How to Read Csv File in C# Form
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 data, comma separated values and tab separated values, respectively. read_csv2() uses ; for the field separator and , for the decimal point. This format is mutual in some European countries.
Usage
read_delim ( file, delim = Nil, quote = "\"", escape_backslash = Simulated, escape_double = TRUE, col_names = TRUE, col_types = Cipher, col_select = Cipher, id = NULL, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = True, annotate = "", trim_ws = Imitation, 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 = Truthful, lazy = should_read_lazy ( ) ) read_csv ( file, col_names = Truthful, col_types = Naught, 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 ( 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_csv2 ( file, col_names = Truthful, col_types = Aught, col_select = Naught, id = NULL, locale = default_locale ( ), na = c ( "", "NA" ), quoted_na = Truthful, 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 ( ) ) read_tsv ( file, col_names = True, col_types = Nix, col_select = Naught, id = Zippo, 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 ending in
.gz,.bz2,.xz, or.zipwill be automatically uncompressed. Files starting withhttp://,https://,ftp://, orftps://will be automatically downloaded. Remote gz files can as well exist automatically downloaded and decompressed.Literal data is most useful for examples and tests. To exist recognised as literal information, the input must be either wrapped with
I(), exist a string containing at least ane new line, or be a vector containing at least 1 cord with a new line.Using a value of
clipboard()will read from the system clipboard. - delim
-
Single grapheme used to separate fields within a record.
- quote
-
Single graphic symbol used to quote strings.
- escape_backslash
-
Does the file employ backslashes to escape special characters? This is more general than
escape_doubleequally backslashes can exist used to escape the delimiter character, the quote graphic symbol, or to add special characters similar\\n. - escape_double
-
Does the file escape quotes past doubling them? i.e. If this selection is
TRUE, the value""""represents a unmarried quote,\". - col_names
-
Either
TRUE,FALSEor a character vector of column names.If
TRUE, the showtime row of the input volition be used as the column names, and volition not be included in the data frame. IfImitation, column names volition 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 first row of the input volition exist read into the first row of the output data frame.Missing (
NA) column names volition generate a alarm, and exist filled in with dummy names...1,...2etc. Duplicate cavalcade names will generate a warning and be made unique, seename_repairto control how this is done. - col_types
-
I of
NULL, acols()specification, or a cord. Seevignette("readr")for more than details.If
NULL, all column types volition exist imputed fromguess_maxrows on the input interspersed throughout the file. This is user-friendly (and fast), just not robust. If the imputation fails, you'll demand to increase theguess_maxor supply the correct types yourself.Column specifications created past
list()orcols()must contain one cavalcade specification for each cavalcade. If you only want to read a subset of the columns, applycols_only().Alternatively, y'all can use a meaty string representation where each character represents one column:
-
c = character
-
i = integer
-
northward = number
-
d = double
-
l = logical
-
f = factor
-
D = date
-
T = date time
-
t = time
-
? = 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, prepareshow_col_types = Imitationor set `options(readr.show_col_types = False).
-
- col_select
-
Columns to include in the results. You can use the aforementioned mini-language as
dplyr::select()to refer to the columns by proper name. Employc()orlist()to utilize more than than one pick expression. Although this usage is less common,col_selectbesides accepts a numeric column index. Run across?tidyselect::languagefor full details on the option language. - id
-
The proper name of a column in which to shop the file path. This is useful when reading multiple input files and in that location is information in the file paths, such as the data drove date. If
Aught(the default) no extra column is created. - locale
-
The locale controls defaults that vary from place to place. The default locale is US-axial (like R), but you can utilise
locale()to create your own locale that controls things like the default time zone, encoding, decimal marking, big mark, and day/month names. - na
-
Character vector of strings to interpret as missing values. Prepare this option to
character()to indicate no missing values. - quoted_na
-
![[Deprecated]](https://readr.tidyverse.org/reference/figures/lifecycle-deprecated.svg)
Should missing values within quotes be treated as missing values (the default) or strings. This parameter is soft deprecated as of readr ii.0.0. - annotate
-
A string used to identify comments. Any text after the annotate 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 before reading data. If
commentis supplied whatsoever commented lines are ignored after skipping. - n_max
-
Maximum number of lines to read.
- guess_max
-
Maximum number of lines to employ for guessing column types. Meet
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 proper noun repair or checks, beyond basic beingness of names. -
"unique"(default value): Make sure names are unique and not empty. -
"check_unique": no name repair, simply check they areunique. -
"universal": Make the namesuniqueand syntactic. -
A function: utilize custom proper noun repair (east.g.,
name_repair = brand.namesfor names in the style of base R). -
A purrr-style bearding function, run into
rlang::as_function().
This statement is passed on equally
repairtovctrs::vec_as_names(). Run across there for more than details on these terms and the strategies used to enforce them. -
- num_threads
-
The number of processing threads to employ for initial parsing and lazy reading of data. If your data contains newlines inside fields the parser should automatically detect this and autumn back to using one thread simply. However if y'all know your file has newlines inside quoted fields it is safest to set
num_threads = 1explicitly. - progress
-
Brandish a progress bar? By default it will only brandish in an interactive session and not while knitting a document. The automatic progress bar can be disabled by setting choice
readr.show_progresstoFalse. - show_col_types
-
If
FALSE, do not show the guessed column types. IfTRUEalways show the cavalcade types, fifty-fifty if they are supplied. IfNULL(the default) only bear witness the column types if they are non explicitly supplied by thecol_typesargument. - skip_empty_rows
-
Should blank rows be ignored altogether? i.eastward. If this selection is
TRUEso blank rows volition not be represented at all. If information technology isFALSEthen 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 you are merely interested in a subset of the full dataset. Note, if you later write to the same file you read from you need to set
lazy = FALSE. On Windows the file will be locked and on other systems the memory map will get invalid.
Value
A tibble(). If there are parsing issues, a warning will alarm you. Yous 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 #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (eleven): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Use `spec()` to think the total cavalcade specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` 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 three.nine 2.62 16.five 0 1 4 4 #> ii 21 6 160 110 3.nine 2.88 17.0 0 ane iv 4 #> 3 22.8 4 108 93 iii.85 ii.32 18.6 1 ane 4 1 #> iv 21.four half-dozen 258 110 3.08 3.22 19.four 1 0 3 i #> 5 18.7 8 360 175 three.15 3.44 17.0 0 0 three ii #> 6 xviii.1 half dozen 225 105 2.76 iii.46 20.ii 1 0 three i #> 7 14.3 8 360 245 3.21 3.57 xv.8 0 0 three four #> 8 24.4 4 147. 62 three.69 3.xix 20 1 0 4 2 #> 9 22.viii 4 141. 95 3.92 3.15 22.9 1 0 four 2 #> x xix.2 6 168. 123 3.92 iii.44 xviii.3 1 0 four 4 #> # … with 22 more than rows read_csv ( readr_example ( "mtcars.csv.zip" ) ) #> Rows: 32 Columns: 11 #> ── Cavalcade specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> ℹ Utilize `spec()` to retrieve the full column specification for this data. #> ℹ Specify the cavalcade types or set `show_col_types = Imitation` 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 vi 160 110 iii.9 ii.62 16.5 0 1 iv iv #> 2 21 6 160 110 three.9 ii.88 17.0 0 i 4 4 #> 3 22.8 4 108 93 iii.85 2.32 18.half-dozen 1 i 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 one 0 three ane #> 5 18.7 8 360 175 three.15 3.44 17.0 0 0 3 2 #> 6 xviii.1 6 225 105 ii.76 iii.46 20.2 1 0 3 one #> 7 14.3 8 360 245 3.21 3.57 15.eight 0 0 three 4 #> eight 24.4 4 147. 62 iii.69 3.nineteen twenty 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.fifteen 22.9 one 0 4 2 #> 10 xix.ii six 168. 123 3.92 three.44 eighteen.3 one 0 iv four #> # … with 22 more than 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 recall the full cavalcade specification for this data. #> ℹ Specify the column types or prepare `show_col_types = False` to tranquillity 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 three.9 2.62 xvi.5 0 1 four 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 four #> 3 22.8 4 108 93 3.85 2.32 18.6 ane one four 1 #> iv 21.4 six 258 110 3.08 three.22 19.4 1 0 three 1 #> 5 18.7 8 360 175 three.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 xx.ii ane 0 3 1 #> vii fourteen.3 8 360 245 iii.21 3.57 fifteen.viii 0 0 3 iv #> viii 24.4 4 147. 62 3.69 three.xix 20 1 0 4 2 #> ix 22.eight 4 141. 95 3.92 3.xv 22.ix 1 0 four 2 #> x nineteen.2 6 168. 123 3.92 iii.44 18.3 1 0 iv four #> # … with 22 more than rows if ( Simulated ) { # 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,ii\n3,4" ) ) #> Rows: ii Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (2): x, y #> #> ℹ Use `spec()` to recall the full column specification for this data. #> ℹ Specify the column types or fix `show_col_types = FALSE` to tranquillity this bulletin. #> # A tibble: 2 × 2 #> x y #> <dbl> <dbl> #> 1 1 ii #> 2 3 4 # Cavalcade types -------------------------------------------------------------- # By default, readr guesses the columns types, looking at `guess_max` rows. # Y'all can override with a compact specification: read_csv ( I ( "10,y\n1,ii\n3,iv" ), col_types = "dc" ) #> # A tibble: 2 × 2 #> 10 y #> <dbl> <chr> #> 1 1 2 #> 2 iii 4 # Or with a listing of column types: read_csv ( I ( "ten,y\n1,2\n3,4" ), col_types = listing ( col_double ( ), col_character ( ) ) ) #> # A tibble: 2 × 2 #> x y #> <dbl> <chr> #> 1 1 ii #> 2 3 iv # If there are parsing problems, y'all get a alarm, and can extract # more details with issues() y <- read_csv ( I ( "10\n1\n2\nb" ), col_types = list ( col_double ( ) ) ) #> Alert: One or more parsing issues, see `bug()` for details y #> # A tibble: three × 1 #> x #> <dbl> #> 1 1 #> 2 ii #> 3 NA problems ( y ) #> # A tibble: ane × 5 #> row col expected actual 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: ii #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "," #> dbl (2): a, b #> #> ℹ Utilize `spec()` to call back the full column specification for this data. #> ℹ Specify the column types or ready `show_col_types = Imitation` to quiet this message. #> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 ane 2 read_csv2 ( I ( "a;b\n1,0;2,0" ) ) #> ℹ Using "','" as decimal and "'.'" as grouping marker. Utilize `read_delim()` for more than control. #> Rows: 1 Columns: 2 #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: ";" #> dbl (2): a, b #> #> ℹ Use `spec()` to remember the full column specification for this data. #> ℹ Specify the column types or ready `show_col_types = Fake` to quiet this message. #> # A tibble: i × 2 #> a b #> <dbl> <dbl> #> ane one 2 read_tsv ( I ( "a\tb\n1.0\t2.0" ) ) #> Rows: i Columns: ii #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "\t" #> 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 message. #> # A tibble: 1 × two #> a b #> <dbl> <dbl> #> i ane 2 read_delim ( I ( "a|b\n1.0|2.0" ), delim = "|" ) #> Rows: one Columns: ii #> ── Column specification ────────────────────────────────────────────────── #> Delimiter: "|" #> dbl (ii): a, b #> #> ℹ Use `spec()` to retrieve the full cavalcade specification for this information. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> # A tibble: one × 2 #> a b #> <dbl> <dbl> #> one 1 2 williamssallithere.blogspot.com
Source: https://readr.tidyverse.org/reference/read_delim.html
0 Response to "How to Read Csv File in C# Form"
Post a Comment