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Split multiple response question column into binary columns

Source: R/dta_mrq.R
dta_mrq.Rd

dta_mrq() splits a specified column in a data frame that contains multiple responses into multiple binary columns. Each binary column represents whether a given response option exists in the original data. It also allows for custom labeling of the new columns, with options for numeric conversion and clean column names.

Usage

dta_mrq(
  dat,
  .column,
  delimeter,
  prefix = NULL,
  as_numeric = FALSE,
  labels = c(TRUE, FALSE),
  is_clean_names = TRUE
)

Arguments

dat

A data frame containing the column to be split.

.column

The name of the column to split.

delimeter

A string representing the delimiter used to separate the different responses in the original column.

prefix

A string that is added as a prefix to the newly created column names. Default is NULL, which means no prefix will be added.

as_numeric

Logical. If TRUE, the new columns will be converted to numeric (1 for TRUE, 0 for FALSE). Default is FALSE.

labels

A vector of length 2 specifying the labels for the TRUE and FALSE values in the binary columns. Default is c(TRUE, FALSE).

is_clean_names

Logical. If TRUE, janitor::clean_names() is used to standardize the column names (e.g., convert to lowercase and replace spaces with underscores). Default is TRUE.

Value

A data frame with new binary columns added. The new columns represent each of the unique responses found in the original column, with the option to clean column names.

Details

This function is typically used when dealing with survey data where multiple options may be selected for a given question, and you want to split those options into individual binary columns indicating the presence or absence of each option.

Examples

data("data_gadgets")
dat <- data_gadgets
dta_gtable(dat)



  
gadgets_owned
    
Smartwatch, Tablet, Smartphone
Tablet, Smartwatch, Smart TV, Desktop Computer
Smartphone
Laptop, Tablet
Tablet, Smart TV, Digital Camera, Laptop
Laptop, Desktop Computer, Digital Camera, Smart TV, Smartphone
Digital Camera, Smartphone, Desktop Computer, Smartwatch
Smartwatch, Smart TV, Laptop, Smartphone
Desktop Computer
Smartphone, Laptop, Smart TV, Smartwatch
Tablet
Digital Camera, Tablet, Desktop Computer
Digital Camera, Desktop Computer, Smart TV, Smartwatch, Laptop
Tablet, Desktop Computer, Smart TV
Digital Camera, Desktop Computer, Smart TV, Smartphone, Laptop

# Split `gadgets_owned` column into separate columns.
# The created columns will be logical (i.e. TRUE / FALSE).

df <- dta_mrq(
 dat = dat,
 .column = gadgets_owned,
 delimeter = ", ",
 is_clean_names = TRUE)
dta_gtable(df)



  
gadgets_owned
      Smartwatch
      Tablet
      Smartphone
      Smart TV
      Desktop Computer
      Laptop
      Digital Camera
    
Smartwatch, Tablet, Smartphone
TRUE
TRUE
TRUE
FALSE
FALSE
FALSE
FALSE
Tablet, Smartwatch, Smart TV, Desktop Computer
TRUE
TRUE
FALSE
TRUE
TRUE
FALSE
FALSE
Smartphone
FALSE
FALSE
TRUE
FALSE
FALSE
FALSE
FALSE
Laptop, Tablet
FALSE
TRUE
FALSE
FALSE
FALSE
TRUE
FALSE
Tablet, Smart TV, Digital Camera, Laptop
FALSE
TRUE
FALSE
TRUE
FALSE
TRUE
TRUE
Laptop, Desktop Computer, Digital Camera, Smart TV, Smartphone
FALSE
FALSE
TRUE
TRUE
TRUE
TRUE
TRUE
Digital Camera, Smartphone, Desktop Computer, Smartwatch
TRUE
FALSE
TRUE
FALSE
TRUE
FALSE
TRUE
Smartwatch, Smart TV, Laptop, Smartphone
TRUE
FALSE
TRUE
TRUE
FALSE
TRUE
FALSE
Desktop Computer
FALSE
FALSE
FALSE
FALSE
TRUE
FALSE
FALSE
Smartphone, Laptop, Smart TV, Smartwatch
TRUE
FALSE
TRUE
TRUE
FALSE
TRUE
FALSE
Tablet
FALSE
TRUE
FALSE
FALSE
FALSE
FALSE
FALSE
Digital Camera, Tablet, Desktop Computer
FALSE
TRUE
FALSE
FALSE
TRUE
FALSE
TRUE
Digital Camera, Desktop Computer, Smart TV, Smartwatch, Laptop
TRUE
FALSE
FALSE
TRUE
TRUE
TRUE
TRUE
Tablet, Desktop Computer, Smart TV
FALSE
TRUE
FALSE
TRUE
TRUE
FALSE
FALSE
Digital Camera, Desktop Computer, Smart TV, Smartphone, Laptop
FALSE
FALSE
TRUE
TRUE
TRUE
TRUE
TRUE

# Convert the created columns from logical (TRUE / FALSE)
# columns to numeric.

df2 <- dta_mrq(
 dat = dat,
 .column = gadgets_owned,
 delimeter = ", ",
 as_numeric = TRUE,
 is_clean_names = TRUE
)
dta_gtable(df2)



  
gadgets_owned
      Smartwatch
      Tablet
      Smartphone
      Smart TV
      Desktop Computer
      Laptop
      Digital Camera
    
Smartwatch, Tablet, Smartphone
1
1
1
0
0
0
0
Tablet, Smartwatch, Smart TV, Desktop Computer
1
1
0
1
1
0
0
Smartphone
0
0
1
0
0
0
0
Laptop, Tablet
0
1
0
0
0
1
0
Tablet, Smart TV, Digital Camera, Laptop
0
1
0
1
0
1
1
Laptop, Desktop Computer, Digital Camera, Smart TV, Smartphone
0
0
1
1
1
1
1
Digital Camera, Smartphone, Desktop Computer, Smartwatch
1
0
1
0
1
0
1
Smartwatch, Smart TV, Laptop, Smartphone
1
0
1
1
0
1
0
Desktop Computer
0
0
0
0
1
0
0
Smartphone, Laptop, Smart TV, Smartwatch
1
0
1
1
0
1
0
Tablet
0
1
0
0
0
0
0
Digital Camera, Tablet, Desktop Computer
0
1
0
0
1
0
1
Digital Camera, Desktop Computer, Smart TV, Smartwatch, Laptop
1
0
0
1
1
1
1
Tablet, Desktop Computer, Smart TV
0
1
0
1
1
0
0
Digital Camera, Desktop Computer, Smart TV, Smartphone, Laptop
0
0
1
1
1
1
1

# You can specify the labels to be used. In the example
# below, the columns will be character with Yes / No.

df3 <- dta_mrq(
 dat = dat,
 .column = gadgets_owned,
 delimeter = ", ",
 labels = c("Yes", "No"),
 is_clean_names = TRUE,
)
dta_gtable(df3)



  
gadgets_owned
      Smartwatch
      Tablet
      Smartphone
      Smart TV
      Desktop Computer
      Laptop
      Digital Camera
    
Smartwatch, Tablet, Smartphone
Yes
Yes
Yes
No
No
No
No
Tablet, Smartwatch, Smart TV, Desktop Computer
Yes
Yes
No
Yes
Yes
No
No
Smartphone
No
No
Yes
No
No
No
No
Laptop, Tablet
No
Yes
No
No
No
Yes
No
Tablet, Smart TV, Digital Camera, Laptop
No
Yes
No
Yes
No
Yes
Yes
Laptop, Desktop Computer, Digital Camera, Smart TV, Smartphone
No
No
Yes
Yes
Yes
Yes
Yes
Digital Camera, Smartphone, Desktop Computer, Smartwatch
Yes
No
Yes
No
Yes
No
Yes
Smartwatch, Smart TV, Laptop, Smartphone
Yes
No
Yes
Yes
No
Yes
No
Desktop Computer
No
No
No
No
Yes
No
No
Smartphone, Laptop, Smart TV, Smartwatch
Yes
No
Yes
Yes
No
Yes
No
Tablet
No
Yes
No
No
No
No
No
Digital Camera, Tablet, Desktop Computer
No
Yes
No
No
Yes
No
Yes
Digital Camera, Desktop Computer, Smart TV, Smartwatch, Laptop
Yes
No
No
Yes
Yes
Yes
Yes
Tablet, Desktop Computer, Smart TV
No
Yes
No
Yes
Yes
No
No
Digital Camera, Desktop Computer, Smart TV, Smartphone, Laptop
No
No
Yes
Yes
Yes
Yes
Yes

# Any other labels could be used. For example
# Positive / Negative e.g. in the case of diseases.

df4 <- dta_mrq(
 dat = dat,
 .column = gadgets_owned,
 delimeter = ", ",
 labels = c("Positive", "Negative"),
 is_clean_names = TRUE,
)
dta_gtable(df4)



  
gadgets_owned
      Smartwatch
      Tablet
      Smartphone
      Smart TV
      Desktop Computer
      Laptop
      Digital Camera
    
Smartwatch, Tablet, Smartphone
Positive
Positive
Positive
Negative
Negative
Negative
Negative
Tablet, Smartwatch, Smart TV, Desktop Computer
Positive
Positive
Negative
Positive
Positive
Negative
Negative
Smartphone
Negative
Negative
Positive
Negative
Negative
Negative
Negative
Laptop, Tablet
Negative
Positive
Negative
Negative
Negative
Positive
Negative
Tablet, Smart TV, Digital Camera, Laptop
Negative
Positive
Negative
Positive
Negative
Positive
Positive
Laptop, Desktop Computer, Digital Camera, Smart TV, Smartphone
Negative
Negative
Positive
Positive
Positive
Positive
Positive
Digital Camera, Smartphone, Desktop Computer, Smartwatch
Positive
Negative
Positive
Negative
Positive
Negative
Positive
Smartwatch, Smart TV, Laptop, Smartphone
Positive
Negative
Positive
Positive
Negative
Positive
Negative
Desktop Computer
Negative
Negative
Negative
Negative
Positive
Negative
Negative
Smartphone, Laptop, Smart TV, Smartwatch
Positive
Negative
Positive
Positive
Negative
Positive
Negative
Tablet
Negative
Positive
Negative
Negative
Negative
Negative
Negative
Digital Camera, Tablet, Desktop Computer
Negative
Positive
Negative
Negative
Positive
Negative
Positive
Digital Camera, Desktop Computer, Smart TV, Smartwatch, Laptop
Positive
Negative
Negative
Positive
Positive
Positive
Positive
Tablet, Desktop Computer, Smart TV
Negative
Positive
Negative
Positive
Positive
Negative
Negative
Digital Camera, Desktop Computer, Smart TV, Smartphone, Laptop
Negative
Negative
Positive
Positive
Positive
Positive
Positive

# Use numeric values and specify a `prefix` for the
# column names.

df5 <- dta_mrq(
 dat = dat,
 .column = gadgets_owned,
 delimeter = ", ",
 prefix = "gad_",
 labels = c(1, 2),
 is_clean_names = TRUE
)
dta_gtable(df5)



  
gadgets_owned
      Smartwatch
      Tablet
      Smartphone
      Smart TV
      Desktop Computer
      Laptop
      Digital Camera
    
Smartwatch, Tablet, Smartphone
1
1
1
2
2
2
2
Tablet, Smartwatch, Smart TV, Desktop Computer
1
1
2
1
1
2
2
Smartphone
2
2
1
2
2
2
2
Laptop, Tablet
2
1
2
2
2
1
2
Tablet, Smart TV, Digital Camera, Laptop
2
1
2
1
2
1
1
Laptop, Desktop Computer, Digital Camera, Smart TV, Smartphone
2
2
1
1
1
1
1
Digital Camera, Smartphone, Desktop Computer, Smartwatch
1
2
1
2
1
2
1
Smartwatch, Smart TV, Laptop, Smartphone
1
2
1
1
2
1
2
Desktop Computer
2
2
2
2
1
2
2
Smartphone, Laptop, Smart TV, Smartwatch
1
2
1
1
2
1
2
Tablet
2
1
2
2
2
2
2
Digital Camera, Tablet, Desktop Computer
2
1
2
2
1
2
1
Digital Camera, Desktop Computer, Smart TV, Smartwatch, Laptop
1
2
2
1
1
1
1
Tablet, Desktop Computer, Smart TV
2
1
2
1
1
2
2
Digital Camera, Desktop Computer, Smart TV, Smartphone, Laptop
2
2
1
1
1
1
1