"text":"Why this book?\nData science is quickly consolidating as a new field, with new tools and user communities emerging seemingly every week! At the same time the field of academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up the emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge."
"text":"Learning to code: my way\nThis guide is a little different from other textbooks targetting learning to code. I remember when I was first starting out, I went through a fair few guides, and they all tended to spend about 200 pages on various theoretical bits, how you form an integer, or data structures, subroutines, the logical structure of algorithms or whatever. It was usuallyweeks of reading before I got to actually do anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention, so it’s not just my personal preference, but also justified! It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I’ve made and the way I’ve structured things. Each chapter focusses on a problem which is particularly salient for the use of data science to conduct research into religion. That problem will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that data set and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a “deep dive” when you feel like it. Otherwise, I’ll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.\nThere are a range of terrific textbooks out there which cover all these elements in greater depth and more slowly. In particular, I’d recommend that many readers will want to check out Hadley Wickham’s “R For Data Science” book. I’ll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail."
"text":"Getting set up\nEvery single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the “hacker way”) they’ve made these tools freely available. This also means that you aren’t restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I’ll make a few opinionated recommendations here based on my own preferences and experience, but it’s really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter explaining other tools you’ve found that you want to share with others.\nThere are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won’t rehash the debates between various factions. For this book, we’ll be using the R language. This is, in part, because the R user community and libraries tend to scale a bit better for the work that I’m commending in this book. However, it’s entirely possible that one could use python for all these exercises, and perhaps in the future we’ll have volume two of this book outlining python approaches to the same operations.\nBearing this in mind, the first step you’ll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on the The Comprehensive R Archive Network (or “CRAN”): https://cran.rstudio.com. Once you’ve installed R, you’ve got some choices to make about the kind of programming environment you’d like to use. You can just use a plain text editor like textedit to write your code and then execute your programs using the R software you’ve just installed. However, most users, myself included, tend to use an integrated development environment (or “IDE”). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages, will have built-in reference tools so you can look up options for libraries you use in your code, they will allow you to visualise the results of your code execution, and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this “debugging”). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor Sublime Text for my coding.\nOnce you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we’ll dive right in and get started!"
"section":"1.1 Your first project: the UK Census",
"text":"1.1 Your first project: the UK Census\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\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nlibrary(here) |> suppressPackageStartupMessages()\nlibrary(tidyverse) |> suppressPackageStartupMessages()\nhere::i_am(\"chapter_1.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\n# Set up local workspace:\nif (dir.exists(\"data\") == FALSE) {\n dir.create(\"data\") \n}\nif (dir.exists(\"figures\") == FALSE) {\n dir.create(\"figures\") \n}\nif (dir.exists(\"derivedData\") == FALSE) {\n dir.create(\"derivedData\")\n}\n\nuk_census_2021_religion <- read.csv(here(\"example_data\", \"census2021-ts030-rgn.csv\"))"
"text":"1.2 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"
"text":"1.3 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 %>% 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."
"section":"1.4 Making your first data visulation: the humble bar chart",
"text":"1.4 Making your first data visulation: the humble bar 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\n1.4.1 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\n1.4.2 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\"whichplacesbarssidebysideratherthanstackedontopofoneanother.Youcangiveitatrywithoutthisinstructiontoseehowthisworks.Wewillusestackedbarsinalaterchapter,sorememberthisfeature.\nIfyouinspectourchart,youcanseethatwe’regettingcloser,butit’snotreallythat
"text":"1.6 Making our script reproducible\nLet’s take a moment to review our hacker code. I’ve just spent some time addressing how we can be truthful in our data science work. We haven’t done much yet to talk abour reproducibility."
"text":"1.7 Multifactor Visualisation\nOne element of R data analysis that can get really interesting is working with multiple variables. Above we’ve looked at the breakdown of religious affiliation across the whole of England and Wales (Scotland operates an independent census), and by placing this data alongside a specific region, we’ve already made a basic entry into working with multiple variables but this can get much more interesting. Adding an additional quantative variable (also known as bivariate data) into the mix, however can also generate a lot more information and we have to think about visualising it in different ways which can still communicate with visual clarity in spite of the additional visual noise which is inevitable with enhanced complexity. Let’s have a look at the way that religion in England and Wales breaks down by ethnicity.\n\n\n\n\n\n\nWhat is Nomis?\n\n\n\nFor the UK, census data is made available for programmatic research like this via an organisation called NOMIS. Luckily for us, there is an R library you can use to access nomis directly which greatly simplifies the process of pulling data down from the platform. It’s worth noting that if you’re not in the UK, there are similar options for other countries. Nearly every R textbook I’ve ever seen works with USA census data, so you’ll find plenty of documentation available on the tools you can use for US Census data. Similarly for the EU, Canada, Austrailia etc.\nIf you want to draw some data from the nomis platform yourself in R, have a look at the companion cookbook repository.\n\n\n\n# Get table of Census 2011 religion data from nomis\nz <- readRDS(file = (here(\"example_data\", \"z.rds\")))\n\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2011_religion <- filter(z, GEOGRAPHY_NAME==\"England and Wales\" & RURAL_URBAN_NAME==\"Total\" & C_RELPUK11_NAME != \"All categories: Religion\")\n# Drop unnecessary columns\nuk_census_2011_religion <- select(uk_census_2011_religion, C_RELPUK11_NAME, OBS_VALUE)\n# Plot results\nplot1 <- ggplot(uk_census_2011_religion, aes(x = C_RELPUK11_NAME, y = OBS_VALUE)) + geom_bar(stat = \"identity\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n# ggsave(filename = \"plot.png\", plot = plot1)\n\n# grab daata from nomis for 2001 census religion / ethnicity\nz0 <- readRDS(file = (here(\"example_data\", \"z0.rds\")))\n\n# select relevant columns\nuk_census_2001_religion_ethnicity <- select(z0, GEOGRAPHY_NAME, C_RELPUK11_NAME, C_ETHHUK11_NAME, OBS_VALUE)\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2001_religion_ethnicity <- filter(uk_census_2001_religion_ethnicity, GEOGRAPHY_NAME==\"England and Wales\" & C_RELPUK11_NAME != \"All categories: Religion\")\n# Simplify data to only include general totals and omit subcategories\nuk_census_2001_religion_ethnicity <- uk_census_2001_religion_ethnicity %>% filter(grepl('Total', C_ETHHUK11_NAME))\n\n# grab data from nomis for 2011 census religion / ethnicity table\nz1 <- readRDS(file = (here(\"example_data\", \"z1.rds\")))\n\n# select relevant columns\nuk_census_2011_religion_ethnicity <- select(z1, GEOGRAPHY_NAME, C_RELPUK11_NAME, C_ETHPUK11_NAME, OBS_VALUE)\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2011_religion_ethnicity <- filter(uk_census_2011_religion_ethnicity, GEOGRAPHY_NAME==\"England and Wales\" & C_RELPUK11_NAME != \"All categories: Religion\" & C_ETHPUK11_NAME != \"All categories: Ethnic group\")\n# Simplify data to only include general totals and omit subcategories\nuk_census_2011_religion_ethnicity <- uk_census_2011_religion_ethnicity %>% filter(grepl('Total', C_ETHPUK11_NAME))\n\n# grab data from nomis for 2021 census religion / ethnicity table\nz2 <- readRDS(file = (here(\"example_data\", \"z2.rds\")))\n\n#selectrelevantcolumns\nuk_census_2021_religion_ethnicity<-select(z2,GEOGRAPHY_NAME,C2021_RELIGION_
"text":"2.1 Loading in some data\n\n# R Setup -----------------------------------------------------------------\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nlibrary(here) |> suppressPackageStartupMessages()\nlibrary(tidyverse) |> suppressPackageStartupMessages()\n# used for importing SPSS .sav files\nlibrary(haven) |> suppressPackageStartupMessages()\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\"))\n\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."
"text":"2.2 How can you ask about religion?\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:\n\n2.2.1 “What is your religion?”\n\nQuestion 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.\n\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.\n\n\n2.2.2 “How religious would you say you are?”\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).\n\n\n2.2.3 Participation in Worship\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?”\n\nMore than once a week (1)\nOnce a week (2)\nAt least once a month (3)\nOnly on special holy days (4)\nNever (5)\n\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.\n\n\n2.2.4 Spirituality\nWe also included a series of questions about spirituality in Q52 and used a slightly overlapping 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.\n\n\n\n\n\n\nSo Who’s Religious?\n\n\n\nAs 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?\nHighlight challenges of various approaches pointing to literature."
"section":"2.3 Exploring data around religious affiliation:",
"text":"2.3 Exploring data around religious affiliation:\nLet’s dive into the data and see how this all works out. We’ll start with the question 56 data, around religious affiliation:\n\nreligious_affiliation <- as_tibble(as_factor(climate_experience_data$Q56))\nnames(religious_affiliation) <- c(\"response\")\nreligious_affiliation <- filter(religious_affiliation, !is.na(response))\n\nThere are few things we need to do here to get the data into initial proper shape. This might be called “cleaning” the data:\n\nBecause 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).\nNext we’ll rename the columns so these names are a bit more useful.\nWe need to omit non-responses so these don’t mess with the counting (these are NA in R)\n\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:\n\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\n\n\n\n\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:\n\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\n\n\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)\n\n\n\n\nI’ve added one feature to our chart that wasn’t in the bar charts in chapter 1, text labels with the actual value on each bar.\nYou may be thinking about the plots we’ve just finished in chapter 1 and wondering how they compare. Let’s use the same facet approach that we’ve just used to render this data in a subsetted way.\n\n# First we need to add in data on ethnic self-identification from our respondents:\ndf <- select(climate_experience_data, Q56, Q0)\nreligious_affiliation_ethnicity <- as_tibble(as_factor(df))\nnames(religious_affiliation_ethnicity) <- c(\"Religion\", \"Ethnicity\")\n\nreligious_affiliation_ethnicity_sums <- religious_affiliation_ethnicity %>% \n group_by(Ethnicity) %>%\n dplyr::count(Religion, sort = TRUE) %>%\n dplyr::mutate(Religion = forcats::fct_rev(forcats::fct_inorder(Religion)))\n\nplot1 <- ggplot(religious_affiliation_ethnicity_sums, aes(x = n, y = Religion)) +\n geom_col(colour = \"white\") + facet_wrap(~Ethnicity, scales=\"free_x\")\n\nggsave(\"chart.png\", plot=plot1, width = 8, height = 10, units=c(\"in\"))"
"text":"2.4 Working With a Continum: Religiosity and Spirituality\nSo far we’ve just worked with bar plots, but there are a lot of other possible visualisations and types of data which demand them.\nAs I’ve mentioned above, on this survey we also asked respondents to tell us on by rating themselves on a scale of 0-10 with 0 being “not religious at all” and 10 being “very religious” in response to the question, “Regardless of whether you belong to a particular religion, how religious would you say you are?”\nWe’ll recycle some code from our previous import to bring in the Q57 data:\n\nreligiosity <- as_tibble(as_factor(climate_experience_data$Q57_1))\nnames(religiosity) <- c(\"response\")\nreligiosity <- filter(religiosity, !is.na(response))\nreligiosity_sums <- religiosity %>% \n1 dplyr::count(response) %>%\n dplyr::mutate(response = forcats::fct_rev(forcats::fct_inorder(response)))\nreligiosity_sums <- religiosity_sums %>% \n dplyr::mutate(perc = scales::percent(n / sum(n), accuracy = .1, trim = FALSE))\n\n\n1\n\nNote: we have removed sort = TRUE in the above statement as it will enforce sorting the data by quantities rather than the factor order. It wouldn’t really make sense to plot this chart in the order of response.\n\n\n\n\nNow, let’s plot that data:\n\ncaption <- \"Respondent Religiosity\"\nggplot(religiosity_sums, aes(x = response, y = n, color=response)) +\n1 geom_col(colour = \"white\", aes(fill = response)) +\n ## get rid of all elements except y axis labels + adjust plot margin\n2 coord_flip() +\n theme(plot.margin = margin(rep(15, 4))) +\n labs(caption = caption)\n\n\n1\n\nWe’ve added colors, because colours are fun.\n\n2\n\nAlso new here is coord_flip to rotate the chart so we have bars going horizontally\n\n\n\n\n\n\n\nSince we’re thinking about how things look just now, let’s play with themes for a minute. ggplot is a really powerful tool for visualising information, but it also has some quite nice features for making things look pretty.\nIf you’d like to take a proper deep dive on all this theme stuff, R-Charts has a great set of examples showing you how a number of different theme packages look in practice, “R-Charts on Themes”.\nR has a number of built-in themes, but these are mostly driven by functional concerns, such as whether you might want to print your chart or have a less heavy look overall. So for example you might use theme_light() in the following way:\n\nggplot(religiosity_sums, aes(x = response, y = n, color=response)) +\n geom_col(colour = \"white\", aes(fill = response)) +\n ## get rid of all elements except y axis labels + adjust plot margin\n coord_flip() +\n theme(plot.margin = margin(rep(15, 4))) +\n labs(caption = caption) +\n theme_light()\n\n\n\n\nYou can also use additional packages like ggthemes() or hrbrthemes() so for example we might want to try the pander theme which has it’s own special (and very cheerful) colour palette.\n\nlibrary(ggthemes) |> suppressPackageStartupMessages()\nggplot(religiosity_sums, aes(x = response, y = n, color=response)) + geom_col(colour = \"white\", aes(fill = response)) + coord_flip() + theme(plot.margin = margin(rep(15, 4))) + labs(caption = caption) + \n theme_pander() +\n scale_fill_pander()\n\n\n\n\nOr, you might try the well-crafted typgraphy from hbrthemes in the theme_ipsum_pub theme:\n\nlibrary(hrbrthemes) |> suppressPackageStartupMessages()\nggplot(religiosity_sums, aes(x = response, y = n, color=response)) + geom_col(colour = \"white\",aes(fill=response))+coord_flip()+theme(plot.margin=margin(rep(15,4)))+labs(caption=caption)+\ntheme_ipsum_pub()+\nscale_fill_pander()\n\n\n\n\nWe’regoingtocomebacktothischart,butlet’ssetittoonesideforamomentandbuildupavisualisationofthespiritualityscaledata.Thespiritualityscalequestionscomefromresearchby___and___.Theseresearchersdevelopedaseriesofquestionswhichtheyaskedrespondentsinasurvey.Theadvantagehereisthatyou’regettingatt
"text":"3.1 Administrative shapes - the UK\nA good starting point is to aquire some adminstrative data. This is a way of referring to political boundaries, whether country borders or those of a local authority or some other administrative unit. For our purposes, we’re going to import several different types of administrative boundary which will be used at different points in our visualisations below. It’s worth noting that the data we use here was prepared to support the 2011 census, and make use of the shapefile format.\n\nlibrary(sf) |> suppressPackageStartupMessages()\nlibrary(here) |> suppressPackageStartupMessages()\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-- 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\n# better video device, more accurate and faster rendering, esp. on macos. Also should enable system fonts for display\nlibrary(ragg) |> suppressPackageStartupMessages()\n\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nhere::i_am(\"chapter_3.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\n# Download administrative boundaries for whole UK at country level\nif (file.exists(here(\"data\", \"infuse_uk_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_uk_2011_clipped.zip\", destfile = \"data/infuse_uk_2011_clipped.zip\")\nunzip(\"data/infuse_uk_2011_clipped.zip\", exdir = \"data\")\n}\nuk_countries <- st_read(here(\"data\", \"infuse_uk_2011_clipped.shp\"), quiet = TRUE)\n\n# Download administrative boundaries for whole UK at regions level\nif (file.exists(here(\"data\", \"infuse_rgn_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_rgn_2011_clipped.zip\", destfile = \"data/infuse_rgn_2011_clipped.zip\")\nunzip(\"data/infuse_rgn_2011_clipped.zip\", exdir = \"data\")\n}\nuk_rgn <- st_read(here(\"data\", \"infuse_rgn_2011_clipped.shp\"), quiet = TRUE)\n\n# Download administrative boundaries for whole UK at local authority level\nif (file.exists(here(\"data\", \"infuse_dist_lyr_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_dist_lyr_2011_clipped.zip\", destfile = \"data/infuse_dist_lyr_2011_clipped.zip\")\nunzip(\"data/infuse_dist_lyr_2011_clipped.zip\", exdir = \"data\")\n}\nlocal_authorities <- st_read(here(\"data\", \"infuse_dist_lyr_2011_clipped.shp\"), quiet = TRUE)\n\n# Download building outlines for whole UK\nif (file.exists(here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\")) == FALSE) {\n download.file(\"https://zenodo.org/record/6395804/files/infuse_dist_lyr_2011_simplified_100m_buildings_overlay_simplified.gpkg\", destfile = here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\"))}\nlocal_authorities_buildings_clip <- st_read(here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\"), quiet = TRUE)\n\nBefore we move on, let’s plot a simple map and have a look at one of our administrative layers. We can use ggplot with a new type of shape geom_sf() to plot the contents of a geospatial data file with polygons which is loaded as a simplefeature in R.\n\nggplot(uk_countries) + geom_sf()"
"title":"3 Mapping churches: geospatial data science",
"section":"3.2 Load in Ordnance Survey OpenMap Points Data",
"text":"3.2 Load in Ordnance Survey OpenMap Points Data\n\n# Note: for more advanced reproducible scripts which demonstrate how these data surces have been \n# obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R\n\nos_openmap_pow <- st_read(here(\"data\", \"os_openmap_pow.gpkg\"), quiet = TRUE)\n\nggplot(os_openmap_pow) + geom_sf()\n\n\n\n\nIt’s worth noting that the way that you load geospatial data in R has changed quite dramatically since 2020 with the introduction of the simplefeature class in R. Much of the documentation you will come across “out there” will make reference to a set of functions which are now deprecated.\nLet’s use that data we’ve just loaded to make our first map:\n\n# Generate choropleth map of respondent locations\n# using temporary palette here so that 0s are white\nlibrary(tmap) |> suppressPackageStartupMessages()\n# palette <- c(white, \"#a8ddb5\", \"#43a2ca\")\n\nmap1 <- tm_shape(local_authorities) + \n# tm_fill(col = \"surveys_count\", , palette = palette, title = \"Concentration of survey respondents\") +\n tm_borders(alpha=.5, lwd=0.1) +\n # for intermediate polygon geometries\n # tm_shape(local_authorities) +\n # tm_borders(lwd=0.6) +\n # for dots from original dataset\n # tm_dots(\"red\", size = .05, alpha = .4) +\n tm_scale_bar(position = c(\"right\", \"bottom\")) +\n tm_style(\"gray\") +\n tm_credits(\"Data: UK Data Service (OGL)\\n& Jeremy H. Kidwell,\\nGraphic is CC-by-SA 4.0\", \n size = 0.4, \n position = c(\"left\", \"bottom\"),\n just = c(\"left\", \"bottom\"),\n align = \"left\") +\n tm_layout(asp = NA,\n frame = FALSE, \n title = \"Figure 1a\", \n title.size = .7,\n legend.title.size = .7,\n inner.margins = c(0.1, 0.1, 0.05, 0.05)\n )\n\nmap1\n\n\n\n# save image\ntmap_save(map1, here(\"figures\", \"map.png\"), width=1920, height=1080, asp=0)\n\nMap saved to /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion/figures/map.png\n\n\nResolution: 1920 by 1080 pixels\n\n\nSize: 6.4 by 3.6 inches (300 dpi)\n\n\n\n# subsetting ordnance survey openmap data for measuring clusters and proximity\n\nos_openmap_important_buildings <- st_read(here(\"data\", \"os_openmap_important_buildings.gpkg\"), quiet = TRUE)\n\n# add pubs, check_cashing, pawnbrokers, SSSI\n## subsets\n\n\nCount the number of churches in Local Authorities\n\n\n# OSM data\n\n# Note: for more advanced reproducible scripts which demonstrate how these data surces have been \n# obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R\n\n\n# osm_uk_points <- st_read(system.file(here(\"data\", \"pow_osm.gpkg\", package = \"spData\")))\n# vector_filepath = system.file(\"data/osm-gb-2018Aug29_pow_osm.pbf\", package = \"sf\")\n# osm_uk_points = st_read(vector_filepath)\n\nGuides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/\nCalculate proximity to pubs"
},
{
"objectID":"chapter_3.html#references",
"href":"chapter_3.html#references",
"title":"3 Mapping churches: geospatial data science",
