Creates a data frame that is a summary table of counts and percentages
Source:R/dataVizCleaning.R
dataVizCleaning.RdCreates a data frame that is a summary table of counts and percentages
Arguments
- df
Required, A tibble/data frame of survey items that are categorical/character variables.
- scale_labels
Required, a character vector of labels for the response scale, must be in the desired order, e.g. if you have a 5 item scale of minimal to extensive it should look like this:
levels_min_ext <- c("Minimal", "Slight", "Moderate", "Good", "Extensive").- pre_post
Logical, default is FALSE. If true, returns a tibble with an additional column of
timingthat is a factor variable of eitherPreorPost.- na_remove
Logical, defaults to TRUE. If TRUE, Drops NA values; if FALSE, turns NA's into "Missing" and adds it as a factor in the first position of
scale_labels.
Value
A tibble with the data in 5 columns: question, response, n_answers, percent_answers and percent_answers_label.
question is the name of the original item, response is all of the categorical responses possible for the item. n_answers is the count of each response,
percent_answers is the percentage of each response and percent_answers_label is a character variable of percentage labelled with percent sign for use as
text label. If pre_post arg is TRUE, an column of timing is added that is a factor variable of either Pre or Post.
Examples
# Fake data for examples, first are single items and the second has pre-post data with
# correct prefixes in variable names:
items_single <- tibble::tibble(
Organization = c("Minimal", "Slight", "Moderate", "Good", "Extensive",
"Good", "Moderate", "Slight", "Minimal"),
Source = c("Slight", "Slight", "Moderate", "Extensive", "Good", "Moderate",
"Slight", "Minimal", "Slight"),
Publish = c("Minimal", "Minimal", "Minimal", "Slight", "Slight", "Slight",
"Moderate", "Moderate", "Moderate"),
Write = c("Slight", "Slight", "Slight", "Moderate", "Moderate", "Moderate",
"Good", "Good", "Good"),
Research = c("Minimal", "Minimal", "Slight", "Slight", "Moderate",
"Moderate", "Good", "Good", "Good")
)
items_pre_post <- tibble::tibble(
pre_Organization = c("Minimal", "Slight", "Moderate", "Good",
"Extensive", "Good", "Moderate", "Slight", "Minimal"),
post_Organization = c("Slight", "Moderate", "Good", "Extensive",
"Extensive", "Extensive", "Good", "Moderate", "Slight"),
pre_Source = c("Slight", "Slight", "Moderate", "Extensive", "Good",
"Moderate", "Slight", "Minimal", "Slight"),
post_Source = c("Good", "Good", "Extensive", "Extensive", "Good",
"Extensive", "Good", "Moderate", "Good"),
pre_Publish = c("Minimal", "Minimal", "Minimal", "Slight", "Slight",
"Slight", "Moderate", "Moderate", "Moderate"),
post_Publish = c("Moderate", "Moderate", "Moderate", "Good", "Good",
"Good", "Extensive", "Extensive", "Extensive"),
pre_Write = c("Slight", "Slight", "Slight", "Moderate", "Moderate",
"Moderate", "Good", "Good", "Good"),
post_Write = c("Moderate", "Moderate", "Moderate", "Good", "Good",
"Good", "Extensive", "Extensive", "Extensive"),
pre_Research = c("Minimal", "Minimal", "Slight", "Slight", "Moderate",
"Moderate", "Good", "Good", "Good"),
post_Research = c("Slight", "Slight", "Moderate", "Moderate", "Good",
"Good", "Extensive", "Extensive", "Extensive")
)
# Add a row of NA values to each fake data set:
items_pre_post_na <- dplyr::rows_append(items_pre_post,
tibble::as_tibble_row(purrr::set_names(rep(NA, NCOL(items_pre_post)),
names(items_pre_post))))
items_single_na <- dplyr::rows_append(items_single,
tibble::as_tibble_row(purrr::set_names(rep(NA, NCOL(items_single)),
names(items_single))))
# Likert scale to pass to `scale_labels` that is the order to arrange each variable:
levels_min_ext <- c("Minimal", "Slight", "Moderate", "Good", "Extensive")
dataVizCleaning(df = items_single, pre_post = FALSE,
scale_labels = levels_min_ext, na_remove = TRUE)
#> # A tibble: 20 × 5
#> question response n_answers percent_answers percent_answers_label
#> <chr> <fct> <int> <dbl> <chr>
#> 1 Organization Minimal 2 0.222 22%
#> 2 Organization Slight 2 0.222 22%
#> 3 Organization Moderate 2 0.222 22%
#> 4 Organization Good 2 0.222 22%
#> 5 Organization Extensive 1 0.111 11%
#> 6 Publish Minimal 3 0.333 33%
#> 7 Publish Slight 3 0.333 33%
#> 8 Publish Moderate 3 0.333 33%
#> 9 Research Minimal 2 0.222 22%
#> 10 Research Slight 2 0.222 22%
#> 11 Research Moderate 2 0.222 22%
#> 12 Research Good 3 0.333 33%
#> 13 Source Minimal 1 0.111 11%
#> 14 Source Slight 4 0.444 44%
#> 15 Source Moderate 2 0.222 22%
#> 16 Source Good 1 0.111 11%
#> 17 Source Extensive 1 0.111 11%
#> 18 Write Slight 3 0.333 33%
#> 19 Write Moderate 3 0.333 33%
#> 20 Write Good 3 0.333 33%
dataVizCleaning(df = items_single_na, pre_post = FALSE,
scale_labels = levels_min_ext, na_remove = FALSE)
#> # A tibble: 25 × 5
#> question response n_answers percent_answers percent_answers_label
#> <chr> <fct> <int> <dbl> <chr>
#> 1 Organization Missing 1 0.1 10%
#> 2 Organization Minimal 2 0.2 20%
#> 3 Organization Slight 2 0.2 20%
#> 4 Organization Moderate 2 0.2 20%
#> 5 Organization Good 2 0.2 20%
#> 6 Organization Extensive 1 0.1 10%
#> 7 Publish Missing 1 0.1 10%
#> 8 Publish Minimal 3 0.3 30%
#> 9 Publish Slight 3 0.3 30%
#> 10 Publish Moderate 3 0.3 30%
#> # ℹ 15 more rows
dataVizCleaning(df = items_pre_post, pre_post = TRUE,
scale_labels = levels_min_ext, na_remove = TRUE)
#> # A tibble: 37 × 6
#> question timing response n_answers percent_answers percent_answers_label
#> <chr> <fct> <fct> <int> <dbl> <chr>
#> 1 Organization Pre Minimal 2 0.222 22%
#> 2 Organization Pre Slight 2 0.222 22%
#> 3 Organization Pre Moderate 2 0.222 22%
#> 4 Organization Pre Good 2 0.222 22%
#> 5 Organization Pre Extensive 1 0.111 11%
#> 6 Organization Post Slight 2 0.222 22%
#> 7 Organization Post Moderate 2 0.222 22%
#> 8 Organization Post Good 2 0.222 22%
#> 9 Organization Post Extensive 3 0.333 33%
#> 10 Publish Pre Minimal 3 0.333 33%
#> # ℹ 27 more rows
dataVizCleaning(df = items_pre_post_na, pre_post = TRUE,
scale_labels = levels_min_ext, na_remove = FALSE)
#> # A tibble: 47 × 6
#> question timing response n_answers percent_answers percent_answers_label
#> <chr> <fct> <fct> <int> <dbl> <chr>
#> 1 Organization Pre Missing 1 0.1 10%
#> 2 Organization Pre Minimal 2 0.2 20%
#> 3 Organization Pre Slight 2 0.2 20%
#> 4 Organization Pre Moderate 2 0.2 20%
#> 5 Organization Pre Good 2 0.2 20%
#> 6 Organization Pre Extensive 1 0.1 10%
#> 7 Organization Post Missing 1 0.1 10%
#> 8 Organization Post Slight 2 0.2 20%
#> 9 Organization Post Moderate 2 0.2 20%
#> 10 Organization Post Good 2 0.2 20%
#> # ℹ 37 more rows