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updated ch2

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Jeremy Kidwell 2023-10-13 07:04:52 +01:00
parent 3eeab10d02
commit 938e88dc4f
7 changed files with 53 additions and 24 deletions
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hacking_religion/chapter_2.qmd
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@ -131,7 +131,6 @@ plot1 <- ggplot(religious_affiliation_ethnicity_sums, aes(x = n, y = Religion))
geom_col(colour = "white") + facet_wrap(~Ethnicity, scales="free_x")
ggsave("chart.png", plot=plot1, width = 8, height = 10, units=c("in"))
```
## Working With a Continum: Religiosity and Spirituality
@ -161,14 +160,13 @@ caption <- "Respondent Religiosity"
ggplot(religiosity_sums, aes(x = response, y = n, color=response)) +
geom_col(colour = "white", aes(fill = response)) + # <1>
## get rid of all elements except y axis labels + adjust plot margin
coord_flip() + # <1>
coord_flip() + # <2>
theme(plot.margin = margin(rep(15, 4))) +
labs(caption = caption)
```
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
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