This is useful if you need to do some manual munging - you can read the columns in as character, clean it up with (e.g.) regular expressions and then let readr take another stab at parsing it. The name is a homage to the base utils::type.convert().

type_convert(df, col_types = NULL, na = c("", "NA"), trim_ws = TRUE,
  locale = default_locale())

Arguments

df

A data frame.

col_types

One of NULL, a cols() specification, or a string. See vignette("readr") for more details.

If NULL, column types will be imputed using all rows.

na

Character vector of strings to interpret as missing values. Set this option to character() to indicate no missing values.

trim_ws

Should leading and trailing whitespace be trimmed from each field before parsing it?

locale

The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use locale() to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names.

Examples

df <- data.frame( x = as.character(runif(10)), y = as.character(sample(10)), stringsAsFactors = FALSE ) str(df)
#> 'data.frame': 10 obs. of 2 variables: #> $ x: chr "0.0807501375675201" "0.834333037259057" "0.600760886212811" "0.157208441523835" ... #> $ y: chr "9" "2" "1" "3" ...
str(type_convert(df))
#> Parsed with column specification: #> cols( #> x = col_double(), #> y = col_double() #> )
#> 'data.frame': 10 obs. of 2 variables: #> $ x: num 0.0808 0.8343 0.6008 0.1572 0.0074 ... #> $ y: num 9 2 1 3 7 8 10 5 6 4
df <- data.frame(x = c("NA", "10"), stringsAsFactors = FALSE) str(type_convert(df))
#> Parsed with column specification: #> cols( #> x = col_double() #> )
#> 'data.frame': 2 obs. of 1 variable: #> $ x: num NA 10
# Type convert can be used to infer types from an entire dataset # first read the data as character data <- read_csv(readr_example("mtcars.csv"), col_types = cols(.default = col_character())) str(data)
#> Classes ‘spec_tbl_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 32 obs. of 11 variables: #> $ mpg : chr "21" "21" "22.8" "21.4" ... #> $ cyl : chr "6" "6" "4" "6" ... #> $ disp: chr "160" "160" "108" "258" ... #> $ hp : chr "110" "110" "93" "110" ... #> $ drat: chr "3.9" "3.9" "3.85" "3.08" ... #> $ wt : chr "2.62" "2.875" "2.32" "3.215" ... #> $ qsec: chr "16.46" "17.02" "18.61" "19.44" ... #> $ vs : chr "0" "0" "1" "1" ... #> $ am : chr "1" "1" "1" "0" ... #> $ gear: chr "4" "4" "4" "3" ... #> $ carb: chr "4" "4" "1" "1" ... #> - attr(*, "spec")= #> .. cols( #> .. .default = col_character(), #> .. mpg = col_character(), #> .. cyl = col_character(), #> .. disp = col_character(), #> .. hp = col_character(), #> .. drat = col_character(), #> .. wt = col_character(), #> .. qsec = col_character(), #> .. vs = col_character(), #> .. am = col_character(), #> .. gear = col_character(), #> .. carb = col_character() #> .. )
# Then convert it with type_convert type_convert(data)
#> Parsed with column specification: #> cols( #> mpg = col_double(), #> cyl = col_double(), #> disp = col_double(), #> hp = col_double(), #> drat = col_double(), #> wt = col_double(), #> qsec = col_double(), #> vs = col_double(), #> am = col_double(), #> gear = col_double(), #> carb = col_double() #> )
#> # A tibble: 32 x 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # ... with 22 more rows