6 Using the
readr package is recently developed by Hadley Wickham to deal with reading in large flat files quickly. The package provides replacements for functions like
read.csv(). The analogous functions in
read_csv(). These functions are often much faster than their base R analogues and provide a few other nice features such as progress meters.
For the most part, you can read use
read_csv() pretty much anywhere you might use
read.csv(). In addition, if there are non-fatal problems that occur while reading in the data, you will get a warning and the returned data frame will have some information about which rows/observations triggered the warning. This can be very helpful for “debugging” problems with your data before you get neck deep in data analysis.
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.
A typical call to
read_csv will look as follows.
> library(readr) > teams <- read_csv("data/team_standings.csv") Parsed with column specification: cols( Standing = col_integer(), Team = col_character() ) > teams # A tibble: 32 × 2 Standing Team <int> <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.). From the
read_csv help page:
If ‘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 specify the type of each column with the
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.
> teams <- read_csv("data/team_standings.csv", col_types = "cc")
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.
> logs <- read_csv("data/2016-07-19.csv.bz2", n_max = 10) Parsed with column specification: cols( date = col_date(format = ""), time = col_time(format = ""), size = col_integer(), r_version = col_character(), r_arch = col_character(), r_os = col_character(), package = col_character(), version = col_character(), country = col_character(), ip_id = col_integer() )
Note that the warnings indicate that
read_csv may have had some difficulty identifying the type of each column. This can be solved by using the
> logs <- read_csv("data/2016-07-19.csv.bz2", col_types = "ccicccccci", n_max = 10) > logs # A tibble: 10 × 10 date time size r_version r_arch r_os package <chr> <chr> <int> <chr> <chr> <chr> <chr> 1 2016-07-19 22:00:00 1887881 3.3.0 x86_64 mingw32 data.table 2 2016-07-19 22:00:05 45436 3.3.1 x86_64 mingw32 assertthat 3 2016-07-19 22:00:03 14259016 3.3.1 x86_64 mingw32 stringi 4 2016-07-19 22:00:05 1887881 3.3.1 x86_64 mingw32 data.table 5 2016-07-19 22:00:06 389615 3.3.1 x86_64 mingw32 foreach 6 2016-07-19 22:00:08 48842 3.3.1 x86_64 linux-gnu tree 7 2016-07-19 22:00:12 525 3.3.1 x86_64 darwin13.4.0 survival 8 2016-07-19 22:00:08 3225980 3.3.1 x86_64 mingw32 Rcpp 9 2016-07-19 22:00:09 556091 3.3.1 x86_64 mingw32 tibble 10 2016-07-19 22:00:10 151527 3.3.1 x86_64 mingw32 magrittr # ... with 3 more variables: version <chr>, country <chr>, ip_id <int>
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
> logdates <- read_csv("data/2016-07-19.csv.bz2", + col_types = cols_only(date = col_date()), + n_max = 10) > logdates # A tibble: 10 × 1 date <date> 1 2016-07-19 2 2016-07-19 3 2016-07-19 4 2016-07-19 5 2016-07-19 6 2016-07-19 7 2016-07-19 8 2016-07-19 9 2016-07-19 10 2016-07-19
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.