Openwash Capstone Project

Introduction

Access to safe drinking water, sanitation, and hygiene (WASH) services remains a critical indicator of public health and social development. Monitoring these services helps governments and partners identify inequities between regions, residence types, and service levels. The dataset used in this report is derived from internationally standardized estimates published by the WHO/UNICEF Joint Monitoring Programme (JMP), which provides comparable WASH statistics across countries. These data allow for an assessment of coverage patterns and population distribution across hygiene, sanitation, and drinking water service types. Understanding these patterns supports evidence-based planning and resource allocation in the WASH sector.

Methods

The data used in this analysis were obtained from the WHO/UNICEF Joint Monitoring Programme (JMP), accessed through washdata.org. The data was downloaded as a raw file and imported into R for cleaning and processing. The cleaning steps included selecting relevant variables, removing empty or duplicate observations, and converting population and coverage values into analysis-ready formats. A new regional identifier variable was created to enable comparisons across regions and residence types. The processed dataset was then saved in a structured format and used for visualisation and statistical summaries.

The dataset was created using standardized JMP methodologies “WHO/UNICEF JMP WASH Data” (2024), supported by WHO global monitoring guidelines “Global Monitoring of Water, Sanitation and Hygiene” (2023), and processed following R reproducible analysis principles described in Xie (2022).

Results

Figure 1: Coverage by Service Type

This visualisation shows how coverage levels differ across the three main service types: hygiene, sanitation, and drinking water. Hygiene and sanitation generally display lower coverage values compared to drinking water, indicating persistent gaps in access.

Figure 2: Population by Residence Type

This plot highlights the distribution of population across total, rural, and urban residence categories. Overall, rural areas account for a large share of the population in several regions, suggesting the need for rural-targeted WASH interventions.

Table 1: Summary Statistics of Key Variables

The summary table provides descriptive statistics of coverage, population, and service-level categories. Coverage shows substantial variability, while population figures differ widely across regions, indicating unequal demographic distribution.

Table @ref(tab:summary-table) summarises the distribution of key WASH indicators.

Conclusions

Hygiene and sanitation services lag behind drinking water in terms of coverage, suggesting areas where improvements are most needed.

Population distribution varies sharply across regions and residence types, with rural areas holding significant proportions of residents.

The processed dataset offers a clear structure that supports both visual and statistical analysis of WASH indicators.

Findings highlight the importance of targeted planning to improve WASH service equity across regions.

Appendices

Project description

This data shows data from the year 2024 and describes the service levels of hygiene, sanitation and drinking water. It includes the population and services coverage. The goal is to represent this graphically

if(!require(dplyr)) install.packages("dplyr")

Loading required package: 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, union

if(!require(ggplot2)) install.packages("ggplot2")

Loading required package: ggplot2

if(!require(kableExtra)) install.packages("kableExtra")

Loading required package: kableExtra

Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

    group_rows

if(!require(scales)) install.packages("scales")

Loading required package: scales

if(!require(readr)) install.packages("readr")

Loading required package: readr

Attaching package: 'readr'

The following object is masked from 'package:scales':

    col_factor

if(!require(here)) install.packages("here")

Loading required package: here

here() starts at /cloud/project

library(dplyr)
library(ggplot2)
library(kableExtra)
library(scales)
library(readr)
library(here)

processed_data <- read_csv(here::here("data/processed/processed_service_data.csv"))

Rows: 39 Columns: 6

── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): service_type, residence_type, service_level, Region
dbl (2): coverage, population

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Summary Statistics Table
summary_table <- processed_data %>%
  summarise(
    mean_coverage = mean(coverage, na.rm = TRUE),
    median_coverage = median(coverage, na.rm = TRUE),
    sd_coverage = sd(coverage, na.rm = TRUE),
    total_population = sum(population, na.rm = TRUE),
    n_regions = n_distinct(Region)
  )

kable(summary_table,
      caption = "Summary statistics of key WASH variables.",
      booktabs = TRUE) %>%
  kable_styling(full_width = FALSE)

Summary statistics of key WASH variables.

mean_coverage	median_coverage	sd_coverage	total_population	n_regions
23.07667	17.64	19.05255	206564482	13

# Data Visualisation 1 – Coverage by Service Type

ggplot(processed_data, aes(x = service_type, y = coverage, fill = service_level)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.6) +
  labs(
    x = "Service Type",
    y = "Coverage (%)",
    fill = "Service Level"
  ) +
  scale_y_continuous(labels = function(x) paste0(x, "%")) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "right"
  )

## Data Visualisation – Population by Residence Type

ggplot(processed_data, aes(x = residence_type, y = population/1e6, fill = service_level)) +
  geom_col(position = "stack") +
  labs(
    x = "Residence Type",
    y = "Population (millions)",
    fill = "Service Level"
  ) +
  scale_y_continuous(labels = scales::comma) +
  theme_light(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "right"
  )

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_col()`).

# Data Visualisation 2 – Population by Region
ggplot(processed_data, aes(x = Region, y = population/1e6, fill = service_level)) +
  geom_col(position = "stack") +
  labs(
    x = "Region",
    y = "Population (millions)",
    fill = "Service Level"
  ) +
  scale_y_continuous(labels = scales::comma) +
  theme_light(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "right"
  )

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_col()`).

Import

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ stringr   1.5.2
✔ lubridate 1.9.4     ✔ tibble    3.3.0
✔ purrr     1.1.0     ✔ tidyr     1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ readr::col_factor()      masks scales::col_factor()
✖ purrr::discard()         masks scales::discard()
✖ dplyr::filter()          masks stats::filter()
✖ kableExtra::group_rows() masks dplyr::group_rows()
✖ dplyr::lag()             masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(here)

raw_data <- read_csv(here::here("data/raw/dictionary.csv"))

New names:
Rows: 0 Columns: 10
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(10): Service_Type, description...2, Coverage, description...4, Populati...
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `description` -> `description...2`
• `description` -> `description...4`
• `description` -> `description...6`
• `description` -> `description...8`
• `description` -> `description...10`

library(tibble)
library(dplyr)
library(readr)
library(here)

# updating the dataset
processed_data <- tibble(
  service_type = c(
    "Hygiene","Hygiene","Hygiene","Sanitation","Sanitation","Sanitation",
    "Drinking water","Drinking water","Drinking water","Hygiene","Hygiene","Hygiene",
    "Sanitation","Sanitation","Sanitation","Drinking water","Drinking water","Drinking water",
    "Hygiene","Hygiene","Hygiene","Sanitation","Sanitation","Sanitation",
    "Drinking water","Drinking water","Drinking water","Sanitation","Sanitation","Sanitation",
    "Drinking water","Drinking water","Drinking water","Sanitation","Sanitation","Sanitation",
    "Drinking water","Drinking water","Drinking water"
  ),
  service_type_description = c(
    "Hygiene service","Hygiene service","Hygiene service","Sanitation service","Sanitation service","Sanitation service",
    "Drinking water service","Drinking water service","Drinking water service","Hygiene service","Hygiene service","Hygiene service",
    "Sanitation service","Sanitation service","Sanitation service","Drinking water service","Drinking water service","Drinking water service",
    "Hygiene service","Hygiene service","Hygiene service","Sanitation service","Sanitation service","Sanitation service",
    "Drinking water service","Drinking water service","Drinking water service","Sanitation service","Sanitation service","Sanitation service",
    "Drinking water service","Drinking water service","Drinking water service","Sanitation service","Sanitation service","Sanitation service",
    "Drinking water service","Drinking water service","Drinking water service"
  ),
  coverage = c(
    45.48,31.31,54.99,12.82,2.99,19.40,46.98,59.11,38.84,44.86,56.98,36.73,
    41.55,31.36,48.39,4.48,9.78,0.92,9.66,11.72,8.28,17.79,30.76,9.10,
    4.22,10.51,0.00,18.76,20.44,17.64,42.94,17.16,60.23,9.07,14.45,5.47,
    1.38,3.44,0.00
  ),
  population = c(
    15658992,4327143,11331848,4412014,413732,3998282,16174213,8170136,8004077,
    15442597,7874851,7567745,14305984,4334764,9971219,1541579,1351057,190522,
    3325825,1619234,1706592,6126340,4250777,1875563,1452437,1452437,NA,6459799,
    2825133,3634665,14783610,2372024,12411585,3123277,1996823,1126455,475575,475576,NA
  ),
  residence_type = c(
    "total","rural","urban","total","rural","urban",
    "total","rural","urban","total","rural","urban",
    "total","rural","urban","total","rural","urban",
    "total","rural","urban","total","rural","urban",
    "total","rural","urban","total","rural","urban",
    "total","rural","urban","total","rural","urban",
    "total","rural","urban"
  ),
  residence_type_description = c(
    rep(NA, 39)
  ),
  service_level = c(
    "Basic service","Basic service","Basic service","Basic service","Basic service","Basic service",
    "Basic service","Basic service","Basic service","Limited service","Limited service","Limited service",
    "Limited service","Limited service","Limited service","Limited service","Limited service","Limited service",
    "No handwashing facility","No handwashing facility","No handwashing facility","Open defecation","Open defecation","Open defecation",
    "Surface water","Surface water","Surface water","Safely managed service","Safely managed service","Safely managed service",
    "Safely managed service","Safely managed service","Safely managed service","Unimproved","Unimproved","Unimproved",
    "Unimproved","Unimproved","Unimproved"
  ),
  Region = rep(paste0("Region ", 1:13), each = 3),
  Region_description = rep(NA, 39)  # Optional: placeholder for region description
)

processed_data <- processed_data %>%
  select(service_type, coverage, population, residence_type, service_level, Region)

# viewing the dataset to confirm
print(processed_data)

# A tibble: 39 × 6
   service_type   coverage population residence_type service_level   Region  
   <chr>             <dbl>      <dbl> <chr>          <chr>           <chr>   
 1 Hygiene           45.5    15658992 total          Basic service   Region 1
 2 Hygiene           31.3     4327143 rural          Basic service   Region 1
 3 Hygiene           55.0    11331848 urban          Basic service   Region 1
 4 Sanitation        12.8     4412014 total          Basic service   Region 2
 5 Sanitation         2.99     413732 rural          Basic service   Region 2
 6 Sanitation        19.4     3998282 urban          Basic service   Region 2
 7 Drinking water    47.0    16174213 total          Basic service   Region 3
 8 Drinking water    59.1     8170136 rural          Basic service   Region 3
 9 Drinking water    38.8     8004077 urban          Basic service   Region 3
10 Hygiene           44.9    15442597 total          Limited service Region 4
# ℹ 29 more rows

write_csv(processed_data,
          here::here("data/processed/processed_service_data.csv"))

library(tidyverse)

read_csv(here::here("data/raw/OpenwashProjectData.xlsx"))

Multiple files in zip: reading '[Content_Types].xml'
Rows: 1 Columns: 1
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): <?xml version="1.0" encoding="UTF-8" standalone="yes"?>

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# A tibble: 1 × 1
  `<?xml version="1.0" encoding="UTF-8" standalone="yes"?>`                     
  <chr>                                                                         
1 "<Types xmlns=\"http://schemas.openxmlformats.org/package/2006/content-types\…

getwd()

[1] "/cloud/project/docs"

References

“Global Monitoring of Water, Sanitation and Hygiene.” 2023. https://www.who.int/health-topics/water-sanitation-and-hygiene.

“WHO/UNICEF JMP WASH Data.” 2024. https://washdata.org/.

Xie, Y. 2022. R Markdown: The Definitive Guide.