The goal of DatGss is to make accessible to all the population and housing census data from Ghana Statistical Survey in 2021. This data has been aggregated for easier manipulation.
You can install the development version of DatGss from GitHub with:
# install.packages("devtools")
devtools::install_github("gkagyen/DatGss")This is a basic example which shows you how to solve a common problem:
library(DatGss)
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
glimpse(Gss_1)
#> Rows: 160
#> Columns: 4
#> $ Religious_Affiliation <chr> "Christian", "Christian", "Christian", "Christia…
#> $ Locality <chr> "Rural", "Rural", "Rural", "Rural", "Rural", "Ru…
#> $ Region <chr> "Western", "Central", "Greater Accra", "Volta", …
#> $ Population <dbl> 798100, 1013371, 393333, 734930, 1213861, 160783…
glimpse(Gss_2)
#> Rows: 320
#> Columns: 4
#> $ Locality <chr> "Rural", "Rural", "Rural", "Rural", "Rural", "Rural", "…
#> $ Household_size <chr> "1 Person", "1 Person", "1 Person", "1 Person", "1 Pers…
#> $ Region <chr> "Western", "Central", "Greater_Accra", "Volta", "Easter…
#> $ Population <dbl> 88359, 102370, 31124, 80499, 122827, 160642, 42839, 183…What is special about using README.Rmd instead of just README.md?
You can include R chunks like so:
summary(cars)
#> speed dist
#> Min. : 4.0 Min. : 2.00
#> 1st Qu.:12.0 1st Qu.: 26.00
#> Median :15.0 Median : 36.00
#> Mean :15.4 Mean : 42.98
#> 3rd Qu.:19.0 3rd Qu.: 56.00
#> Max. :25.0 Max. :120.00You’ll still need to render README.Rmd regularly, to keep README.md
up-to-date. devtools::build_readme() is handy for this.
You can also embed plots, for example:
In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.