Introduction
A quick look at the newly published Urban/Rural classification data and a visualisation of the classification at Local Authority level.
I am using the population tables available at this link
Load packages
library(tidyverse)
library(magrittr)
library(readxl)
library(janitor)
library(ggthemr)
ggthemr("light", "plain")Load data
pop_table <- read_xlsx("2018-03-29-urban-rural-plot_data/00534380.xlsx",
sheet = "CA8FOLD", range = "A3:I36")Tidy up
Data is all good but I need to tidy up the variable names and factorise the local authority variable
pop_table <- clean_names(pop_table)
pop_table$council_area <- factor(pop_table$council_area)I also want to make the table “tidy” and in long format for easier plotting
#gather
pop_table %<>%
gather(class, percent, large_urban:very_remote_rural)
#factorise class
pop_table$class <- factor(pop_table$class,
levels = c("large_urban", "other_urban",
"accessible_small_towns",
"remote_small_towns",
"very_remote_small_towns",
"accessible_rural",
"remote_rural", "very_remote_rural"),
labels = c("Large urban", "Other urban",
"Small town accessible",
"Small town remote",
"Small town very remote",
"Rural accessible",
"Rural remote", "Rural very remote")
)Visualise
Ready to plot now. I am using the ggthemr package for themes. Only found it today and love how it creates such a professional finish so simply!!!
pop_table %>%
ggplot(aes(class, percent, fill = class)) +
geom_col() +
guides(fill = guide_legend(nrow = 1)) +
facet_wrap(~council_area, strip.position = "bottom") +
theme(
axis.text.x = element_blank(),
legend.position = "top"
) +
labs(
title = "Percentage of individuals living in urban/remote areas",
subtitle = "Eight-fold classification",
y = "%",
x = "",
fill = ""
) -> urbrur_plot
urbrur_plot
A very quick and simple summary but a nice plot in the end!
Session Info
devtools::session_info()## Session info -------------------------------------------------------------## setting value
## version R version 3.5.1 (2018-07-02)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United Kingdom.1252
## tz Europe/London
## date 2018-10-02## Packages -----------------------------------------------------------------## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.5.1)
## backports 1.1.2 2017-12-13 CRAN (R 3.5.0)
## base * 3.5.1 2018-07-02 local
## bindr 0.1.1 2018-03-13 CRAN (R 3.5.1)
## bindrcpp 0.2.2 2018-03-29 CRAN (R 3.5.1)
## blogdown 0.8 2018-07-15 CRAN (R 3.5.1)
## bookdown 0.7 2018-02-18 CRAN (R 3.5.1)
## broom 0.5.0 2018-07-17 CRAN (R 3.5.1)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.5.1)
## cli 1.0.0 2017-11-05 CRAN (R 3.5.1)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.5.1)
## compiler 3.5.1 2018-07-02 local
## crayon 1.3.4 2017-09-16 CRAN (R 3.5.1)
## datasets * 3.5.1 2018-07-02 local
## devtools 1.13.6 2018-06-27 CRAN (R 3.5.1)
## digest 0.6.17 2018-09-12 CRAN (R 3.5.1)
## dplyr * 0.7.6 2018-06-29 CRAN (R 3.5.1)
## evaluate 0.11 2018-07-17 CRAN (R 3.5.1)
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## ggplot2 * 3.0.0 2018-07-03 CRAN (R 3.5.1)
## ggthemr * 1.1.0 2018-10-02 Github (cttobin/ggthemr@0a31bb5)
## glue 1.3.0 2018-07-17 CRAN (R 3.5.1)
## graphics * 3.5.1 2018-07-02 local
## grDevices * 3.5.1 2018-07-02 local
## grid 3.5.1 2018-07-02 local
## gtable 0.2.0 2016-02-26 CRAN (R 3.5.1)
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