# Create_interactive_map_displaying_Ghana_2019_School_Attendance_Indicators


library(rGhanaCensus)
library(sf)
#> Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
library(tmap)
#> Warning: multiple methods tables found for 'direction'
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, union
library(magrittr)

### Example 1

Create a interactive map with the package tmap and data from the package rGhanaCensus that displays the regional distribution of the Percentage of students 3 years or older who have dropped out of school.

Load geometry data Ghana_2021_school_attendance_geometry from rGhanaCensus package.

data("Ghana_2021_school_attendance_geometry", package = "rGhanaCensus")

Convert it to sf data frame and assign a new name. In this example, “Ghana_edu_sf” will be the name of the sf data frame created.

Ghana_edu_sf<- sf::st_as_sf(Ghana_2021_school_attendance_geometry)

The code Ghana_edu_sf %>%filter(Locality=="Urban") subsets the data frame and retains only the rows that the survey respondents came from Urban areas.

Ghana_edu_sf %>%filter(Locality=="Rural") can be used to retain observations from Rural areas.

Use tmap_mode("view") to create interactive map. A static map is plotted here with tmap_mode("plot")

### Map displaying Percentage of School Drop-outs from Total Respondents in each Region


#Use tmap to create interactive map
tmap_mode("plot")
#> tmap mode set to plotting

Ghana_edu_sf %>%
dplyr::filter(Locality=="Urban") %>%
tm_shape()+
tm_polygons(id="Region", col="Region",palette="YlOrRd",
title="Percentage of School drop-outs")+
tm_text(text="Percent_Dropped_out_of_School", size=0.7)+
tm_facets(by="Gender")
#> Some legend labels were too wide. These labels have been resized to 0.66, 0.49, 0.66, 0.61, 0.58, 0.49. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

The code tm_facets(by="Gender") specifies facets (multiple maps) by Gender.

### Example 2

Create a interactive map with the package tmap and data from the package rGhanaCensus that displays the regional distribution of population densities of students 3 years or older who have dropped out of school.


data("Ghana_2021_school_attendance_geometry", package = "rGhanaCensus")

#Convert to sf data frame
Ghana_edu_sf<- sf::st_as_sf(Ghana_2021_school_attendance_geometry)

Use tmap_mode("view") to create interactive map. A static map is plotted here with tmap_mode("plot") .

The convert2density argument in the tm_polygon function calculates the population density using the raw count values of the variable Dropped_out_of_School where the area size is in this case approximated from the shape object.

### Map displaying the Regional Population Density of School Drop-outs


tmap_mode("plot")
#> tmap mode set to plotting

Ghana_edu_sf %>%
dplyr::filter(Locality=="Urban") %>%
tm_shape()+
tm_polygons(id="Region",col="Dropped_out_of_School", palette = "RdPu",
style="kmeans", convert2density = TRUE,
title="Population density of School drop-outs")+
tm_text(text="Region", size=0.7)+
tm_facets(by="Gender")
#> Some legend labels were too wide. These labels have been resized to 0.65, 0.52, 0.48. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.