It's useful for reading the most common types of flat file data, comma separated values and tab separated values.
Usage
read_chr(
file,
delim = ",",
locale = NULL,
...,
date_names = "en",
date_format = "%AD",
time_format = "%AT",
decimal_mark = ".",
grouping_mark = "",
tz = "CET",
encoding = "UTF-8",
asciify = FALSE
)Arguments
- file
Either a path to a file, a connection, or literal data (either a single string or a raw vector).
- delim
Single character used to separate fields within a record.
- 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.
- ...
Other parameters to readr::read_delim.
- date_names
"en" from readr::locale
- date_format
"%AD" from readr::locale
- time_format
"%AT" from readr::locale
- decimal_mark
"." from readr::locale
- grouping_mark
"" from readr::locale
- tz
"CET"
- encoding
"UTF-8"
- asciify
FALSE
Details
The read_chr function works like readr::read_delim, except that
column sreturned would be characters and with clean names. It requires readr and janitor packages installed.
Examples
read_chr(readr::readr_example("mtcars.csv"), delim = ",")
#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 21 6 160 110 3.9 2.62 16.46 0 1 4 4
#> 2 21 6 160 110 3.9 2.875 17.02 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
#> 8 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows