qol (version 1.2.0)

transpose_plus: Fast And Powerful Yet Simple To Use Transpose

Description

transpose_plus() is able to reshape a data frame from long to wide and from wide to long. In the long to wide transposition variables can be nested or placed side by side. With the wide to long transposition it is also possible to transpose multiple variables at once.

Additionally transpose_plus() is able to weight results before transposing them from long to wide.

The function also makes use of formats, which means you don't need to create variables storing the new variable names before transposition. You can just use formats to name the new variables and with multilabels you can even generate new variable expressions at the same time.

Usage

transpose_plus(
  data_frame,
  preserve = NULL,
  pivot,
  values = NULL,
  formats = c(),
  weight = NULL,
  na.rm = .qol_options[["na.rm"]],
  monitor = .qol_options[["monitor"]]
)

Value

Returns a transposed data table.

Arguments

data_frame

A data frame to transpose

preserve

Variables to keep and preserve in their current form.

pivot

A vector that provides the expressions of single variables or od variable combinations that should be transposed. To nest variables use the form: "var1 + var2 + var3 + ...".

values

A vector containing all value variables that should be transposed.

formats

A list in which is specified which formats should be applied to which variables.

weight

Put in a weight variable to compute weighted results.

na.rm

FALSE by default. If TRUE removes all NA values from the preserve and pivot variables.

monitor

FALSE by default. If TRUE, outputs two charts to visualize the functions time consumption.

Details

transpose_plus() is just very loosely based on the 'SAS' procedure Proc Transpose, and the possibilities of a Data-Step transposition using loops.

The transposition methods 'SAS' has to offer are actually fairly weak. Which is weird because all tools are there to have another powerful function. So transpose_plus() tries to create the function 'SAS' should have.

The function is able to interpret which transposition direction the user wants by just looking at what the user provided with the function parameters. For a long to wide transposition it is natural to just provide variables to transpose. While it is also just natural to provide new variable names when transposing from wide to long. That alone reduces the number of parameters the user has to enter to perform a simple transposition.

The real magic happens when formats come into play. With their help you can not only name new variables or their expressions, but you can also generate completely new expressions with no effort, just with the help of multilabels.

See Also

Creating formats: discrete_format() and interval_format().

Functions that also make use of formats: frequencies(), crosstabs(), any_table(), recode(), recode_multi(), sort_plus().

Examples

# Example formats
age. <- discrete_format(
    "Total"          = 0:100,
    "under 18"       = 0:17,
    "18 to under 25" = 18:24,
    "25 to under 55" = 25:54,
    "55 to under 65" = 55:64,
    "65 and older"   = 65:100)

sex. <- discrete_format(
    "Total"  = 1:2,
    "Male"   = 1,
    "Female" = 2)

sex2. <- discrete_format(
    "Total"  = c("Male", "Female"),
    "Male"   = "Male",
    "Female" = "Female")

income. <- interval_format(
    "Total"              = 0:99999,
    "below 500"          = 0:499,
    "500 to under 1000"  = 500:999,
    "1000 to under 2000" = 1000:1999,
    "2000 and more"      = 2000:99999)

# Example data frame
my_data <- dummy_data(1000)

# Transpose from long to wide and use a multilabel to generate additional categories
long_to_wide <- my_data |>
    transpose_plus(preserve = c(year, age),
                   pivot    = c("sex", "education"),
                   values   = income,
                   formats  = list(sex = sex., age = age.),
                   weight   = weight,
                   na.rm    = TRUE)

# Transpose back from wide to long
wide_to_long <- long_to_wide |>
    transpose_plus(preserve = c(year, age),
                   pivot    = list(sex       = c("Total", "Male", "Female"),
                                   education = c("low", "middle", "high")))

# Nesting variables in long to wide transposition
nested <- my_data |>
    transpose_plus(preserve = c(year, age),
                   pivot    = "sex + education",
                   values   = income,
                   formats  = list(sex = sex., age = age.),
                   weight   = weight,
                   na.rm    = TRUE)

# Or both, nested and un-nested, at the same time
both <- my_data |>
    transpose_plus(preserve = c(year, age),
                   pivot    = c("sex + education", "sex", "education"),
                   values   = income,
                   formats  = list(sex = sex., age = age.),
                   weight   = weight,
                   na.rm    = TRUE)