C++ How to Read Bulk Data From Disk and Turn It Into Objects Without Reinterpret_cast
read_csv() and read_tsv() are special cases of the more general read_delim(). They're useful for reading the almost mutual types of flat file data, comma separated values and tab separated values, respectively. read_csv2() uses ; for the field separator and , for the decimal bespeak. This format is common in some European countries.
Usage
read_delim ( file, delim = NULL, quote = "\"", escape_backslash = FALSE, escape_double = TRUE, col_names = TRUE, col_types = NULL, col_select = NULL, id = Nothing, 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 = TRUE, lazy = should_read_lazy ( ) ) read_csv ( file, col_names = TRUE, col_types = Zippo, 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 ( chiliad, n_max ), name_repair = "unique", num_threads = readr_threads ( ), progress = show_progress ( ), show_col_types = should_show_types ( ), skip_empty_rows = TRUE, lazy = should_read_lazy ( ) ) read_csv2 ( 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 ( 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 = 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 ( 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 cord or a raw vector).
Files catastrophe in
.gz,.bz2,.xz, or.zipwill be automatically uncompressed. Files starting withhttp://,https://,ftp://, orftps://will be automatically downloaded. Remote gz files tin can too be automatically downloaded and decompressed.Literal data is most useful for examples and tests. To be recognised every bit literal data, the input must be either wrapped with
I(), exist a string containing at to the lowest degree one new line, or be a vector containing at least one cord with a new line.Using a value of
clipboard()will read from the system clipboard. - delim
-
Single character used to separate fields within a record.
- quote
-
Single grapheme used to quote strings.
- escape_backslash
-
Does the file utilize backslashes to escape special characters? This is more general than
escape_doubleas backslashes tin can be used to escape the delimiter character, the quote grapheme, or to add special characters similar\\northward. - escape_double
-
Does the file escape quotes past doubling them? i.e. If this selection is
Truthful, the value""""represents a single quote,\". - col_names
-
Either
True,Falseor a grapheme vector of column names.If
TRUE, the first row of the input will be used as the column names, and volition non be included in the data frame. IfFALSE, column names will be generated automatically: X1, X2, X3 etc.If
col_namesis a grapheme vector, the values will exist used as the names of the columns, and the commencement row of the input will be read into the first row of the output data frame.Missing (
NA) column names volition generate a warning, and exist filled in with dummy names...one,...2etc. Indistinguishable column names will generate a warning and be made unique, seename_repairto command how this is washed. - col_types
-
One of
Zero, acols()specification, or a string. Seevignette("readr")for more than details.If
Nada, all column types volition be imputed fromguess_maxrows on the input interspersed throughout the file. This is convenient (and fast), but not robust. If the imputation fails, you'll need to increase theguess_maxor supply the right types yourself.Column specifications created by
list()orcols()must comprise one column specification for each column. If you only want to read a subset of the columns, usecols_only().Alternatively, you can utilize a meaty string representation where each character represents one cavalcade:
-
c = graphic symbol
-
i = integer
-
northward = number
-
d = double
-
l = logical
-
f = factor
-
D = date
-
T = date time
-
t = time
-
? = guess
-
_ or - = skip
Past default, reading a file without a cavalcade specification will print a message showing what
readrguessed they were. To remove this message, setshow_col_types = FALSEor set `options(readr.show_col_types = False).
-
- col_select
-
Columns to include in the results. You can apply the aforementioned mini-language as
dplyr::select()to refer to the columns by name. Usec()orlist()to apply more one option expression. Although this usage is less common,col_selecttoo accepts a numeric column alphabetize. See?tidyselect::languagefor full details on the selection linguistic communication. - id
-
The proper name of a column in which to shop the file path. This is useful when reading multiple input files and there is data in the file paths, such equally the data drove engagement. If
Cypher(the default) no extra column is created. - locale
-
The locale controls defaults that vary from identify to identify. The default locale is United states-centric (like R), simply you lot can use
locale()to create your own locale that controls things like the default time zone, encoding, decimal marker, large marking, and solar day/month names. - na
-
Character vector of strings to interpret as missing values. Set this option to
grapheme()to indicate no missing values. - quoted_na
-
![[Deprecated]](https://readr.tidyverse.org/reference/figures/lifecycle-deprecated.svg)
Should missing values inside quotes be treated as missing values (the default) or strings. This parameter is soft deprecated as of readr two.0.0. - comment
-
A string used to identify comments. Any text afterwards the comment characters will exist 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 earlier reading information. If
commentis supplied any commented lines are ignored later on skipping. - n_max
-
Maximum number of lines to read.
- guess_max
-
Maximum number of lines to use for guessing column types. See
vignette("column-types", package = "readr")for more details. - name_repair
-
Handling of column names. The default behaviour is to ensure cavalcade names are
"unique". Various repair strategies are supported:-
"minimal": No name repair or checks, beyond bones existence of names. -
"unique"(default value): Brand sure names are unique and not empty. -
"check_unique": no name repair, only check they areunique. -
"universal": Make the namesuniqueand syntactic. -
A function: utilize custom name repair (e.yard.,
name_repair = make.namesfor names in the mode of base R). -
A purrr-style anonymous function, encounter
rlang::as_function().
This argument is passed on as
repairtovctrs::vec_as_names(). Run into in that location for more 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 within fields the parser should automatically detect this and fall back to using one thread simply. However if you know your file has newlines within quoted fields it is safest to ready
num_threads = 1explicitly. - progress
-
Display a progress bar? By default it volition but brandish in an interactive session and not while knitting a document. The automatic progress bar can be disabled by setting option
readr.show_progresstoFALSE. - show_col_types
-
If
FALSE, do non show the guessed column types. IfTRUEalways show the column types, even if they are supplied. IfZip(the default) simply show the column types if they are not explicitly supplied by thecol_typesargument. - skip_empty_rows
-
Should blank rows be ignored altogether? i.e. If this pick is
Truethen blank rows will not exist represented at all. If information technology isFalseand so they will be represented byNAvalues in all the columns. - lazy
-
Read values lazily? By default the file is initially simply indexed and the values are read lazily when accessed. Lazy reading is useful interactively, particularly if you lot are merely interested in a subset of the full dataset. Note, if you later write to the aforementioned file you read from you demand to fix
lazy = FALSE. On Windows the file will be locked and on other systems the memory map volition become invalid.
Value
A tibble(). If in that location are parsing bug, a warning volition warning y'all. You tin can retrieve the full details by calling problems() on your dataset.
Examples
# Input sources ------------------------------------------------------------- # Read from a path read_csv ( readr_example ( "mtcars.csv" ) ) #> Rows: 32 Columns: xi #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Use `spec()` to retrieve the full cavalcade specification for this data. #> ℹ Specify the column types or prepare `show_col_types = FALSE` to tranquility this bulletin. #> # 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.ix 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 iv 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 one four i #> iv 21.iv 6 258 110 3.08 3.22 nineteen.4 1 0 3 1 #> v xviii.7 8 360 175 3.xv three.44 17.0 0 0 3 two #> 6 18.1 6 225 105 2.76 iii.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 three.21 3.57 fifteen.8 0 0 iii 4 #> 8 24.4 4 147. 62 3.69 three.xix 20 1 0 4 2 #> 9 22.8 four 141. 95 3.92 3.15 22.9 1 0 4 two #> ten nineteen.two 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows read_csv ( readr_example ( "mtcars.csv.zip" ) ) #> Rows: 32 Columns: 11 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (xi): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Use `spec()` to recall the full column specification for this information. #> ℹ Specify the column types or fix `show_col_types = Imitation` to quiet this bulletin. #> # 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 iii.9 2.62 16.v 0 1 iv four #> two 21 half dozen 160 110 3.ix 2.88 17.0 0 1 4 4 #> iii 22.8 iv 108 93 iii.85 2.32 18.6 ane 1 4 one #> iv 21.iv six 258 110 3.08 3.22 19.4 ane 0 3 1 #> 5 eighteen.7 8 360 175 iii.15 3.44 17.0 0 0 three ii #> 6 eighteen.1 6 225 105 ii.76 3.46 20.2 1 0 three 1 #> 7 14.3 viii 360 245 three.21 3.57 15.8 0 0 3 iv #> eight 24.4 4 147. 62 3.69 3.nineteen xx i 0 4 2 #> 9 22.viii four 141. 95 three.92 3.15 22.nine one 0 4 2 #> x 19.ii vi 168. 123 iii.92 3.44 18.3 ane 0 iv 4 #> # … with 22 more than rows read_csv ( readr_example ( "mtcars.csv.bz2" ) ) #> Rows: 32 Columns: xi #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (eleven): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Apply `spec()` to retrieve the total column specification for this information. #> ℹ Specify the column types or prepare `show_col_types = Faux` to quiet this message. #> # 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 6 160 110 3.ix 2.62 16.5 0 1 4 4 #> two 21 half dozen 160 110 3.9 ii.88 17.0 0 1 4 4 #> three 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> four 21.4 vi 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 xviii.7 viii 360 175 iii.fifteen 3.44 17.0 0 0 iii ii #> 6 18.1 6 225 105 two.76 3.46 twenty.two 1 0 three 1 #> seven 14.three 8 360 245 iii.21 3.57 15.8 0 0 3 4 #> viii 24.4 four 147. 62 3.69 iii.xix 20 1 0 iv two #> ix 22.viii 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> ten xix.2 6 168. 123 3.92 iii.44 eighteen.3 1 0 4 four #> # … 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 ( "x,y\n1,2\n3,iv" ) ) #> Rows: two Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (2): x, y #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to placidity this bulletin. #> # A tibble: 2 × 2 #> 10 y #> <dbl> <dbl> #> 1 ane 2 #> 2 3 four # Column types -------------------------------------------------------------- # By default, readr guesses the columns types, looking at `guess_max` rows. # You can override with a compact specification: read_csv ( I ( "x,y\n1,2\n3,4" ), col_types = "dc" ) #> # A tibble: ii × ii #> ten y #> <dbl> <chr> #> 1 1 two #> ii three 4 # Or with a list of cavalcade types: read_csv ( I ( "x,y\n1,ii\n3,4" ), col_types = list ( col_double ( ), col_character ( ) ) ) #> # A tibble: 2 × 2 #> x y #> <dbl> <chr> #> i 1 two #> 2 3 iv # If there are parsing problems, you become a alarm, and can excerpt # more details with problems() y <- read_csv ( I ( "x\n1\n2\nb" ), col_types = listing ( col_double ( ) ) ) #> Warning: One or more parsing issues, see `problems()` for details y #> # A tibble: three × 1 #> 10 #> <dbl> #> one 1 #> 2 2 #> 3 NA problems ( y ) #> # A tibble: 1 × 5 #> row col expected bodily file #> <int> <int> <chr> <chr> <chr> #> 1 4 1 a double b /tmp/RtmpHUcdNA/file272e3ec33855 # File types ---------------------------------------------------------------- read_csv ( I ( "a,b\n1.0,2.0" ) ) #> Rows: 1 Columns: 2 #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (two): a, b #> #> ℹ Use `spec()` to retrieve the total column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 1 2 read_csv2 ( I ( "a;b\n1,0;ii,0" ) ) #> ℹ Using "','" as decimal and "'.'" as grouping marking. Use `read_delim()` for more control. #> Rows: 1 Columns: ii #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: ";" #> dbl (2): a, b #> #> ℹ Use `spec()` to retrieve the full cavalcade specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to tranquility this bulletin. #> # A tibble: one × ii #> a b #> <dbl> <dbl> #> i 1 two read_tsv ( I ( "a\tb\n1.0\t2.0" ) ) #> Rows: 1 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "\t" #> dbl (ii): a, b #> #> ℹ Use `spec()` to retrieve the total column specification for this data. #> ℹ Specify the column types or set `show_col_types = Imitation` to quiet this bulletin. #> # A tibble: one × 2 #> a b #> <dbl> <dbl> #> 1 1 2 read_delim ( I ( "a|b\n1.0|2.0" ), delim = "|" ) #> Rows: 1 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "|" #> dbl (2): a, b #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the cavalcade types or prepare `show_col_types = FALSE` to tranquility this message. #> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> i ane ii Source: https://readr.tidyverse.org/reference/read_delim.html
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