Module 5: Apply 4

Import data

# excel file
data <- read_excel("../00_data/myWaterData.xlsx")
data

## # A tibble: 473,315 × 13
##    row_id lat_deg lon_deg report_date         status_id water_source  water_tech
##     <dbl>   <dbl>   <dbl> <dttm>              <chr>     <chr>         <chr>     
##  1   3957   8.07     38.6 2017-04-06 00:00:00 y         NA            NA        
##  2  33512   7.37     40.5 2020-08-04 00:00:00 y         Protected Sp… NA        
##  3  35125   0.773    34.9 2015-03-18 00:00:00 y         Protected Sh… NA        
##  4  37760   0.781    35.0 2015-03-18 00:00:00 y         Borehole      NA        
##  5  38118   0.779    35.0 2015-03-18 00:00:00 y         Protected Sh… NA        
##  6  38501   0.308    34.1 2015-03-19 00:00:00 y         Borehole      NA        
##  7  46357   0.419    34.3 2015-05-19 00:00:00 y         Unprotected … NA        
##  8  46535   0.444    34.3 2015-05-19 00:00:00 y         Protected Sh… NA        
##  9  46560   0.456    34.3 2015-05-19 00:00:00 y         Protected Sh… NA        
## 10  46782   0.467    34.3 2015-05-20 00:00:00 y         Protected Sh… NA        
## # ℹ 473,305 more rows
## # ℹ 6 more variables: facility_type <chr>, country_name <chr>,
## #   install_year <chr>, installer <chr>, pay <chr>, status <chr>

State one question

• What does the water availability look like in areas of water scarcity?

Plot data

ggplot(data, aes(x = facility_type)) +
    geom_bar()

Interpret

• It appears that majority of the facility (the water wells) have been improved with still some areas with no facilities or unimproved.
• The values are very large so it is difficult to read the values.