R语言数据导入导出知识

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read_*(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"),

quoted_na = TRUE, comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000,

n_max), progress = interactive())

Try one of the following packages to import

other types of files

• haven - SPSS, Stata, and SAS files

• readxl - excel files (.xls and .xlsx)

• DBI - databases

• jsonlite - json

• xml2 - XML

• httr - Web APIs

• rvest - HTML (Web Scraping)

Save Data

Data Import : : CHEAT SHEET

Read Tabular Data - These functions share the common arguments: Data types

USEFUL ARGUMENTS

OTHER TYPES OF DATA

Comma delimited file

write_csv(x, path, na = "NA", append = FALSE,

col_names = !append)

File with arbitrary delimiter

write_delim(x, path, delim = " ", na = "NA",

append = FALSE, col_names = !append)

CSV for excel

write_excel_csv(x, path, na = "NA", append =

FALSE, col_names = !append)

String to file

write_file(x, path, append = FALSE)

String vector to file, one element per line

write_lines(x,path, na = "NA", append = FALSE)

Object to RDS file

write_rds(x, path, compress = c("none", "gz",

"bz2", "xz"), ...)

Tab delimited files

write_tsv(x, path, na = "NA", append = FALSE,

col_names = !append)

Save x, an R object, to path, a file path, as:

Skip lines

read_csv(f, skip = 1)

Read in a subset

read_csv(f, n_max = 1)

Missing Values

read_csv(f, na = c("1", "."))

Comma Delimited Files

read_csv("file.csv")

To make file.csv run:

write_file(x = "a,b,c1,2,34,5,NA", path = "file.csv")

Semi-colon Delimited Files

read_csv2("file2.csv")

write_file(x = "a;b;c1;2;34;5;NA", path = "file2.csv")

Files with Any Delimiter

read_delim("file.txt", delim = "|")

write_file(x = "a|b|c1|2|34|5|NA", path = "file.txt")

Fixed Width Files

read_fwf("file.fwf", col_positions = c(1, 3, 5))

write_file(x = "a b c1 2 34 5 NA", path = "file.fwf")

Tab Delimited Files

read_tsv("file.tsv") Also read_table().

write_file(x = "a b c1 2 34 5 NA", path = "file.tsv")

a,b,c

1,2,3

4,5,NA

a;b;c

1;2;3

4;5;NA

a|b|c

1|2|3

4|5|NA

a b c

1 2 3

4 5 NA

a,b,c

1,2,3

4,5,NA

Example file

write_file("a,b,c1,2,34,5,NA","file.csv")

f <- "file.csv"

No header

read_csv(f, col_names = FALSE)

Provide header

read_csv(f, col_names = c("x", "y", "z"))

Read a file into a single string

read_file(file, locale = default_locale())

Read each line into its own string

read_lines(file, skip = 0, n_max = -1L, na = character(),

locale = default_locale(), progress = interactive())

Read a file into a raw vector

read_file_raw(file)

Read each line into a raw vector

read_lines_raw(file, skip = 0, n_max = -1L,

progress = interactive())

Read Non-Tabular Data

Read Apache style log files

read_log(file, col_names = FALSE, col_types = NULL, skip = 0, n_max = -1, progress = interactive())

## Parsed with column specification:

## cols(

## age = col_integer(),

## sex = col_character(),

## earn = col_double()

## )

1. Use problems() to diagnose problems.

x <- read_csv("file.csv"); problems(x)

2. Use a col_ function to guide parsing.

• col_guess() - the default

• col_character()

• col_double(), col_euro_double()

• col_datetime(format = "") Also

col_date(format = ""), col_time(format = "")

• col_factor(levels, ordered = FALSE)

• col_integer()

• col_logical()

• col_number(), col_numeric()

• col_skip()

x <- read_csv("file.csv", col_types = cols(

A = col_double(),

B = col_logical(),

C = col_factor()))

3. Else, read in as character vectors then parse

with a parse_ function.

• parse_guess()

• parse_character()

• parse_datetime() Also parse_date() and

parse_time()

• parse_double()

• parse_factor()

• parse_integer()

• parse_logical()

• parse_number()

x$A <- parse_number(x$A)

readr functions guess

the types of each column and

convert types when appropriate (but will NOT

convert strings to factors automatically).

A message shows the type of each column in the

result.

earn is a double (numeric)

sex is a

character

age is an

integer

RStudio® is a trademark of RStudio, Inc. • CC BY SA RStudio • info@rstudio.com • 844-448-1212 • rstudio.com • Learn more at tidyverse.org • readr 1.1.0 • tibble 1.2.12 • tidyr 0.6.0 • Updated: 2017-01

R’s tidyverse is built around tidy data stored

in tibbles, which are enhanced data frames.

The front side of this sheet shows

how to read text files into R with

readr.

The reverse side shows how to

create tibbles with tibble and to

layout tidy data with tidyr.

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