Read a Column From Csv in R
read_csv() and read_tsv() are special cases of the more general read_delim(). They're useful for reading the most common types of flat file information, comma separated values and tab separated values, respectively. read_csv2() uses ; for the field separator and , for the decimal point. This format is common in some European countries.
Usage
read_delim ( file, delim = NULL, quote = "\"", escape_backslash = FALSE, escape_double = Truthful, col_names = True, col_types = Naught, col_select = NULL, id = NULL, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = TRUE, comment = "", trim_ws = FALSE, skip = 0, n_max = Inf, guess_max = min ( 1000, n_max ), name_repair = "unique", num_threads = readr_threads ( ), progress = show_progress ( ), show_col_types = should_show_types ( ), skip_empty_rows = Truthful, lazy = should_read_lazy ( ) ) read_csv ( file, col_names = Truthful, col_types = NULL, col_select = NULL, id = NULL, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = True, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min ( thou, n_max ), name_repair = "unique", num_threads = readr_threads ( ), progress = show_progress ( ), show_col_types = should_show_types ( ), skip_empty_rows = Truthful, lazy = should_read_lazy ( ) ) read_csv2 ( file, col_names = TRUE, col_types = NULL, col_select = Zero, id = Nil, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = Truthful, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min ( 1000, n_max ), progress = show_progress ( ), name_repair = "unique", num_threads = readr_threads ( ), show_col_types = should_show_types ( ), skip_empty_rows = TRUE, lazy = should_read_lazy ( ) ) read_tsv ( file, col_names = True, col_types = Aught, col_select = Nil, id = Cypher, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min ( 1000, n_max ), progress = show_progress ( ), name_repair = "unique", num_threads = readr_threads ( ), show_col_types = should_show_types ( ), skip_empty_rows = TRUE, lazy = should_read_lazy ( ) ) Arguments
- file
-
Either a path to a file, a connection, or literal data (either a single string or a raw vector).
Files catastrophe in
.gz,.bz2,.xz, or.zipwill exist automatically uncompressed. Files starting withhttp://,https://,ftp://, orftps://will be automatically downloaded. Remote gz files tin can also be automatically downloaded and decompressed.Literal information is most useful for examples and tests. To exist recognised every bit literal data, the input must exist either wrapped with
I(), be a string containing at least one new line, or be a vector containing at to the lowest degree 1 string with a new line.Using a value of
clipboard()will read from the system clipboard. - delim
-
Unmarried character used to dissever fields within a record.
- quote
-
Single character used to quote strings.
- escape_backslash
-
Does the file apply backslashes to escape special characters? This is more than full general than
escape_doubleas backslashes can exist used to escape the delimiter character, the quote graphic symbol, or to add special characters like\\due north. - escape_double
-
Does the file escape quotes by doubling them? i.due east. If this option is
Truthful, the value""""represents a single quote,\". - col_names
-
Either
True,FALSEor a graphic symbol vector of column names.If
True, the kickoff row of the input will be used as the cavalcade names, and will not be included in the data frame. IfFaux, cavalcade names will be generated automatically: X1, X2, X3 etc.If
col_namesis a graphic symbol vector, the values will exist used every bit the names of the columns, and the first row of the input will be read into the first row of the output data frame.Missing (
NA) column names will generate a alarm, and exist filled in with dummy names...1,...iietc. Indistinguishable cavalcade names volition generate a warning and be made unique, seename_repairto control how this is done. - col_types
-
1 of
NULL, acols()specification, or a cord. Seevignette("readr")for more than details.If
NULL, all column types will exist imputed fromguess_maxrows on the input interspersed throughout the file. This is convenient (and fast), only not robust. If the imputation fails, you lot'll need to increase theguess_maxor supply the correct types yourself.Column specifications created by
list()orcols()must contain 1 cavalcade specification for each column. If you merely want to read a subset of the columns, usecols_only().Alternatively, yous tin can use a compact string representation where each character represents one column:
-
c = character
-
i = integer
-
n = number
-
d = double
-
l = logical
-
f = factor
-
D = date
-
T = engagement time
-
t = time
-
? = guess
-
_ or - = skip
By default, reading a file without a cavalcade specification will impress a message showing what
readrguessed they were. To remove this message, set upshow_col_types = FALSEor set `options(readr.show_col_types = FALSE).
-
- col_select
-
Columns to include in the results. You can use the same mini-linguistic communication every bit
dplyr::select()to refer to the columns by name. Utilisec()orlist()to use more i selection expression. Although this usage is less common,col_selectlikewise accepts a numeric column index. See?tidyselect::languagefor full details on the selection linguistic communication. - id
-
The name of a column in which to store the file path. This is useful when reading multiple input files and there is data in the file paths, such as the data collection appointment. If
NULL(the default) no actress column is created. - locale
-
The locale controls defaults that vary from place to place. The default locale is U.s.-centric (like R), merely you can use
locale()to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/calendar month names. - na
-
Character vector of strings to interpret as missing values. Ready this option to
grapheme()to point no missing values. - quoted_na
-
![[Deprecated]](https://readr.tidyverse.org/reference/figures/lifecycle-deprecated.svg)
Should missing values inside quotes exist treated as missing values (the default) or strings. This parameter is soft deprecated equally of readr ii.0.0. - comment
-
A cord used to identify comments. Whatsoever text later the annotate characters will be silently ignored.
- trim_ws
-
Should leading and trailing whitespace (ASCII spaces and tabs) be trimmed from each field before parsing it?
- skip
-
Number of lines to skip before reading data. If
commentis supplied whatsoever commented lines are ignored later on skipping. - n_max
-
Maximum number of lines to read.
- guess_max
-
Maximum number of lines to employ for guessing column types. Come across
vignette("cavalcade-types", package = "readr")for more details. - name_repair
-
Treatment of column names. The default behaviour is to ensure column names are
"unique". Diverse repair strategies are supported:-
"minimal": No name repair or checks, beyond bones existence of names. -
"unique"(default value): Make certain names are unique and not empty. -
"check_unique": no name repair, merely check they areunique. -
"universal": Make the namesuniqueand syntactic. -
A function: utilise custom name repair (e.g.,
name_repair = make.namesfor names in the style of base R). -
A purrr-style bearding part, see
rlang::as_function().
This argument is passed on equally
repairtovctrs::vec_as_names(). See there for more than details on these terms and the strategies used to enforce them. -
- num_threads
-
The number of processing threads to utilize for initial parsing and lazy reading of data. If your data contains newlines inside fields the parser should automatically find this and autumn back to using i thread just. However if you know your file has newlines within quoted fields it is safest to set
num_threads = 1explicitly. - progress
-
Brandish a progress bar? Past default it will merely display in an interactive session and non while knitting a certificate. The automatic progress bar can be disabled past setting option
readr.show_progresstoSimulated. - show_col_types
-
If
FALSE, do non show the guessed column types. IfTRUEalways show the cavalcade types, even if they are supplied. IfAught(the default) only show the column types if they are not explicitly supplied by thecol_typesargument. - skip_empty_rows
-
Should blank rows be ignored birthday? i.e. If this option is
TRUEthen blank rows will not be represented at all. If information technology isSimulatedthen they will be represented pastNAvalues in all the columns. - lazy
-
Read values lazily? By default the file is initially only indexed and the values are read lazily when accessed. Lazy reading is useful interactively, particularly if y'all are only interested in a subset of the full dataset. Notation, if you lot subsequently write to the same file yous read from you need to set
lazy = FALSE. On Windows the file will exist locked and on other systems the memory map will become invalid.
Value
A tibble(). If at that place are parsing problems, a alert volition alert you. Y'all can retrieve the full details past calling problems() on your dataset.
Examples
# Input sources ------------------------------------------------------------- # Read from a path read_csv ( readr_example ( "mtcars.csv" ) ) #> Rows: 32 Columns: 11 #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Use `spec()` to retrieve the full column specification for this information. #> ℹ Specify the cavalcade types or set `show_col_types = Faux` to serenity this message. #> # A tibble: 32 × 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 ii.62 sixteen.five 0 1 four 4 #> 2 21 6 160 110 3.nine 2.88 17.0 0 1 4 4 #> three 22.eight 4 108 93 three.85 ii.32 18.six 1 i 4 one #> iv 21.four 6 258 110 iii.08 iii.22 19.4 one 0 iii 1 #> 5 xviii.7 eight 360 175 iii.15 three.44 17.0 0 0 3 2 #> half dozen 18.one six 225 105 2.76 3.46 20.2 ane 0 3 1 #> 7 xiv.iii viii 360 245 3.21 3.57 15.eight 0 0 3 4 #> viii 24.4 four 147. 62 3.69 3.xix 20 1 0 four ii #> 9 22.eight 4 141. 95 3.92 iii.15 22.ix ane 0 iv 2 #> 10 19.two half-dozen 168. 123 three.92 3.44 18.3 1 0 iv 4 #> # … with 22 more rows read_csv ( readr_example ( "mtcars.csv.zero" ) ) #> Rows: 32 Columns: eleven #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Use `spec()` to recall the total column specification for this data. #> ℹ Specify the column types or gear up `show_col_types = Faux` to placidity this bulletin. #> # A tibble: 32 × eleven #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 six 160 110 iii.9 2.62 16.5 0 1 4 4 #> 2 21 half-dozen 160 110 3.9 two.88 17.0 0 i 4 4 #> iii 22.8 4 108 93 three.85 ii.32 18.6 1 1 4 1 #> 4 21.iv 6 258 110 3.08 3.22 19.iv i 0 3 1 #> 5 xviii.seven eight 360 175 3.15 three.44 17.0 0 0 3 2 #> half-dozen 18.1 six 225 105 2.76 three.46 twenty.2 one 0 3 1 #> 7 xiv.3 8 360 245 iii.21 3.57 15.eight 0 0 3 4 #> 8 24.four 4 147. 62 3.69 iii.19 20 one 0 4 ii #> 9 22.eight 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> x 19.2 6 168. 123 three.92 3.44 xviii.3 1 0 iv 4 #> # … with 22 more rows read_csv ( readr_example ( "mtcars.csv.bz2" ) ) #> Rows: 32 Columns: 11 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Use `spec()` to retrieve the full column specification for this information. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this bulletin. #> # A tibble: 32 × eleven #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> i 21 vi 160 110 three.9 ii.62 16.5 0 1 four 4 #> 2 21 half-dozen 160 110 3.nine ii.88 17.0 0 1 four 4 #> 3 22.eight 4 108 93 3.85 2.32 18.6 i one four one #> 4 21.4 6 258 110 iii.08 iii.22 xix.4 1 0 3 1 #> 5 18.seven 8 360 175 3.fifteen 3.44 17.0 0 0 3 2 #> 6 eighteen.i 6 225 105 two.76 iii.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 fifteen.8 0 0 three 4 #> 8 24.four four 147. 62 iii.69 3.19 20 1 0 4 2 #> 9 22.viii 4 141. 95 3.92 3.xv 22.9 1 0 4 ii #> 10 nineteen.2 6 168. 123 3.92 3.44 18.iii one 0 iv iv #> # … with 22 more rows if ( FALSE ) { # Including remote paths read_csv ( "https://github.com/tidyverse/readr/raw/main/inst/extdata/mtcars.csv" ) } # Or directly from a string with `I()` read_csv ( I ( "10,y\n1,2\n3,four" ) ) #> Rows: 2 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (two): x, y #> #> ℹ Use `spec()` to recollect the full column specification for this data. #> ℹ Specify the cavalcade types or set `show_col_types = Faux` to quiet this bulletin. #> # A tibble: 2 × ii #> x y #> <dbl> <dbl> #> 1 1 2 #> ii 3 4 # Column types -------------------------------------------------------------- # By default, readr guesses the columns types, looking at `guess_max` rows. # You can override with a compact specification: read_csv ( I ( "10,y\n1,2\n3,4" ), col_types = "dc" ) #> # A tibble: 2 × 2 #> x y #> <dbl> <chr> #> i ane two #> 2 three iv # Or with a list of column types: read_csv ( I ( "ten,y\n1,2\n3,4" ), col_types = list ( col_double ( ), col_character ( ) ) ) #> # A tibble: 2 × 2 #> x y #> <dbl> <chr> #> 1 ane 2 #> 2 3 4 # If in that location are parsing problems, you lot get a warning, and can extract # more than details with problems() y <- read_csv ( I ( "x\n1\n2\nb" ), col_types = list ( col_double ( ) ) ) #> Warning: Ane or more parsing issues, see `problems()` for details y #> # A tibble: 3 × ane #> x #> <dbl> #> 1 i #> 2 2 #> 3 NA problems ( y ) #> # A tibble: ane × 5 #> row col expected bodily file #> <int> <int> <chr> <chr> <chr> #> ane 4 ane a double b /tmp/RtmpHUcdNA/file272e3ec33855 # File types ---------------------------------------------------------------- read_csv ( I ( "a,b\n1.0,2.0" ) ) #> Rows: i Columns: two #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (2): a, b #> #> ℹ Use `spec()` to retrieve the full column specification for this information. #> ℹ Specify the column types or fix `show_col_types = Fake` to quiet this message. #> # A tibble: 1 × ii #> a b #> <dbl> <dbl> #> 1 1 two read_csv2 ( I ( "a;b\n1,0;2,0" ) ) #> ℹ Using "','" as decimal and "'.'" as grouping marker. Employ `read_delim()` for more than control. #> Rows: 1 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: ";" #> dbl (ii): a, b #> #> ℹ Use `spec()` to remember the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = Fake` to tranquility this bulletin. #> # A tibble: 1 × ii #> a b #> <dbl> <dbl> #> 1 i 2 read_tsv ( I ( "a\tb\n1.0\t2.0" ) ) #> Rows: 1 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "\t" #> dbl (2): a, b #> #> ℹ Use `spec()` to call up the total column specification for this information. #> ℹ Specify the column types or gear up `show_col_types = FALSE` to repose this message. #> # A tibble: ane × 2 #> a b #> <dbl> <dbl> #> 1 one 2 read_delim ( I ( "a|b\n1.0|2.0" ), delim = "|" ) #> Rows: 1 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "|" #> dbl (2): a, b #> #> ℹ Utilise `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> # A tibble: 1 × ii #> a b #> <dbl> <dbl> #> 1 1 2 Source: https://readr.tidyverse.org/reference/read_delim.html
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