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zi(C1h7a!f;){jqwW%rf84S?0UyM$c^x0sHM7;7Cv8TP9>fQ!l+;KXZZ>*`_U?uW#ucvW!p`P9l_iQiH_%ji8Vmx=G6-)U_Cc$qFS1Zb>whwQ^)ES z5!Ri@=$6|4WA4xj_VK#AJ4gb+Gx_OjzlrgGBVo4x>De1ueZjj7om@PeOwHiFSfbo) PY#d*?@&GY;ak&2jv0ei( diff --git a/docs/chapter_1.html b/docs/chapter_1.html index 6530475..c393883 100644 --- a/docs/chapter_1.html +++ b/docs/chapter_1.html @@ -572,7 +572,7 @@ div.csl-indent {

2
-We’ll re-order the column by size. +We’ll re-order the column by size.
@@ -595,19 +595,19 @@ div.csl-indent {
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!
diff --git a/docs/chapter_2.html b/docs/chapter_2.html index e10c7af..6e351b4 100644 --- a/docs/chapter_2.html +++ b/docs/chapter_2.html @@ -336,15 +336,15 @@ 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
@@ -413,25 +413,36 @@ So Who’s Religious?
1
-Note: 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. +Note: 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.

Now, let’s plot that data:

-
caption <- "Respondent Religiosity"
+
caption <- "Respondent Religiosity"
 ggplot(religiosity_sums, aes(x = response, y = n, color=response)) +
-  geom_col(colour = "white", aes(fill = response)) +
+1  geom_col(colour = "white", aes(fill = response)) +
   ## get rid of all elements except y axis labels + adjust plot margin
-  coord_flip() +
+2  coord_flip() +
   theme(plot.margin = margin(rep(15, 4))) +
-  labs(caption = caption)
+ labs(caption = caption)
+
+
+
1
+
+We’ve added colors, because colours are fun. +
+
2
+
+Also new here is coord_flip to rotate the chart so we have bars going horizontally +
+
+

-

We’ve added a few elements here: 1. Colors, because colours are fun. 2. coord_flip to rotate the chart so we have bars going horizontally

Since 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.

If 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”.

R 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:

@@ -479,8 +490,10 @@ So Who’s Religious? # Spirituality scale -# stat_summary(climate_experience_data$Q52_score) -mean(climate_experience_data$Q52_score) +# JK note to self: need to fix stat_summary plot here + +# stat_summary(climate_experience_data$Q52_score) +mean(climate_experience_data$Q52_score)
[1] 6.047454
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Colors, because colours are fun. -2. `coord_flip` to rotate the chart so we have bars going horizontally +1. We've added colors, because colours are fun. +2. Also new here is `coord_flip` to rotate the chart so we have bars going horizontally Since 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. @@ -213,15 +211,33 @@ climate_experience_data$Q52_score <- rowMeans(select(climate_experience_data, Q5 Like we did in chapter 1, let's start by exploring the data and get a bit of a sense of the character of the responses overall. One good place to start is to find out the mean response for our two continum questions: +```{r} +# Spirituality first: +mean(climate_experience_data$Q52_score) + +# Then religiosity +mean(climate_experience_data$Q57_1) +``` + + + ```{r} # t_testing and means # Spirituality scale +library(rstatix) +religiosity_stats <- as.tibble(climate_experience_data$Q57_1) +spirituality_stats <- as.tibble(climate_experience_data$Q52_score) + +plot(religiosity_stats ~ spirituality_stats, data=CO2) + +stats %>% get_summary_stats(value, type="mean_sd") + # JK note to self: need to fix stat_summary plot here - # stat_summary(climate_experience_data$Q52_score) -mean(climate_experience_data$Q52_score) + + # Q57 Regardless of whether you belong to a particular religion, how religious would you say you are? # 0-10, Not religious at all => Very religious; mean=5.58