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:

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

uk_census_2021_religion <- 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(uk_census_2021_religion)

                 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(uk_census_2021_religion))

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(uk_census_2021_religion))

	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:

uk_census_2021_religion_wmids <- uk_census_2021_religion %>% 
  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:

uk_census_2021_religion_wmids <- uk_census_2021_religion_wmids %>% select(no_religion:no_response)
uk_census_2021_religion_wmids <- gather(uk_census_2021_religion_wmids)

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 <- uk_census_2021_religion_wmids[order(uk_census_2021_religion_wmids$value,decreasing = TRUE),]
barplot(height=df$value, names=df$key)

2.2.0.2 GGPlot

ggplot(uk_census_2021_religion_wmids, aes(x = key, y = value)) +
  geom_bar(stat = "identity")

2

We’ll re-order the column by size.

2ggplot(uk_census_2021_religion_wmids, aes(x= reorder(key,-value),value)) + geom_bar(stat ="identity")

Let’s assume we’re working with a data set that doesn’t include a “totals” column and that we might want to get sums for each column. This is pretty easy to do in R:

1uk_census_2021_religion_totals <- uk_census_2021_religion %>% select(no_religion:no_response)
uk_census_2021_religion_totals <- uk_census_2021_religion_totals %>%
2   summarise(across(everything(), ~ sum(., na.rm = TRUE)))
3uk_census_2021_religion_totals <- gather(uk_census_2021_religion_totals)
4ggplot(uk_census_2021_religion_totals, aes(x= reorder(key,-value),value)) + geom_bar(stat ="identity")

1

First, remove the column with region names and the totals for the regions as we want just integer data.

2

Second calculate the totals. In this example we use the tidyverse library dplyr(), but you can also do this using base R with colsums() like this: uk_census_2021_religion_totals <- colSums(uk_census_2021_religion_totals, na.rm = TRUE). The downside with base R is that you’ll also need to convert the result into a dataframe for ggplot like this: uk_census_2021_religion_totals <- as.data.frame(uk_census_2021_religion_totals)

3

In order to visualise this data using ggplot, we need to shift this data from wide to long format. This is a quick job using gather()

4

Now plot it out and have a look!

You might have noticed that these two dataframes give us somewhat different results. But with data science, it’s much more interesting to compare these two side-by-side in a visualisation. We can join these two dataframes and plot the bars side by side using bind() - which can be done by columns with cbind() and rows using rbind():

uk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)

Do you notice there’s going to be a problem here? How can we tell one set from the other? We need to add in something idenfiable first! This isn’t too hard to do as we can simply create a new column for each with identifiable information before we bind them:

uk_census_2021_religion_totals$dataset <- c("totals")
uk_census_2021_religion_wmids$dataset <- c("wmids")
uk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)

Now we’re ready to plot out our data as a grouped barplot:

ggplot(uk_census_2021_religion_merged, aes(fill=dataset, x= reorder(key,-value), value)) + geom_bar(position="dodge", stat ="identity")

If you’re looking closely, you will notice that I’ve added two elements to our previous ggplot. I’ve asked ggplot to fill in the columns with reference to the dataset column we’ve just created. Then I’ve also asked ggplot to alter the position="dodge" which places bars side by side rather than stacked on top of one another. You can give it a try without this instruction to see how this works. We will use stacked bars in a later chapter, so remember this feature.

If you inspect our chart, you can see that we’re getting closer, but it’s not really that helpful to compare the totals. What we need to do is get percentages that can be compared side by side. This is easy to do using another dplyr feature mutate:

uk_census_2021_religion_totals <- uk_census_2021_religion_totals %>% 
  dplyr::mutate(perc = scales::percent(value / sum(value), accuracy = 0.1, trim = FALSE))
uk_census_2021_religion_wmids <- uk_census_2021_religion_wmids %>% 
  dplyr::mutate(perc = scales::percent(value / sum(value), accuracy = 0.1, trim = FALSE))
uk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)
ggplot(uk_census_2021_religion_merged, aes(fill=dataset, x=key, y=perc)) + geom_bar(position="dodge", stat ="identity")

Now you can see a very rough comparison

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

References