NDMA_DeIdentification_Garissa_2000_2020
Pheroze
2024-10-18
NDMA Data Pre processing
The following are steps undertaken for deidentifying NDMA data. The data is dis aggregated per county for all 23 counties - ASAL. The information covers the years of 2000 - 2020, where data prior to 2016 was stored in a different database (REWAS) and data from 2016 henceforth in the new database (DEWS). In each county data set workbook there are 6 different sheets:
HHA REWAS, HHA DEWS, KIA REWAS, KIA DEWS, MUAC REWAS, MUAC DEWS
The process involves inspecting individual sheets for each data set, dropping P.I.I columns, and then writing all the sheets to a single workbook - Garissa.
GARISSA
1. HHA REWAS
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.2library(geosphere)## Warning: package 'geosphere' was built under R version 4.3.3## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
## (status 2 uses the sf package in place of rgdal)library(openxlsx)## Warning: package 'openxlsx' was built under R version 4.3.3file_path <- "C:/Users/AAH USER/Downloads/Garissa.xlsx"
# Read the specific sheet into a data frame
hha_rewas_data <- read.xlsx(file_path, sheet = "HHA REWAS")This data set has P.I.I’s in the “housename” column so we will drop that.
# Drop the specified PII columns
hha_rewas_data <- hha_rewas_data %>%
select(-housename)
# Check the updated dataset
head(hha_rewas_data)## district_name divisioncode year month purchamt_bean soldamt_bean
## 1 GARISSA 142 2006 October 0 0
## 2 GARISSA 148 2006 June 10 0
## 3 GARISSA 146 2006 July 0 0
## 4 GARISSA 145 2006 March 5 0
## 5 GARISSA 147 2006 June 0 0
## 6 GARISSA 152 2006 February 6 0
## purchamt_oil purchamt_cowpea soldamt_cowpea purchamt_greengram
## 1 3 0 0 0
## 2 0 0 0 0
## 3 3 0 0 0
## 4 4 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## soldamt_greengram purchamt_milk soldamt_milk purchamt_millet soldamt_millet
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 2 0 0 0
## purchamt_other soldamt_other purchamt_pigeon soldamt_pigeon purchamt_posho
## 1 0 0 0 0 14
## 2 0 0 0 0 20
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 50
## 6 0 0 0 0 0
## purchamt_rice purchamt_siftmaize purchamt_sorg soldamt_sorg purchamt_sugar
## 1 0 0 0 0 28
## 2 10 30 0 0 30
## 3 10 10 0 0 15
## 4 10 0 0 0 15
## 5 0 0 0 0 15
## 6 14 12 0 0 45
## purchamt_wheat purchamt_wholemaize soldamt_wholemaize inc_dailyrate app_cam
## 1 7 0 0 NA NA
## 2 10 20 0 NA NA
## 3 0 0 0 NA NA
## 4 15 0 0 NA NA
## 5 0 0 0 NA NA
## 6 0 30 0 NA NA
## born_cam death_cam deathreason_cam slaughtreason_cam slaught_cam sold_cam
## 1 0 0 <NA> <NA> 0 0
## 2 0 0 <NA> <NA> 0 0
## 3 0 0 <NA> <NA> 0 0
## 4 0 0 <NA> <NA> 0 0
## 5 0 0 <NA> <NA> 0 0
## 6 0 0 <NA> <NA> 0 0
## total_cam app_cat born_cat death_cat deathreason_cat slaughtreason_cat
## 1 0 NA 0 0 <NA> <NA>
## 2 4 7000 0 0 <NA> <NA>
## 3 0 9000 0 0 <NA> <NA>
## 4 0 NA 0 0 <NA> <NA>
## 5 15 NA 0 0 <NA> <NA>
## 6 0 3500 1 2 Drought <NA>
## slaught_cat sold_cat total_cat app_don born_don death_don deathreason_don
## 1 0 0 0 NA 0 0 <NA>
## 2 0 1 13 NA 0 0 <NA>
## 3 0 1 50 NA 0 0 <NA>
## 4 0 0 24 NA 0 0 <NA>
## 5 0 0 2 NA 0 0 <NA>
## 6 0 1 22 NA 1 0 <NA>
## sold_don total_don app_goa born_goa death_goa deathreason_goa
## 1 0 1 NA 5 0 <NA>
## 2 0 1 600 0 0 <NA>
## 3 0 0 NA 0 0 <NA>
## 4 0 2 NA 0 0 <NA>
## 5 0 2 900 0 0 <NA>
## 6 0 2 NA 2 1 Predation
## slaughtreason_goa slaught_goa sold_goa total_goa hhaid survf_borrowfood
## 1 <NA> 0 0 27 37058 Never
## 2 <NA> 0 1 30 35605 Rarely
## 3 <NA> 0 0 100 35888 Rarely
## 4 <NA> 0 0 36 34424 Never
## 5 <NA> 0 1 80 35502 Rarely
## 6 <NA> 0 0 45 34233 Rarely
## aid_cfw child_schooldrop aid_ffw aidkg_ffw surv_migrate aid_gift hvst_cereal
## 1 no no no 0 no no no
## 2 no no no 0 no no no
## 3 no no no 0 no no no
## 4 no no no 0 no no no
## 5 no no no 0 no no no
## 6 no no no 0 no no no
## hvst_legume hh_totalmembers survf_lesspreffood survf_limitportion
## 1 no 5 Never Never
## 2 no 10 Never Rarely
## 3 no 7 Rarely Never
## 4 no 4 Rarely Rarely
## 5 no 7 Never Never
## 6 no 7 Often Often
## survf_skipmeal survdesc_other surv_other hh_ownslivestock survf_foodcredit
## 1 Never <NA> no yes Rarely
## 2 Rarely <NA> no yes Rarely
## 3 Never <NA> no yes Rarely
## 4 Never <NA> no yes Rarely
## 5 Never <NA> no yes Never
## 6 Rarely <NA> no no Often
## purch_foodstuff inc_remittance survf_reducemeals aid_food aidkg_food
## 1 yes no Never no 0
## 2 yes no Rarely yes 35
## 3 yes no Never yes 40
## 4 yes no Rarely no 0
## 5 yes no Never yes 60
## 6 yes yes Rarely yes 15
## aid_remittance surv_sellbreedingstock surv_selldraught surv_selltools
## 1 no no no no
## 2 no no no no
## 3 no yes no no
## 4 no no no no
## 5 no yes no no
## 6 no no no no
## surv_sellvaluables surv_sellmilkanimal sold_foodstuff stock_cereal
## 1 2 no no no
## 2 2 no no no
## 3 2 no no no
## 4 2 no no no
## 5 2 no no no
## 6 2 no no no
## stock_legume aid_suppfood aidkg_suppfood stockexp_cereal stockexp_legume
## 1 no no 0 0 0
## 2 no no 0 0 0
## 3 no no 0 0 0
## 4 no no 0 0 0
## 5 no no 0 0 0
## 6 no no 0 0 0
## inc_relynormalsource item ldisease_cbpp ldisease_ccpp ldisease_diarrhea
## 1 yes 5282 no no NA
## 2 no 4067 no no NA
## 3 yes 4457 no no NA
## 4 no 3265 no no NA
## 5 yes 4170 no yes NA
## 6 yes 2825 no no NA
## ldisease_other ldisease_ecfever ldisease_fmd ldisease_lumpy ldisease_ncastle
## 1 <NA> no no no no
## 2 <NA> no no no no
## 3 <NA> no no no no
## 4 <NA> no no no no
## 5 <NA> no no no no
## 6 <NA> no no no no
## vacc_anthrax vacc_cbpp vacc_ccpp vacc_diarrhea vacc_fmd vacc_lumpy
## 1 no no no no no 2
## 2 no no no no no 2
## 3 no no no no no 2
## 4 no no no no no 2
## 5 no no no no no 2
## 6 no no no no no 2
## vacc_ncastle vacc_other vacc_worms inc_currentsource milksold_cat
## 1 no <NA> no Sale of Livestock 0
## 2 no <NA> no Sale of Livestock 0
## 3 no <NA> no Sale of Livestock 0
## 4 no <NA> no Casual Labor 0
## 5 no <NA> no Sale of Livestock 0
## 6 no <NA> no Sale of Livestock 0
## milksold_goa milksold_cam total_milkcam total_milkcat total_milkgoa
## 1 0 0 0 0 5
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 2 8
## total_milkshe milkdaily_cam milkdaily_cat milkdaily_goa milkdaily_she
## 1 2 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 1 1 0
## milksold_she inc_emplcasualwork inc_seekcasualwork inc_normalsource
## 1 0 0 0 NA
## 2 0 0 1 NA
## 3 0 0 0 NA
## 4 0 0 0 NA
## 5 0 0 1 NA
## 6 0 0 1 NA
## pest_otherdesc inc_otherdesc pest app_pou death_pou deathreason_pou
## 1 <NA> <NA> 0 NA 0 <NA>
## 2 <NA> <NA> 0 NA 0 <NA>
## 3 <NA> <NA> 0 NA 0 <NA>
## 4 NONE NONE 0 NA 0 <NA>
## 5 NONE NONE 0 NA 0 <NA>
## 6 NONE NONE 0 NA 0 <NA>
## slaughtreason_pou slaught_pou sold_pou total_pou ppkpurch_bean ppksold_bean
## 1 <NA> 0 0 0 NA NA
## 2 <NA> 0 0 0 60 NA
## 3 <NA> 0 0 0 NA NA
## 4 <NA> 0 0 0 48 NA
## 5 <NA> 0 0 0 NA NA
## 6 <NA> 0 0 0 60 NA
## ppkpurch_oil ppkpurch_cowpea ppksold_cowpea ppkpurch_greengram
## 1 250 NA NA NA
## 2 NA NA NA NA
## 3 83 NA NA NA
## 4 100 NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## ppksold_greengram pplpurch_milk pplsold_milk ppkpurch_millet ppksold_millet
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA 20 NA NA NA
## ppurch_other psold_other ppkpurch_pigeon ppksold_pigeon ppkpurch_posho
## 1 NA NA NA NA 20
## 2 NA NA NA NA 20
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA 20
## 6 NA NA NA NA NA
## ppkpurch_rice ppkpurch_siftmaize ppkpurch_sorg ppksold_sorg ppkpurch_sugar
## 1 NA NA NA NA 66
## 2 50 25 NA NA 70
## 3 50 40 NA NA 64
## 4 48 NA NA NA 56
## 5 NA NA NA NA 80
## 6 50 30 NA NA 80
## ppkpurch_wheat ppkpurch_wholemaize ppksold_wholemaize qtymilkproduced sacode
## 1 46 NA NA 0 370
## 2 50 12 NA 0 369
## 3 NA NA NA 0 367
## 4 48 NA NA 0 368
## 5 NA NA NA 0 372
## 6 NA 15 NA 0 373
## app_she born_she death_she deathreason_she slaughtreason_she slaught_she
## 1 800 2 0 <NA> <NA> 0
## 2 500 0 0 <NA> <NA> 0
## 3 NA 0 0 <NA> <NA> 0
## 4 NA 0 0 <NA> <NA> 0
## 5 NA 0 0 <NA> <NA> 0
## 6 NA 1 0 <NA> Ceremony 1
## sold_she total_she tag whodrankmilk wrhh age1 age2 age3 age4 age5 age6
## 1 1 15 1 no one Poor NA NA NA NA NA NA
## 2 1 20 1 no one Poor NA NA NA NA NA NA
## 3 0 0 1 no one Middle NA NA NA NA NA NA
## 4 0 10 1 no one Poor NA NA NA NA NA NA
## 5 0 5 1 no one Poor NA NA NA NA NA NA
## 6 0 6 1 no one Poor NA NA NA NA NA NA
## soldamt_banana_old ppu_banana_old bled_cam bled_cat bled_goa bled_she
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA
## 5 NA NA NA NA NA NA
## 6 NA NA NA NA NA NA
## purch_cereals aid_cfw_old weather_old community csvname districtcode divname
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA
## fieldmonitorname aid_ffw_old aidkg_unimix child_unimix child_under5
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## lhzone soldamt_maize_old ppu_maize_old
## 1 Pastoral: Camel/Goats/Shoats NA NA
## 2 Pastoral-All Species NA NA
## 3 Pastoral: Cattle/Sheep NA NA
## 4 Pastoral-All Species NA NA
## 5 Agropastoral NA NA
## 6 Agropastoral NA NA
## soldamt_mango_old ppu_mango_old milkyest_cam milkyest_cat milkyest_goa
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## milkyest_she mon_date mon_office muac1 muac2 muac3 muac4 muac5 muac6 name1
## 1 NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA
## name2 name3 name4 name5 name6 aid_received aid_receivedother
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA
## soldamt_other_old ppu_other_old pest_old aid_food_old soldamt_rice_old
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## ppu_rice_old saname soldamt_sorg_old ppu_sorg_old
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NAWe save it to a new workbook and populate the rest of the sheets sequentially to the same Garissa workbook after relevant pre processing steps have been taken.
# Define the path for the new Excel workbook
new_file_path <- "C:/Users/AAH USER/OneDrive - Action Against Hunger USA/Documents/NDMA_DeIdentified/Garissa.xlsx"
# Create a new workbook
wb <- createWorkbook()
# Add the HHA-REWAS data to the new Garissa workbook
addWorksheet(wb, "HHA REWAS")
writeData(wb, "HHA REWAS", hha_rewas_data)
# Save the new workbook
saveWorkbook(wb, new_file_path, overwrite = TRUE)2. HHA DEWS
The geocoordinates in the HHA dataset represent household coordinates, we will mask them (random displacement) using the Haversine Formula to randomly distribute a point around a central coordinate within a radius of 2.5 KM and drop other P.I.I.s.
library(openxlsx)
file_path <- "C:/Users/AAH USER/Downloads/Garissa.xlsx"
# Read the specific sheet into a data frame
hha_dews_data <- read.xlsx(file_path, sheet = "HHA DEWS")The dataset contains household coordinates in columns “Lat” and “Long” which are considered P.I.I’s so we mask the coordinates, verify by plotting a histogram of the distribution of displacement distances of the original and displaced coordinates to establish uniformity.
We check for and deal with outliers if any in the “Lat” and “Long” columns
# Verify the dataset
summary(hha_dews_data$Lat)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -2.000 -0.033 0.000 -0.079 0.000 0.745 3453summary(hha_dews_data$Long)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.00 39.17 20.22 39.87 41.00 3453There seems to be erroneous entries. 3453 rows also dont have entries for coordinates.
# Replace (0,0) coordinates with NA only in the Lat and Long columns
hha_dews_data$Lat[hha_dews_data$Lat == 0 & hha_dews_data$Long == 0] <- NA
hha_dews_data$Long[hha_dews_data$Lat == 0 & hha_dews_data$Long == 0] <- NA
# Calculate the mean for Lat and Long, ignoring NA values
lat_mean <- mean(hha_dews_data$Lat, na.rm = TRUE)
lon_mean <- mean(hha_dews_data$Long, na.rm = TRUE)
lat_sd <- sd(hha_dews_data$Lat, na.rm = TRUE)
lon_sd <- sd(hha_dews_data$Long, na.rm = TRUE)
# Calculate Z-scores
hha_dews_data <- hha_dews_data %>%
mutate(lat_z = (Lat - lat_mean) / lat_sd,
lon_z = (Long - lon_mean) / lon_sd)
# Set threshold for identifying outliers
threshold <- 3 # Common threshold for Z-scores
# Replace outliers with NA
hha_dews_data <- hha_dews_data %>%
mutate(Lat = ifelse(abs(lat_z) > threshold & !is.na(lat_z), NA, Lat),
Long = ifelse(abs(lon_z) > threshold & !is.na(lon_z), NA, Long))
# Remove the Z-score columns
hha_dews_data <- hha_dews_data %>%
select(-lat_z, -lon_z)
# Verify the dataset
summary(hha_dews_data$Lat)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -1.598 -0.492 -0.032 -0.143 0.216 0.745 7664summary(hha_dews_data$Long)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.00 39.17 20.22 39.87 41.00 34534211 rows in total are affected by the outlier and are replaced with NAs, bringing the total to 7664 NAs. We proceed to mask the coordinates.
# Create backup columns for original coordinates
hha_dews_data$Original_Lat <- hha_dews_data$Lat
hha_dews_data$Original_Long <- hha_dews_data$Long
# Function to generate random displaced coordinates with uniform distance distribution
mask_coordinates_uniform <- function(lat, lon, radius_km) {
R <- 6371 # Earth radius in kilometers
# Random bearing angle (in radians)
bearing <- runif(1, 0, 2 * pi)
# Random distance uniformly sampled from [0, radius_km]
rand_dist <- runif(1, 0, radius_km) / R # Uniformly sampled distance in radians
# Convert original coordinates to radians
lat_rad <- lat * pi / 180
lon_rad <- lon * pi / 180
# Calculate new latitude (in radians)
new_lat <- asin(sin(lat_rad) * cos(rand_dist) +
cos(lat_rad) * sin(rand_dist) * cos(bearing))
# Calculate new longitude (in radians)
new_lon <- lon_rad + atan2(sin(bearing) * sin(rand_dist) * cos(lat_rad),
cos(rand_dist) - sin(lat_rad) * sin(new_lat))
# Convert back to degrees
new_lat <- new_lat * 180 / pi
new_lon <- new_lon * 180 / pi
return(c(new_lat, new_lon))
}
# Set displacement radius in kilometers
radius_km <- 2.5
# Generate masked coordinates for each row using the modified function
masked_coords <- t(apply(hha_dews_data, 1, function(row) {
mask_coordinates_uniform(as.numeric(row["Original_Lat"]), as.numeric(row["Original_Long"]), radius_km)
}))
# Replace the original Lat and Long columns with the masked coordinates
hha_dews_data$Lat <- masked_coords[, 1]
hha_dews_data$Long <- masked_coords[, 2]We then evaluate the distribution of the displacement distances before dropping the original coordinates by plotting a histogram.
# Calculate displacement distances (in kilometers) as before
displacement_distances <- distHaversine(
cbind(hha_dews_data$Long, hha_dews_data$Lat), # Masked coordinates
cbind(hha_dews_data$Original_Long, hha_dews_data$Original_Lat) # Original coordinates
) / 1000 # Convert meters to kilometers
# Add displacement distances to the dataset for further analysis
hha_dews_data$Displacement_Distance <- displacement_distances
# Plot: Histogram of displacement distances
ggplot(hha_dews_data, aes(x = Displacement_Distance)) +
geom_histogram(binwidth = 0.1, fill = "skyblue", color = "black") +
labs(title = "Distribution of Displacement Distances",
x = "Displacement Distance (km)", y = "Count") +
theme_minimal()## Warning: Removed 7664 rows containing non-finite values (`stat_bin()`).
There are no significant peaks or valleys in the histogram, suggesting that the displacements are indeed more uniformly distributed, as intended.
We then drop the original coordinates column leaving only the masked coordinates columns. We also drop other PII’s which are the “HouseholdName”, “HouseHoldHead”, and “RespondentName”.
# Drop the specified PII columns along with original coordinates
hha_dews_data <- hha_dews_data %>%
select(-c(Original_Lat, Original_Long, Displacement_Distance, HouseholdName, HouseHoldHead, RespondentName))
# Check the updated dataset
head(hha_dews_data)## QID County SubCounty Ward LivelihoodZone Month Year Lat Long
## 1 8045 Garissa Fafi Nanighi Pastoral July 2016 NA NA
## 2 8046 Garissa Fafi Nanighi Pastoral July 2016 NA NA
## 3 8049 Garissa Fafi Nanighi Pastoral July 2016 NA NA
## 4 8050 Garissa Fafi Nanighi Pastoral July 2016 NA NA
## 5 8051 Garissa Fafi Nanighi Pastoral July 2016 NA NA
## 6 8052 Garissa Fafi Nanighi Pastoral July 2016 NA NA
## InterviewDate HouseholdCode HeadEducationLevel MainHHIncomeSource HeadGender
## 1 42556 GSA0408 <NA> <NA> Female
## 2 42556 GSA0416 <NA> <NA> Male
## 3 42556 GSA0419 <NA> <NA> Male
## 4 42556 GSA0422 <NA> <NA> Male
## 5 42556 20 <NA> <NA> Male
## 6 42557 GSA0423 <NA> <NA> Male
## RespondentGender MaleMembers FemaleMembers ChildrenBelow5 KeepLivestock
## 1 Female 3 4 3 TRUE
## 2 Female 9 2 4 TRUE
## 3 Female 4 3 3 TRUE
## 4 Female 2 3 0 TRUE
## 5 Female 3 5 2 TRUE
## 6 Female 3 3 2 TRUE
## MilkAnimals MilkSource HowOftenMilked AverageMilkedPerDay
## 1 FALSE <NA> NA NA
## 2 TRUE <NA> NA NA
## 3 TRUE <NA> NA NA
## 4 FALSE <NA> NA NA
## 5 TRUE <NA> NA NA
## 6 TRUE <NA> NA NA
## AverageMilkConsumedPerDay WhoDrankMilk AverageMilkPrice
## 1 NA Children under 5 years NA
## 2 NA Children under 5 years NA
## 3 NA Children under 5 years NA
## 4 NA Children under 5 years NA
## 5 NA Children under 5 years NA
## 6 NA Children under 5 years NA
## HarvestedInLastWeeks AcresHarvested BagsHarvested HaveFoodStock
## 1 FALSE NA NA FALSE
## 2 FALSE NA NA FALSE
## 3 FALSE NA NA FALSE
## 4 FALSE NA NA FALSE
## 5 FALSE NA NA FALSE
## 6 FALSE NA NA FALSE
## FoodStockSources DaysStockLast WaterSource1 WaterSource2 WaterSource3
## 1 Production 0 Rivers Piped Water System -Select-
## 2 Production 0 Rivers Piped Water System -Select-
## 3 Purchase 7 Rivers Piped Water System -Select-
## 4 Production 0 Rivers Piped Water System -Select-
## 5 Purchase 0 Rivers Piped Water System -Select-
## 6 Purchase 15 Rivers Piped Water System -Select-
## NormalWaterSource WhyNotNormalWaterSource DaysWaterSourceExpectedToLast
## 1 FALSE Breakdown of water source 5
## 2 FALSE Breakdown of water source 5
## 3 FALSE Breakdown of water source 5
## 4 FALSE Breakdown of water source 5
## 5 FALSE Breakdown of water source 5
## 6 FALSE Breakdown of water source 5
## DistanceFromWaterSource NoWaterJerryCans JerryCansCost
## 1 1 10 2
## 2 1 10 3
## 3 1 5 4
## 4 2 0 4
## 5 2 4 3
## 6 3 4 3
## NormalHHWaterConsumption HHPayForWater CostTransportJerryCan
## 1 200 FALSE 0
## 2 0 FALSE 0
## 3 300 FALSE 0
## 4 300 FALSE 0
## 5 200 FALSE 0
## 6 0 FALSE 0
## TreatWaterBeforeDrinking WaterTreatmentMethodUsed CSI_ReliedOnLess
## 1 FALSE <NA> 2
## 2 FALSE <NA> 3
## 3 FALSE <NA> 3
## 4 FALSE <NA> 3
## 5 FALSE <NA> 3
## 6 FALSE <NA> 3
## CSI_BorrowedFood CSI_ReducedNoOfMeals CSI_ReducedPortionMealSize
## 1 3 2 2
## 2 2 3 2
## 3 3 3 2
## 4 3 2 0
## 5 3 2 3
## 6 2 2 3
## CSI_QuantityForAdult CSI_SoldHouseholdAssets CSI_ReducedNonFoodExpenses
## 1 0 1 1
## 2 0 3 3
## 3 0 3 3
## 4 0 1 1
## 5 0 4 4
## 6 0 4 4
## CSI_SoldProductiveAssets CSI_SpentSavings CSI_BorrowedMoney CSI_SoldHouseLand
## 1 1 1 NA 1
## 2 1 3 NA 4
## 3 4 4 NA 4
## 4 1 1 NA 1
## 5 4 4 NA 4
## 6 4 4 NA 4
## CSI_WithdrewChildrenSchool CSI_SoldLastFemaleAnimal CSI_Begging
## 1 1 1 1
## 2 4 4 4
## 3 4 4 4
## 4 1 1 1
## 5 4 4 4
## 6 4 4 4
## CSI_SoldMoreAnimals HFC_GrainDays HFC_GrainSource HFC_RootsDays
## 1 1 7 5 NA
## 2 3 7 5 NA
## 3 4 7 5 NA
## 4 1 7 5 NA
## 5 4 7 5 NA
## 6 4 7 5 NA
## HFC_RootsSource HFC_PulsesNutsDays HFC_PulsesNutsSource HFC_OrangeVegDays
## 1 NA 3 5 NA
## 2 NA 5 5 NA
## 3 NA 3 5 NA
## 4 NA 5 5 NA
## 5 NA 7 5 NA
## 6 NA 4 5 NA
## HFC_OrangeVegSource HFC_GreenLeafyDays HFC_GreenLeafySource HFC_OtherVegDays
## 1 NA NA NA 4
## 2 NA NA NA 0
## 3 NA NA NA 4
## 4 NA NA NA 0
## 5 NA NA NA 0
## 6 NA NA NA 6
## HFC_OtherVegSource HFC_OrangeFruitsDays HFC_OrangeFruitsSource
## 1 3 NA NA
## 2 10 NA NA
## 3 3 NA NA
## 4 10 NA NA
## 5 10 NA NA
## 6 5 NA NA
## HFC_OtherFruitsDays HFC_OtherFruitsSource HFC_MeatDays HFC_MeatSource
## 1 0 10 4 1
## 2 2 5 4 1
## 3 0 10 4 1
## 4 0 10 4 1
## 5 0 10 2 5
## 6 0 10 4 1
## HFC_LiverDays HFC_LiverSource HFC_FishDays HFC_EggsDays HFC_EggsSource
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## HFC_MilkDays HFC_MilkSource HFC_OilDays HFC_OilSource HFC_SugarDays
## 1 2 5 7 5 4
## 2 3 1 7 5 4
## 3 3 1 7 5 4
## 4 3 1 7 5 2
## 5 2 5 3 5 3
## 6 3 1 7 5 0
## HFC_SugarSource HFC_CondimentsDays HFC_CondimentsSource MainIncomeSource
## 1 2 7 5 5. Sale of wood
## 2 3 7 5 2. Sale of livestock
## 3 1 7 5 2. Sale of livestock
## 4 5 7 5 1. Sale of crops
## 5 2 7 5 2. Sale of livestock
## 6 10 7 5 4. Casual labour
## MaleCasualLabour FemaleCasualLabour CasualLabourEarn CharcoalSaleEarn
## 1 1 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 1 0 300 0
## WoodSaleEarn DivisionID CountyID SiteID LivelihoodZoneID DateCaptured
## 1 800 1140 2 1397 1 NA
## 2 0 1140 2 1397 1 NA
## 3 0 1140 2 1397 1 NA
## 4 0 1140 2 1397 1 NA
## 5 0 1140 2 1397 1 NA
## 6 0 1140 2 1397 1 NAWe also have to ensure that the “InterviewDate” column is parsed correctly as a date before saving the worksheet to the new workbook.
# Ensure the column is numeric
hha_dews_data$InterviewDate <- as.numeric(hha_dews_data$InterviewDate)
# Convert the numeric date to Date format
hha_dews_data$InterviewDate <- as.Date(hha_dews_data$InterviewDate, origin = "1899-12-30")
# View the first few dates to verify the conversion
head(hha_dews_data$InterviewDate)## [1] "2016-07-05" "2016-07-05" "2016-07-05" "2016-07-05" "2016-07-05"
## [6] "2016-07-06"Save the cleaned data set as a different sheet in the Garissa workbook
# Define the path for the existing Excel workbook
existing_file_path <- "C:/Users/AAH USER/OneDrive - Action Against Hunger USA/Documents/NDMA_DeIdentified/Garissa.xlsx"
# Load the existing workbook
wb <- loadWorkbook(existing_file_path)
# Add the cleaned HHA DEWS data to the existing workbook
addWorksheet(wb, "HHA DEWS")
writeData(wb, "HHA DEWS", hha_dews_data)
# Save the updated workbook
saveWorkbook(wb, existing_file_path, overwrite = TRUE)3. KIA REWAS
library(openxlsx)
file_path <- "C:/Users/AAH USER/Downloads/Garissa.xlsx"
# Read the specific sheet into a data frame
kia_rewas_data <- read.xlsx(file_path, sheet = "KIA REWAS")There are no P.I.I columns in this particular sheet.We save this new sheet alongside the previous two in the Garissa workbook created
# Define the path for the existing Excel workbook
existing_file_path <- "C:/Users/AAH USER/OneDrive - Action Against Hunger USA/Documents/NDMA_DeIdentified/Garissa.xlsx"
# Load the existing workbook
wb <- loadWorkbook(existing_file_path)
# Add the KIA REWAS data to the existing workbook
addWorksheet(wb, "KIA REWAS")
writeData(wb, "KIA REWAS", kia_rewas_data)
# Save the updated workbook
saveWorkbook(wb, existing_file_path, overwrite = TRUE)4. KIA DEWS
library(openxlsx)
file_path <- "C:/Users/AAH USER/Downloads/Garissa.xlsx"
# Read the specific sheet into a data frame
kia_dews_data <- read.xlsx(file_path, sheet = "KIA DEWS")There are no P.I.I columns in this particular sheet. We ensure that the “InterviewDate” column is parsed correctly as a date
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union# Convert the numeric date to Date format
kia_dews_data$InterviewDate <- as.Date(kia_dews_data$InterviewDate, origin = "1899-12-30")
# View the first few dates to verify the conversion
head(kia_dews_data$InterviewDate)## [1] "2016-07-25" "2016-07-25" "2016-07-16" "2016-07-16" "2016-07-16"
## [6] "2016-07-16"We save this new sheet alongside the previous three in the Garissa workbook created
# Define the path for the existing Excel workbook
existing_file_path <- "C:/Users/AAH USER/OneDrive - Action Against Hunger USA/Documents/NDMA_DeIdentified/Garissa.xlsx"
# Load the existing workbook
wb <- loadWorkbook(existing_file_path)
# Add the KIA DEWS data to the existing workbook
addWorksheet(wb, "KIA DEWS")
writeData(wb, "KIA DEWS", kia_dews_data)
# Save the updated workbook
saveWorkbook(wb, existing_file_path, overwrite = TRUE)5. MUAC REWAS
library(openxlsx)
file_path <- "C:/Users/AAH USER/Downloads/Garissa.xlsx"
# Read the specific sheet into a data frame
muac_rewas_data <- read.xlsx(file_path, sheet = "MUAC REWAS")We drop PII’s which are the “fname”, and “hhname”.
# Drop the specified PII columns
muac_rewas_data <- muac_rewas_data %>%
select(-c(fname, hhname ))
# Check the updated dataset
head(muac_rewas_data)## district_name year admin6id child_age batchid child_sickcode district
## 1 GARISSA 2011 <NA> 29 1340 0 2
## 2 GARISSA 2011 <NA> 33 1340 0 2
## 3 GARISSA 2011 <NA> 36 1340 0 2
## 4 GARISSA 2011 <NA> 38 1340 0 2
## 5 GARISSA 2011 <NA> 39 1340 0 2
## 6 GARISSA 2011 <NA> 37 1340 0 2
## division child_sex child_sick hhaid hhamuacid item child_hh lzonehh month
## 1 145 Female No 1419 200508 1 No 4 1
## 2 145 Female No 1419 200507 2 Yes 4 1
## 3 145 Male No 1418 200506 3 Yes 4 1
## 4 145 Female No 1418 200505 4 Yes 4 1
## 5 145 Male No 1417 200504 5 No 4 1
## 6 145 Male No 1417 200503 6 Yes 4 1
## muac sacode serialno
## 1 135 368 146
## 2 134 368 146
## 3 134 368 145
## 4 144 368 145
## 5 133 368 144
## 6 149 368 144We save this new sheet alongside the previous four in the Garissa workbook created
# Define the path for the existing Excel workbook
existing_file_path <- "C:/Users/AAH USER/OneDrive - Action Against Hunger USA/Documents/NDMA_DeIdentified/Garissa.xlsx"
# Load the existing workbook
wb <- loadWorkbook(existing_file_path)
# Add the KIA DEWS data to the existing workbook
addWorksheet(wb, "MUAC REWAS")
writeData(wb, "MUAC REWAS", muac_rewas_data)
# Save the updated workbook
saveWorkbook(wb, existing_file_path, overwrite = TRUE)6. MUAC DEWS
library(openxlsx)
file_path <- "C:/Users/AAH USER/Downloads/Garissa.xlsx"
# Read the specific sheet into a data frame
muac_dews_data <- read.xlsx(file_path, sheet = "MUAC DEWS")This data set has P.I.I’s in the “ChildName” column so we will drop that.
# Drop the specified PII columns
muac_dews_data <- muac_dews_data %>%
select(-ChildName)
# Check the updated dataset
head(muac_dews_data)## MUACIndicatorID QID County SubCounty Ward LivelihoodZone Month Year
## 1 13753 8045 Garissa Fafi Nanighi Pastoral July 2016
## 2 13754 8045 Garissa Fafi Nanighi Pastoral July 2016
## 3 13755 8045 Garissa Fafi Nanighi Pastoral July 2016
## 4 13756 8045 Garissa Fafi Nanighi Pastoral July 2016
## 5 13757 8045 Garissa Fafi Nanighi Pastoral July 2016
## 6 13758 8046 Garissa Fafi Nanighi Pastoral July 2016
## HouseholdCode Gender MUAC MUAC_Color AgeInMonths
## 1 GSA0408 Male 142 59 TRUE
## 2 GSA0408 Female 144 36 TRUE
## 3 GSA0408 Female 130 59 TRUE
## 4 GSA0408 Male 150 6 FALSE
## 5 GSA0408 Male 144 59 FALSE
## 6 GSA0416 Male 160 36 TRUE
## LiveInHousehold SufferedIllnesses InterviewDate DivisionID
## 1 Fever with chills like malaria 42556 1140 2
## 2 Diarrhea 42556 1140 2
## 3 <NA> 42556 1140 2
## 4 <NA> 42556 1140 2
## 5 <NA> 42556 1140 2
## 6 <NA> 42556 1140 2
## CountyID SiteID LivelihoodZoneID
## 1 1397 1 NA
## 2 1397 1 NA
## 3 1397 1 NA
## 4 1397 1 NA
## 5 1397 1 NA
## 6 1397 1 NAWe also have to ensure that the “InterviewDate” column is parsed correctly as a date. The row values are displaced to the “SufferedIllnesses” column. We align this and proceed to parse the date correctly.
# Create a mask for rows to modify
rows_to_modify <- which(muac_dews_data$Year %in% 2016:2019)
# Ensure the columns being shifted are correctly specified
# We will create an index to specify which columns to shift
shift_columns <- c("MUAC_Color", "AgeInMonths", "LiveInHousehold",
"SufferedIllnesses", "InterviewDate",
"DivisionID", "CountyID", "SiteID", "LivelihoodZoneID")
# Create an empty data frame for the shifted values
shifted_values <- muac_dews_data[rows_to_modify, shift_columns]
# Replace the values in the original DataFrame with NA in the selected rows
muac_dews_data[rows_to_modify, shift_columns] <- NA
# Move the values one column to the right
for (i in seq_along(shift_columns)[-length(shift_columns)]) {
muac_dews_data[rows_to_modify, shift_columns[i + 1]] <- shifted_values[[i]]
}library(lubridate)
# Ensure the column is numeric
muac_dews_data$InterviewDate <- as.numeric(muac_dews_data$InterviewDate)
# Convert the Excel serial date to R Date
muac_dews_data$InterviewDate <- as.Date(muac_dews_data$InterviewDate, origin = "1899-12-30")
# Verify the output
head(muac_dews_data$InterviewDate)## [1] "2016-07-05" "2016-07-05" "2016-07-05" "2016-07-05" "2016-07-05"
## [6] "2016-07-05"Save this final sheet to the existing workbook
# Define the path for the existing Excel workbook
existing_file_path <- "C:/Users/AAH USER/OneDrive - Action Against Hunger USA/Documents/NDMA_DeIdentified/Garissa.xlsx"
# Load the existing workbook
wb <- loadWorkbook(existing_file_path)
# Add the KIA DEWS data to the existing workbook
addWorksheet(wb, "MUAC DEWS")
writeData(wb, "MUAC DEWS", muac_dews_data)
# Save the updated workbook
saveWorkbook(wb, existing_file_path, overwrite = TRUE)