> 6 Don't know/not applicable NA 0 0 0 0 NA
#> 7 Don't know/not applicable NA 0 0 0 0 NA
#> 8 Don't know/not applicable NA 0 0 0 0 NA
#> 9 Don't know/not applicable NA 0 0 0 0 NA
#> 10 Don't know/not applicable NA 0 0 0 0 NA
#> adequateSan accessSan acceptSan acceptScore
#> 1 0 NA 0 3
#> 2 0 NA 0 3
#> 3 0 NA 0 2
#> 4 0 NA 0 3
#> 5 0 NA 0 3
#> 6 0 NA 0 4
#> 7 0 NA 0 3
#> 8 0 NA 0 3
#> 9 0 NA 0 3
#> 10 0 0 0 2
- Water indicators
Water data is obtained and recoded as
follows:
get_water_vars(washdata::surveyDataBGD)
#> uniqueID waterSource water1 water2 water2a water3 water3a water4
#> 1 120101 4 0 2 1 2 0 5
#> 2 120102 9 0 Don't know NA 2 0 NA
#> 3 120201 5 0 2 1 Don't know NA 12
#> 4 120202 4 0 2 1 2 0 3
#> 5 120301 4 0 2 1 2 0 4
#> 6 120302 4 0 2 1 Don't know NA 12
#> 7 120401 4 0 2 1 2 0 2
#> 8 120402 4 0 2 1 Don't know NA 12
#> 9 120501 4 0 2 1 Don't know NA 4
#> 10 120502 5 0 2 1 2 0 4
#> water4a water4b water5 water5a water5b water6 water6a water7 water7a water7b
#> 1 0 0 7 1 1 Yes 1 0 1 1
#> 2 NA 0 7 1 1 Yes 1 0 1 1
#> 3 1 0 7 1 1 Yes 1 0 1 1
#> 4 0 0 7 1 1 Yes 1 0 1 1
#> 5 0 0 7 1 1 Yes 1 0 1 1
#> 6 1 0 7 1 1 Yes 1 0 1 1
#> 7 0 0 7 1 1 Yes 1 0 1 1
#> 8 1 0 7 1 1 Yes 1 0 1 1
#> 9 0 0 7 1 1 Yes 1 0 1 1
#> 10 0 0 7 1 1 No 0 0 1 1
#> water7c water8 water8a water9a water9b water9c water9d water9e water9f
#> 1 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 2 1 2 NA <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 4 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 5 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 6 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 7 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 8 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 9 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 10 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> water9g water9h water9i water9j water9k water9l water9m water9n water9o
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> 21 <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> water9p water9q water9r water9s water9t water9u water10 water10a water10b
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> 9 3 0
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> water11 water11a water11b water11c water11d water12 water12a water12b
#> 1 2 0 <NA> 0 0 425 3 0
#> 2 2 0 <NA> 0 0 125 3 0
#> 3 2 0 <NA> 0 0 NA 3 0
#> 4 2 0 <NA> 0 0 NA 3 0
#> 5 2 0 <NA> 0 0 100 3 0
#> 6 2 0 <NA> 0 0 125 3 0
#> 7 2 0 <NA> 0 0 60 3 0
#> 8 2 0 <NA> 0 0 NA 3 0
#> 9 2 0 <NA> 0 0 75 3 0
#> 10 2 0 <NA> 0 0 25 3 0
#> water13 water13a water14 water14a water16 water16a water17 water18 jmpWater1
#> 1 2 0 1 0 3 0 0 1 0
#> 2 2 0 1 0 3 0 0 1 0
#> 3 3 0 2 0 3 0 0 1 0
#> 4 2 0 2 0 3 0 0 1 0
#> 5 2 0 2 0 3 0 0 1 0
#> 6 2 0 2 0 3 0 0 1 0
#> 7 2 0 2 0 3 0 0 1 0
#> 8 2 0 2 0 3 0 0 1 0
#> 9 2 0 2 0 3 0 0 1 0
#> 10 2 0 2 0 3 0 0 1 0
#> jmpWater2 jmpWater3 jmpWater4 jmpWater5 accessWater
#> 1 1 0 0 NA 0
#> 2 1 0 0 NA 0
#> 3 1 0 0 NA 0
#> 4 1 0 0 NA 0
#> 5 1 0 0 NA 0
#> 6 1 0 0 NA 0
#> 7 1 0 0 NA 0
#> 8 1 0 0 NA 0
#> 9 1 0 0 NA 0
#> 10 1 0 0 NA 0
- Poverty indicators
Poverty data is obtained and recoded as follows:
get_poverty_vars(washdata::surveyDataBGD,
ccode = "BGD",
ppiTable = ppitables::ppiBGD2013)
#> uniqueID ppi pQuintile score nl nu100 nu150 nu200 extreme ppp125 ppp175
#> 1 120101 74 5 74 0.2 2.3 24.6 51.0 0.0 5.6 31.5
#> 2 120102 80 5 80 0.0 0.5 17.0 32.0 0.0 2.7 19.7
#> 3 120201 45 2 45 6.6 19.6 65.8 86.6 5.4 33.5 68.8
#> 4 120202 85 5 85 0.0 0.0 8.3 24.9 0.0 0.0 10.7
#> 5 120301 53 3 53 3.9 14.7 55.0 81.3 4.5 24.2 60.3
#> 6 120302 87 5 87 0.0 0.0 8.3 24.9 0.0 0.0 10.7
#> 7 120401 52 3 52 3.9 14.7 55.0 81.3 4.5 24.2 60.3
#> 8 120402 59 3 59 1.5 7.1 42.6 75.6 1.8 14.5 50.4
#> 9 120501 67 4 67 0.4 4.4 28.6 52.5 0.1 8.7 32.2
#> 10 120502 67 4 67 0.4 4.4 28.6 52.5 0.1 8.7 32.2
#> ppp200 ppp250
#> 1 42.9 60.4
#> 2 26.7 40.9
#> 3 79.6 91.5
#> 4 14.6 33.3
#> 5 74.2 87.9
#> 6 14.6 33.3
#> 7 74.2 87.9
#> 8 65.2 84.3
#> 9 44.5 63.3
#> 10 44.5 63.3
- Overall summary indicators
Overall summary WASH data is obtained and recoded as follows:
get_overall_vars(adminDF = get_admin_vars(washdata::surveyDataBGD),
waterDF = get_water_vars(washdata::surveyDataBGD),
sanDF = get_sanitation_vars(washdata::surveyDataBGD))
#> uniqueID overall1 overall2 overall3 overall4 overallSpend
#> 1 120101 1 0 0 0 505
#> 2 120102 1 0 0 0 195
#> 3 120201 1 0 0 0 0
#> 4 120202 1 0 0 0 70
#> 5 120301 1 0 0 0 130
#> 6 120302 1 0 0 0 225
#> 7 120401 1 0 0 0 80
#> 8 120402 1 0 0 0 40
#> 9 120501 1 0 0 0 110
#> 10 120502 1 0 0 0 45
Data processing and handling
This family of functions include 3 utilities for 1) combining all WASH
indicators; 2) assigning datasets to North or South Dhaka Corporations;
and, 3) getting variables names.
Data analysis
The wsup package includes 3 data analysis functions that 1) performs
the LQAS classification on single data; 2) performs the LQAS
classification on the full dataset; and, 3) estimates results of
indicators at various stratification levels.
Shiny web app
A single function that runs the built-in Shiny web application in the
wsup package.
validmeasures/wsup documentation built on Dec. 16, 2019, 4:50 a.m.
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wsup: An Implementation of the Water and Sanitation for the Urban Poor (WSUP) Urban Water and Sanitation Survey in R
Project on developing sampling and survey methods to assess water, sanitation and hygiene (WASH) indicators in urban areas. Project commissioned by Water and Sanitation for the Urban Poor (WSUP).
This package contains functions for producing WASH indicator results from datasets collected in the various WSUP focus cities i.e., Dhaka, Bangladesh; Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. These functions can be used by a competent useR in producing indicators results from datasets collected using the specific Urban Water and Sanitation Survey design.
This package also implements a Shiny application that provides a graphical user interface to performing the data processing, analysis and visualisation using the various functions contained in this package. This application can be used by anyone wanting to produce indicator results from datasets collected using the specific Urban Water and Sanitation Survey design without having to know how to use R.
This package supersedes the RAnalyticFlow-based implementation of the Urban Water and Sanitation Survey found in this repository.
Installation
This latest wsup version is in active development in preparation for
submission to CRAN. You can install this development version of wsup
from GitHub with:
if(!require(remotes)) install.packages("remotes")
remotes::install_github("validmeasures/wsup")
Usage
The wsup package includes four main families of functions: 1) recode
functions; 2) data processing and handling functions; 3) data analysis
functions; and 4) Shiny web application function.
Recode functions
All recode functions in wsup package start with the verb get_ then
followed by the indicator set to be recoded. All recode functions take a
survey dataset as its one and only argument. The survey dataset is
expected to be collected using the specific Urban Water and Sanitation
Survey survey questionnaire developed by WSUP. Survey dataset/s based on
WSUP’s survey questionnaire is available via the washdata package
which is available via CRAN and is installed as a dependency when the
wsup package is installed.
There are eight recode functions which recodes the following indicator sets:
- Administrative indicators
Administrative data is obtained as follows:
get_admin_vars(washdata::surveyDataBGD)
#> country ccode uniqueID psu zone type quadrat hhid month year longitude
#> 1 Bangladesh BGD 120101 1201 1 2 1 1 Mar 2017 90.42397
#> 2 Bangladesh BGD 120102 1201 1 2 1 2 Mar 2017 90.42417
#> 3 Bangladesh BGD 120201 1202 1 2 2 1 Mar 2017 90.42329
#> 4 Bangladesh BGD 120202 1202 1 2 2 2 Mar 2017 90.42329
#> 5 Bangladesh BGD 120301 1203 1 2 3 1 Mar 2017 90.43500
#> 6 Bangladesh BGD 120302 1203 1 2 3 2 Mar 2017 90.43477
#> 7 Bangladesh BGD 120401 1204 1 2 4 1 Mar 2017 90.42366
#> 8 Bangladesh BGD 120402 1204 1 2 4 2 Mar 2017 90.42353
#> 9 Bangladesh BGD 120501 1205 1 2 5 1 Mar 2017 90.43361
#> 10 Bangladesh BGD 120502 1205 1 2 5 2 Mar 2017 90.43337
#> latitude
#> 1 23.80840
#> 2 23.80886
#> 3 23.82047
#> 4 23.82047
#> 5 23.81953
#> 6 23.81957
#> 7 23.83110
#> 8 23.83080
#> 9 23.83472
#> 10 23.83457
- Demographic indicators
Demographic data is obtained as follows:
get_demo_vars(washdata::surveyDataBGD)
#> uniqueID gender landOwnStatus nWomen nMen nOldWomen nOldMen nGirls nBoys
#> 1 120101 Female Owner-occupier 1 1 0 0 2 2
#> 2 120102 Female Tenant 2 2 0 0 1 0
#> 3 120201 Female Tenant 1 1 0 0 1 0
#> 4 120202 Female Tenant 2 2 0 0 0 0
#> 5 120301 Female Tenant 1 1 0 0 0 0
#> 6 120302 Female Owner-occupier 2 1 0 0 0 0
#> 7 120401 Female Tenant 1 1 0 0 0 0
#> 8 120402 Female Tenant 1 1 0 0 0 1
#> 9 120501 Female Tenant 2 1 0 0 0 1
#> 10 120502 Female Tenant 3 2 1 0 0 0
#> nInfants nMobility nMembers
#> 1 0 0 6
#> 2 0 0 5
#> 3 1 0 4
#> 4 0 0 4
#> 5 1 0 3
#> 6 0 0 3
#> 7 1 0 3
#> 8 0 0 3
#> 9 1 0 5
#> 10 1 0 7
- Handwashing indicators
Handwashing data is obtained and recoded as follows:
get_handwashing_vars(washdata::surveyDataBGD)
#> uniqueID jmpHand1 jmpHand2 jmpHand3
#> 1 120101 NA NA NA
#> 2 120102 NA NA NA
#> 3 120201 NA NA NA
#> 4 120202 NA NA NA
#> 5 120301 NA NA NA
#> 6 120302 NA NA NA
#> 7 120401 NA NA NA
#> 8 120402 NA NA NA
#> 9 120501 NA NA NA
#> 10 120502 NA NA NA
- Hygiene indicators
Hygiene data is obtained and recoded as follows:
get_hygiene_vars(washdata::surveyDataBGD)
#> uniqueID san26 san27 san28 san28a san29 san30 jmpWoman jmpWomenHygiene
#> 1 120101 1 1 0 2 0 0 1 0
#> 2 120102 1 1 0 2 0 0 1 0
#> 3 120201 1 1 0 2 0 0 1 0
#> 4 120202 1 1 0 3 0 0 1 0
#> 5 120301 1 1 0 2 0 0 1 0
#> 6 120302 1 1 0 3 0 0 1 0
#> 7 120401 1 1 0 2 0 0 1 0
#> 8 120402 1 1 0 2 0 0 1 0
#> 9 120501 1 1 0 3 0 0 1 0
#> 10 120502 1 1 0 3 0 0 1 0
- Sanitation indicators
Sanitation data is obtained and recoded as follows:
get_sanitation_vars(washdata::surveyDataBGD)
#> uniqueID san1 san1a san2 san2a san3 san4 san5 san6 san7 san8a san8b san8c
#> 1 120101 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 2 120102 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 3 120201 1 1 26 1 1 2 NA 0 0 <NA> <NA> <NA>
#> 4 120202 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 5 120301 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 6 120302 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 7 120401 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 8 120402 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 9 120501 2 1 0 0 1 4 NA 1 1 1 <NA> <NA>
#> 10 120502 1 1 24 1 1 4 NA 0 0 <NA> <NA> <NA>
#> san8d san8e san8f san8g san8h san8i san8j san8k san8l san8m san8n san8o
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san8p san8q san8r san8s san8t san8u san8v san8w san8x san8y san8z san8NA
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> 25 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 33
#> san8NA.1 san8NA.2 san8NA.3 san8NA.4 san8NA.5 san8NA.6 san8NA.7 san8NA.8
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san8NA.9 san9 san10 san11 san12 san13 san13a san14 san15 san16 san17 san18
#> 1 <NA> 1 1 0 0 0 1 80 1 0 0 0
#> 2 <NA> 1 1 0 0 0 1 70 1 0 0 0
#> 3 <NA> 1 1 0 1 0 1 0 NA 0 0 0
#> 4 <NA> 1 1 0 0 0 1 70 1 0 0 0
#> 5 <NA> 1 1 0 1 0 1 30 1 0 0 0
#> 6 <NA> 1 1 1 0 0 1 100 1 0 0 0
#> 7 <NA> 1 1 0 1 0 1 20 1 0 0 0
#> 8 <NA> 1 1 0 0 0 1 40 1 0 0 0
#> 9 <NA> 1 1 0 1 0 1 35 1 0 0 0
#> 10 <NA> 1 1 0 0 2 1 20 1 0 0 0
#> san19 san20a san20b san20c san20d san20e san20f san20g san20h san20i san20j
#> 1 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 1 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san20k san20l san20m san20n san20o san20p san20q san20r san20s san20t san20u
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> 21 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 27 <NA>
#> san20v san20w san20x san20y san20z san20NA san20NA.1 san20NA.2 san20NA.3
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> san20NA.4 san20NA.5 san21 san22a san22b san22c san23 san24a san24b san24c
#> 1 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 2 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 3 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 4 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 5 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 6 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 7 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 8 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 9 <NA> <NA> 1 <NA> <NA> <NA> NA <NA> <NA> <NA>
#> 10 <NA> <NA> 0 2 <NA> <NA> NA 2 <NA> <NA>
#> san24d san25 san32 san33 san34 san34a san35 san35a
#> 1 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 2 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 3 <NA> 0 0 NA NA NA 0 Don't know/not applicable
#> 4 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 5 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 6 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 7 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 8 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 9 <NA> 0 1 NA NA NA 0 Don't know/not applicable
#> 10 <NA> 0 0 NA NA NA 0 Don't know/not applicable
#> san36 san37 jmpSan1 jmpSan2 jmpSan3 jmpSan4 jmpSan5
#> 1 Don't know/not applicable NA 0 0 0 0 NA
#> 2 Don't know/not applicable 1 0 0 0 0 NA
#> 3 Don't know/not applicable NA 0 0 0 0 NA
#> 4 Don't know/not applicable NA 0 0 0 0 NA
#> 5 Don't know/not applicable NA 0 0 0 0 NA
#> 6 Don't know/not applicable NA 0 0 0 0 NA
#> 7 Don't know/not applicable NA 0 0 0 0 NA
#> 8 Don't know/not applicable NA 0 0 0 0 NA
#> 9 Don't know/not applicable NA 0 0 0 0 NA
#> 10 Don't know/not applicable NA 0 0 0 0 NA
#> adequateSan accessSan acceptSan acceptScore
#> 1 0 NA 0 3
#> 2 0 NA 0 3
#> 3 0 NA 0 2
#> 4 0 NA 0 3
#> 5 0 NA 0 3
#> 6 0 NA 0 4
#> 7 0 NA 0 3
#> 8 0 NA 0 3
#> 9 0 NA 0 3
#> 10 0 0 0 2
- Water indicators
Water data is obtained and recoded as follows:
get_water_vars(washdata::surveyDataBGD)
#> uniqueID waterSource water1 water2 water2a water3 water3a water4
#> 1 120101 4 0 2 1 2 0 5
#> 2 120102 9 0 Don't know NA 2 0 NA
#> 3 120201 5 0 2 1 Don't know NA 12
#> 4 120202 4 0 2 1 2 0 3
#> 5 120301 4 0 2 1 2 0 4
#> 6 120302 4 0 2 1 Don't know NA 12
#> 7 120401 4 0 2 1 2 0 2
#> 8 120402 4 0 2 1 Don't know NA 12
#> 9 120501 4 0 2 1 Don't know NA 4
#> 10 120502 5 0 2 1 2 0 4
#> water4a water4b water5 water5a water5b water6 water6a water7 water7a water7b
#> 1 0 0 7 1 1 Yes 1 0 1 1
#> 2 NA 0 7 1 1 Yes 1 0 1 1
#> 3 1 0 7 1 1 Yes 1 0 1 1
#> 4 0 0 7 1 1 Yes 1 0 1 1
#> 5 0 0 7 1 1 Yes 1 0 1 1
#> 6 1 0 7 1 1 Yes 1 0 1 1
#> 7 0 0 7 1 1 Yes 1 0 1 1
#> 8 1 0 7 1 1 Yes 1 0 1 1
#> 9 0 0 7 1 1 Yes 1 0 1 1
#> 10 0 0 7 1 1 No 0 0 1 1
#> water7c water8 water8a water9a water9b water9c water9d water9e water9f
#> 1 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 2 1 2 NA <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 4 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 5 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 6 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 7 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 8 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 9 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> 10 1 0 0 1 <NA> <NA> <NA> <NA> <NA>
#> water9g water9h water9i water9j water9k water9l water9m water9n water9o
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> 21 <NA> <NA>
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> water9p water9q water9r water9s water9t water9u water10 water10a water10b
#> 1 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 2 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 3 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 4 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 5 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 6 <NA> <NA> <NA> <NA> <NA> <NA> 9 3 0
#> 7 <NA> <NA> <NA> <NA> <NA> <NA> 7 3 0
#> 8 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 9 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> 10 <NA> <NA> <NA> <NA> <NA> <NA> 8 3 0
#> water11 water11a water11b water11c water11d water12 water12a water12b
#> 1 2 0 <NA> 0 0 425 3 0
#> 2 2 0 <NA> 0 0 125 3 0
#> 3 2 0 <NA> 0 0 NA 3 0
#> 4 2 0 <NA> 0 0 NA 3 0
#> 5 2 0 <NA> 0 0 100 3 0
#> 6 2 0 <NA> 0 0 125 3 0
#> 7 2 0 <NA> 0 0 60 3 0
#> 8 2 0 <NA> 0 0 NA 3 0
#> 9 2 0 <NA> 0 0 75 3 0
#> 10 2 0 <NA> 0 0 25 3 0
#> water13 water13a water14 water14a water16 water16a water17 water18 jmpWater1
#> 1 2 0 1 0 3 0 0 1 0
#> 2 2 0 1 0 3 0 0 1 0
#> 3 3 0 2 0 3 0 0 1 0
#> 4 2 0 2 0 3 0 0 1 0
#> 5 2 0 2 0 3 0 0 1 0
#> 6 2 0 2 0 3 0 0 1 0
#> 7 2 0 2 0 3 0 0 1 0
#> 8 2 0 2 0 3 0 0 1 0
#> 9 2 0 2 0 3 0 0 1 0
#> 10 2 0 2 0 3 0 0 1 0
#> jmpWater2 jmpWater3 jmpWater4 jmpWater5 accessWater
#> 1 1 0 0 NA 0
#> 2 1 0 0 NA 0
#> 3 1 0 0 NA 0
#> 4 1 0 0 NA 0
#> 5 1 0 0 NA 0
#> 6 1 0 0 NA 0
#> 7 1 0 0 NA 0
#> 8 1 0 0 NA 0
#> 9 1 0 0 NA 0
#> 10 1 0 0 NA 0
- Poverty indicators
Poverty data is obtained and recoded as follows:
get_poverty_vars(washdata::surveyDataBGD,
ccode = "BGD",
ppiTable = ppitables::ppiBGD2013)
#> uniqueID ppi pQuintile score nl nu100 nu150 nu200 extreme ppp125 ppp175
#> 1 120101 74 5 74 0.2 2.3 24.6 51.0 0.0 5.6 31.5
#> 2 120102 80 5 80 0.0 0.5 17.0 32.0 0.0 2.7 19.7
#> 3 120201 45 2 45 6.6 19.6 65.8 86.6 5.4 33.5 68.8
#> 4 120202 85 5 85 0.0 0.0 8.3 24.9 0.0 0.0 10.7
#> 5 120301 53 3 53 3.9 14.7 55.0 81.3 4.5 24.2 60.3
#> 6 120302 87 5 87 0.0 0.0 8.3 24.9 0.0 0.0 10.7
#> 7 120401 52 3 52 3.9 14.7 55.0 81.3 4.5 24.2 60.3
#> 8 120402 59 3 59 1.5 7.1 42.6 75.6 1.8 14.5 50.4
#> 9 120501 67 4 67 0.4 4.4 28.6 52.5 0.1 8.7 32.2
#> 10 120502 67 4 67 0.4 4.4 28.6 52.5 0.1 8.7 32.2
#> ppp200 ppp250
#> 1 42.9 60.4
#> 2 26.7 40.9
#> 3 79.6 91.5
#> 4 14.6 33.3
#> 5 74.2 87.9
#> 6 14.6 33.3
#> 7 74.2 87.9
#> 8 65.2 84.3
#> 9 44.5 63.3
#> 10 44.5 63.3
- Overall summary indicators
Overall summary WASH data is obtained and recoded as follows:
get_overall_vars(adminDF = get_admin_vars(washdata::surveyDataBGD),
waterDF = get_water_vars(washdata::surveyDataBGD),
sanDF = get_sanitation_vars(washdata::surveyDataBGD))
#> uniqueID overall1 overall2 overall3 overall4 overallSpend
#> 1 120101 1 0 0 0 505
#> 2 120102 1 0 0 0 195
#> 3 120201 1 0 0 0 0
#> 4 120202 1 0 0 0 70
#> 5 120301 1 0 0 0 130
#> 6 120302 1 0 0 0 225
#> 7 120401 1 0 0 0 80
#> 8 120402 1 0 0 0 40
#> 9 120501 1 0 0 0 110
#> 10 120502 1 0 0 0 45
Data processing and handling
This family of functions include 3 utilities for 1) combining all WASH indicators; 2) assigning datasets to North or South Dhaka Corporations; and, 3) getting variables names.
Data analysis
The wsup package includes 3 data analysis functions that 1) performs
the LQAS classification on single data; 2) performs the LQAS
classification on the full dataset; and, 3) estimates results of
indicators at various stratification levels.
Shiny web app
A single function that runs the built-in Shiny web application in the
wsup package.
R Package Documentation
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