Clean Up and Format Open-ended Text

Usage

openendedCleanup(df, var, remove_values)

Arguments

df: Required, A tibble/data frame containing the character variable of text.
var: Required, the character variable to clean up from the tibble/data frame.
remove_values: Required, a character vector of additional text to remove from the text, see example.

Value

A tibble containg one character variable of clean text ready for use or output.

Examples

# Example data:
#  Training usefulness composite scale- 5 variables of that make up a scale:
# Responsible, Ethics, Standards, Practices, Morals
#  these are all on a 5-point likert scale of 1 to 5 needs to be
#  recoded to: c("Not at all useful", "Slightly useful", "Somewhat useful",
#                "Very useful", "Extremely useful")
# levels useful:
levels_useful <- c("Not at all useful", "Slightly useful", "Somewhat useful",
                   "Very useful", "Extremely useful")
# Data:
data <- dplyr::tibble(
 Responsible = sample(levels_useful, size = 100, replace = TRUE,
                       prob = c(0.1, 0.2, 0.3, 0.2, 0.1)),
 Ethics = sample(levels_useful, size = 100, replace = TRUE, prob = c(0.1, 0.2, 0.3, 0.2, 0.1)),
 Standards = sample(levels_useful, size = 100, replace = TRUE, prob = c(0.1, 0.1, 0.2, 0.3, 0.3)),
 Practices = sample(levels_useful, size = 100, replace = TRUE, prob = c(0.1, 0.1, 0.2, 0.3, 0.3)),
 Morals = sample(levels_useful, size = 100, replace = TRUE, prob = c(0.05, 0.05, 0.2, 0.3, 0.4)),
 Responsible_oe = ifelse(Responsible == "Not at all useful",
                 stringi::stri_rand_lipsum(sample(1:3, replace = TRUE,
                 prob = c(0.4, 0.2, 0.1))), NA_character_),
 Ethics_oe = ifelse(Ethics == "Not at all useful",
                 stringi::stri_rand_lipsum(sample(1:3, replace = TRUE,
                 prob = c(0.4, 0.2, 0.1))), NA_character_),
 Standards_oe = ifelse(Standards == "Not at all useful",
                 stringi::stri_rand_lipsum(sample(1:3, replace = TRUE,
                 prob = c(0.4, 0.2, 0.1))), NA_character_),
 Practices_oe = ifelse(Practices == "Not at all useful",
                 stringi::stri_rand_lipsum(sample(1:3, replace = TRUE,
                 prob = c(0.4, 0.2, 0.1))), NA_character_),
 Morals_oe = ifelse(Morals == "Not at all useful",
                 stringi::stri_rand_lipsum(sample(1:3, replace = TRUE,
                 prob = c(0.4, 0.2, 0.1))), NA_character_)
 ) %>% dplyr::select(dplyr::ends_with("_oe"))

# Set up character vector of text or other things like punctuation to remove from the text data:
remove_values <- c("N/A", ".", "A")

# Make a nice table with the function:
data %>% openendedCleanup(., Responsible_oe, remove_values)
#> # A tibble: 5 × 1
#>   Responsible_oe                                                                
#>   <chr>                                                                         
#> 1 "Amet ut duis lacus donec dis lobortis at. Congue tincidunt cursus sed diam\n…
#> 2 "Amet ut duis lacus donec dis lobortis at. Congue tincidunt cursus sed diam\n…
#> 3 "Amet ut duis lacus donec dis lobortis at. Congue tincidunt cursus sed diam\n…
#> 4 "Varius primis orci tincidunt class in duis sed blandit elit, et. Elementum,\…
#> 5 "Varius primis orci tincidunt class in duis sed blandit elit, et. Elementum,\…