Vb.net Read Csv, Data Grid, Row.add, Row 2
read_csv() and read_tsv() are special cases of the more than general read_delim(). They're useful for reading the most 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 point. This format is common in some European countries.
Usage
read_delim ( file, delim = NULL, quote = "\"", escape_backslash = Fake, escape_double = TRUE, col_names = Truthful, col_types = NULL, col_select = NULL, id = NULL, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = TRUE, comment = "", trim_ws = Fake, 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 = NULL, col_select = NULL, id = NULL, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = TRUE, quote = "\"", annotate = "", trim_ws = True, skip = 0, n_max = Inf, guess_max = min ( m, 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 = Nada, col_select = NULL, id = NULL, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = TRUE, quote = "\"", annotate = "", trim_ws = Truthful, skip = 0, n_max = Inf, guess_max = min ( thou, 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 = NULL, col_select = Zilch, id = Nothing, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = TRUE, quote = "\"", annotate = "", trim_ws = Truthful, 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 unmarried string 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 can also be automatically downloaded and decompressed.Literal data is almost useful for examples and tests. To be recognised equally literal data, the input must be either wrapped with
I(), be a string containing at least one new line, or be a vector containing at least 1 string with a new line.Using a value of
clipboard()will read from the organisation clipboard. - delim
-
Single character used to split fields within a record.
- quote
-
Single graphic symbol used to quote strings.
- escape_backslash
-
Does the file use backslashes to escape special characters? This is more than general than
escape_doubleas backslashes can be used to escape the delimiter character, the quote character, or to add together special characters like\\n. - escape_double
-
Does the file escape quotes by doubling them? i.e. If this option is
TRUE, the value""""represents a single quote,\". - col_names
-
Either
TRUE,FALSEor a character vector of column names.If
TRUE, the start row of the input will be used as the column names, and will non be included in the data frame. IfFalse, cavalcade names volition be generated automatically: X1, X2, X3 etc.If
col_namesis a grapheme vector, the values volition be used as the names of the columns, and the first row of the input will exist read into the first row of the output information frame.Missing (
NA) column names volition generate a warning, and exist filled in with dummy names...1,...2etc. Duplicate column names will generate a warning and be made unique, meetname_repairto command how this is done. - col_types
-
One of
Nix, acols()specification, or a string. Seevignette("readr")for more than details.If
Goose egg, 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, y'all'll demand to increase theguess_maxor supply the correct types yourself.Cavalcade specifications created past
list()orcols()must contain ane cavalcade specification for each cavalcade. If you just want to read a subset of the columns, utilisecols_only().Alternatively, you lot can employ a meaty string representation where each character represents 1 column:
-
c = character
-
i = integer
-
n = number
-
d = double
-
50 = logical
-
f = factor
-
D = date
-
T = date fourth dimension
-
t = fourth dimension
-
? = guess
-
_ or - = skip
By default, reading a file without a column specification will impress a message showing what
readrguessed they were. To remove this message, readyshow_col_types = Falseor ready `options(readr.show_col_types = FALSE).
-
- col_select
-
Columns to include in the results. You can use the same mini-language as
dplyr::select()to refer to the columns past name. Usec()orlisting()to use more than than one selection expression. Although this usage is less common,col_selectalso accepts a numeric column alphabetize. See?tidyselect::languagefor total details on the selection language. - 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 as the data drove date. If
Nada(the default) no extra column is created. - locale
-
The locale controls defaults that vary from identify to identify. The default locale is US-axial (like R), but you tin can apply
locale()to create your own locale that controls things like the default fourth dimension zone, encoding, decimal mark, big mark, and solar day/month names. - na
-
Character vector of strings to translate equally missing values. Set this pick to
character()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 2.0.0. - comment
-
A string used to identify comments. Any text after the comment characters volition 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 earlier reading data. If
annotateis supplied any commented lines are ignored after skipping. - n_max
-
Maximum number of lines to read.
- guess_max
-
Maximum number of lines to use for guessing column types. Meet
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 basic existence of names. -
"unique"(default value): Brand certain names are unique and not empty. -
"check_unique": no name repair, only cheque they areunique. -
"universal": Make the namesuniqueand syntactic. -
A function: apply custom name repair (e.g.,
name_repair = brand.namesfor names in the way of base R). -
A purrr-fashion anonymous function, run into
rlang::as_function().
This statement is passed on as
repairtovctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them. -
- num_threads
-
The number of processing threads to use for initial parsing and lazy reading of data. If your information contains newlines within fields the parser should automatically detect this and autumn back to using one thread only. Yet if you know your file has newlines within quoted fields information technology is safest to set
num_threads = 1explicitly. - progress
-
Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The automatic progress bar tin can be disabled by setting option
readr.show_progresstoFALSE. - show_col_types
-
If
Imitation, do not show the guessed column types. IfTRUEalways testify the column types, even if they are supplied. IfZippo(the default) merely prove 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 option is
TRUEand so blank rows volition not exist represented at all. If it isFALSEthen they will be represented byNAvalues in all the columns. - lazy
-
Read values lazily? By default the file is initially but indexed and the values are read lazily when accessed. Lazy reading is useful interactively, specially if you are only interested in a subset of the total dataset. Notation, if you subsequently write to the aforementioned file you lot read from y'all demand to set
lazy = Fake. On Windows the file will exist locked and on other systems the memory map will go invalid.
Value
A tibble(). If there are parsing problems, a warning will warning you. You can retrieve the total details by calling bug() 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 column types or gear up `show_col_types = Fake` to quiet 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 half dozen 160 110 3.9 2.62 xvi.5 0 1 4 4 #> ii 21 6 160 110 three.ix two.88 17.0 0 1 4 4 #> 3 22.viii 4 108 93 three.85 2.32 18.6 1 i iv ane #> 4 21.4 6 258 110 3.08 3.22 xix.4 one 0 3 1 #> 5 18.7 viii 360 175 3.xv three.44 17.0 0 0 3 ii #> 6 18.one half dozen 225 105 two.76 3.46 xx.2 ane 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 iii iv #> 8 24.4 4 147. 62 3.69 iii.xix twenty ane 0 4 2 #> 9 22.8 four 141. 95 iii.92 iii.15 22.9 ane 0 4 ii #> 10 19.2 6 168. 123 iii.92 3.44 18.3 one 0 iv 4 #> # … with 22 more rows read_csv ( readr_example ( "mtcars.csv.goose egg" ) ) #> 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 cavalcade specification for this information. #> ℹ Specify the column types or fix `show_col_types = FALSE` to quiet 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 six 160 110 3.9 2.62 16.five 0 1 four 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 i 4 4 #> 3 22.eight four 108 93 3.85 2.32 xviii.6 one ane iv 1 #> 4 21.iv 6 258 110 3.08 3.22 19.4 1 0 iii 1 #> five 18.7 8 360 175 iii.xv 3.44 17.0 0 0 iii 2 #> 6 xviii.1 six 225 105 2.76 3.46 20.ii 1 0 3 ane #> vii fourteen.iii viii 360 245 3.21 3.57 15.8 0 0 3 four #> 8 24.4 iv 147. 62 three.69 3.xix xx 1 0 4 2 #> ix 22.viii 4 141. 95 3.92 three.fifteen 22.9 1 0 4 2 #> ten xix.2 vi 168. 123 3.92 iii.44 18.3 1 0 4 4 #> # … with 22 more rows read_csv ( readr_example ( "mtcars.csv.bz2" ) ) #> Rows: 32 Columns: 11 #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (eleven): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to tranquility 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> #> one 21 half-dozen 160 110 3.nine two.62 16.5 0 i 4 4 #> 2 21 6 160 110 3.nine two.88 17.0 0 i iv iv #> 3 22.8 4 108 93 3.85 2.32 18.half-dozen 1 1 4 1 #> iv 21.four 6 258 110 3.08 three.22 nineteen.four ane 0 iii 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 two #> vi 18.one 6 225 105 2.76 3.46 20.ii 1 0 iii 1 #> 7 14.three viii 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 iii.xix 20 1 0 4 ii #> 9 22.viii four 141. 95 iii.92 3.15 22.nine 1 0 4 two #> 10 19.2 6 168. 123 3.92 three.44 18.3 i 0 4 4 #> # … with 22 more than rows if ( Fake ) { # Including remote paths read_csv ( "https://github.com/tidyverse/readr/raw/main/inst/extdata/mtcars.csv" ) } # Or direct from a string with `I()` read_csv ( I ( "x,y\n1,two\n3,4" ) ) #> Rows: ii Columns: 2 #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (2): x, y #> #> ℹ Employ `spec()` to retrieve the full column specification for this information. #> ℹ Specify the column types or set `show_col_types = FALSE` to placidity this bulletin. #> # A tibble: 2 × two #> ten y #> <dbl> <dbl> #> 1 one 2 #> 2 iii iv # Column types -------------------------------------------------------------- # By default, readr guesses the columns types, looking at `guess_max` rows. # You lot can override with a compact specification: read_csv ( I ( "x,y\n1,two\n3,four" ), col_types = "dc" ) #> # A tibble: 2 × 2 #> x y #> <dbl> <chr> #> i 1 2 #> 2 iii 4 # Or with a list of column types: read_csv ( I ( "10,y\n1,2\n3,4" ), col_types = list ( col_double ( ), col_character ( ) ) ) #> # A tibble: 2 × 2 #> 10 y #> <dbl> <chr> #> 1 i two #> 2 three 4 # If there are parsing problems, you get a warning, and can extract # more than details with problems() y <- read_csv ( I ( "ten\n1\n2\nb" ), col_types = list ( col_double ( ) ) ) #> Warning: Ane or more parsing issues, see `problems()` for details y #> # A tibble: three × i #> ten #> <dbl> #> 1 i #> 2 2 #> 3 NA problems ( y ) #> # A tibble: ane × v #> row col expected bodily file #> <int> <int> <chr> <chr> <chr> #> 1 4 ane a double b /tmp/RtmpHUcdNA/file272e3ec33855 # File types ---------------------------------------------------------------- read_csv ( I ( "a,b\n1.0,2.0" ) ) #> Rows: 1 Columns: two #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (two): a, b #> #> ℹ Use `spec()` to call back the total column specification for this data. #> ℹ Specify the cavalcade types or set up `show_col_types = Imitation` to tranquility this bulletin. #> # A tibble: i × ii #> a b #> <dbl> <dbl> #> 1 1 ii read_csv2 ( I ( "a;b\n1,0;2,0" ) ) #> ℹ Using "','" as decimal and "'.'" as grouping marker. Use `read_delim()` for more command. #> Rows: 1 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: ";" #> dbl (2): a, b #> #> ℹ Use `spec()` to recollect the total column specification for this data. #> ℹ Specify the cavalcade types or set `show_col_types = Fake` to quiet this message. #> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 1 two read_tsv ( I ( "a\tb\n1.0\t2.0" ) ) #> Rows: one Columns: 2 #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "\t" #> dbl (ii): a, b #> #> ℹ Employ `spec()` to recollect the full column specification for this information. #> ℹ Specify the column types or set `show_col_types = Faux` to repose this bulletin. #> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 1 2 read_delim ( I ( "a|b\n1.0|ii.0" ), delim = "|" ) #> Rows: ane Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "|" #> dbl (ii): a, b #> #> ℹ Use `spec()` to call back the total cavalcade specification for this data. #> ℹ Specify the cavalcade types or set `show_col_types = FALSE` to quiet this bulletin. #> # A tibble: 1 × ii #> a b #> <dbl> <dbl> #> 1 1 2 Source: https://readr.tidyverse.org/reference/read_delim.html
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