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Surveyexplorer

CRAN status

Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data.

Installation

install.packages("surveyexplorer")
# or devtools::install_github("liamhaller/surveyexplorer") for the devlopment version 

Examples

library(surveyexplorer)

The data used in the following examples is from the berlinbears dataset, a fictional survey of bears in Berlin, that is included in the surveyexplorer package.

Single-choice questions

#Basic table
single_table(berlinbears, 
             question = income)
Question: income
n freq
<1000 82 0.164
1000-2000 50 0.100
2000-3000 177 0.354
3000-4000 109 0.218
5000+ 57 0.114
No answer 22 0.044
NA 3 0.006
Column Total 500

Use group_by = to partition the question into several groups

single_table(berlinbears,
             question = income,
             group_by = gender)
Question: income
grouped by: gender
female male NA Rowwise Total
Frequency Count Frequency Count Frequency Count Frequency Count
<1000 16.74% 39 15.73% 39 21.05% 4 16.40% 82
1000-2000 9.87% 23 9.68% 24 15.79% 3 10.00% 50
2000-3000 35.62% 83 35.89% 89 26.32% 5 35.40% 177
3000-4000 21.89% 51 22.18% 55 15.79% 3 21.80% 109
5000+ 11.59% 27 10.89% 27 15.79% 3 11.40% 57
No answer 3.86% 9 4.84% 12 5.26% 1 4.40% 22
NA 0.43% 1 0.81% 2 0.00% 0 0.60% 3
Columnwise Total 46.60% 233 49.60% 248 3.80% 19 100.00% 500

Ignore unwanted subgroups with subgroups_to_exclude

single_table(berlinbears,
             question = income, 
             group_by = gender, 
             subgroups_to_exclude = NA) 
Question: income
grouped by: gender
female male Rowwise Total
Frequency Count Frequency Count Frequency Count
<1000 16.74% 39 15.73% 39 16.22% 78
1000-2000 9.87% 23 9.68% 24 9.77% 47
2000-3000 35.62% 83 35.89% 89 35.76% 172
3000-4000 21.89% 51 22.18% 55 22.04% 106
5000+ 11.59% 27 10.89% 27 11.23% 54
No answer 3.86% 9 4.84% 12 4.37% 21
NA 0.43% 1 0.81% 2 0.62% 3
Columnwise Total 48.44% 233 51.56% 248 100.00% 481

Remove NAs from the question variable with na.rm

single_table(berlinbears,
             question = income, 
             group_by = gender, 
             subgroups_to_exclude = NA,
             na.rm = TRUE)
Question: income
grouped by: gender
female male Rowwise Total
Frequency Count Frequency Count Frequency Count
<1000 16.81% 39 15.85% 39 16.32% 78
1000-2000 9.91% 23 9.76% 24 9.83% 47
2000-3000 35.78% 83 36.18% 89 35.98% 172
3000-4000 21.98% 51 22.36% 55 22.18% 106
5000+ 11.64% 27 10.98% 27 11.30% 54
No answer 3.88% 9 4.88% 12 4.39% 21
Columnwise Total 48.54% 232 51.46% 246 100.00% 478

Finally, you can specify survey weights using the weight option

single_table(berlinbears,
             question = income, 
             group_by = gender, 
             subgroups_to_exclude = NA,
             na.rm = TRUE,
             weights = weights)
Question: income
grouped by: gender
female male Rowwise Total
Frequency Count Frequency Count Frequency Count
<1000 15.96% 59.6 17.21% 75.2 16.63% 134.8
1000-2000 10.46% 39.1 10.19% 44.5 10.31% 83.6
2000-3000 33.79% 126.3 33.88% 148.0 33.84% 274.3
3000-4000 25.08% 93.7 25.34% 110.7 25.22% 204.4
5000+ 9.82% 36.7 8.68% 37.9 9.21% 74.6
No answer 4.90% 18.3 4.70% 20.5 4.79% 38.8
Columnwise Total 46.10% 373.6 53.90% 436.9 100.00% 810.5
Frequencies and counts are weighted

The same syntax can be applied to the single_freq function to plot frequencies of the question optionally partitioned by subgroups.

single_freq(berlinbears,
             question = income, 
             group_by = gender, 
             subgroups_to_exclude = NA,
             na.rm = TRUE,
             weights = weights)

Multiple-choice questions

The options and syntax for multiple-choice tables multi_table and graphs multi_graphs are the same. The only difference is the question input also accommodates tidyselect syntax to select several columns for each answer option. For example, the question “will_eat” has five answer options each prefixed by “will_eat”

berlinbears |> 
  dplyr::select(starts_with('will_eat')) |> 
  head()
#>   will_eat.SQ001 will_eat.SQ002 will_eat.SQ003 will_eat.SQ004 will_eat.SQ005
#> 1              0              1              0              1              1
#> 2              0              1              1              1              1
#> 3              1              1              0              1              1
#> 4              0              0              0              1              0
#> 5              0              0              0              1              1
#> 6              0              0              0              1              0

The same syntax can be used to select the question for the multiple choice tables and graphs

multi_table(berlinbears, 
            question = dplyr::starts_with('will_eat'), 
            group_by = genus, 
            subgroups_to_exclude = NA,
            na.rm = TRUE)
Question: dplyr::starts_with(“will_eat”)
grouped by: genus
Ailuropoda Ursus Rowwise Total
Frequency Count Frequency Count Frequency Count
will_eat.SQ004 97.54% 278 91.62% 175 40.12% 453
will_eat.SQ002 59.30% 169 66.49% 127 26.22% 296
will_eat.SQ005 43.86% 125 46.60% 89 18.95% 214
will_eat.SQ001 24.91% 71 27.23% 52 10.89% 123
will_eat.SQ003 8.42% 24 9.95% 19 3.81% 43
Columnwise Total 59.08% 667 40.92% 462 100.00% 1129

For graphing, the multi_freq function creates an UpSet plot to visualize the frequencies of the intersecting sets for each answer combination and also includes the ability to specify weights.

multi_freq(berlinbears, 
            question = dplyr::starts_with('will_eat'), 
            na.rm = TRUE,
            weights = weights)
#> Estimes are only preciese to one significant digit, weights may have been rounded

The graphs can also be grouped

multi_freq(berlinbears, 
            question = dplyr::starts_with('will_eat'), 
            group_by = genus,
            subgroups_to_exclude = NA,
            na.rm = FALSE,
            weights = weights)
#> Estimes are only preciese to one significant digit, weights may have been rounded

Matrix Questions

matrix_table has the same syntax as above and works with array or categorical questions

matrix_table(berlinbears, 
             dplyr::starts_with('c_'),
             group_by = is_parent)
Question: dplyr::starts_with(“c_”)
grouped by: is_parent
high low medium NA
0
c_diet 6.02% (20) 71.99% (239) 16.57% (55) 5.42% (18)
c_exercise 25% (83) 27.71% (92) 24.1% (80) 23.19% (77)
1
c_diet 3.57% (6) 75% (126) 17.26% (29) 4.17% (7)
c_exercise 19.05% (32) 27.38% (46) 23.81% (40) 29.76% (50)

matrix_freq visualizes the frequencies of responses

matrix_freq(berlinbears, 
             dplyr::starts_with('p_'), 
             na.rm = TRUE)

For array/matrix style questions that are numeric matrix_mean plots the mean values and confidence intervals

matrix_mean(berlinbears, 
             question = dplyr::starts_with('p_'),
             na.rm = TRUE)

#Can also apply grouping + survey weights
matrix_mean(berlinbears, 
            question = dplyr::starts_with('p_'),
            na.rm = TRUE,
            group_by = species, 
            subgroups_to_exclude = NA)

Finally, for Likert questions (scales of 3,5,7,9…) matrix_likert provides a custom plot

#you can specify custom labels with the `label` argument
matrix_likert(berlinbears,
              question = dplyr::starts_with('p_'),
              labels = c('Strongly disagree', 'Disagree','Neutral','Agree','Strongly agree'))


#can also apply pass custom colors and specify weights weights 
matrix_likert(berlinbears, 
              question = dplyr::starts_with('p_'),
              labels = c('Strongly disagree', 'Disagree','Neutral','Agree','Strongly agree'), 
              colors = c("#E1AA28", "#1E5F46", "#7E8F75", "#EFCD83", "#E17832"),
              weights = weights) 

Overview

Functions

*_table functions return a gt table of the cross tabulations and frequencies for each question while *_freq returns the same data but as a plot.

For matrix-style questions with numerical input, matrix_mean plots the mean value value and ± two standard deviations. matrix_likert visualizes questions that accept Likert responses (strongly agree-strongly disagree) or questions with 3,5,7,9… categories.

Syntax

Each function contains the following options

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.