1.3 Reading Tabular Data with the
The learning objectives for this section are to:
- Read tabular data into R and read in web data via web scraping tools and APIs
readr package is the primary means by which we will read tablular data, most notably, comma-separated-value (CSV) files. The
readr package has a few functions in it for reading and writing tabular data—we will focus on the
read_csv function. The
readr package is available on CRAN and the code for the package is maintained on GitHub.
The importance of the
read_csv function is perhaps better understood from an historical perspective. R’s built in
read.csv function similarly reads CSV files, but the
read_csv function in
readr builds on that by removing some of the quirks and “gotchas” of
read.csv as well as dramatically optimizing the speed with which it can read data into R. The
read_csv function also adds some nice user-oriented features like a progress meter and a compact method for specifying column types.
The only required argument to
read_csv is a character string specifying the path to the file to read. A typical call to
read_csv will look as follows.
library(readr) <- read_csv("data/team_standings.csv") teams ── Column specification ────────────────────────────────────────────────────────cols( Standing = col_double(), Team = col_character() ) teams# A tibble: 32 x 2 Standing Team <dbl> <chr> 1 1 Spain 2 2 Netherlands 3 3 Germany 4 4 Uruguay 5 5 Argentina 6 6 Brazil 7 7 Ghana 8 8 Paraguay 9 9 Japan 10 10 Chile # … with 22 more rows
read_csv will open a CSV file and read it in line-by-line. It will also (by default), read in the first few rows of the table in order to figure out the type of each column (i.e. integer, character, etc.). In the code example above, you can see that
read_csv has correctly assigned an integer class to the “Standing” variable in the input data and a character class to the “Team” variable. From the
read_csv help page:
If [the argument for
col_typesis] ‘NULL,’ all column types will be imputed from the first 1000 rows on the input. This is convenient (and fast), but not robust. If the imputation fails, you’ll need to supply the correct types yourself.
You can also specify the type of each column with the
col_types argument. In general, it’s a good idea to specify the column types explicitly. This rules out any possible guessing errors on the part of
read_csv. Also, specifying the column types explicitly provides a useful safety check in case anything about the dataset should change without you knowing about it.
<- read_csv("data/team_standings.csv", col_types = "cc")teams
Note that the
col_types argument accepts a compact representation. Here
"cc" indicates that the first column is
character and the second column is
character (there are only two columns). Using the
col_types argument is useful because often it is not easy to automatically figure out the type of a column by looking at a few rows (especially if a column has many missing values).
read_csv function will also read compressed files automatically. There is no need to decompress the file first or use the
gzfile connection function. The following call reads a gzip-compressed CSV file containing download logs from the RStudio CRAN mirror.
<- read_csv("data/2016-07-19.csv.gz", n_max = 10) logs ── Column specification ────────────────────────────────────────────────────────cols( date = col_date(format = ""), time = col_time(format = ""), size = col_double(), r_version = col_character(), r_arch = col_character(), r_os = col_character(), package = col_character(), version = col_character(), country = col_character(), ip_id = col_double() )
Note that the message (“Parse with column specification …”) printed after the call indicates that
read_csv may have had some difficulty identifying the type of each column. This can be solved by using the
<- read_csv("data/2016-07-20.csv.gz", col_types = "ccicccccci", n_max = 10) logs logs# A tibble: 10 x 10 date time size r_version r_arch r_os package version country ip_id<chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <int> 1 2016-0… 06:04… 144723 3.3.1 i386 mingw… gtools 3.5.0 US 1 2 2016-0… 06:04… 2049711 3.3.0 i386 mingw… rmarkdo… 1.0 DK 2 3 2016-0… 06:04… 26252 <NA> <NA> <NA> R.metho… 1.7.1 AU 3 4 2016-0… 06:04… 556091 2.13.1 x86_64 mingw… tibble 1.1 CN 4 5 2016-0… 06:03… 313363 2.13.1 x86_64 mingw… iterato… 1.0.5 US 5 6 2016-0… 06:03… 378892 3.3.1 x86_64 mingw… foreach 1.3.2 US 5 7 2016-0… 06:04… 41228 3.3.1 x86_64 linux… moments 0.14 US 6 8 2016-0… 06:04… 403177 <NA> <NA> <NA> R.oo 1.20.0 AU 3 9 2016-0… 06:04… 525 3.3.0 x86_64 linux… rgl 0.95.1… KR 7 10 2016-0… 06:04… 755720 3.2.5 x86_64 mingw… geosphe… 1.5-5 US 8
You can specify the column type in a more detailed fashion by using the various
col_* functions. For example, in the log data above, the first column is actually a date, so it might make more sense to read it in as a Date variable. If we wanted to just read in that first column, we could do
<- read_csv("data/2016-07-20.csv.gz", logdates col_types = cols_only(date = col_date()), n_max = 10) logdates# A tibble: 10 x 1 date <date> 1 2016-07-20 2 2016-07-20 3 2016-07-20 4 2016-07-20 5 2016-07-20 6 2016-07-20 7 2016-07-20 8 2016-07-20 9 2016-07-20 10 2016-07-20
date column is stored as a
Date object which can be used for relevant date-related computations (for example, see the
read_csv function has a
progress option that defaults to TRUE. This options provides a nice progress meter while the CSV file is being read. However, if you are using
read_csv in a function, or perhaps embedding it in a loop, it’s probably best to set
progress = FALSE.
readr package includes a variety of functions in the
read_* family that allow you to read in data from different formats of flat files. The following table gives a guide to several functions in the
||Reads comma-separated file|
||Reads semicolon-separated file|
||Reads tab-separated file|
||General function for reading delimited files|
||Reads fixed width files|
||Reads log files|