Data Import:: Cheatsheet
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.
OTHERTYPESOFDATA
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
Save x, an R object, to path, a file path, as:
Comma delimited file
write_csv(x, path, na = "NA", append = FALSE, col_names = lappend)
File with arbitrary delimiter write_delim(x, path, delim = "", na = "NA", append = FALSE, col_names = lappend)
CSV for excel write_excel_csv(x, path, na = "NA", append = FALSE, col_names = lappend)
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 = lappend)
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Read Tabular Data
These functions share the common arguments:
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 = interactiveQ)
a,b,c 1,2,3 4,5,NA
a;b;c 1;2;3 4;5;NA
1\2\3 4\5\NA
K
a b c 1 2 3 4 5 NA
D		
1	2	3
4	5	NA
D 1	□ 2	B 3
4	5	NA
□ 1	El 2	B 3
4	5	NA
□ 1	El 2	B 3
4	5	NA
Comma Delimited Files
read_csv("file.csv")
To make file.csv run:
write_file(x = "a,b,c\nl,2,3\n4,5,NA", path = "file.csv")
Semi-colon Delimited Files read_cs v2 (" f i I e2. cs v")
write_file(x = "a;b;c\nl;2;3\n4;5;NA", path = "file2.csv")
Files with Any Delimiter
read_delim("file.txt", delim = "|")
write_file(x = "a|b|c\nl|2|3\n4|5|NA", path = "file.txt")
Fixed Width Files
read_fwf("file.fwf", coLpositions = c(l, 3,5))
write_file(x = "a b c\nl 2 3\n4 5 NA", path = "file.fwf")
Tab Delimited Files
read_tsv("file.tsv") Also read_table().
write_file(x = Ma\tb\tc\nl\t2\t3\n4\t5\tNA", path = "file.tsv";
USEFULARGUMENTS
l\    Example file
a,b,c 1,2,3 4,5,NA
A	B	C
1	2	3
4	5	NA
Q A	n B	B c
1	2	3
4	5	NA
write_file("a,b,c\nl,2,3\n4,5,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"))
	□	Skip lines
4 5	NA	read_csv(f, skip = 1)
□ □ 1 2	□ 3	Read in a subset read_csv(f, n_max = 1)
DB NA 2	B 3	Missing Values read_csv(f,na = c("l","."))
4 5	NA	
Read Non-Tabular Data
Read a file into a single string
read_file(file, locale = defaultJocale()) Read each line into its own string
read_Iines(file, skip = 0, n_max = -1L, na = character), locale = default_locale(), progress = interactive())
Read Apache style log files
read_Iog(file, col_names = FALSE, col_types = NULL, skip = 0, n_max = -1, progress = interactive())
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Read a file into a raw vector
read_file_raw(file)
Read each line into a raw vector
read_!ines_raw(file, skip = 0, n_max = -1L, progress = interactiveQ)
Data types
readrfunctions 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.
## 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 = colJactorQ))
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)
Eg • readr 1.1.0- tibble 1.2.12- tidyr 0.6.0- Updated:2019-oe
Tibbies
an enhanced data frame
The tibble package provides a new S3 class for storing tabular data, the tibble. Tibbies inherit the data frame class, but improve three behaviors:
• Subsetting- [ always returns a new tibble, [[ and $ always return a vector.
• No partial matching - You must use full column names when subsetting
• Display - When you print a tibble, R provides a concise view of the ^— data that fits on        | # a tlbble: 234 „ 6
inufacturer model displ
<chr> <chr> <dbl>
one screen
audi a4 quattn audi a4 quattn audi a4 quattn
It 1:8
a4 3.1
tibble display
A large table to display
156 1999 6 157 1999 6	auto(l4) auto(l4)
158 2668 6 mim %* 161 1999 4	auto!14} auto(l4}
ill V 166 1999 4	anuallmi! luíBltfl auto(l4}
[ reached qe1 — omitted 68	Option! "max. print"}
data frame display
• Control the default appearance with options:
options(tibble.print_max = n,
tibble.print_min = m, tibble.width = Inf)
• View full data set with View)) or glimpse))
• Revert to data frame with as.data.frame))
CONSTRUCT A TIBBLE IN TWO WAYS
tibble)...) Construct by columns. tibble(x = l:3,y = c("a", "b", "c")j
tribble)...)
Construct by rows. tribble( ~x, ~y
1, "a",
2, "b",
3, "c")
as_tibble(x,...) Convert data frame to tibble.
enframefx, name = "name", value = "value") Convert named vector to a tibble
is_tibble(x) Test whether x is a tibble.
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Tidy Data with tidyr
Tidy data is a way to organize tabular data. It provides a consistent data structure across packages.
A table is tidy if:
Tidy data:
&
Each variable is in its own column
Each observation, or case, is in its own row
A * B -> C □ * Q Q
Makes variables easy to access as vectors
Preserves cases during vectorized operations
Reshape Data Ch3 nge the layout of values in a table
Use gather)) and spread)) to reorganize the values of a table into a new layout.
spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL)
spread() moves the unique values of a key column into the column names, spreading the values of a value column across the new columns.
table2
gather(data, key, value,na.rm = FALSE, convert = FALSE, factor_key = FALSE)
gather() moves column names into a key column, gathering the column values into a single value column.
table4a
Bwrmirmi-Miiii
A 0.7k 2k B 37k 80k C      212k  213k
A     11=1=1=1 0.7k
b 1	IEEE	37k
c		212k
A	aililil	2k
b		80k
0 1	ailiiii	213k
	key	value
					
A	1999 E	3  0.7k _w 9 19m	A	1999 0.7k	19m
A	1999 U		A	2000 2k	20m
A	2000 B	j 2k	B	1999 37k	172m
A	2000 K	9 20m	B	2000 80k	174m
B	1999 E	HS 37k	C	1999  212k	1t
B	1999 M	9 172m	C	2000  213k	1t
B	2000 E	B 80k			
B	2000 U	9 174m			
C	1999 E	Hä 212k			
C	1999 K	9 1t			
C	2000 E	!3S 213k			
C	2000 M	9 1t			
gather(table4a, '1999', '2000' key= "year", value = "cases")
key valu*
spread(table2, type, count)
Handle Missing Values
drop_na(data,...)
Drop rows containing NA'sin ... columns.
X
D 3 E NA
drop_na(x, x2)
fill(data,.direction = cf'down", "up")) Fill in NA's in ... columns with most recent non-NA values.
replace_na(data,
replace = list(),...) Replace NA's by column.
niia	X				X	wswwta
A 1	A	1	_W A	1	A 1	\     A 1
D 3	B	NA	B	1	B NA	~    B 2
	C	NA	C	1	C	C 2
	D	3	D	3	D 3	D 3
	E	NA	E	3	E NA	E 2
fill(x, x2)
replace_na(x, list(x2=2))
Expand Tables quickly create tables with combinations of values complete(data,fill = listQ) expand(data,...)
Adds to the data missing combinations of the values of the variables listed in ...
complete(mtcars, cyl, gear, carb)
Create new tibble with all possible combinations of the values of the variables listed in ...
expand(mtcars, cyl, gear, carb)
Split Cells
Use these functions to split or combine cells into individual, isolated values.
separate(data, col, into, sep = "[A[:alnum:]] +", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn",...)
Separate each cell in a column to make several columns. table3
A      1999   0.7kai9m               A      1999   0.7k B3H	
A      2000    2k/20m      ^      A 2000 B      1999  37k/172m              B 1999	2k Biffil
	37k WBS
B      2000  80k/174m              B 2000 C      1999   212k/1t               C 1999 C      2000   213k/1t               C 2000	80k 1
separate(table3, rate, sep = into = c(''cases", "pop"))	
separate_rows(data, ...,sep= "[A[:alnum:].]	
+", convert = FALSE)
Separate each cell in a column to make several rows. table3
			A      1999 0.7k		
A 1999	1.7k/19m				
A 2000	2k/20m	->■	A	1999	mm
B 1999	37kai72m		A	2000	2k
B 2000	80kai74m		A	2000	
C 1999	12kait		B	1999	37k
C 2000	13kait		B	1999	
			B	2000	80k
			B	2000	
			C	1999	212k
			C	1999	■EB
			C	2000	213k
			C	2000	■m
separate_rows(table3, rate, sep = "/")
unite(data, col,sep =     remove = TRUE)
Collapse cells across several columns to make a single column. table5
Afghan 19				Afghan   1999
Afghan	20		->	Afghan 2000
Brazil	19			Brazil 1999
Brazil	20			Brazil 2000
China	19			China 1999
China	20			China 2000
unite(table5, century, year, col= "year", sep = "")
RStudio8 isa trademark of RStudio, Inc. • CCBYSA RStudio- info@rstudio.com • 844-448-1212-rstudio.com- Learn more at tidwerse.org • readr 1.1.0- tibble 1.2.12- tidyr 0.6.0- Updated: 2019-'