3 Toplines and crosstabs

In many, maybe most, situations, your dataset will have so many rows that it would not be realistic to plot every single observations.

This means that you will want to:

Calculate some aggregate statisitics first. Think of this “new” data object as just a new spreadsheet.
Pass the smaller object (often a tibble or a data frame) into ggplot(). This the first layer.
Choose the parts (rows and columns) which are most relevant.
Experiment with…
… arrangements within aes()
… different geoms
… sub-groups of the data (faceting may or may not be informative)
… themes, labels, and so on.

In this chapter, we’ll look at some instructive way to summarize public opinion data:

Read in the responses from the first 10 waves of Nationscape surveys.

library(tidyverse)
library(haven)
library(labelled)
library(pollster)

a <- readRDS("data_nationscape2019/Nationscape_first10waves.rds")

3.1 Ocasio-Cortez toplines

First, what percentage of respondents expressed favorable views of AOC?

You can look at the distribution of responses directly:

a %>% count(cand_favorability_cortez)

# A tibble: 5 × 2
  cand_favorability_cortez      n
  <dbl+lbl>                 <int>
1  1 [Very favorable]        7569
2  2 [Somewhat favorable]    9424
3  3 [Somewhat unfavorable]  5988
4  4 [Very unfavorable]     13908
5 NA                        25519

And you can report what proportion of respondents falls into each of the 4 relevant cells:

a %>% count(cand_favorability_cortez) %>%
  mutate(prop = n / sum(n))

# A tibble: 5 × 3
  cand_favorability_cortez      n   prop
  <dbl+lbl>                 <int>  <dbl>
1  1 [Very favorable]        7569 0.121 
2  2 [Somewhat favorable]    9424 0.151 
3  3 [Somewhat unfavorable]  5988 0.0959
4  4 [Very unfavorable]     13908 0.223 
5 NA                        25519 0.409

Naturally, we’ll want to filter out missing observations (respondnets who were not asked the favorability quesiton):

a %>% count(cand_favorability_cortez) %>%
  filter(!is.na(cand_favorability_cortez)) %>%
  mutate(prop = n / sum(n),
         percent = prop*100)

# A tibble: 4 × 4
  cand_favorability_cortez     n  prop percent
  <dbl+lbl>                <int> <dbl>   <dbl>
1 1 [Very favorable]        7569 0.205    20.5
2 2 [Somewhat favorable]    9424 0.255    25.5
3 3 [Somewhat unfavorable]  5988 0.162    16.2
4 4 [Very unfavorable]     13908 0.377    37.7

The dataset does come with a coarsened version of the favorability variable, which is useful if we’ll want to report raw favorability rates (i.e. if we don’t care about expressed intensity of opinion).

table(a$aoc_Favorable)


    0     1 
19896 16993

And it’s always good to check a variable is coded in the way we expect. One way to do that would be to run the following:

a %>% count(cand_favorability_cortez, aoc_Favorable)

# A tibble: 5 × 3
  cand_favorability_cortez  aoc_Favorable     n
  <dbl+lbl>                         <dbl> <int>
1  1 [Very favorable]                   1  7569
2  2 [Somewhat favorable]               1  9424
3  3 [Somewhat unfavorable]             0  5988
4  4 [Very unfavorable]                 0 13908
5 NA                                   NA 25519

The preceding output suggests nothing went wrong.

So, we could use the binarized version of the cand_favorability_cortez variable (i.e. aoc_Favorable) to directly calculate an unweighted favorability rate

mean(a$aoc_Favorable)

[1] NA

But we need to drop missing variables!

mean(a$aoc_Favorable, na.rm=T)

[1] 0.4606522

# OR
a %>% filter(!is.na(aoc_Favorable)) %>% pull(aoc_Favorable) %>% mean()

[1] 0.4606522

But to get a more accurate picture of the what the population thinks, we’ll want to upweight some observations and downweight others. To do, we could run:

a %>% filter(!is.na(aoc_Favorable)) %>%
  summarise(weighted_favorability = weighted.mean(aoc_Favorable,w=weight))

# A tibble: 1 × 1
  weighted_favorability
                  <dbl>
1                 0.445

Or we can used the pollster::topline() function:

library(pollster)
topline(df=a,
        variable = aoc_Favorable,
        weight=weight)

# A tibble: 3 × 5
  Response  Frequency Percent `Valid Percent` `Cumulative Percent`
  <fct>         <dbl>   <dbl>           <dbl>                <dbl>
1 0            20364.    32.6            55.5                 55.5
2 1            16303.    26.1            44.5                100  
3 (Missing)    25741.    41.2            NA                   NA

We get the same answer here: 44.5%.

Going forward, we’ll only want to be reporting weighted calculations.

3.2 Favorability by vote choice (intent)

Let’s calculate the AOC favorability rates again, but this time for different “types” of respondents. In other words, we want to show “cross-tabs”. (We won’t actually be showing tables - we’ll be displaying data from cross-tabs).

Let’s consider the following types of voters:

Joe Biden supporters
Donald Trump supporters

First we need to calculate what we eventually want to display visually. Here pollster::crosstab will be useful:

pollster::crosstab(df = a, 
                   x = trump_biden, 
                   y = cand_favorability_cortez,
                   weight = weight)

# A tibble: 3 × 6
  trump_biden  `Very favorable` `Somewhat favorable` `Somewhat unfavorable`
  <fct>                   <dbl>                <dbl>                  <dbl>
1 Joe Biden               30.8                 40.5                    18.2
2 Donald Trump             3.91                 7.31                   13.4
3 Don't Know              13.9                 24.7                    26.0
# ℹ 2 more variables: `Very unfavorable` <dbl>, n <dbl>

This is very good, but longer data would be even better - just add the format = "long" option:

pollster::crosstab(df = a, 
                   x = trump_biden, 
                   y = cand_favorability_cortez,
                   weight = weight,
                   format = "long")

# A tibble: 12 × 4
   trump_biden  cand_favorability_cortez   pct      n
   <fct>        <fct>                    <dbl>  <dbl>
 1 Joe Biden    Very favorable           30.8  18881.
 2 Joe Biden    Somewhat favorable       40.5  18881.
 3 Joe Biden    Somewhat unfavorable     18.2  18881.
 4 Joe Biden    Very unfavorable         10.5  18881.
 5 Donald Trump Very favorable            3.91 14695.
 6 Donald Trump Somewhat favorable        7.31 14695.
 7 Donald Trump Somewhat unfavorable     13.4  14695.
 8 Donald Trump Very unfavorable         75.4  14695.
 9 Don't Know   Very favorable           13.9   2989.
10 Don't Know   Somewhat favorable       24.7   2989.
11 Don't Know   Somewhat unfavorable     26.0   2989.
12 Don't Know   Very unfavorable         35.3   2989.

Keeping only the rows we care about, i.e. the first 8 rows, we can use the data object to make the following stacked bar chart:

pollster::crosstab(df = a, 
                   x = trump_biden, 
                   y = cand_favorability_cortez,
                   weight = weight,
                   format = "long") %>%
  filter(trump_biden!="Don't Know") %>%
  ggplot(aes(x= fct_reorder(trump_biden,pct), 
             y = pct,fill=cand_favorability_cortez)) + 
  geom_col(width = .3) + 
      theme_classic() +
  scale_fill_brewer(palette = "RdGy") +
  labs(y="Percent", x = "2020 vote intent", title = "AOC favorability")

A different way to display the equivalent information would be the following:

pollster::crosstab(df = a, 
                   x = trump_biden, 
                   y = cand_favorability_cortez,
                   weight = weight,
                   format = "long") %>%
  filter(trump_biden!="Don't Know") %>%
  ggplot(aes(x= fct_reorder(trump_biden,pct), 
             y = pct,fill=cand_favorability_cortez)) + 
  geom_col(width = .3, position = position_dodge()) + 
      theme_classic() +
  scale_fill_brewer(palette = "RdGy") +
  labs(y="Percent", x = "2020 vote intent", title = "AOC favorability", fill="Evaluations\nof AOC")

Or we can plot the (weighted) distribution of the binarized variable (aoc_Favorable), then simply use filter(aoc_Favorable==1), and again apply our friend geom_col():

pollster::crosstab(df = a, 
                   x = trump_biden, 
                   y = aoc_Favorable,
                   weight = weight,
                   format = "long") %>%
  filter(aoc_Favorable==1, trump_biden!="Don't Know") %>%
  ggplot(aes(y= fct_reorder(trump_biden,pct), 
             x = pct)) + 
  geom_col(width = .3,fill=c("blue3","firebrick2")) + 
      theme_classic() +
  labs(x="Percent", y = "Supporters of...", title = "AOC favorability") +
  geom_label(aes(label=round(pct,1)))

3.3 Taking a step back: getting to know your dataset

3.3.1 List of variables

dim(a)

[1] 62408   889

names(a) %>% head(n=40)

 [1] "response_id"                     "start_date"                     
 [3] "right_track"                     "economy_better"                 
 [5] "interest"                        "registration"                   
 [7] "news_sources_facebook"           "news_sources_cnn"               
 [9] "news_sources_msnbc"              "news_sources_fox"               
[11] "news_sources_network"            "news_sources_localtv"           
[13] "news_sources_telemundo"          "news_sources_npr"               
[15] "news_sources_amtalk"             "news_sources_new_york_times"    
[17] "news_sources_local_newspaper"    "news_sources_other"             
[19] "news_sources_other_TEXT"         "pres_approval"                  
[21] "vote_intention"                  "vote_2016"                      
[23] "vote_2016_other_text"            "consider_trump"                 
[25] "not_trump"                       "primary_party"                  
[27] "group_favorability_whites"       "group_favorability_blacks"      
[29] "group_favorability_latinos"      "group_favorability_asians"      
[31] "group_favorability_christians"   "group_favorability_socialists"  
[33] "group_favorability_muslims"      "group_favorability_labor_unions"
[35] "group_favorability_the_police"   "group_favorability_undocumented"
[37] "group_favorability_lgbt"         "group_favorability_republicans" 
[39] "group_favorability_democrats"    "cand_favorability_trump"

3.3.2 How many respondents were recruited each week?

# Weekly number of respondents
a %>% group_by(week) %>% tally() %>% head()

# A tibble: 6 × 2
  week           n
  <date>     <int>
1 2019-07-14  3906
2 2019-07-21  5990
3 2019-07-28  6236
4 2019-08-04  6082
5 2019-08-11  5097
6 2019-08-18  7685

3.3.3 What is the date range?

a %>% summarise(first = min(date), last = max(date))

# A tibble: 1 × 2
  first      last      
  <date>     <date>    
1 2019-07-18 2019-09-25

3.3.4 Plotting some decriptive statistics

a %>% group_by(week) %>% tally() %>%
  ggplot(aes(x=week,y=n)) + geom_bar(stat="identity") +
  labs(x="",y="Respondents",title="Weekly number of Nationscape respondents")

We could choose other options for the x-axis, using scale_x_date():

a %>% group_by(week) %>% tally() %>%
  ggplot(aes(x=week,y=n)) + geom_bar(stat="identity") +
  labs(x="Week of the year",y="Respondents",title="Weekly number of Nationscape respondents") +
  scale_x_date(date_breaks = "2 weeks", date_labels = "%W")

Note that date_labels include various combinations of "%B %Y" or "%b %y", etc.:

a %>% group_by(week) %>% tally() %>%
  ggplot(aes(x=week,y=n)) + geom_bar(stat="identity") +
  labs(x="",y="Respondents",title="Weekly number of Nationscape respondents") +
  scale_x_date(date_breaks = "1 months", minor_breaks = "1 weeks",date_labels = "%B %Y")

3.3.5 Switching to a line graph

a %>% group_by(week) %>% tally() %>%
  ggplot(aes(x=week,y=n)) + 
  geom_line() + 
  geom_point() +
  labs(x="",y="Respondents",title="Weekly number of Nationscape respondents") +
  scale_x_date(date_breaks = "1 months", minor_breaks = "1 days",date_labels = "%b %y")

3.3.6 Modifying the y-axis range

a %>% group_by(week) %>% tally() %>%
  ggplot(aes(x=week,y=n)) + geom_line() + geom_point() +
  labs(x="",y="Respondents",title="Weekly number of Nationscape respondents") +
  scale_x_date(date_breaks = "1 months", minor_breaks = "1 days",date_labels = "%B %Y") +
  ylim(c(0,8000))

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long")

# A tibble: 6 × 4
  trump_biden  aoc_Favorable   pct      n
  <fct>        <fct>         <dbl>  <dbl>
1 Joe Biden    0              28.7 18881.
2 Joe Biden    1              71.3 18881.
3 Donald Trump 0              88.8 14695.
4 Donald Trump 1              11.2 14695.
5 Don't Know   0              61.3  2989.
6 Don't Know   1              38.7  2989.

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1)

# A tibble: 3 × 4
  trump_biden  aoc_Favorable   pct      n
  <fct>        <fct>         <dbl>  <dbl>
1 Joe Biden    1              71.3 18881.
2 Donald Trump 1              11.2 14695.
3 Don't Know   1              38.7  2989.

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) 
# ggplot(aes(x= ..., y = ...))

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(x= trump_biden, y = pct)) + geom_col()

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(x= trump_biden, y = pct)) + geom_col()

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(y= trump_biden, x = pct)) + geom_col()

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(y= trump_biden, x = pct)) + geom_col() + theme_classic()

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(y= fct_reorder(trump_biden,pct), 
             x = pct)) + geom_col() + theme_classic() + ggtitle("AOC favorability")

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(y= fct_reorder(trump_biden,pct), 
             x = pct)) + geom_col() + theme_classic() +
  labs(x="Percent", y = "", title = "AOC favorability")

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(y= fct_reorder(trump_biden,pct), 
             x = pct)) + geom_col(width = .3,fill="darkorange") + theme_classic() +
  labs(x="Percent", y = "", title = "AOC favorability")

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(y= fct_reorder(trump_biden,pct), 
             x = pct)) + geom_col(width = .3,fill="darkorange") + theme_classic() +
  labs(x="Percent", y = "", title = "AOC favorability") +
  geom_label(aes(label=pct))

pollster::crosstab(df = a, x = trump_biden, 
                        y = aoc_Favorable,weight = weight,format = "long") %>%
  filter(aoc_Favorable==1) %>%
  ggplot(aes(y= fct_reorder(trump_biden,pct), 
             x = pct)) + geom_col(width = .3,fill="darkorange") + theme_classic() +
  labs(x="Percent", y = "", title = "AOC favorability") +
  geom_label(aes(label=round(pct,1)))