knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Load required packages
library(rGhanaCensus) library(sf) library(tmap) library(dplyr) library(magrittr)
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")
#Use tmap to create interactive map tmap_mode("plot") 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")
The code tm_facets(by="Gender")
specifies facets (multiple maps) by Gender.
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.
#Load geometry data 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.
tmap_mode("plot") 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")
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