A helper function to help you format the participants data (age, sex, ...) in the participants section.
Usage
report_participants(
data,
age = NULL,
sex = NULL,
gender = NULL,
education = NULL,
country = NULL,
race = NULL,
participants = NULL,
group = NULL,
spell_n = FALSE,
digits = 1,
threshold = 10,
...
)Arguments
- data
A data frame.
- age
The name of the column containing the age of the participant.
- sex
The name of the column containing the sex of the participant. The classes should be one of
c("Male", "M", "Female", "F"). Note that you can specify other characters here as well (e.g.,"Intersex"), but the function will group all individuals in those groups as"Other".- gender
The name of the column containing the gender of the classes should be one of
c("Man", "M", "Male", Woman", "W", F", "Female", Non-Binary", "N"). Note that you can specify other characters here as well (e.g.,"Gender Fluid"), but the function will group all individuals in those groups as"Non-Binary".- education
The name of the column containing education information.
- country
The name of the column containing country information.
- race
The name of the column containing race/ethnicity information.
- participants
The name of the participants' identifier column (for instance in the case of repeated measures).
- group
A character vector indicating the name(s) of the column(s) used for stratified description.
- spell_n
Logical, fully spell the sample size (
"Three participants"instead of"3 participants").- digits
Number of significant digits.
- threshold
Percentage after which to combine, e.g., countries (default is 10%, so countries that represent less than 10% will be combined in the "other" category).
- ...
Arguments passed to or from other methods.
Value
A character vector with description of the "participants", based on
the information provided in data.
Examples
library(report)
data <- data.frame(
"Age" = c(22, 23, 54, 21, 8, 42),
"Sex" = c("Intersex", "F", "M", "M", "NA", NA),
"Gender" = c("N", "W", "W", "M", "NA", NA)
)
report_participants(data, age = "Age", sex = "Sex")
#> [1] "6 participants (Mean age = 28.3, SD = 16.6, range: [8, 54]; Sex: 16.7% females, 33.3% males, 16.7% other, 33.33% missing; Gender: 33.3% women, 16.7% men, 16.67% non-binary, 33.33% missing)"
# Years of education (relative to high school graduation)
data$Education <- c(0, 8, -3, -5, 3, 5)
report_participants(data,
age = "Age", sex = "Sex", gender = "Gender",
education = "Education"
)
#> [1] "6 participants (Mean age = 28.3, SD = 16.6, range: [8, 54]; Sex: 16.7% females, 33.3% males, 16.7% other, 33.33% missing; Gender: 33.3% women, 16.7% men, 16.67% non-binary, 33.33% missing; Mean education = 1.3, SD = 4.9, range: [-5, 8])"
# Education as factor
data$Education2 <- c(
"Bachelor", "PhD", "Highschool",
"Highschool", "Bachelor", "Bachelor"
)
report_participants(data, age = "Age", sex = "Sex", gender = "Gender", education = "Education2")
#> [1] "6 participants (Mean age = 28.3, SD = 16.6, range: [8, 54]; Sex: 16.7% females, 33.3% males, 16.7% other, 33.33% missing; Gender: 33.3% women, 16.7% men, 16.67% non-binary, 33.33% missing; Education: Bachelor, 50.00%; Highschool, 33.33%; PhD, 16.67%)"
# Country
data <- data.frame(
"Age" = c(22, 23, 54, 21, 8, 42, 18, 32, 24, 27, 45),
"Sex" = c("Intersex", "F", "F", "M", "M", "M", "F", "F", "F", "F", "F"),
"Gender" = c("N", "W", "W", "M", "M", "M", "W", "W", "W", "W", "W"),
"Country" = c(
"USA", NA, "Canada", "Canada", "India", "Germany",
"USA", "USA", "USA", "USA", "Canada"
)
)
report_participants(data)
#> [1] "11 participants (Mean age = 28.7, SD = 13.4, range: [8, 54]; Sex: 63.6% females, 27.3% males, 9.1% other; Gender: 63.6% women, 27.3% men, 9.09% non-binary; Country: 45.45% USA, 27.27% Canada, 27.27% other)"
# Country, control presentation treshold
report_participants(data, threshold = 5)
#> [1] "11 participants (Mean age = 28.7, SD = 13.4, range: [8, 54]; Sex: 63.6% females, 27.3% males, 9.1% other; Gender: 63.6% women, 27.3% men, 9.09% non-binary; Country: 45.45% USA, 27.27% Canada, 9.09% Germany, 9.09% India, 9.09% missing)"
# Race/ethnicity
data <- data.frame(
"Age" = c(22, 23, 54, 21, 8, 42, 18, 32, 24, 27, 45),
"Sex" = c("Intersex", "F", "F", "M", "M", "M", "F", "F", "F", "F", "F"),
"Gender" = c("N", "W", "W", "M", "M", "M", "W", "W", "W", "W", "W"),
"Race" = c(
"Black", NA, "White", "Asian", "Black", "Arab", "Black",
"White", "Asian", "Southeast Asian", "Mixed"
)
)
report_participants(data)
#> [1] "11 participants (Mean age = 28.7, SD = 13.4, range: [8, 54]; Sex: 63.6% females, 27.3% males, 9.1% other; Gender: 63.6% women, 27.3% men, 9.09% non-binary; Race: 27.27% Black, 18.18% Asian, 18.18% White, 36.36% other)"
# Race/ethnicity, control presentation treshold
report_participants(data, threshold = 5)
#> [1] "11 participants (Mean age = 28.7, SD = 13.4, range: [8, 54]; Sex: 63.6% females, 27.3% males, 9.1% other; Gender: 63.6% women, 27.3% men, 9.09% non-binary; Race: 27.27% Black, 18.18% Asian, 18.18% White, 9.09% Arab, 9.09% Mixed, 9.09% Southeast Asian, 9.09% missing)"
# Repeated measures data
data <- data.frame(
"Age" = c(22, 22, 54, 54, 8, 8),
"Sex" = c("I", "F", "M", "M", "F", "F"),
"Gender" = c("N", "W", "W", "M", "M", "M"),
"Participant" = c("S1", "S1", "s2", "s2", "s3", "s3")
)
report_participants(data, age = "Age", sex = "Sex", gender = "Gender", participants = "Participant")
#> [1] "3 participants (Mean age = 28.0, SD = 23.6, range: [8, 54]; Sex: 33.3% females, 33.3% males, 33.3% other; Gender: 33.3% women, 33.3% men, 33.33% non-binary)"
# Grouped data
data <- data.frame(
"Age" = c(22, 22, 54, 54, 8, 8, 42, 42),
"Sex" = c("I", "I", "M", "M", "F", "F", "F", "F"),
"Gender" = c("N", "N", "W", "M", "M", "M", "Non-Binary", "Non-Binary"),
"Participant" = c("S1", "S1", "s2", "s2", "s3", "s3", "s4", "s4"),
"Condition" = c("A", "A", "A", "A", "B", "B", "B", "B")
)
report_participants(data,
age = "Age",
sex = "Sex",
gender = "Gender",
participants = "Participant",
group = "Condition"
)
#> [1] "For the 'Condition - A' group: 2 participants (Mean age = 38.0, SD = 22.6, range: [22, 54]; Sex: 0.0% females, 50.0% males, 50.0% other; Gender: 50.0% women, 0.0% men, 50.00% non-binary) and for the 'Condition - B' group: 2 participants (Mean age = 25.0, SD = 24.0, range: [8, 42]; Sex: 100.0% females, 0.0% males, 0.0% other; Gender: 0.0% women, 50.0% men, 50.00% non-binary)"
# Spell sample size
paste(
report_participants(data, participants = "Participant", spell_n = TRUE),
"were recruited in the study by means of torture and coercion."
)
#> [1] "Four participants (Mean age = 31.5, SD = 20.5, range: [8, 54]; Sex: 50.0% females, 25.0% males, 25.0% other; Gender: 25.0% women, 25.0% men, 50.00% non-binary) were recruited in the study by means of torture and coercion."