diff --git a/docs/Hacking-Religion--TRS---Data-Science-in-Action.pdf b/docs/Hacking-Religion--TRS---Data-Science-in-Action.pdf index 446532c..3f1604d 100644 Binary files a/docs/Hacking-Religion--TRS---Data-Science-in-Action.pdf and b/docs/Hacking-Religion--TRS---Data-Science-in-Action.pdf differ diff --git a/docs/chapter_1.html b/docs/chapter_1.html index dbb00ff..c93eee9 100644 --- a/docs/chapter_1.html +++ b/docs/chapter_1.html @@ -84,7 +84,7 @@ div.csl-indent { - + @@ -155,12 +155,6 @@ div.csl-indent {
  • - Preface -
    -
    • -
  • -
    - Introduction: Hacking Religion
    • @@ -227,7 +221,6 @@ div.csl-indent {
  • 1.5 Is your chart accurate? Telling the truth in data science
  • 1.6 Making our script reproducible
  • 1.7 Multifactor Visualisation
    • -
  • References
    • @@ -579,7 +572,7 @@ div.csl-indent {
    2
    -We’ll re-order the column by size. +We’ll re-order the column by size.
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    1
    -First, remove the column with region names and the totals for the regions as we want just integer data. +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) +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() +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! +Now plot it out and have a look!
    @@ -876,9 +869,6 @@ What is Nomis? ggsave("chart.png", plot=plot1, width = 8, height = 10, units=c("in")) - -
    -

    References

    @@ -1122,7 +1112,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) { @@ -273,8 +240,8 @@ div.csl-indent {

    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.

    -
    -

    3 Loading in some data

    +
    +

    2.1 Loading in some data

    # R Setup -----------------------------------------------------------------
 setwd("/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion")
@@ -290,8 +257,8 @@ div.csl-indent {

    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.

    -
    -

    4 How can you ask about religion?

    +
    +

    2.2 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:

    1. 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.
      2. @@ -345,15 +312,15 @@ So who’s religious?
      1
      -First we generate new a dataframe with sums per category and +First we generate new a dataframe with sums per category and
      2
      -…sort in descending order +…sort in descending order
      3
      -Then we add new column with percentages based on the sums you’ve just generated +Then we add new column with percentages based on the sums you’ve just generated
      @@ -403,82 +370,220 @@ So who’s religious? ggsave("chart.png", plot=plot1, width = 8, height = 10, units=c("in"))       -

      Use mutate to put “prefer not to say” at the bottom # Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels

      -
    -
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    5 Q56 follow-ups

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    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”)

    -

    christian_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

    -
    -
    -

    6 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”)

    -
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    7 Q57

    -
    -
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    8 Religiosity

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    caption <- “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”)

    -
    -
    -

    9 Q58

    -

    caption <- “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”)

    -
    -
    -

    10 Faceted plot working with 3x3 grid

    -

    df <- 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”)

    -
    -
    -

    11 Q59

    -

    caption <- “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”)

    -
    -
    -

    12 Faceted plot working with 3x3 grid

    -

    df <- 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”)

    -
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    13 Comparing with attitudes surrounding climate change

    -
    -
    -

    14 Q6

    -

    q6_data <- qualtrics_process_single_multiple_choice_unsorted_streamlined(climate_experience_data$Q6)

    -

    title <- “Do you think the climate is changing?”

    -

    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”)

    -
    -
    -

    15 Subsetting

    -
    -

    15.1 Q57 subsetting based on Religiosity ————————————————————–

    -

    climate_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”)) )

    -
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    15.2 Subsetting based on Spirituality ————————————————————–

    -
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    15.2.1 Nature relatedness ————————————————————–

    -
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    16 Calculate overall mean nature-relatedness score based on six questions:

    -

    climate_experience_data$Q51_score <- rowMeans(select(climate_experience_data, Q51_remote_vacation:Q51_heritage))

    -
    -
    -

    17 Create low/med/high bins based on Mean and +1/-1 Standard Deviation

    -

    climate_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”)) )

    -
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    17.0.1 Spirituality scale ————————————————————–

    -
    -
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    18 Calculate overall mean spirituality score based on six questions:

    -

    climate_experience_data$Q52_score <- rowMeans(select(climate_experience_data, Q52a_1:Q52f_1))

    -
    -
    -

    19 Create low/med/high bins based on Mean and +1/-1 Standard Deviation

    -

    climate_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”)) )

    + +
    @@ -519,8 +624,8 @@ What is Secularisation?
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    -

    References

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    +

    References

    diff --git a/docs/chapter_3.html b/docs/chapter_3.html index 888493d..156d3ba 100644 --- a/docs/chapter_3.html +++ b/docs/chapter_3.html @@ -155,12 +155,6 @@ div.csl-indent {
  • - Preface -
    -
    • -
  • -
    - Introduction: Hacking Religion
    • @@ -216,8 +210,8 @@ div.csl-indent {

    Table of contents

    @@ -249,8 +243,8 @@ div.csl-indent {

    The most complex aspect of point data relates to the ways that coordinates are encoded, as they will aways need to be associated with a coordinate reference system (CRS) which describes how they are situated with respect to the planet earth. The most common CRS is the WGS, though for our data sets we’ll also come into contact with the BGS, a specifically British coordinate reference system. There are dozens of CRS, usually mapping onto a specific geographical region. Bearing in mind the way that you need to use a CRS to understand how coordinates can be associated with specific parts of the earth, you can see how this is a bit like survey data, where you also need a “codebook” to understand what the specific response values map onto, e.g. a “1” means “strongly agree” and so on. We also saw, in a previous chapter, how some forms of data have the codebook already baked into the factor data, simplifying the process of interpreting the data. In a similar way, some types of geospatial data sets can also come with CRS “baked in” while we’ll need to define CRS for other types. Here are some of the most common types of geospatial data files:

    CSV: “comma separated values” a plain text file containing various coordinates Shapefile: a legacy file format, often still in use, but being replaced by others for a variety of good reasons. For more on this see [http://switchfromshapefile.org/] Geopackage: one of the more recent ways of packaging up geospatial data. Geopackages can contain a wide variety of different data and are easily portable. GeoJSON: a file format commonly used in other forms of coding, the “JSON” (an acronym for JavaScript Object Notation) is meant to be easily interchangeable across various platforms. GeoJSON is an augmented version of JSON data with coordinates added in.

    Now that you have a sense of some of the basic aspects of geospatial data, let’s dive in and do a bit of learning in action.

    -
    -

    4 Administrative shapes - the UK

    +
    +

    3.1 Administrative shapes - the UK

    A 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.

    library(sf) |> suppressPackageStartupMessages()
@@ -310,8 +304,8 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
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    5 Load in Ordnance Survey OpenMap Points Data

    +
    +

    3.2 Load in Ordnance Survey OpenMap Points Data

    # Note: for more advanced reproducible scripts which demonstrate how these data surces have been 
 # obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R
@@ -395,8 +389,8 @@ i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all
 
    Guides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/
    
 
    Calculate proximity to pubs
    -
    -

    References

    +
    +

    References

    diff --git a/docs/chapter_4.html b/docs/chapter_4.html index 1edca7f..10e0ee6 100644 --- a/docs/chapter_4.html +++ b/docs/chapter_4.html @@ -121,12 +121,6 @@ div.csl-indent {
  • - Preface -
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  • -
    - Introduction: Hacking Religion
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  • - Preface -
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    - Introduction: Hacking Religion
    • diff --git a/docs/index.html b/docs/index.html index 8dbfba3..606fa94 100644 --- a/docs/index.html +++ b/docs/index.html @@ -32,7 +32,7 @@ ul.task-list li input[type="checkbox"] { - + @@ -75,7 +75,7 @@ ul.task-list li input[type="checkbox"] { - + - + - - - - - - - -
    - - -
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    Introduction: Hacking Religion

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    - - - -
    - - - - -
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    Why this book?

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    Data 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.

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    The hacker way

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    It’s worth emphasising at the outset that this isn’t meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a “hacker”. Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation.

    -

    Back in the 1980s Steven Levy tried to capture some of this in his book “Hackers: Heroes of the Computer Revolution”. As Levy put it, the “hacker ethic” included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn’t just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code.

    -

    This emphasis on ethics is especially important when we’re doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I’ve met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it’s possible that this can quickly get out of hand. It’s important to have a clear sense of what sorts of convictions guide your work in this field, a “hacker code” of sorts. With this in mind, I’d like to share with you my own set of principles:

    -

    It never ceases to amaze me how often people think that, when they’re working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you’ve been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation.

    -

    Presenting through “facts” an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it’s vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I’ll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it’s also a lot more fun and interesting to work alongside others.

    -

    Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There’s also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of “learning by doing” and it’s this approach that guides my approach in this book. This isn’t just a book about data analysis, I’m proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you’ll gain access to a set of tools and a research philosophy which is much more powerful.

    -

    Here’s a quick summary of these principles, which I’ll return to periodically as we work through the coding and data in this book:

    -
      -
    1. Tell the truth: Be candid about your limits, use visualisation responsibly
      2. -
    2. Work transparently: Open data, open code
      3. -
    3. Work in community, draw others in by producing reproducible research
      4. -
    4. Work with reality, learn by doing
      5. -
    -
    -
    -

    Learning to code: my way

    -

    This 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.

    -

    There 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.

    -
    -
    -

    Getting set up

    -

    Every 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.

    -

    There 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.

    -

    Bearing 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.

    -

    Once 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!

    - - -
    - -
    - - -
    - - - - \ No newline at end of file diff --git a/hacking_religion/_quarto.yml b/hacking_religion/_quarto.yml index f47ad38..226aba4 100644 --- a/hacking_religion/_quarto.yml +++ b/hacking_religion/_quarto.yml @@ -7,7 +7,6 @@ book: date: "10/16/2023" chapters: - index.qmd - - intro.qmd - chapter_1.qmd - chapter_2.qmd - chapter_3.qmd diff --git a/hacking_religion/chapter_2.qmd b/hacking_religion/chapter_2.qmd index 063db21..7d89c29 100644 --- a/hacking_religion/chapter_2.qmd +++ b/hacking_religion/chapter_2.qmd @@ -8,7 +8,7 @@ While I've framed my comments above in terms of climate change research, it is a 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. -# Loading in some data +## Loading in some data ```{r, results = 'hide'} # R Setup ----------------------------------------------------------------- @@ -23,7 +23,7 @@ climate_experience_data <- read_sav(here("example_data", "climate_experience_dat 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. -# How can you ask about religion? +## 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: @@ -120,6 +120,7 @@ ggsave("chart.png", plot=plot1, width = 8, height = 10, units=c("in")) ``` + ::: {.callout-tip} -## What is Religion? +### What is Religion? Content tbd ::: @@ -341,7 +343,7 @@ Content tbd ::: {.callout-tip} -## Hybrid Religious Identity +### Hybrid Religious Identity Content tbd ::: @@ -350,11 +352,11 @@ Content tbd ::: {.callout-tip} -## What is Secularisation? +### What is Secularisation? Content tbd ::: -# References {.unnumbered} +## References {.unnumbered} ::: {#refs} ::: diff --git a/hacking_religion/chapter_3.qmd b/hacking_religion/chapter_3.qmd index ddb0e5d..ec72fd9 100644 --- a/hacking_religion/chapter_3.qmd +++ b/hacking_religion/chapter_3.qmd @@ -1,4 +1,4 @@ -# Mapping churches: geospatial data science +## Mapping churches: geospatial data science Until recently, most data science books didn't have a section on geospatial data. It was considered a specialist form of research best left to GIS technicians who tended to use proprietary tools like ArcGIS. This has changed significantly in the past five years, but you'll still be hard pressed to find an introduction to the subject which strays very far from a few simple data sets (mostly of the USA) and relatively uncomplicated geospatial operations. I actually first began learning R, back in 2013, right when open source geospatial research tools were beginning to be developed with quite a lot more energy and geospatial data is my personal favourite data science playground, so in this book we're going to go much deeper than is usual. There are also good reasons to take things a few steps further in the particular forms of data and inquiry that religion takes us into. @@ -17,7 +17,7 @@ GeoJSON: a file format commonly used in other forms of coding, the "JSON" (an ac Now that you have a sense of some of the basic aspects of geospatial data, let's dive in and do a bit of learning in action. -# Administrative shapes - the UK +## Administrative shapes - the UK A 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. @@ -63,7 +63,7 @@ Before we move on, let's plot a simple map and have a look at one of our adminis ggplot(uk_countries) + geom_sf() ``` -# Load in Ordnance Survey OpenMap Points Data +## Load in Ordnance Survey OpenMap Points Data ```{r} # Note: for more advanced reproducible scripts which demonstrate how these data surces have been @@ -142,7 +142,7 @@ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistic Calculate proximity to pubs -# References {.unnumbered} +## References {.unnumbered} ::: {#refs} ::: \ No newline at end of file diff --git a/hacking_religion/index.qmd b/hacking_religion/index.qmd index 2062215..7d124a8 100644 --- a/hacking_religion/index.qmd +++ b/hacking_religion/index.qmd @@ -1,5 +1,45 @@ -# Preface {.unnumbered} +# Introduction: Hacking Religion {.unnumbered} -This is a Quarto book. +## Why this book? -To learn more about Quarto books visit . +Data 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. + + +## The hacker way + +It's worth emphasising at the outset that this isn't meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a "hacker". Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation. + +Back in the 1980s Steven Levy tried to capture some of this in his book "Hackers: Heroes of the Computer Revolution". As Levy put it, the "hacker ethic" included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn't just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code. + +This emphasis on ethics is especially important when we're doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I've met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it's possible that this can quickly get out of hand. It's important to have a clear sense of what sorts of convictions guide your work in this field, a "hacker code" of sorts. With this in mind, I'd like to share with you my own set of principles: + +It never ceases to amaze me how often people think that, when they're working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you've been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation. + +Presenting through "facts" an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it's vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I'll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it's also a lot more fun and interesting to work alongside others. + +Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There's also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of "learning by doing" and it's this approach that guides my approach in this book. This isn't just a book about data analysis, I'm proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you'll gain access to a set of tools and a research philosophy which is much more powerful. + +Here's a quick summary of these principles, which I'll return to periodically as we work through the coding and data in this book: + +1. Tell the truth: Be candid about your limits, use visualisation responsibly +2. Work transparently: Open data, open code +3. Work in community, draw others in by producing reproducible research +4. Work with reality, learn by doing + + +## Learning to code: my way + +This 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. + +There 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. + + +## Getting set up + +Every 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. + +There 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. + +Bearing 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. + +Once 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! diff --git a/hacking_religion/intro.qmd b/hacking_religion/intro.qmd deleted file mode 100644 index 7d124a8..0000000 --- a/hacking_religion/intro.qmd +++ /dev/null @@ -1,45 +0,0 @@ -# Introduction: Hacking Religion {.unnumbered} - -## Why this book? - -Data 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. - - -## The hacker way - -It's worth emphasising at the outset that this isn't meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a "hacker". Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation. - -Back in the 1980s Steven Levy tried to capture some of this in his book "Hackers: Heroes of the Computer Revolution". As Levy put it, the "hacker ethic" included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn't just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code. - -This emphasis on ethics is especially important when we're doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I've met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it's possible that this can quickly get out of hand. It's important to have a clear sense of what sorts of convictions guide your work in this field, a "hacker code" of sorts. With this in mind, I'd like to share with you my own set of principles: - -It never ceases to amaze me how often people think that, when they're working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you've been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation. - -Presenting through "facts" an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it's vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I'll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it's also a lot more fun and interesting to work alongside others. - -Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There's also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of "learning by doing" and it's this approach that guides my approach in this book. This isn't just a book about data analysis, I'm proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you'll gain access to a set of tools and a research philosophy which is much more powerful. - -Here's a quick summary of these principles, which I'll return to periodically as we work through the coding and data in this book: - -1. Tell the truth: Be candid about your limits, use visualisation responsibly -2. Work transparently: Open data, open code -3. Work in community, draw others in by producing reproducible research -4. Work with reality, learn by doing - - -## Learning to code: my way - -This 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. - -There 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. - - -## Getting set up - -Every 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. - -There 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. - -Bearing 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. - -Once 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!