3 min read

Data Recode

2021-06-01

Import and transform Relig-incom.csv

library(readxl)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.1     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
rel_inc <- read_excel("relig-income.xlsx")
rel_inc <- read_excel(file.choose())


rel_inc_long <- rel_inc %>%
  rename(religion = `Religious tradition`, n = `Sample Size`) %>%
  pivot_longer(cols = -c(religion, n),   # all but religion and n 
    names_to = "income",  
    values_to = "proportion") %>%
  mutate(frequency = round(proportion * n))

rel_inc_long
## # A tibble: 48 x 5
##    religion                   n income            proportion frequency
##    <chr>                  <dbl> <chr>                  <dbl>     <dbl>
##  1 Buddhist                 233 Less than $30,000       0.36        84
##  2 Buddhist                 233 $30,000-$49,999         0.18        42
##  3 Buddhist                 233 $50,000-$99,999         0.32        75
##  4 Buddhist                 233 $100,000 or more        0.13        30
##  5 Catholic                6137 Less than $30,000       0.36      2209
##  6 Catholic                6137 $30,000-$49,999         0.19      1166
##  7 Catholic                6137 $50,000-$99,999         0.26      1596
##  8 Catholic                6137 $100,000 or more        0.19      1166
##  9 Evangelical Protestant  7462 Less than $30,000       0.35      2612
## 10 Evangelical Protestant  7462 $30,000-$49,999         0.22      1642
## # ... with 38 more rows

Visualize using the Barplot

rel_inc_long <- rel_inc_long %>%
  mutate(religion = case_when(
    religion == "Evangelical Protestant"           ~ "Ev. Protestant",
    religion == "Historically Black Protestant"    ~ "Hist. Black Protestant",
    religion == 'Unaffiliated (religious "nones")' ~ "Unaffiliated",
    TRUE                                           ~ religion
  ))


rel_inc_long <- rel_inc_long %>%
  mutate(religion = fct_rev(religion))
ggplot(rel_inc_long, aes(y = religion, x = frequency)) +
  geom_col()

## Fill Barplot with Income

ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col()

ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d()

## Change theme of the plot

ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d() +
  theme_minimal()

ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d() +
  theme_minimal() +
  theme(legend.position = "bottom")

ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d() +
  theme_minimal() +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(nrow = 2, byrow = TRUE))

ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d() +
  theme_minimal() +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(nrow = 2, byrow = TRUE)) +
  labs(
    x = "Proportion", y = "", 
    title = "Income distribution by religious group", 
    subtitle = "Source: Pew Research Center, Religious Landscape Study", 
    fill = "Income" 
    )