groupedTable() creates a summary table of frequencies and percentages that can show breakdowns by groups and the totals for
the data passed to it. The table shows each item and all the responses by row.
Arguments
- df
Required, a tibble or data frame of categorical/factor data that also can contain a categorical group variable to split up the data, e.g. role, gender, education level, etc.
- col_group
Default is NULL. The name of the categorical group variable to split up the data, e.g. role, gender, education level, etc. Must be in quotes (e.g. "role").
- question_labels
Default is NULL. Takes in a named character vector to both supply labels the questions and sort the order of the questions.The named character vector should have the new labels as the "name" and the old labels as the "variable" sorted in the desired order of appearing in the table, first item will appear at the top of the table. See examples.
- str_width
Default is 20. The character length to wrap the question column. If question_labels are supplied and very long use this to keep the question column from being too large for the table.
Value
a flextable object with columns for question, response, and counts and percentages for each group and a total column. Colors are set to The Mark USA branding
Examples
data <- dplyr::tibble(
Cohort = factor(c(1,2,1,2,1,2,1,2,1,2,1,2), levels = c(1, 2)),
gender = factor(c(
"Female", "Female","Female","Male", "Female","Male",
"Male", "Female","Male", "Female", "Male", "Female"
), levels = c("Female", "Male")),
year = factor(c(
"grad", "undergrad", "grad", "undergrad", "grad","undergrad",
"undergrad", "undergrad", "grad", "undergrad", "grad","undergrad"
), levels = c("grad", "undergrad")),
department = factor(c(
"Chemistry", "Biology", "Chemistry", "Biology", "Physics","Biology",
"Biology", "Physics", "Chemistry", "Chemistry", "Physics","Biology"
), levels = c("Biology", "Chemistry", "Physics")),
ethnicity = factor(c(
"Asian", "Black", "white", "Hispanic or Latino", "white","Asian",
"Black", "Asian", "white", "white", "Hispanic or Latino","Hispanic or Latino"
), levels = c( "Asian", "Black", "Hispanic or Latino", "white"))
)
# Labels for questions column of table, pass to question_labels argument:
labels <- c('Gender' = "gender",
'Ethnicity' = "ethnicity",
'Year in school' = "year",
'Department of Affiliation' = "department")
# Call groupedTable with a grouping variable:
data %>% groupedTable(col_group = "Cohort", question_labels = labels)
Question
Response
1
(n = 6)
2
(n = 6)
Total
(n = 12)1
Gender
Female
3 (25%)
4 (33%)
7 (58%)
Male
3 (25%)
2 (17%)
5 (42%)
Ethnicity
Asian
1 (8%)
2 (17%)
3 (25%)
Black
1 (8%)
1 (8%)
2 (17%)
Hispanic or Latino
1 (8%)
2 (17%)
3 (25%)
white
3 (25%)
1 (8%)
4 (33%)
Year in school
grad
5 (42%)
-
5 (42%)
undergrad
1 (8%)
6 (50%)
7 (58%)
Department of
Affiliation
Biology
1 (8%)
4 (33%)
5 (42%)
Chemistry
3 (25%)
1 (8%)
4 (33%)
Physics
2 (17%)
1 (8%)
3 (25%)
1n (%)
# Call groupedTable without a grouping variable:
data %>% groupedTable(question_labels = labels)
Question
Response
n = 121
Gender
Female
7 (58%)
Male
5 (42%)
Ethnicity
Asian
3 (25%)
Black
2 (17%)
Hispanic or Latino
3 (25%)
white
4 (33%)
Year in school
grad
5 (42%)
undergrad
7 (58%)
Department of
Affiliation
Biology
5 (42%)
Chemistry
4 (33%)
Physics
3 (25%)
Cohort
1
6 (50%)
2
6 (50%)
1n (%)