- Introduction and Install
- Benefits of GroupThink
- Core Functionality
- Further Functionality
- Even More Functionality
- Future Plans
- Bug Reports and Feature Requests
GroupThink is a package designed to assist in the analysis in
categorical survey data. It mainly acts as an interface for existing
tidyverse functions - but makes it easier to aggregate responses, do
cross-question analysis, and avoid classic mistakes typical of survey
data analysis.
It currently has two functions:unify() and assess() (though others
are planned for the future…!).
GroupThink isn’t on CRAN, so you’ll need to use devtools to install
it. Run:
install.packages("devtools")
library(devtools)
devtools::install_github("Samuel-Osian-Andrews/GroupThink")
library(GroupThink)As GroupThink is still in development, you should periodically reinstall the package to get updates.
GroupThink depends on dplyr, tidyr and gt libraries. If these
aren’t installed automatically when you install GroupThink, you may need
to run:
install.packages(c("dplyr", "tidyr", "gt"))
GroupThink is a response to key bottlenecks and common mistakes when analysing survey data. The function is beneficial because it…
-
Allows for easy groupings.
unify()makes it very easy to group together different Likert-style responses (e.g. combiningSomewhat agreewithStrongly agree, orExcellentandVery good). It’s now extremely difficult to make mistakes with incorrect groupings, asunify()alerts you of any unassigned responses. -
Automates your calculations.
unify()handles n and proportion calculations for you, meaning you no longer have to undertake complex data manipulation tasks, avoiding functions such aspivot_longer(), which can result in inaccurate figures if you’re not careful! -
Works with full questions as column headers. Typically, exporting survey responses (such as from Microsoft Forms or SurveyMonkey) will leave you with full questions (e.g. “Do you agree or disagree that…”) as column headers. This is usually a nightmare to work with in R. Because
unify()works on column indexes, rather than column names, you don’t need to worry about recoding your columns or typing out full survey questions throughout your code. -
Gives usable outputs..
unify()neatly integrates with ggplot, allowing you to visualise your aggregated data. Alternatively, you can produce formatted tables through the gtTable argument. -
Presents clear, readable syntax. Even for those unfamiliar with R syntax,
unify()makes it very clear exactly how you’ve grouped together your responses, improving readability and reproducibility. -
Means faster insights. With just a few lines of code, this function could save you hours worth of work for large survey projects.
The unify() function groups together Likert-style responses for a
given question or set of questions, returning a summarised output that
contains the n and proportion for each of these groupings.
unify(data, cols = 1, # ...dataframe name and column index number(s) to analyse
# Below, we 'group' responses via custom grouping labels (e.g. 'Agree'):
Agree = c("Somewhat agree", "Strongly agree"),
Disagree = c("Somewhat disagree", "Strongly disagree"),
Neutral = "Neither agree nor disagree",
ignore = "Don't know") # ...optionally, set response(s) to ignore from calcs## # A tibble: 1 × 7
## Question `Agree (n)` `Disagree (n)` `Neutral (n)` `Agree (Proportion)`
## <chr> <int> <int> <int> <dbl>
## 1 I find the cour… 38 32 18 43.2
## # ℹ 2 more variables: `Disagree (Proportion)` <dbl>,
## # `Neutral (Proportion)` <dbl>
The grouping labels can be anything you like. For example, Agree could
instead be Positive, Good, Satisifed or something else entirely.
Similarly, Don't know could be its own group, instead of being
ignored. You may include as many grouping labels as you’d like.
There’s of course nothing wrong with having just 1 response option per
group (e.g. "Somewhat agree" = "Somewhat agree"). The main purpose of
unify() is that it forces you to be intentional with how you
handle your data, to improve consistency and avoid mistakes.
If you forgot to include a response in your custom groupings, unify()
will throw an error. This is crucial for avoiding mistakes in your
proportion calculations. For example:
unify(data, 1, Agree = "Somewhat agree",
#"Strongly agree"), -- let's stop unify() from seeing this line
Disagree = c("Somewhat disagree", "Strongly disagree"),
Neutral = "Neither agree nor disagree",
ignore = "Don't know") ## Error in unify(data, 1, Agree = "Somewhat agree", Disagree = c("Somewhat disagree", : The following responses are not accounted for in the provided groups: 'Strongly agree'. Please check your grouping arguments.
As seen above, the output tells you that you forgot to assign “Strongly agree” to a grouping variable.
These errors are crucial, since other R functions do not warn you if you
haven’t accounted for a group, or mistyped “Strongly Agree”, as
“Strongly agree”, for example.
The unify() function expects data that looks like this:
## # A tibble: 10 × 3
## I find the course material en…¹ The course workload …² Feedback from assign…³
## <fct> <fct> <fct>
## 1 Somewhat disagree Agree Disagree
## 2 Strongly agree Disagree No opinion
## 3 Somewhat agree Agree <NA>
## 4 Somewhat disagree Unsure Disagree
## 5 Somewhat agree <NA> Strongly agree
## 6 Strongly agree Neither agree nor dis… <NA>
## 7 Strongly disagree Neither agree nor dis… Strongly agree
## 8 Somewhat agree Highly disagree Strongly agree
## 9 Somewhat disagree Neither agree nor dis… Strongly disagree
## 10 Somewhat agree Neither agree nor dis… Agree
## # ℹ abbreviated names: ¹`I find the course material engaging and relevant.`,
## # ²`The course workload is manageable within my schedule.`,
## # ³`Feedback from assignments is helpful for my learning.`
Responses do not need to be consistently labelled either within or
between different questions/columns, and can contain missing data
(you’ll likely want to assign NA to the ignore parameter).
Since GroupThink functions work with column index numbers, not
column names, you’ll likely want to summarise all index numbers of your
dataset. For this, run colnames() from base-R.
colnames(data)## [1] "I find the course material engaging and relevant."
## [2] "The course workload is manageable within my schedule."
## [3] "Feedback from assignments is helpful for my learning."
You might find it beneficial to run GroupThink’s assess() function,
which provides an overview of the different response options in your
specified columns.
assess(data, cols = c(2, 3))## Examining the following columns:
##
## Column [2]: The course workload is manageable within my schedule.
## Column [3]: Feedback from assignments is helpful for my learning.
##
##
## 11 unique responses were found across these columns. Please make sure each response
## below is accounted for within your `unify()` function call:
##
##
## Agree
## Disagree
## Highly agree
## Highly disagree
## Indifferent
## Neither agree nor disagree
## No opinion
## Strongly agree
## Strongly disagree
## Unsure
## NA
You are not restricted to analysing just one question/column with
unify(). You can specify multiple columns/questions to use for the
output:
unify(data, c(1, 2, 3), # ...analyse Columns 1, 2 and 3
Positive = c("Somewhat agree", "Strongly agree", "Highly agree", "Agree"),
Negative = c("Somewhat disagree", "Strongly disagree", "Highly disagree",
"Disagree"),
ignore = c(NA, "Don't know", "Unsure", "Neither agree nor disagree",
"No opinion", "Indifferent"),
hideN = TRUE) # ...(optional) hide n column from output (a lot cleaner!)## # A tibble: 3 × 3
## Question Positive (Proportion…¹ Negative (Proportion…²
## <chr> <dbl> <dbl>
## 1 Feedback from assignments is he… 57.8 42.2
## 2 I find the course material enga… 54.3 45.7
## 3 The course workload is manageab… 36.7 63.3
## # ℹ abbreviated names: ¹`Positive (Proportion)`, ²`Negative (Proportion)`
…Just make sure that you’ve accounted for each response option across your range of columns, otherwise you’ll get an error.
Using the gtTable argument, unify() makes it simple to create nice,
formatted tables.
unify(data, 1, Agree = c("Somewhat agree", "Strongly agree"),
Disagree = c("Somewhat disagree", "Strongly disagree"),
Neutral = "Neither agree nor disagree",
ignore = "Don't know",
filter = c("Agree", "Disagree"),
hideN = TRUE, # ...optionally, hide N column
gtTable = TRUE) # ...set gtTable to TRUE| Question | Agree (Proportion) | Disagree (Proportion) |
|---|---|---|
| I find the course material engaging and relevant. | 43.2 | 36.4 |
If you want to only display one response option in the output, we can
use the filter argument.
Note that this is different from the ignore parameter: filter
removes unwanted responses after the calculations have been performed,
while ignore removes them before.
unify(data, 3,
Agree = c("Agree", "Strongly agree"),
Disagree = c("Disagree", "Strongly disagree"),
Neither = c("No opinion", "Indifferent"),
ignore = c(NA, "Don't know"),
filter = "Agree") # ...only include the Agree group in the output## # A tibble: 1 × 3
## Question `Agree (n)` `Agree (Proportion)`
## <chr> <int> <dbl>
## 1 Feedback from assignments is helpful for my … 37 38.9
The other variable groupings are used for the calculations, but only “Agree” responses are shown in the final output.
Unless you’ve set unify()’s gtTable() argument to TRUE, it will
output as a tibble. This means it integrates neatly into ggplot()
function calls.
Let’s pretend we’ve already run unify() on columns 1, 2 & 3, and
assigned it to the name united…
ggplot(data = united, # ...unify() output becomes ggplot()'s data argument
aes(x = Question, y = `Agree (Proportion)`, fill = Question)) +
geom_col() +
# ...below are just optional customisation options:
coord_flip() +
theme_bw() +
scale_fill_manual(values = c("cornflowerblue", "coral", "chartreuse3")) +
scale_y_continuous(limits = c(0, 70)) +
theme(legend.position = "none")
For other functionality not covered in this document, please run
?unify() and ?assess() to view the help files, which covers all
function parameters.
-
Add
unify()support for demographic comparison (comparing figures between factors such as age, sex, etc.) -
Add support for
stargazertables intounify(). -
Develop a separate function for analysing multiple choice data for data formats typical of exported survey data.
Please do let me know of any issues you come across. You can use the Issues tab in GitHub for any bug reports.
If you have any ideas for existing features, or perhaps even new ones, then I’d love to hear them. Let me know in the Discussions tab in GitHub.