diff --git a/docs/chapter_1.html b/docs/chapter_1.html
index 05b0e3f..4e0c6a2 100644
--- a/docs/chapter_1.html
+++ b/docs/chapter_1.html
@@ -271,13 +271,13 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
here() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion
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)
+
head(uk_census_2021_religion)
geography total no_religion christian buddhist hindu jewish
1 North East 2647012 1058122 1343948 7026 10924 4389
@@ -297,7 +297,7 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
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))
+
knitr::kable(head(uk_census_2021_religion))
@@ -413,7 +413,7 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
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))
+
knitr::kable(tail(uk_census_2021_religion))
@@ -541,7 +541,7 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
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:
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:
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:
+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():
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:
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:
For this chapter, I’m going to walk you through a data set that a colleague (Charles Ogunbode) and I collected in 2021. Another problem with smaller, more selective samples is that researchers can often undersample minoritised ethnic groups. This is particularly the case with climate change research. Until the time we conducted this research, there had not been a single study investigating the specific experiences of people of colour in relation to climate change in the UK. Past researchers had been content to work with large samples, and assumed that if they had done 1000 surveys and 50 of these were completed by people of colour, they could “tick” the box. But 5% is actually well below levels of representation in the UK generally, and even more sharply the case for specific communities. And if we bear in mind that non-white respondents are (of course!) a highly heterogenous group, we’re even more behind in terms of collecting data that can improve our knowledge. Up until recently researchers just haven’t been paying close enough attention to catch the significant neglect of the empirical field that this represents.
While I’ve framed my comments above in terms of climate change research, it is also the case that, especially in diverse societies like the USA, Canada, the UK etc., paying attention to non-majority groups and people and communities of colour automatically draws in a strongly religious sample. This is highlighted in one recent study done in the UK, the “Black British Voices Report” in which the researchers observed that “84% of respondents described themselves as religious and/or spiritual”. My comments above in terms of controlling for other factors remains important here - these same researchers also note that “despire their significant important to the lives of Black Britons, only 7% of survey respondents reported that their religion was more defining of their identity than their race”.
We’ve decided to open up access to our data and I’m highlighting it in this book because it’s a unique opportunitiy to explore a dataset that emphasises diversity from the start, and by extension, provides some really interesting ways to use data science techniques to explore religion in the UK.
+
+
4 Loading in some data
+
+
# R Setup -----------------------------------------------------------------
+setwd("/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion")
+library(here)
+
+
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
+
+
library(haven) # used for importing SPSS .sav files
+here::i_am("chapter_2.qmd")
+
+
here() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion
The first thing to note here is that we’ve drawn in a different type of data file, this time from an .sav file, usully produced by the statistics software package SPSS. This uses a different R Library (I use haven for this). The upside is that in some cases where you have survey data with both a code and a value like “1” is eqivalent to “very much agree” this will preserve both in the R dataframe that is created. Now that you’ve loaded in data, you have a new R dataframe called “climate_experience_data” with a lot of columns with just under 1000 survey responses.
+
+
+
5 How can you ask about religion?
+
One of the challenges we faced when running this study is how to gather responsible data from surveys regarding religious identity. We’ll dive into this in depth as we do analysis and look at some of the agreements and conflicts in terms of respondent attribution. Just to set the stage, we used the following kinds of question to ask about religion and spirituality:
+
+
Question 56 asks respondents simply, “What is your religion?” and then provides a range of possible answers. We included follow-up questions regarding denomination for respondents who indicated they were “Christian” or “Muslim”. For respondents who ticked “Christian” we asked, “What is your denomination?” nad for respondents who ticked “Muslim” we asked “Which of the following would you identify with?” and then left a range of possible options which could be ticked such as “Sunni,” “Shia,” “Sufi” etc.
+
+
This is one way of measuring religion, that is, to ask a person if they consider themselves formally affiliated with a particular group. This kind of question has some (serious) limitations, but we’ll get to that in a moment.
+
We also asked respondents (Q57): “Regardless of whether you belong to a particular religion, how religious would you say you are?” and then provided a slider from 0 (not religious at all) to 10 (very religious).
+
We included some classic indicators about how often respondents go to worship (Q58): Apart from weddings, funerals and other special occasions, how often do you attend religious services? and (Q59): “Q59 Apart from when you are at religious services, how often do you pray?”
+
+
More than once a week (1)
+
Once a week (2)
+
At least once a month (3)
+
Only on special holy days (4)
+
Never (5)
+
+
Each of these measures a particular kind of dimension, and it is interesting to note that sometimes there are stronger correlations between how often a person attends worship services (weekly versus once a year) and a particular view, than there is between their affiliation (if they are Christian or Pagan). We’ll do some exploratory work shortly to see how this is the case in our sample. We also included a series of questions about spirituality in Q52 and used a nature relatedness scale Q51.
+
You’ll find that many surveys will only use one of these forms of question and ignore the rest. I think this is a really bad idea as religious belonging, identity, and spirituality are far too complex to work off a single form of response. We can also test out how these different attributions relate to other demographic features, like interest in politics, economic attainment, etc.
-How can we measure religion?
+So who’s religious?
+
+
+
+
As I’ve already hinted in the previous chapter, measuring religiosity is complicated. I suspect some readers may be wondering something like, “what’s the right question to ask?” here. Do we get the most accurate representation by asking people to self-report their religious affiliation? Or is it more accurate to ask individuals to report on how religious they are? Is it, perhaps, better to assume that the indirect query about practice, e.g. how frequently one attends services at a place of worship may be the most reliable proxy?
+
Highlight challenges of various approaches pointing to literature.
+
+
+
Let’s dive into the data and see how this all works out. We’ll start with the question 56 data, around religious affiliation:
There are few things we need to do here to get the data into initial proper shape. This might be called “cleaning” the data:
+
+
Because we imported this data from an SPSS .sav file format using the R haven() library, we need to start by adapting the data into a format that our visualation engine ggplot can handle (a dataframe).
+
Next we’ll rename the columns so these names are a bit more useful.
+
We need to omit non-responses so these don’t mess with the counting (these are NA in R)
+
+
If we pause at this point to view the data, you’ll see it’s basically just a long list of survey responses. What we need is a count of each unique response (or factor). This will take a few more steps:
+First we generate new a dataframe with sums per category and
+
+
2
+
+…sort in descending order
+
+
3
+
+Then we add new column with percentages based on the sums you’ve just generated
+
+
+
+
+
That should give us a tidy table of results, which you can see if you view the contents of our new religious_affiliation_sums dataframe:
+
+
head(religious_affiliation_sums)
+
+
# A tibble: 6 x 3
+ response n perc
+ <fct> <int> <chr>
+1 Christian 342 "33.9%"
+2 Muslim 271 "26.9%"
+3 No religion 108 "10.7%"
+4 Hindu 72 " 7.1%"
+5 Atheist 54 " 5.4%"
+6 Spiritual but not religious 38 " 3.8%"
+
+
+
+
# make plot
+ggplot(religious_affiliation_sums, aes(x = n, y = response)) +
+geom_col(colour ="white") +
+## add percentage labels
+geom_text(aes(label = perc),
+## make labels left-aligned and white
+hjust =1, nudge_x =-.5, colour ="white", size=3)
+
+
+
+
+
Add colours Use mutate to put “prefer not to say” at the bottom # Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels
+
+
+
6 Q56 follow-ups
+
caption <- “Christian Denomination” # TODO: copy plot above for Q56 to add two additional plots using climate_experience_data_named\(Q56b and climate_experience_data_named\)Q56c # Religious Affiliation b - Christian Denomination Subquestion christian_denomination <- qualtrics_process_single_multiple_choice(climate_experience_data_named\(Q56b) christian_denomination_table <- chart_single_result_flextable(climate_experience_data_named\)Q56b, desc(Count)) christian_denomination_table save_as_docx(christian_denomination_table, path = “./figures/q56_religious_affiliation_xn_denomination.docx”)
7 Religious Affiliation c - Muslim Denomination Subquestion
+
caption <- “Islamic Identity” # Should the label be different than income since the data examined is the Affiliation? # TODO: adjust plot to factor using numbered responses on this question (perhaps also above) religious_affiliationc <- qualtrics_process_single_multiple_choice(climate_experience_data_named\(Q56c) religious_affiliationc_plot <- plot_horizontal_bar(religious_affiliationc) religious_affiliationc_plot <- religious_affiliationc_plot + labs(caption = caption, x = "", y = "") religious_affiliationc_plot ggsave("figures/q56c_religious_affiliation.png", width = 20, height = 10, units = "cm") religious_affiliationc_table <- chart_single_result_flextable(climate_experience_data_named\)Q56c, Count) religious_affiliationc_table save_as_docx(religious_affiliationc_table, path = “./figures/q56_religious_affiliation_islam.docx”)
level_order <- c(“Don<80><99>t know”, “Definitely not changing”, “Probably not changing”, “Probably changing”, “Definitely changing”) ## code if a specific palette is needed for matching fill = wheel(ochre, num = as.integer(count(q6_data[1]))) # make plot q6_data_plot <- ggplot(q6_data, aes(x = n, y = response, fill = fill)) + geom_col(colour = “white”) + ## add percentage labels geom_text(aes(label = perc), ## make labels left-aligned and white hjust = 1, colour = “black”, size=4) + # use nudge_x = 30, to shift position ## reduce spacing between labels and bars scale_fill_identity(guide = “none”) + ## get rid of all elements except y axis labels + adjust plot margin theme_ipsum_rc() + theme(plot.margin = margin(rep(15, 4))) + easy_center_title() + # with thanks for helpful info on doing wrap here: https://stackoverflow.com/questions/21878974/wrap-long-axis-labels-via-labeller-label-wrap-in-ggplot2 scale_y_discrete(labels = wrap_format(30), limits = level_order) + theme(plot.title = element_text(size =18, hjust = 0.5), axis.text.y = element_text(size =16)) + labs(title = title, x = ““, y =”“)
+
q6_data_plot
+
ggsave(“figures/q6.png”, width = 18, height = 12, units = “cm”)
+
+
+
16 Subsetting
+
+
16.1 Q57 subsetting based on Religiosity ————————————————————–
Guides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/
+
Extact places of worship from Ordnance survey open data set Calculate proximity to pubs
References
diff --git a/docs/search.json b/docs/search.json
index ebadfc3..00820a3 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -60,28 +60,42 @@
"href": "chapter_1.html#your-first-project-building-a-pie-chart",
"title": "2 The 2021 UK Census",
"section": "2.1 Your first project: building a pie chart",
- "text": "2.1 Your first project: building a pie chart\nLet’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.\nIf 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”.\nIn 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:\n\n# R Setup -----------------------------------------------------------------\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nlibrary(here) # much better way to manage working paths in R across multiple instances\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook\n\nlibrary(tidyverse)\n\n-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --\nv dplyr 1.1.3 v readr 2.1.4\nv forcats 1.0.0 v stringr 1.5.0\nv ggplot2 3.4.3 v tibble 3.2.1\nv lubridate 1.9.3 v tidyr 1.3.0\nv purrr 1.0.2 \n\n\n-- Conflicts ------------------------------------------ tidyverse_conflicts() --\nx dplyr::filter() masks stats::filter()\nx dplyr::lag() masks stats::lag()\ni Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n\nhere::i_am(\"chapter_1.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\nreligion_uk <- read.csv(here(\"example_data\", \"census2021-ts030-rgn.csv\")) \n\n\n2.1.1 Examining data:\nWhat’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:\n\nhead(religion_uk)\n\n geography total no_religion christian buddhist hindu jewish\n1 North East 2647012 1058122 1343948 7026 10924 4389\n2 North West 7417397 2419624 3895779 23028 49749 33285\n3 Yorkshire and The Humber 5480774 2161185 2461519 15803 29243 9355\n4 East Midlands 4880054 1950354 2214151 14521 120345 4313\n5 West Midlands 5950756 1955003 2770559 18804 88116 4394\n6 East 6335072 2544509 2955071 26814 86631 42012\n muslim sikh other no_response\n1 72102 7206 9950 133345\n2 563105 11862 28103 392862\n3 442533 24034 23618 313484\n4 210766 53950 24813 286841\n5 569963 172398 31805 339714\n6 234744 24284 36380 384627\n\n\nThis 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:\n\nknitr::kable(head(religion_uk))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ngeography\ntotal\nno_religion\nchristian\nbuddhist\nhindu\njewish\nmuslim\nsikh\nother\nno_response\n\n\n\n\nNorth East\n2647012\n1058122\n1343948\n7026\n10924\n4389\n72102\n7206\n9950\n133345\n\n\nNorth West\n7417397\n2419624\n3895779\n23028\n49749\n33285\n563105\n11862\n28103\n392862\n\n\nYorkshire and The Humber\n5480774\n2161185\n2461519\n15803\n29243\n9355\n442533\n24034\n23618\n313484\n\n\nEast Midlands\n4880054\n1950354\n2214151\n14521\n120345\n4313\n210766\n53950\n24813\n286841\n\n\nWest Midlands\n5950756\n1955003\n2770559\n18804\n88116\n4394\n569963\n172398\n31805\n339714\n\n\nEast\n6335072\n2544509\n2955071\n26814\n86631\n42012\n234744\n24284\n36380\n384627\n\n\n\n\n\nYou 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:\n\nknitr::kable(tail(religion_uk))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ngeography\ntotal\nno_religion\nchristian\nbuddhist\nhindu\njewish\nmuslim\nsikh\nother\nno_response\n\n\n\n\n5\nWest Midlands\n5950756\n1955003\n2770559\n18804\n88116\n4394\n569963\n172398\n31805\n339714\n\n\n6\nEast\n6335072\n2544509\n2955071\n26814\n86631\n42012\n234744\n24284\n36380\n384627\n\n\n7\nLondon\n8799728\n2380404\n3577681\n77425\n453034\n145466\n1318754\n144543\n86759\n615662\n\n\n8\nSouth East\n9278068\n3733094\n4313319\n54433\n154748\n18682\n309067\n74348\n54098\n566279\n\n\n9\nSouth West\n5701186\n2513369\n2635872\n24579\n27746\n7387\n80152\n7465\n36884\n367732\n\n\n10\nWales\n3107494\n1446398\n1354773\n10075\n12242\n2044\n66947\n4048\n15926\n195041\n\n\n\n\n\n\n\n2.1.2 Parsing and Exploring your data\nThe 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:\nYou can use the filter command to do this. To give an example, filter can pick a single row in the following way:\n\nwmids_data <- religion_uk %>% \n filter(geography==\"West Midlands\") \n\nNow we’ll use select in a different way to narrow our data to specific columns that are needed (no totals!).\nSome 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.\nIn 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."
+ "text": "2.1 Your first project: building a pie chart\nLet’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.\nIf 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”.\nIn 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:\n\n# R Setup -----------------------------------------------------------------\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nlibrary(here) # much better way to manage working paths in R across multiple instances\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook\n\nlibrary(tidyverse)\n\n-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --\nv dplyr 1.1.3 v readr 2.1.4\nv forcats 1.0.0 v stringr 1.5.0\nv ggplot2 3.4.3 v tibble 3.2.1\nv lubridate 1.9.3 v tidyr 1.3.0\nv purrr 1.0.2 \n\n\n-- Conflicts ------------------------------------------ tidyverse_conflicts() --\nx dplyr::filter() masks stats::filter()\nx dplyr::lag() masks stats::lag()\ni Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n\nhere::i_am(\"chapter_1.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\nuk_census_2021_religion <- read.csv(here(\"example_data\", \"census2021-ts030-rgn.csv\")) \n\n\n2.1.1 Examining data:\nWhat’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:\n\nhead(uk_census_2021_religion)\n\n geography total no_religion christian buddhist hindu jewish\n1 North East 2647012 1058122 1343948 7026 10924 4389\n2 North West 7417397 2419624 3895779 23028 49749 33285\n3 Yorkshire and The Humber 5480774 2161185 2461519 15803 29243 9355\n4 East Midlands 4880054 1950354 2214151 14521 120345 4313\n5 West Midlands 5950756 1955003 2770559 18804 88116 4394\n6 East 6335072 2544509 2955071 26814 86631 42012\n muslim sikh other no_response\n1 72102 7206 9950 133345\n2 563105 11862 28103 392862\n3 442533 24034 23618 313484\n4 210766 53950 24813 286841\n5 569963 172398 31805 339714\n6 234744 24284 36380 384627\n\n\nThis 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:\n\nknitr::kable(head(uk_census_2021_religion))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ngeography\ntotal\nno_religion\nchristian\nbuddhist\nhindu\njewish\nmuslim\nsikh\nother\nno_response\n\n\n\n\nNorth East\n2647012\n1058122\n1343948\n7026\n10924\n4389\n72102\n7206\n9950\n133345\n\n\nNorth West\n7417397\n2419624\n3895779\n23028\n49749\n33285\n563105\n11862\n28103\n392862\n\n\nYorkshire and The Humber\n5480774\n2161185\n2461519\n15803\n29243\n9355\n442533\n24034\n23618\n313484\n\n\nEast Midlands\n4880054\n1950354\n2214151\n14521\n120345\n4313\n210766\n53950\n24813\n286841\n\n\nWest Midlands\n5950756\n1955003\n2770559\n18804\n88116\n4394\n569963\n172398\n31805\n339714\n\n\nEast\n6335072\n2544509\n2955071\n26814\n86631\n42012\n234744\n24284\n36380\n384627\n\n\n\n\n\nYou 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:\n\nknitr::kable(tail(uk_census_2021_religion))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ngeography\ntotal\nno_religion\nchristian\nbuddhist\nhindu\njewish\nmuslim\nsikh\nother\nno_response\n\n\n\n\n5\nWest Midlands\n5950756\n1955003\n2770559\n18804\n88116\n4394\n569963\n172398\n31805\n339714\n\n\n6\nEast\n6335072\n2544509\n2955071\n26814\n86631\n42012\n234744\n24284\n36380\n384627\n\n\n7\nLondon\n8799728\n2380404\n3577681\n77425\n453034\n145466\n1318754\n144543\n86759\n615662\n\n\n8\nSouth East\n9278068\n3733094\n4313319\n54433\n154748\n18682\n309067\n74348\n54098\n566279\n\n\n9\nSouth West\n5701186\n2513369\n2635872\n24579\n27746\n7387\n80152\n7465\n36884\n367732\n\n\n10\nWales\n3107494\n1446398\n1354773\n10075\n12242\n2044\n66947\n4048\n15926\n195041\n\n\n\n\n\n\n\n2.1.2 Parsing and Exploring your data\nThe 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:\nYou can use the filter command to do this. To give an example, filter can pick a single row in the following way:\n\nuk_census_2021_religion_wmids <- uk_census_2021_religion %>% \n filter(geography==\"West Midlands\") \n\nNow we’ll use select in a different way to narrow our data to specific columns that are needed (no totals!).\nSome 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.\nIn 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."
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- "text": "2.2 Making your first chart\nWe’ve got a nice lean set of data, so now it’s time to visualise this. We’ll start by making a pie chart:\n\nwmids_data <- wmids_data %>% select(no_religion:no_response)\nwmids_data <- gather(wmids_data)\n\nThere 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:\n\n2.2.0.1 Base R\n\ndf <- wmids_data[order(wmids_data$value,decreasing = TRUE),]\nbarplot(height=df$value, names=df$key)\n\n\n\n\n\n\n2.2.0.2 GGPlot\n\n# unsorted\nggplot(wmids_data, aes(x = key, y = value)) +\n geom_bar(stat = \"identity\")\n\n\n\n# with sorting added in\nggplot(wmids_data, aes(x= reorder(key,-value),value)) + geom_bar(stat =\"identity\")\n\n\n\n\nClean up chart features\nAdd time series data for 2001 and 2011 census, change to grouped bar plot:\nhttps://r-graphics.org/recipe-bar-graph-grouped-bar#discussion-8\n\n\n\n\n\n\n\nWhat is Religion?\n\n\n\nContent tbd\n\n\n\n\n\n\n\n\nHybrid Religious Identity\n\n\n\nContent tbd\n\n\n\n\n\n\n\n\nWhat is Secularisation?\n\n\n\nContent tbd"
+ "text": "2.2 Making your first chart\nWe’ve got a nice lean set of data, so now it’s time to visualise this. We’ll start by making a pie chart:\n\nuk_census_2021_religion_wmids <- uk_census_2021_religion_wmids %>% select(no_religion:no_response)\nuk_census_2021_religion_wmids <- gather(uk_census_2021_religion_wmids)\n\nThere are two basic ways to do visualisations in R. You can work with basic functions in R, often called “base R” or you can work with an alternative library called ggplot:\n\n2.2.0.1 Base R\n\ndf <- uk_census_2021_religion_wmids[order(uk_census_2021_religion_wmids$value,decreasing = TRUE),]\nbarplot(height=df$value, names=df$key)\n\n\n\n\n\n\n2.2.0.2 GGPlot\n\nggplot(uk_census_2021_religion_wmids, aes(x = key, y = value)) +\n geom_bar(stat = \"identity\")\n\n\n2\n\nWe’ll re-order the column by size.\n\n\n\n\n\n\n2ggplot(uk_census_2021_religion_wmids, aes(x= reorder(key,-value),value)) + geom_bar(stat =\"identity\")\n\n\n\n\nLet’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:\n\n1uk_census_2021_religion_totals <- uk_census_2021_religion %>% select(no_religion:no_response)\nuk_census_2021_religion_totals <- uk_census_2021_religion_totals %>%\n2 summarise(across(everything(), ~ sum(., na.rm = TRUE)))\n3uk_census_2021_religion_totals <- gather(uk_census_2021_religion_totals)\n4ggplot(uk_census_2021_religion_totals, aes(x= reorder(key,-value),value)) + geom_bar(stat =\"identity\")\n\n\n1\n\nFirst, remove the column with region names and the totals for the regions as we want just integer data.\n\n2\n\nSecond 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)\n\n3\n\nIn 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()\n\n4\n\nNow plot it out and have a look!\n\n\n\n\n\n\n\nYou 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():\n\nuk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)\n\nDo 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:\n\nuk_census_2021_religion_totals$dataset <- c(\"totals\")\nuk_census_2021_religion_wmids$dataset <- c(\"wmids\")\nuk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)\n\nNow we’re ready to plot out our data as a grouped barplot:\n\nggplot(uk_census_2021_religion_merged, aes(fill=dataset, x= reorder(key,-value), value)) + geom_bar(position=\"dodge\", stat =\"identity\")\n\n\n\n\nIf 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.\nIf 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:\n\nuk_census_2021_religion_totals <- uk_census_2021_religion_totals %>% \n dplyr::mutate(perc = scales::percent(value / sum(value), accuracy = 0.1, trim = FALSE))\nuk_census_2021_religion_wmids <- uk_census_2021_religion_wmids %>% \n dplyr::mutate(perc = scales::percent(value / sum(value), accuracy = 0.1, trim = FALSE))\nuk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)\nggplot(uk_census_2021_religion_merged, aes(fill=dataset, x=key, y=perc)) + geom_bar(position=\"dodge\", stat =\"identity\")\n\n\n\n\nNow you can see a very rough comparison\nAdd time series data for 2001 and 2011 census, change to grouped bar plot:\nhttps://r-graphics.org/recipe-bar-graph-grouped-bar#discussion-8"
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"title": "3 Survey Data: Spotlight Project",
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- "text": "In the last chapter we explored some high level data about religion in the UK. This was a census sample, which usually refers to an attempt to get as comprehensive a sample as possible. But this is actually fairly unusual in practice. Depending on how complex a subject is, and how representative we want our data to be, it’s much more common to use selective sampling, that is survey responses at n=100 or n=1000 at a maximum. The advantage of a census sample is that you can explore how a wide range of other factors - particularly demographics - intersect with your question. And this can be really valuable in the study of religion, particularly as you will see as we go along that responses to some questions are more strongly correlated to things like economic status or educational attainment than they are to religious affiliation. It can be hard to tell if this is the case unless you have enough of a sample to break down into a number of different kinds of subsets. But census samples are complex and expensive to gather, so they’re quite rare in practice.\nFor this chapter, I’m going to walk you through a data set that a colleague (Charles Ogunbode) and I collected in 2021. Another problem with smaller, more selective samples is that researchers can often undersample minoritised ethnic groups. This is particularly the case with climate change research. Until the time we conducted this research, there had not been a single study investigating the specific experiences of people of colour in relation to climate change in the UK. Past researchers had been content to work with large samples, and assumed that if they had done 1000 surveys and 50 of these were completed by people of colour, they could “tick” the box. But 5% is actually well below levels of representation in the UK generally, and even more sharply the case for specific communities. And if we bear in mind that non-white respondents are (of course!) a highly heterogenous group, we’re even more behind in terms of collecting data that can improve our knowledge. Up until recently researchers just haven’t been paying close enough attention to catch the significant neglect of the empirical field that this represents.\nWhile I’ve framed my comments above in terms of climate change research, it is also the case that, especially in diverse societies like the USA, Canada, the UK etc., paying attention to non-majority groups and people and communities of colour automatically draws in a strongly religious sample. This is highlighted in one recent study done in the UK, the “Black British Voices Report” in which the researchers observed that “84% of respondents described themselves as religious and/or spiritual”. My comments above in terms of controlling for other factors remains important here - these same researchers also note that “despire their significant important to the lives of Black Britons, only 7% of survey respondents reported that their religion was more defining of their identity than their race”.\nWe’ve decided to open up access to our data and I’m highlighting it in this book because it’s a unique opportunitiy to explore a dataset that emphasises diversity from the start, and by extension, provides some really interesting ways to use data science techniques to explore religion in the UK.\n\n\n\n\n\n\nHow can we measure religion?\n\n\n\nContent tbd\n\n\n\nReferences"
+ "text": "4 Loading in some data\n# R Setup -----------------------------------------------------------------\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nlibrary(here)\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook\n\nlibrary(tidyverse)\n\n-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --\nv dplyr 1.1.3 v readr 2.1.4\nv forcats 1.0.0 v stringr 1.5.0\nv ggplot2 3.4.3 v tibble 3.2.1\nv lubridate 1.9.3 v tidyr 1.3.0\nv purrr 1.0.2 \n\n\n-- Conflicts ------------------------------------------ tidyverse_conflicts() --\nx dplyr::filter() masks stats::filter()\nx dplyr::lag() masks stats::lag()\ni Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n\nlibrary(haven) # used for importing SPSS .sav files\nhere::i_am(\"chapter_2.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\nclimate_experience_data <- read_sav(here(\"example_data\", \"climate_experience_data.sav\"))\nThe first thing to note here is that we’ve drawn in a different type of data file, this time from an .sav file, usully produced by the statistics software package SPSS. This uses a different R Library (I use haven for this). The upside is that in some cases where you have survey data with both a code and a value like “1” is eqivalent to “very much agree” this will preserve both in the R dataframe that is created. Now that you’ve loaded in data, you have a new R dataframe called “climate_experience_data” with a lot of columns with just under 1000 survey responses.\nOne of the challenges we faced when running this study is how to gather responsible data from surveys regarding religious identity. We’ll dive into this in depth as we do analysis and look at some of the agreements and conflicts in terms of respondent attribution. Just to set the stage, we used the following kinds of question to ask about religion and spirituality:\nThis is one way of measuring religion, that is, to ask a person if they consider themselves formally affiliated with a particular group. This kind of question has some (serious) limitations, but we’ll get to that in a moment.\nWe also asked respondents (Q57): “Regardless of whether you belong to a particular religion, how religious would you say you are?” and then provided a slider from 0 (not religious at all) to 10 (very religious).\nWe included some classic indicators about how often respondents go to worship (Q58): Apart from weddings, funerals and other special occasions, how often do you attend religious services? and (Q59): “Q59 Apart from when you are at religious services, how often do you pray?”\nEach of these measures a particular kind of dimension, and it is interesting to note that sometimes there are stronger correlations between how often a person attends worship services (weekly versus once a year) and a particular view, than there is between their affiliation (if they are Christian or Pagan). We’ll do some exploratory work shortly to see how this is the case in our sample. We also included a series of questions about spirituality in Q52 and used a nature relatedness scale Q51.\nYou’ll find that many surveys will only use one of these forms of question and ignore the rest. I think this is a really bad idea as religious belonging, identity, and spirituality are far too complex to work off a single form of response. We can also test out how these different attributions relate to other demographic features, like interest in politics, economic attainment, etc.\nLet’s dive into the data and see how this all works out. We’ll start with the question 56 data, around religious affiliation:\nreligious_affiliation <- as_tibble(as_factor(climate_experience_data$Q56))\nnames(religious_affiliation) <- c(\"response\")\nreligious_affiliation <- filter(religious_affiliation, !is.na(response))\nThere are few things we need to do here to get the data into initial proper shape. This might be called “cleaning” the data:\nIf we pause at this point to view the data, you’ll see it’s basically just a long list of survey responses. What we need is a count of each unique response (or factor). This will take a few more steps:\nreligious_affiliation_sums <- religious_affiliation %>% \n1 dplyr::count(response, sort = TRUE) %>%\n2 dplyr::mutate(response = forcats::fct_rev(forcats::fct_inorder(response)))\nreligious_affiliation_sums <- religious_affiliation_sums %>% \n3 dplyr::mutate(perc = scales::percent(n / sum(n), accuracy = .1, trim = FALSE))\n\n\n1\n\nFirst we generate new a dataframe with sums per category and\n\n2\n\n…sort in descending order\n\n3\n\nThen we add new column with percentages based on the sums you’ve just generated\nThat should give us a tidy table of results, which you can see if you view the contents of our new religious_affiliation_sums dataframe:\nhead(religious_affiliation_sums)\n\n# A tibble: 6 x 3\n response n perc \n <fct> <int> <chr> \n1 Christian 342 \"33.9%\"\n2 Muslim 271 \"26.9%\"\n3 No religion 108 \"10.7%\"\n4 Hindu 72 \" 7.1%\"\n5 Atheist 54 \" 5.4%\"\n6 Spiritual but not religious 38 \" 3.8%\"\n# make plot\nggplot(religious_affiliation_sums, aes(x = n, y = response)) +\n geom_col(colour = \"white\") + \n ## add percentage labels\n geom_text(aes(label = perc),\n ## make labels left-aligned and white\n hjust = 1, nudge_x = -.5, colour = \"white\", size=3)\nAdd colours Use mutate to put “prefer not to say” at the bottom # Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels\ncaption <- “Christian Denomination” # TODO: copy plot above for Q56 to add two additional plots using climate_experience_data_named\\(Q56b and climate_experience_data_named\\)Q56c # Religious Affiliation b - Christian Denomination Subquestion christian_denomination <- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q56b) christian_denomination_table <- chart_single_result_flextable(climate_experience_data_named\\)Q56b, desc(Count)) christian_denomination_table save_as_docx(christian_denomination_table, path = “./figures/q56_religious_affiliation_xn_denomination.docx”)\nchristian_denomination_hi <- filter(climate_experience_data_named, Q56 == “Christian”, Q57_bin == “high”) christian_denomination_hi <- qualtrics_process_single_multiple_choice(christian_denomination_hi$Q56b) christian_denomination_hi\ncaption <- “Islamic Identity” # Should the label be different than income since the data examined is the Affiliation? # TODO: adjust plot to factor using numbered responses on this question (perhaps also above) religious_affiliationc <- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q56c) religious_affiliationc_plot <- plot_horizontal_bar(religious_affiliationc) religious_affiliationc_plot <- religious_affiliationc_plot + labs(caption = caption, x = \"\", y = \"\") religious_affiliationc_plot ggsave(\"figures/q56c_religious_affiliation.png\", width = 20, height = 10, units = \"cm\") religious_affiliationc_table <- chart_single_result_flextable(climate_experience_data_named\\)Q56c, Count) religious_affiliationc_table save_as_docx(religious_affiliationc_table, path = “./figures/q56_religious_affiliation_islam.docx”)\ncaption <- “Respondent Religiosity” religiosity <- qualtrics_process_single_multiple_choice(as.character(climate_experience_data_named\\(Q57_1)) religiosity_plot <- plot_horizontal_bar(religiosity) religiosity_plot <- religiosity_plot + labs(caption = caption, x = \"\", y = \"\") religiosity_plot ggsave(\"figures/q57_religiosity_plot.png\", width = 20, height = 10, units = \"cm\") religiosity_table <- chart_single_result_flextable(climate_experience_data_named\\)Q57_1, desc(Variable)) religiosity_table save_as_docx(religious_affiliationc_table, path = “./figures/q57_religiousity.docx”)\ncaption <- “Respondent Attendance of Religious Services” religious_service_attend <- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q58) religious_service_attend_plot <- plot_horizontal_bar(religious_service_attend) religious_service_attend_plot <- religious_service_attend_plot + labs(title = caption, x = \"\", y = \"\") religious_service_attend_plot ggsave(\"figures/q58_religious_service_attend.png\", width = 20, height = 10, units = \"cm\") religious_service_attend_table <- chart_single_result_flextable(climate_experience_data_named\\)Q58, Count) religious_service_attend_table save_as_docx(religious_service_attend_table, path = “./figures/q58_religious_service_attend.docx”)\ndf <- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q58) names(df) <- c(“Q52_bin”, “Q53_bin”, “Q57_bin”, “response”) facet_names <- c(Q52_bin = “Spirituality”, Q53_bin = “Politics L/R”, Q57_bin = “Religiosity”, low=“low”, medium=“medium”, high=“high”) facet_labeller <- function(variable,value){return(facet_names[value])} df\\(response <- factor(df\\)response, ordered = TRUE, levels = c(“1”, “2”, “3”, “4”, “5”)) df\\(response <- fct_recode(df\\)response, “More than once a week” = “1”, “Once a week” = “2”, “At least once a month” = “3”, “Only on special holy days” = “4”, “Never” = “5”) df %>% # we need to get the data including facet info in long format, so we use pivot_longer() pivot_longer(!response, names_to = “bin_name”, values_to = “b”) %>% # add counts for plot below count(response, bin_name, b) %>% group_by(bin_name,b) %>% mutate(perc=paste0(round(n*100/sum(n),1),“%”)) %>% # run ggplot ggplot(aes(x = n, y = ““, fill = response)) + geom_col(position=position_fill(), aes(fill=response)) + geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) + scale_fill_brewer(palette =”Dark2”, type = “qual”) + scale_x_continuous(labels = scales::percent_format()) + facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + labs(caption = caption, x = ““, y =”“) + guides(fill = guide_legend(title = NULL)) ggsave(”figures/q58_faceted.png”, width = 30, height = 10, units = “cm”)\ncaption <- “Respondent Prayer Outside of Religious Services” prayer <- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q59) prayer_plot <- plot_horizontal_bar(prayer) prayer_plot <- prayer_plot + labs(caption = caption, x = \"\", y = \"\") prayer_plot ggsave(\"figures/q59_prayer.png\", width = 20, height = 10, units = \"cm\") prayer_table <- chart_single_result_flextable(climate_experience_data_named\\)Q59, Count) prayer_table save_as_docx(prayer_table, path = “./figures/q59_prayer.docx”)\ndf <- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q59) names(df) <- c(“Q52_bin”, “Q53_bin”, “Q57_bin”, “response”) facet_names <- c(Q52_bin = “Spirituality”, Q53_bin = “Politics L/R”, Q57_bin = “Religiosity”, low=“low”, medium=“medium”, high=“high”) facet_labeller <- function(variable,value){return(facet_names[value])} df\\(response <- factor(df\\)response, ordered = TRUE, levels = c(“1”, “2”, “3”, “4”, “5”)) df\\(response <- fct_recode(df\\)response, “More than once a week” = “1”, “Once a week” = “2”, “At least once a month” = “3”, “Only on special holy days” = “4”, “Never” = “5”) df %>% # we need to get the data including facet info in long format, so we use pivot_longer() pivot_longer(!response, names_to = “bin_name”, values_to = “b”) %>% # add counts for plot below count(response, bin_name, b) %>% group_by(bin_name,b) %>% mutate(perc=paste0(round(n*100/sum(n),1),“%”)) %>% # run ggplot ggplot(aes(x = n, y = ““, fill = response)) + geom_col(position=position_fill(), aes(fill=response)) + geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) + scale_fill_brewer(palette =”Dark2”, type = “qual”) + scale_x_continuous(labels = scales::percent_format()) + facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + labs(caption = caption, x = ““, y =”“) + guides(fill = guide_legend(title = NULL)) ggsave(”figures/q59_faceted.png”, width = 30, height = 10, units = “cm”)\nq6_data <- qualtrics_process_single_multiple_choice_unsorted_streamlined(climate_experience_data$Q6)\ntitle <- “Do you think the climate is changing?”\nlevel_order <- c(“Don<80><99>t know”, “Definitely not changing”, “Probably not changing”, “Probably changing”, “Definitely changing”) ## code if a specific palette is needed for matching fill = wheel(ochre, num = as.integer(count(q6_data[1]))) # make plot q6_data_plot <- ggplot(q6_data, aes(x = n, y = response, fill = fill)) + geom_col(colour = “white”) + ## add percentage labels geom_text(aes(label = perc), ## make labels left-aligned and white hjust = 1, colour = “black”, size=4) + # use nudge_x = 30, to shift position ## reduce spacing between labels and bars scale_fill_identity(guide = “none”) + ## get rid of all elements except y axis labels + adjust plot margin theme_ipsum_rc() + theme(plot.margin = margin(rep(15, 4))) + easy_center_title() + # with thanks for helpful info on doing wrap here: https://stackoverflow.com/questions/21878974/wrap-long-axis-labels-via-labeller-label-wrap-in-ggplot2 scale_y_discrete(labels = wrap_format(30), limits = level_order) + theme(plot.title = element_text(size =18, hjust = 0.5), axis.text.y = element_text(size =16)) + labs(title = title, x = ““, y =”“)\nq6_data_plot\nggsave(“figures/q6.png”, width = 18, height = 12, units = “cm”)\nclimate_experience_data$Q51_score <- rowMeans(select(climate_experience_data, Q51_remote_vacation:Q51_heritage))\nclimate_experience_data <- climate_experience_data %>% mutate( Q51_bin = case_when( Q51_score > mean(Q51_score) + sd(Q51_score) ~ “high”, Q51_score < mean(Q51_score) - sd(Q51_score) ~ “low”, TRUE ~ “medium” ) %>% factor(levels = c(“low”, “medium”, “high”)) )\nclimate_experience_data$Q52_score <- rowMeans(select(climate_experience_data, Q52a_1:Q52f_1))\nclimate_experience_data <- climate_experience_data %>% mutate( Q52_bin = case_when( Q52_score > mean(Q52_score) + sd(Q52_score) ~ “high”, Q52_score < mean(Q52_score) - sd(Q52_score) ~ “low”, TRUE ~ “medium” ) %>% factor(levels = c(“low”, “medium”, “high”)) )"
+ },
+ {
+ "objectID": "chapter_2.html#q57-subsetting-based-on-religiosity",
+ "href": "chapter_2.html#q57-subsetting-based-on-religiosity",
+ "title": "3 Survey Data: Spotlight Project",
+ "section": "16.1 Q57 subsetting based on Religiosity ————————————————————–",
+ "text": "16.1 Q57 subsetting based on Religiosity ————————————————————–\nclimate_experience_data <- climate_experience_data %>% mutate( Q57_bin = case_when( Q57_1 > mean(Q57_1) + sd(Q57_1) ~ “high”, Q57_1 < mean(Q57_1) - sd(Q57_1) ~ “low”, TRUE ~ “medium” ) %>% factor(levels = c(“low”, “medium”, “high”)) )"
+ },
+ {
+ "objectID": "chapter_2.html#subsetting-based-on-spirituality",
+ "href": "chapter_2.html#subsetting-based-on-spirituality",
+ "title": "3 Survey Data: Spotlight Project",
+ "section": "16.2 Subsetting based on Spirituality ————————————————————–",
+ "text": "16.2 Subsetting based on Spirituality ————————————————————–\n\n16.2.1 Nature relatedness ————————————————————–"
},
{
"objectID": "chapter_3.html",
"href": "chapter_3.html",
"title": "4 Mapping churches: geospatial data science",
"section": "",
- "text": "Guides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/\n\nReferences"
+ "text": "Guides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/\nExtact places of worship from Ordnance survey open data set Calculate proximity to pubs\n\nReferences"
},
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"objectID": "chapter_4.html",
diff --git a/hacking_religion/_book/chapter_1.html b/hacking_religion/_book/chapter_1.html
index 6455542..4e0c6a2 100644
--- a/hacking_religion/_book/chapter_1.html
+++ b/hacking_religion/_book/chapter_1.html
@@ -271,13 +271,13 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
here() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion
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)
+
head(uk_census_2021_religion)
geography total no_religion christian buddhist hindu jewish
1 North East 2647012 1058122 1343948 7026 10924 4389
@@ -297,7 +297,7 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
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))
+
knitr::kable(head(uk_census_2021_religion))
@@ -413,7 +413,7 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
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))
+
knitr::kable(tail(uk_census_2021_religion))
@@ -541,7 +541,7 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
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:
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:
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:
+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():
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:
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:
diff --git a/hacking_religion/chapter_1.qmd b/hacking_religion/chapter_1.qmd
index cef2d13..d6d43b4 100644
--- a/hacking_religion/chapter_1.qmd
+++ b/hacking_religion/chapter_1.qmd
@@ -24,8 +24,6 @@ What's in the table? You can take a quick look at either the top of the data fra
```{r .column-page}
head(uk_census_2021_religion)
-summarise(uk_census_2021_religion)
-rowSums(uk_census_2021_religion)
```
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:
