Using for loops

As always, I need to make a disclaimer that I know I should be using some type of functional like lapply or purrr::map, but again since I’m newer to programming, I find it best to make a for loop first to better understand what’s happening.

This example uses a sample of the General Social Survey found in the forcats package.

library(tidyverse)

gss <- forcats::gss_cat %>% as_tibble()

gss
## # A tibble: 21,483 x 9
##     year marital     age race  rincome    partyid     relig     denom    tvhours
##    <int> <fct>     <int> <fct> <fct>      <fct>       <fct>     <fct>      <int>
##  1  2000 Never ma~    26 White $8000 to ~ Ind,near r~ Protesta~ Souther~      12
##  2  2000 Divorced     48 White $8000 to ~ Not str re~ Protesta~ Baptist~      NA
##  3  2000 Widowed      67 White Not appli~ Independent Protesta~ No deno~       2
##  4  2000 Never ma~    39 White Not appli~ Ind,near r~ Orthodox~ Not app~       4
##  5  2000 Divorced     25 White Not appli~ Not str de~ None      Not app~       1
##  6  2000 Married      25 White $20000 - ~ Strong dem~ Protesta~ Souther~      NA
##  7  2000 Never ma~    36 White $25000 or~ Not str re~ Christian Not app~       3
##  8  2000 Divorced     44 White $7000 to ~ Ind,near d~ Protesta~ Luthera~      NA
##  9  2000 Married      44 White $25000 or~ Not str de~ Protesta~ Other          0
## 10  2000 Married      47 White $25000 or~ Strong rep~ Protesta~ Souther~       3
## # ... with 21,473 more rows

What I want to do is quick count of the responses for each column of the survey. First, I just try to do it for the first column:

gss %>% 
  group_by(year) %>% 
  summarize(n = n())
## # A tibble: 8 x 2
##    year     n
##   <int> <int>
## 1  2000  2817
## 2  2002  2765
## 3  2004  2812
## 4  2006  4510
## 5  2008  2023
## 6  2010  2044
## 7  2012  1974
## 8  2014  2538

Easy enough. I could have wrote one less line of code with count(), but the reason I am not has to do with how the for loops work. I found that count() inside a for loop was nearly impossible, for reasons I have yet to understand.

Now let’s write the for loop. As always we want three things: output, sequence, and body.

gss_list <- vector("list", ncol(gss)) # 1. output
for (i in 1:ncol(gss)) {              # 2. sequence
  gss_list[[i]] <- gss %>%            # 3. body
    group_by(gss[[i]]) %>% 
    summarize(n = n())
}
#printing the 8th item in the list for an example.
gss_list[[7]]
## # A tibble: 15 x 2
##    `gss[[i]]`                  n
##    <fct>                   <int>
##  1 No answer                  93
##  2 Don't know                 15
##  3 Inter-nondenominational   109
##  4 Native american            23
##  5 Christian                 689
##  6 Orthodox-christian         95
##  7 Moslem/islam              104
##  8 Other eastern              32
##  9 Hinduism                   71
## 10 Buddhism                  147
## 11 Other                     224
## 12 None                     3523
## 13 Jewish                    388
## 14 Catholic                 5124
## 15 Protestant              10846

Awesome! That wasn’t so bad. And what’s nice is, if I print the whole list, I get a nice quick summary of counts for every column in the dataframe.

Here’s the problem: I want the column name in the dataframe, not gss[[i]], which isn’t meaningful. If I had 40 columns for instance, how could I keep track of what’s what?

Below, I add another line inside the for loop that replaces gss[[i]] in each dataframe in gss_list to the original column names.

gss_list <- vector("list", ncol(gss)) # 1. output
for (i in 1:ncol(gss)) {              # 2. sequence
  gss_list[[i]] <- gss %>%            # 3. body
    group_by(gss[[i]]) %>% 
    summarize(n = n()) %>% 
    ungroup()
  colnames(gss_list[[i]])[1] <- names(gss)[i] #here I rename the column to its orig name.
}
#printing the 8th item in the list for an example.
gss_list[[7]]
## # A tibble: 15 x 2
##    relig                       n
##    <fct>                   <int>
##  1 No answer                  93
##  2 Don't know                 15
##  3 Inter-nondenominational   109
##  4 Native american            23
##  5 Christian                 689
##  6 Orthodox-christian         95
##  7 Moslem/islam              104
##  8 Other eastern              32
##  9 Hinduism                   71
## 10 Buddhism                  147
## 11 Other                     224
## 12 None                     3523
## 13 Jewish                    388
## 14 Catholic                 5124
## 15 Protestant              10846

It’s not the prettiest code, but it does the trick. I’d love a more elegant solution, but for now, it works.

A quick plot

I always end by doing a quick plot, because really what’s the point of summarizing data like this without visualizing it in some way?

First, though, I want to group by the first column year for all my counts, so I’ll tweak the for loop again.

gss_list <- vector("list", ncol(gss)) # 1. output
for (i in 1:ncol(gss)) {              # 2. sequence
  gss_list[[i]] <- gss %>%            # 3. body
    group_by(year, gss[[i]]) %>% #adding the year column
    summarize(n = n()) %>% 
    ungroup()
  colnames(gss_list[[i]])[2] <- names(gss)[i] #here I rename the column to its orig name. Note: it's the second column now!
}
#printing the 8th item in the list for an example.
gss_list[[7]]
## # A tibble: 118 x 3
##     year relig                       n
##    <int> <fct>                   <int>
##  1  2000 No answer                   3
##  2  2000 Don't know                  1
##  3  2000 Inter-nondenominational    17
##  4  2000 Native american             4
##  5  2000 Christian                  39
##  6  2000 Orthodox-christian         12
##  7  2000 Moslem/islam               12
##  8  2000 Other eastern               1
##  9  2000 Hinduism                    8
## 10  2000 Buddhism                   17
## # ... with 108 more rows

Okay, now on to plotting! This is also a chance to show off Julia Silge’s awesome reorder_within() function that allows you to easily reorder factors within each facet using facet_wrap().

library(scales)
library(tidytext) #this has reorder_within() along with a lot of great functions for working with text.

theme_set(theme_minimal(base_size = 10) +
  theme(plot.title = element_text(face = "bold"),
        axis.text = element_text(size = 8))
)

gss_list[[7]] %>% 
  mutate(relig = reorder_within(relig, n, year)) %>% #use this inside of mutate
  ggplot(aes(x = relig, y = n, fill = relig)) +
  geom_col() +
  coord_flip() +
  facet_wrap(~year, scales = "free_y") +
  scale_x_reordered() + #and this to scale it properly.
  scale_fill_viridis_d(direction = -1) +
  scale_y_log10(labels = comma_format()) +
  theme(legend.position = "none") +
  labs(
    title = "Although the top five have remained steady,\nthere's been lots of movement in minority religions.",
    subtitle = "Count of religious affiliation, 2000-2014.",
    x = element_blank(),
    y = element_blank(),
    caption = "A sample of categorical variables from the General Social survey."
  )

Another thing to note that David Robinson got me hooked on is scale_y_log10(); without it in this particular plot, it would be difficult to see how the smaller minority religions have changed across time.