Introduction:

I will explore a dataset containing information about water, sanitation, and hygiene (WASH) services and my goal is to analyze 5 random subsamples of the data to gain insights into the variability of WASH data and its implications.

Consistency in Characteristics:

The analysis of the subsamples reveals a remarkable consistency in key characteristics such as Year, Population, Coverage, and Service level across different random samples.
This consistency suggests that the dataset captures representative information about water, sanitation, and hygiene (WASH) services across various regions and residence types.

Variability Within Subsamples:

While the overall characteristics remain consistent, there is variability within each subsample, particularly in the distribution of Population and Coverage. This variability underscores the diversity in WASH service provision and coverage levels, which may be influenced by factors such as infrastructure development, socioeconomic conditions, and policy interventions.

Stability of Coverage and Service Levels:

Despite the variability in population characteristics, the distribution of Coverage and Service.level remains relatively stable across all subsamples. This stability suggests a consistent approach to categorizing and assessing WASH services, which could facilitate comparative analyses and policy evaluations over time.

Implications for Analysis:

The consistent patterns observed across subsamples enhance the reliability of analyses conducted on the dataset. Draw insights and make informed decisions based on the aggregated findings, knowing that they are reflective of broader trends in WASH service provision.

Further Investigation Opportunities:

While the analysis provides valuable insights, further investigation is warranted to delve deeper into specific aspects of WASH service provision. Exploring correlations between variables, identifying outliers or underserved regions, and assessing the impact of interventions could uncover additional nuances and inform targeted interventions.

Conclusions:

the analysis of the subsamples reinforces the reliability of the dataset and highlights the importance of understanding both the consistency and variability within data when making meaningful interpretations and policy decisions related to WASH services.

# Read the CSV file
data <- read.csv("C:\\Users\\am790\\Downloads\\washdash-download (1).csv")
# View summary of the data
summary(data)

##      Type              Region          Residence.Type     Service.Type      
##  Length:3367        Length:3367        Length:3367        Length:3367       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       Year         Coverage         Population        Service.level     
##  Min.   :2010   Min.   :  0.000   Min.   :0.000e+00   Length:3367       
##  1st Qu.:2013   1st Qu.:  2.486   1st Qu.:4.366e+06   Class :character  
##  Median :2016   Median : 12.110   Median :3.306e+07   Mode  :character  
##  Mean   :2016   Mean   : 22.447   Mean   :1.497e+08                     
##  3rd Qu.:2019   3rd Qu.: 34.190   3rd Qu.:1.755e+08                     
##  Max.   :2022   Max.   :100.000   Max.   :2.173e+09

# Set seed for reproducibility
set.seed(123)
# Set the display format to avoid scientific notation
options(scipen = 999)
# Determine the size of each subsample (roughly 50% of the original data size)
subsample_size <- round(nrow(data) * 0.5)

# Create 5 random subsamples
subsample_1 <- data[sample(nrow(data), subsample_size, replace = TRUE), ]
subsample_2 <- data[sample(nrow(data), subsample_size, replace = TRUE), ]
subsample_3 <- data[sample(nrow(data), subsample_size, replace = TRUE), ]
subsample_4 <- data[sample(nrow(data), subsample_size, replace = TRUE), ]
subsample_5 <- data[sample(nrow(data), subsample_size, replace = TRUE), ]

# Analyze and compare the characteristics of each subsample
summary_subsample_1 <- summary(subsample_1)
summary_subsample_2 <- summary(subsample_2)
summary_subsample_3 <- summary(subsample_3)
summary_subsample_4 <- summary(subsample_4)
summary_subsample_5 <- summary(subsample_5)

# Display summary statistics for each subsample
print(summary_subsample_1)

##      Type              Region          Residence.Type     Service.Type      
##  Length:1684        Length:1684        Length:1684        Length:1684       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       Year         Coverage         Population         Service.level     
##  Min.   :2010   Min.   :  0.000   Min.   :         0   Length:1684       
##  1st Qu.:2013   1st Qu.:  2.476   1st Qu.:   4154243   Class :character  
##  Median :2016   Median : 12.313   Median :  34402660   Mode  :character  
##  Mean   :2016   Mean   : 22.714   Mean   : 157647595                     
##  3rd Qu.:2019   3rd Qu.: 34.640   3rd Qu.: 181490195                     
##  Max.   :2022   Max.   :100.000   Max.   :2173068885

print(summary_subsample_2)

##      Type              Region          Residence.Type     Service.Type      
##  Length:1684        Length:1684        Length:1684        Length:1684       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       Year         Coverage         Population         Service.level     
##  Min.   :2010   Min.   :  0.000   Min.   :         0   Length:1684       
##  1st Qu.:2013   1st Qu.:  2.724   1st Qu.:   5175632   Class :character  
##  Median :2016   Median : 12.793   Median :  34759598   Mode  :character  
##  Mean   :2016   Mean   : 22.597   Mean   : 146462007                     
##  3rd Qu.:2019   3rd Qu.: 33.992   3rd Qu.: 174690481                     
##  Max.   :2022   Max.   :100.000   Max.   :2165634558

print(summary_subsample_3)

##      Type              Region          Residence.Type     Service.Type      
##  Length:1684        Length:1684        Length:1684        Length:1684       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       Year         Coverage         Population         Service.level     
##  Min.   :2010   Min.   :  0.000   Min.   :         0   Length:1684       
##  1st Qu.:2013   1st Qu.:  2.493   1st Qu.:   5009428   Class :character  
##  Median :2016   Median : 12.460   Median :  39553047   Mode  :character  
##  Mean   :2016   Mean   : 22.354   Mean   : 155646582                     
##  3rd Qu.:2019   3rd Qu.: 33.734   3rd Qu.: 176074957                     
##  Max.   :2022   Max.   :100.000   Max.   :2173068885

print(summary_subsample_4)

##      Type              Region          Residence.Type     Service.Type      
##  Length:1684        Length:1684        Length:1684        Length:1684       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       Year         Coverage         Population         Service.level     
##  Min.   :2010   Min.   :  0.000   Min.   :         0   Length:1684       
##  1st Qu.:2013   1st Qu.:  1.995   1st Qu.:   3897266   Class :character  
##  Median :2016   Median :  9.267   Median :  25851633   Mode  :character  
##  Mean   :2016   Mean   : 20.472   Mean   : 136173254                     
##  3rd Qu.:2019   3rd Qu.: 31.066   3rd Qu.: 137711310                     
##  Max.   :2022   Max.   :100.000   Max.   :2122935806

print(summary_subsample_5)

##      Type              Region          Residence.Type     Service.Type      
##  Length:1684        Length:1684        Length:1684        Length:1684       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       Year         Coverage         Population         Service.level     
##  Min.   :2010   Min.   :  0.000   Min.   :         0   Length:1684       
##  1st Qu.:2013   1st Qu.:  2.476   1st Qu.:   4134665   Class :character  
##  Median :2016   Median : 11.075   Median :  30676280   Mode  :character  
##  Mean   :2016   Mean   : 21.873   Mean   : 142638708                     
##  3rd Qu.:2019   3rd Qu.: 33.293   3rd Qu.: 165223167                     
##  Max.   :2022   Max.   :100.000   Max.   :2173068885

# calculate the mean population for each combination of Region and Residence.Type in each subsample
library(dplyr)

## Warning: package 'dplyr' was built under R version 4.3.3

## 
## 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

mean_population_subsample_1 <- subsample_1 %>%
  group_by(Region, Residence.Type) %>%
  summarise(mean_population = mean(Population, na.rm = TRUE))

## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.

mean_population_subsample_2 <- subsample_2 %>%
  group_by(Region, Residence.Type) %>%
  summarise(mean_population = mean(Population, na.rm = TRUE))

## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.

mean_population_subsample_3 <- subsample_3 %>%
  group_by(Region, Residence.Type) %>%
  summarise(mean_population = mean(Population, na.rm = TRUE))

## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.

mean_population_subsample_4 <- subsample_4 %>%
  group_by(Region, Residence.Type) %>%
  summarise(mean_population = mean(Population, na.rm = TRUE))

## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.

mean_population_subsample_5 <- subsample_5 %>%
  group_by(Region, Residence.Type) %>%
  summarise(mean_population = mean(Population, na.rm = TRUE))

## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.

# Display mean population for each combination of Region and Residence.
print(mean_population_subsample_1)

## # A tibble: 24 × 3
## # Groups:   Region [8]
##    Region                         Residence.Type mean_population
##    <chr>                          <chr>                    <dbl>
##  1 Australia and New Zealand      rural                  552201.
##  2 Australia and New Zealand      total                 5699598.
##  3 Australia and New Zealand      urban                 6630438.
##  4 Central and Southern Asia      rural               290875621.
##  5 Central and Southern Asia      total               551402921.
##  6 Central and Southern Asia      urban               174334992.
##  7 Eastern and South-Eastern Asia rural               185809073.
##  8 Eastern and South-Eastern Asia total               541939177.
##  9 Eastern and South-Eastern Asia urban               255742312.
## 10 Europe and Northern America    rural                60843089.
## # ℹ 14 more rows

print(mean_population_subsample_2)

## # A tibble: 24 × 3
## # Groups:   Region [8]
##    Region                         Residence.Type mean_population
##    <chr>                          <chr>                    <dbl>
##  1 Australia and New Zealand      rural                 1368316.
##  2 Australia and New Zealand      total                 8632325.
##  3 Australia and New Zealand      urban                 6046107.
##  4 Central and Southern Asia      rural               308488678.
##  5 Central and Southern Asia      total               440200014.
##  6 Central and Southern Asia      urban               203248365.
##  7 Eastern and South-Eastern Asia rural               172907559.
##  8 Eastern and South-Eastern Asia total               414326521.
##  9 Eastern and South-Eastern Asia urban               253826928.
## 10 Europe and Northern America    rural                45032284.
## # ℹ 14 more rows

print(mean_population_subsample_3)

## # A tibble: 24 × 3
## # Groups:   Region [8]
##    Region                         Residence.Type mean_population
##    <chr>                          <chr>                    <dbl>
##  1 Australia and New Zealand      rural                 1266910.
##  2 Australia and New Zealand      total                 7251198.
##  3 Australia and New Zealand      urban                 4052628.
##  4 Central and Southern Asia      rural               206851144.
##  5 Central and Southern Asia      total               433677903.
##  6 Central and Southern Asia      urban               179174357.
##  7 Eastern and South-Eastern Asia rural               242079099.
##  8 Eastern and South-Eastern Asia total               602445506.
##  9 Eastern and South-Eastern Asia urban               227816176.
## 10 Europe and Northern America    rural                45564835.
## # ℹ 14 more rows

print(mean_population_subsample_4)

## # A tibble: 24 × 3
## # Groups:   Region [8]
##    Region                         Residence.Type mean_population
##    <chr>                          <chr>                    <dbl>
##  1 Australia and New Zealand      rural                  896972.
##  2 Australia and New Zealand      total                 4616919.
##  3 Australia and New Zealand      urban                 2737720.
##  4 Central and Southern Asia      rural               238224639.
##  5 Central and Southern Asia      total               390874890.
##  6 Central and Southern Asia      urban               169680439.
##  7 Eastern and South-Eastern Asia rural               152135302.
##  8 Eastern and South-Eastern Asia total               447944252.
##  9 Eastern and South-Eastern Asia urban               233357763.
## 10 Europe and Northern America    rural                48765644.
## # ℹ 14 more rows

print(mean_population_subsample_5)

## # A tibble: 24 × 3
## # Groups:   Region [8]
##    Region                         Residence.Type mean_population
##    <chr>                          <chr>                    <dbl>
##  1 Australia and New Zealand      rural                  621285.
##  2 Australia and New Zealand      total                 7995427.
##  3 Australia and New Zealand      urban                 6280446.
##  4 Central and Southern Asia      rural               251934330.
##  5 Central and Southern Asia      total               416112248.
##  6 Central and Southern Asia      urban               157086007.
##  7 Eastern and South-Eastern Asia rural               213714405.
##  8 Eastern and South-Eastern Asia total               447537617.
##  9 Eastern and South-Eastern Asia urban               269456967.
## 10 Europe and Northern America    rural                48030766.
## # ℹ 14 more rows

visualization for the mean population for each combination of Region and Residence:

# Load the ggplot2 library for visualization
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.3

# Create a function to plot the mean population for each combination of Region and Residence.Type
plot_mean_population <- function(mean_population_data, subsample_number) {
  ggplot(mean_population_data, aes(x = Region, y = mean_population, fill = Residence.Type)) +
    geom_bar(stat = "identity", position = "dodge") +
    labs(title = paste("Mean Population for Subsample", subsample_number),
         x = "Region",
         y = "Mean Population",
         fill = "Residence Type") +
    theme_minimal() +
    theme(legend.position = "top") +  # Adjust legend position
    coord_flip()  # Flip the coordinates for better readability
}

# Plot for subsample 1
plot_mean_population(mean_population_subsample_1, 1)
plot of chunk unnamed-chunk-2
# Plot for subsample 2
plot_mean_population(mean_population_subsample_2, 2)
plot of chunk unnamed-chunk-2
# Plot for subsample 3
plot_mean_population(mean_population_subsample_3, 3)
plot of chunk unnamed-chunk-2
# Plot for subsample 4
plot_mean_population(mean_population_subsample_4, 4)
plot of chunk unnamed-chunk-2
# Plot for subsample 5
plot_mean_population(mean_population_subsample_5, 5)
plot of chunk unnamed-chunk-2