2  The 2021 UK Census

2.1 Your first project: building a pie chart

Let’s start by importing some data into R. Because R is what is called an object-oriented programming language, we’ll always take our information and give it a home inside a named object. There are many different kinds of objects, which you can specify, but usually R will assign a type that seems to fit best.

If you’d like to explore this all in a bit more depth, you can find a very helpful summary in R for Data Science, chapter 8, “data import”.

In the example below, we’re going to read in data from a comma separated value file (“csv”) which has rows of information on separate lines in a text file with each column separated by a comma. This is one of the standard plain text file formats. R has a function you can use to import this efficiently called “read.csv”. Each line of code in R usually starts with the object, and then follows with instructions on what we’re going to put inside it, where that comes from, and how to format it:

# R Setup -----------------------------------------------------------------
setwd("/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion")
library(here) # much better way to manage working paths in R across multiple instances
here() starts at /Users/kidwellj/gits/hacking_religion_textbook
library(tidyverse)
-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
v dplyr     1.1.3     v readr     2.1.4
v forcats   1.0.0     v stringr   1.5.0
v ggplot2   3.4.3     v tibble    3.2.1
v lubridate 1.9.3     v tidyr     1.3.0
v purrr     1.0.2     
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
here::i_am("chapter_1.qmd")
here() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion
religion_uk <- read.csv(here("example_data", "census2021-ts030-rgn.csv"))

2.1.1 Examining data:

What’s in the table? You can take a quick look at either the top of the data frame, or the bottom using one of the following commands:

head(religion_uk)
                 geography   total no_religion christian buddhist  hindu jewish
1               North East 2647012     1058122   1343948     7026  10924   4389
2               North West 7417397     2419624   3895779    23028  49749  33285
3 Yorkshire and The Humber 5480774     2161185   2461519    15803  29243   9355
4            East Midlands 4880054     1950354   2214151    14521 120345   4313
5            West Midlands 5950756     1955003   2770559    18804  88116   4394
6                     East 6335072     2544509   2955071    26814  86631  42012
  muslim   sikh other no_response
1  72102   7206  9950      133345
2 563105  11862 28103      392862
3 442533  24034 23618      313484
4 210766  53950 24813      286841
5 569963 172398 31805      339714
6 234744  24284 36380      384627

This is actually a fairly ugly table, so I’ll use an R tool called kable to give you prettier tables in the future, like this:

knitr::kable(head(religion_uk))
geography total no_religion christian buddhist hindu jewish muslim sikh other no_response
North East 2647012 1058122 1343948 7026 10924 4389 72102 7206 9950 133345
North West 7417397 2419624 3895779 23028 49749 33285 563105 11862 28103 392862
Yorkshire and The Humber 5480774 2161185 2461519 15803 29243 9355 442533 24034 23618 313484
East Midlands 4880054 1950354 2214151 14521 120345 4313 210766 53950 24813 286841
West Midlands 5950756 1955003 2770559 18804 88116 4394 569963 172398 31805 339714
East 6335072 2544509 2955071 26814 86631 42012 234744 24284 36380 384627

You can see how I’ve nested the previous command inside the kable command. For reference, in some cases when you’re working with really complex scripts with many different libraries and functions, they may end up with functions that have the same name. You can specify the library where the function is meant to come from by preceding it with :: as we’ve done knitr:: above. The same kind of output can be gotten using tail:

knitr::kable(tail(religion_uk))
geography total no_religion christian buddhist hindu jewish muslim sikh other no_response
5 West Midlands 5950756 1955003 2770559 18804 88116 4394 569963 172398 31805 339714
6 East 6335072 2544509 2955071 26814 86631 42012 234744 24284 36380 384627
7 London 8799728 2380404 3577681 77425 453034 145466 1318754 144543 86759 615662
8 South East 9278068 3733094 4313319 54433 154748 18682 309067 74348 54098 566279
9 South West 5701186 2513369 2635872 24579 27746 7387 80152 7465 36884 367732
10 Wales 3107494 1446398 1354773 10075 12242 2044 66947 4048 15926 195041

2.1.2 Parsing and Exploring your data

The first thing you’re going to want to do is to take a smaller subset of a large data set, either by filtering out certain columns or rows. Now let’s say we want to just work with the data from the West Midlands, and we’d like to omit some of the columns. We can choose a specific range of columns using select, like this:

You can use the filter command to do this. To give an example, filter can pick a single row in the following way:

wmids_data <- religion_uk %>% 
  filter(geography=="West Midlands")

Now we’ll use select in a different way to narrow our data to specific columns that are needed (no totals!).

Some readers will want to pause here and check out Hadley Wickham’s “R For Data Science” book, in the section, “Data visualisation” to get a fuller explanation of how to explore your data.

In keeping with my goal to demonstrate data science through examples, we’re going to move on to producing some snappy looking charts for this data.

2.2 Making your first chart

We’ve got a nice lean set of data, so now it’s time to visualise this. We’ll start by making a pie chart:

wmids_data <- wmids_data %>% select(no_religion:no_response)
wmids_data <- gather(wmids_data)

There are two basic ways to do visualisations in R. You can work with basic functions in R, often called “base R” or you can work with an alternative library called ggplot:

2.2.0.1 Base R

df <- wmids_data[order(wmids_data$value,decreasing = TRUE),]
barplot(height=df$value, names=df$key)

2.2.0.2 GGPlot

# unsorted
ggplot(wmids_data, aes(x = key, y = value)) +
  geom_bar(stat = "identity")

# with sorting added in
ggplot(wmids_data, aes(x= reorder(key,-value),value)) + geom_bar(stat ="identity")

Clean up chart features

Add time series data for 2001 and 2011 census, change to grouped bar plot:

https://r-graphics.org/recipe-bar-graph-grouped-bar#discussion-8

What is Religion?

Content tbd

Hybrid Religious Identity

Content tbd

What is Secularisation?

Content tbd

References