Encode variables to a specific format

Function to quick encode vector or columns to a specific format.

• as_numeric(): Encode columns to numeric using as.numeric().

• as_integer(): Encode columns to integer using as.integer().

• as_logical(): Encode columns to logical using as.logical().

• as_character(): Encode columns to character using as.character().

• as_factor(): Encode columns to factor using as.factor().

as_numeric(.data, ..., .keep = "all", .pull = FALSE)

as_integer(.data, ..., .keep = "all", .pull = FALSE)

as_logical(.data, ..., .keep = "all", .pull = FALSE)

as_character(.data, ..., .keep = "all", .pull = FALSE)

as_factor(.data, ..., .keep = "all", .pull = FALSE)

Arguments

.data	A data frame or a vector.
...	<tidy-select>. If .data is a data frame, then ... are the variable(s) to encode to a format.
.keep	Allows you to control which columns from .data are retained in the output. "all" (default) retains all variables. "used" keeps any variables used to make new variables.
.pull	Allows you to pull out the last column of the output. It is useful in combination with .keep = "used". In this case, a vector will be created with the used column.

Value

An object of the same class of .data with the variables in ... encoded to the specified format.

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)
library(tibble)
#> Warning: package 'tibble' was built under R version 4.0.4
#> 
#> Attaching package: 'tibble'
#> The following objects are masked from 'package:metan':
#> 
#>     column_to_rownames, remove_rownames, rownames_to_column
df <-
  tibble(y = rnorm(5),
         x1 = c(1:5),
         x2 = c(TRUE, TRUE, FALSE, FALSE, FALSE),
         x3 = letters[1:5],
         x4 = as.factor(x3))
df
#> # A tibble: 5 x 5
#>         y    x1 x2    x3    x4   
#>     <dbl> <int> <lgl> <chr> <fct>
#> 1  1.25       1 TRUE  a     a    
#> 2  0.433      2 TRUE  b     b    
#> 3 -0.0765     3 FALSE c     c    
#> 4 -0.139      4 FALSE d     d    
#> 5 -0.241      5 FALSE e     e    

# Convert y to integer
as_integer(df, y)
#> # A tibble: 5 x 5
#>       y    x1 x2    x3    x4   
#>   <int> <int> <lgl> <chr> <fct>
#> 1     1     1 TRUE  a     a    
#> 2     0     2 TRUE  b     b    
#> 3     0     3 FALSE c     c    
#> 4     0     4 FALSE d     d    
#> 5     0     5 FALSE e     e    
as_integer(df$y)
#> [1] 1 0 0 0 0

# convert x3 to factor
as_factor(df, x3)
#> # A tibble: 5 x 5
#>         y    x1 x2    x3    x4   
#>     <dbl> <int> <lgl> <fct> <fct>
#> 1  1.25       1 TRUE  a     a    
#> 2  0.433      2 TRUE  b     b    
#> 3 -0.0765     3 FALSE c     c    
#> 4 -0.139      4 FALSE d     d    
#> 5 -0.241      5 FALSE e     e    

# Convert all columns to character
as_character(df, everything())
#> # A tibble: 5 x 5
#>   y                   x1    x2    x3    x4   
#>   <chr>               <chr> <chr> <chr> <chr>
#> 1 1.24779392896803    1     TRUE  a     a    
#> 2 0.432763432791496   2     TRUE  b     b    
#> 3 -0.0764867483287394 3     FALSE c     c    
#> 4 -0.139495740422391  4     FALSE d     d    
#> 5 -0.241223117185153  5     FALSE e     e    

# Convert x2 to numeric and coerce to a vector
as_numeric(df, x2, .keep = "used", .pull = TRUE)
#> [1] 1 1 0 0 0
# }