# Importing Flat Files Into R

There are many tutorials for importing data into R focusing on a specific function/package. This one focuses on 3 different packages. You will learn how to import all common formats of flat file data with base R functions and the dedicated readr and data.table packages. I first present these three packages and finish with a comparison table between them.

Import a flat file into R: create an R object that contains the data from a flat file.

## What is a flat file?

A flat file can be a plain text file that contains table data. A form of flat file is one in which table data is gathered in lines with the value from each table cell separated by a comma and each row represented with a new line. This type of flat file is also known as a comma-separated values (CSV) file. An alternative is a tab-delimited file where each field value is separated from the next using tabs.

The following sections describe various options for importing flat files. The ultimate goal is to convey, “translate”, them into an R data.frame.

## What are we going to import?

For illustration purposes we use the Happiness dataset. It is based on the European quality of life survey with questions related to income, life satisfaction or perceived quality of society. The file is quite small but enough to sharpen your importing skills. It provides the average rating for the question “How happy would you say you are these days?”. Rating 1 (low) to 10 (high) by country and gender.

##    Country Gender Mean   N.
## 1       AT   Male  7.3  471
## 2          Female  7.3  570
## 3            Both  7.3 1041
## 4       BE   Male  7.8  468
## 5          Female  7.8  542
## 6            Both  7.8 1010
## 7       BG   Male  5.8  416
## 8          Female  5.8  555
## 9            Both  5.8  971
## 10      CY   Male  7.8  433

## Let’s get going… the utils

We start with the utils package. This package is loaded by default when you start your R session. This means that you can access its functions without further due. Here, we are interested in three of them: read.table(), read.csv(), and read.delim().

### Reading data with read.table()

Reads a file in table format and creates an R data.frame from it, with cases corresponding to rows and variables to columns. Let’s see how it works for our dataset.

happiness <- read.table("happiness.csv")
head(happiness)
##                       V1
## 1 Country,Gender,Mean,N=
## 2        AT,Male,7.3,471
## 3        ,Female,7.3,570
## 4         ,Both,7.3,1041
## 5        BE,Male,7.8,468
## 6        ,Female,7.8,542

Not what we wanted?! This data frame contains 108 rows and 1 column instead of 105 rows and 4 columns. That’s because additional arguments need to be specified in order to tell R what it has to deal with.

happiness <- read.table(file = "happiness.csv",     # path to flat file
header = TRUE,              # first row lists variables' names
sep = ",",                  # field separator is a comma
stringsAsFactors = FALSE)   # not import strings as categorical variables

Let’s take a look now

head(happiness)
##   Country Gender Mean   N.
## 1      AT   Male  7.3  471
## 2         Female  7.3  570
## 3           Both  7.3 1041
## 4      BE   Male  7.8  468
## 5         Female  7.8  542
## 6           Both  7.8 1010
str(happiness)
## 'data.frame':    105 obs. of  4 variables:
##  $Country: chr "AT" "" "" "BE" ... ##$ Gender : chr  "Male" "Female" "Both" "Male" ...
##  $Mean : num 7.3 7.3 7.3 7.8 7.8 7.8 5.8 5.8 5.8 7.8 ... ##$ N.     : int  471 570 1041 468 542 1010 416 555 971 433 ...

By specifying header = TRUE R sees the that the first line contains the names of the variables. With stringsAsFactors = FALSE we specify that we wanted Country and Gender to be character variables. The sep = "," identifies the field separator to be a comma. There are many more arguments you can specify and each one can take many values! For further details, consult the R documentation or type help(read.table) on the console.

Note: In order to use read.table(), in same manner, you need to give the full path name of the target file if it’s not in your working directory. You can use the R Function of the Day, namely setwd("<location of your dataset>"), to change your working directory. The same is valid for any other function we are going to encounter in this tutorial. Alternatively, you can specify the location of the flat file inside read.table(). Keep in mind that the specification of the file is platform dependent (Windows, Unix/Linux and OSX).

read.table(file = "<location of your dataset>", ...) 

Another option is to use file.path(). It constructs the path to a file from components in a platform-independent way. For example,

path <- file.path("~", "datasets", "happiness.csv")

sep = ",",
stringsAsFactors = FALSE)

Comment The stringsAsFactors argument is true by default which means that character variables are imported into R as factors, the data type to store categorical variables.

happiness_2 <- read.table(file = "happiness.csv",
sep = ",",
stringsAsFactors = TRUE)

At first sight you do not notice anything different and you shouldn’t! But for R it’s a big deal! For character variables each element is a string of one or more characters. On the other hand, factor variables are stored, internally, as numeric variables together with their levels. This has major impact in computations that R maybe has to carry out later.

str(happiness_2)
## 'data.frame':    105 obs. of  4 variables:
##  $Country: Factor w/ 36 levels "","AT","BE","BG",..: 2 1 1 3 1 1 4 1 1 6 ... ##$ Gender : Factor w/ 3 levels "Both","Female",..: 3 2 1 3 2 1 3 2 1 3 ...
##  $Mean : num 7.3 7.3 7.3 7.8 7.8 7.8 5.8 5.8 5.8 7.8 ... ##$ N.     : int  471 570 1041 468 542 1010 416 555 971 433 ...

### Reading data with read.csv()

It is a wrapper around read.table(). This means that read.csv() calls read.table() behind the scenes but with different default arguments. More specifically, the defaults are header = TRUE and sep = ",". These match with the standardized CSV format, where , is used as a separator and usually the first line contains the names of the columns. Therefore, it saves you time since you need to specify less arguments.

read.csv(file = "happiness.csv",
stringsAsFactors = FALSE)

which is equivalent to

read.table(file = "happiness.csv",
sep = ",",
stringsAsFactors = FALSE)

### Reading data with read.delim()

It is also a wrapper of read.table(). Now the default arguments match with tab-delimited files. More specifically, the defaults are header = TRUE and sep = "\t", since \t is the field separator in tab-delimited files.

read.delim(file = "happiness.txt",
stringsAsFactors = FALSE)

which is equivalent to

read.table(file = "happiness.txt",
sep = "\t",
stringsAsFactors = FALSE)

Both these functions make our lives easier since less arguments need to be specified.

Note Locale differences. The standard field delimiters for CSV files are commas. On US versions, the comma is set as default for the “List Separator”, which is okay for CSV files. But on European versions this character is reserved as the Decimal Symbol and the “List Separator” is set by default to the semicolon. Why you should care?

Suppose you try to import the European CSV version happiness_eu.csv.

head(happiness_eu)
##   Country.Gender.Mean.N.
## 1        AT,Male,7.3,471
## 2        ,Female,7.3,570
## 3         ,Both,7.3,1041
## 4        BE,Male,7.8,468
## 5        ,Female,7.8,542
## 6         ,Both,7.8,1010

R performs the operation but clearly not the one we wanted. It’s a data frame with 105 rows but a single variable! To deal with such problems you can use the read.csv2() function. The defaults are sep = ";" and dec = ",".

happiness_eu <- read.csv2(file = "happiness_eu.csv",
stringsAsFactors = FALSE)
head(happiness_eu)
##   Country Gender Mean   N.
## 1      AT   Male  7.3  471
## 2         Female  7.3  570
## 3           Both  7.3 1041
## 4      BE   Male  7.8  468
## 5         Female  7.8  542
## 6           Both  7.8 1010

Similarly, there is read.delim2(). The logic is the same.

To summarize, the read.table() is to read delimited data files. Some variants are: read.csv() and read.delim(), which have different default values and are tailored for CSV and tab-delimited files, respectively.

• In read.csv() default values are: header = T, sep = ???,???, dec = ???.???
• In read.csv2() default values are: header = T, sep = ???;???, dec = ???,???
• In read.delim() default values are: header = T, sep = ???\t???, dec = ???.???
• In read.delim2() default values are: header = T, sep = ???\t???, dec = ???,???

##readr … an alternative to import flat files

An alternative to the utils package is the readr. Compared the read.table family of functions, it is faster, easier to use and with a consistent naming scheme. We start by installing and loading it.

install.packages("readr")
library(readr)

Let’s import our dataset. In readr you can use read_delim() for flat files. It can be considered the correspondent to read.table().

happiness_readr <- read_delim("happiness.csv",        # path to flat file
delim = ",")            # character that separates fields in the file
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Country = col_character(),
##   Gender = col_character(),
##   Mean = col_double(),
##   N= = col_double()
## )
str(happiness_readr)
## tibble [105 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $Country: chr [1:105] "AT" NA NA "BE" ... ##$ Gender : chr [1:105] "Male" "Female" "Both" "Male" ...
##  $Mean : num [1:105] 7.3 7.3 7.3 7.8 7.8 7.8 5.8 5.8 5.8 7.8 ... ##$ N=     : num [1:105] 471 570 1041 468 542 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Country = col_character(),
##   ..   Gender = col_character(),
##   ..   Mean = col_double(),
##   ..   N= = col_double()
##   .. )
head(happiness_readr)
## # A tibble: 6 x 4
##   Country Gender  Mean  N=
##   <chr>   <chr>  <dbl> <dbl>
## 1 AT      Male     7.3   471
## 2 <NA>    Female   7.3   570
## 3 <NA>    Both     7.3  1041
## 4 BE      Male     7.8   468
## 5 <NA>    Female   7.8   542
## 6 <NA>    Both     7.8  1010

Notice, that the output is the same as when using the read.table(), previously. But we did not have to specify header=TRUE because by default read_delim() expects the first row to contain the column names. This is done through the col_names argument, set equal to true by default. Also, strings are never automatically converted to factors. Hence, stringsAsFactors = FALSE is not necessary. To control the types of the columns readr uses the col_types argument. Let’s see how these two work.

col_names is true by default meaning that it will use the the first row of data as column names. If your file does not have column names you can set col_names = FALSE and columns will be numbered sequentially.

head(read_delim("happiness2.csv", delim = ",",
col_names = FALSE))
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   X1 = col_character(),
##   X2 = col_character(),
##   X3 = col_double(),
##   X4 = col_character()
## )
## # A tibble: 6 x 4
##   X1    X2        X3 X4
##   <chr> <chr>  <dbl> <chr>
## 1 'AT   Male     7.3 471'
## 2 '     Female   7.3 570'
## 3 '     Both     7.3 1041'
## 4 'BE   Male     7.8 468'
## 5 '     Female   7.8 542'
## 6 '     Both     7.8 1010'

Note Instead of assigning the output of read_delim() to a variable I directly use the head() function to print the first 6 lines of the data frame. It is equivalent to

happiness_delim <- read_delim("happiness2.csv", delim = ",",
col_names = FALSE)
head(happiness_delim)

You can also manually set the column names.

head(read_delim("happiness2.csv", delim = ",",
col_names = c("Country", "Gender", "Mean", "N")))
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Country = col_character(),
##   Gender = col_character(),
##   Mean = col_double(),
##   N = col_character()
## )
## # A tibble: 6 x 4
##   Country Gender  Mean N
##   <chr>   <chr>  <dbl> <chr>
## 1 'AT     Male     7.3 471'
## 2 '       Female   7.3 570'
## 3 '       Both     7.3 1041'
## 4 'BE     Male     7.8 468'
## 5 '       Female   7.8 542'
## 6 '       Both     7.8 1010'

As mentioned, there is col_types to control the column classes. If you leave the default value readr heuristically inspects the first 100 rows to guess the type of each column.

sapply(happiness_readr, class)
##     Country      Gender        Mean          N=
## "character" "character"   "numeric"   "numeric"

If you want to override the default column types you can also specify them manually. An option would be

happiness_readr2 <- read_delim("happiness.csv", delim = ",",
col_types = "ccni")
sapply(happiness_readr2, class)
##     Country      Gender        Mean          N=
## "character" "character"   "numeric"   "integer"

Where

• c = character
• d = double
• i = integer
• l = logical
• _ = skip

Let see how skip works

head(read_delim("happiness.csv", delim = ",",
col_types = "ccn_"))
## # A tibble: 6 x 3
##   Country Gender  Mean
##   <chr>   <chr>  <dbl>
## 1 AT      Male     7.3
## 2 <NA>    Female   7.3
## 3 <NA>    Both     7.3
## 4 BE      Male     7.8
## 5 <NA>    Female   7.8
## 6 <NA>    Both     7.8

Notice the fourth column has been skipped.

Yet another way of setting the types of the imported columns is using collectors. Collector functions can be passed in a list() to the col_types argument of read_ functions to tell them how to interpret values in a column.

car <- col_character()
fac <- col_factor(levels = c("Male", "Female", "Both"))
num <- col_number()
int <- col_integer()

col_types = list(car, fac, num, int)))
## tibble [105 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $Country: chr [1:105] "AT" NA NA "BE" ... ##$ Gender : Factor w/ 3 levels "Male","Female",..: 1 2 3 1 2 3 1 2 3 1 ...
##  $Mean : num [1:105] 7.3 7.3 7.3 7.8 7.8 7.8 5.8 5.8 5.8 7.8 ... ##$ N=     : int [1:105] 471 570 1041 468 542 1010 416 555 971 433 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Country = col_character(),
##   ..   Gender = col_factor(levels = c("Male", "Female", "Both"), ordered = FALSE, include_na = FALSE),
##   ..   Mean = col_number(),
##   ..   N= = col_integer()
##   .. )

For a complete list of collector functions, you can take a look at the collector documentation.

If you are working on large datasets you may prefer handling the data in smaller parts. In readr you can achieve this with the combination of skip and n_max arguments.

head(read_delim("happiness.csv", delim = ",",
skip=2, n_max= 4))
## Warning: Missing column names filled in: 'X1' [1]
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   X1 = col_character(),
##   Female = col_character(),
##   7.3 = col_double(),
##   570 = col_double()
## )
## # A tibble: 4 x 4
##   X1    Female 7.3 570
##   <chr> <chr>  <dbl> <dbl>
## 1 <NA>  Both     7.3  1041
## 2 BE    Male     7.8   468
## 3 <NA>  Female   7.8   542
## 4 <NA>  Both     7.8  1010

We skipped two rows and then read four lines. There is something wrong though! Since the col_names is true by default the first line is used for the column names. Therefore, we need to manually specify the column names.

head(read_delim("happiness2.csv", delim = ",",
col_names = c("Country","Gender", "Mean", "N"),
skip=2, n_max= 4))
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Country = col_character(),
##   Gender = col_character(),
##   Mean = col_double(),
##   N = col_character()
## )
## # A tibble: 4 x 4
##   Country Gender  Mean N
##   <chr>   <chr>  <dbl> <chr>
## 1 '       Both     7.3 1041'
## 2 'BE     Male     7.8 468'
## 3 '       Female   7.8 542'
## 4 '       Both     7.8 1010'

Like the utils package readr provides alternatives to read_delim(). The read_csv() and read_tsv() are used for CSV files and tab-delimited files, respectively. The functions of both packages are presented below. Notice the _ is used in readr instead of the ..

read.table() read_delim()
read.csv() read_csv()
read.delim() read.tsv

## data.table … yet another alternative to read data into R

The data.table package is designed mainly for fast data manipulation. It also features a powerful function to import your data into R, the fread(). Once more you need to install and load the package.

install.packages("data.table")
library(data.table)

Let’s see how it works with two versions of our dataset.

head(fread("happiness.csv"))
##    Country Gender Mean   N=
## 1:      AT   Male  7.3  471
## 2:         Female  7.3  570
## 3:           Both  7.3 1041
## 4:      BE   Male  7.8  468
## 5:         Female  7.8  542
## 6:           Both  7.8 1010
head(fread("happiness2.csv"))
##     V1     V2  V3    V4
## 1: 'AT   Male 7.3  471'
## 2:   ' Female 7.3  570'
## 3:   '   Both 7.3 1041'
## 4: 'BE   Male 7.8  468'
## 5:   ' Female 7.8  542'
## 6:   '   Both 7.8 1010'

Remember that the first row of happiness2.csv does not contain the column names. That’s not a problem for fread() as it automatically assignees names to the columns. As in this case, often simply specifying the path to the file is enough to successfully import your flat file using fread. Moreover, it can infer the column types and separators.

str(fread("happiness.csv"))
## Classes 'data.table' and 'data.frame':   105 obs. of  4 variables:
##  $Country: chr "AT" "" "" "BE" ... ##$ Gender : chr  "Male" "Female" "Both" "Male" ...
##  $Mean : num 7.3 7.3 7.3 7.8 7.8 7.8 5.8 5.8 5.8 7.8 ... ##$ N=     : int  471 570 1041 468 542 1010 416 555 971 433 ...
##  - attr(*, ".internal.selfref")=<externalptr>

Two more useful arguments of fread() are drop and select. They enable you to drop or select variables of interest in your flat file. Suppose I want to select the 2nd and 3rd column.

head(fread("happiness.csv", select = c(2,3)))
##    Gender Mean
## 1:   Male  7.3
## 2: Female  7.3
## 3:   Both  7.3
## 4:   Male  7.8
## 5: Female  7.8
## 6:   Both  7.8

Alternatively,

head(fread("happiness.csv", select = c("Gender","Mean")))
##    Gender Mean
## 1:   Male  7.3
## 2: Female  7.3
## 3:   Both  7.3
## 4:   Male  7.8
## 5: Female  7.8
## 6:   Both  7.8

or

head(fread("happiness.csv", drop = c(1,4)))
##    Gender Mean
## 1:   Male  7.3
## 2: Female  7.3
## 3:   Both  7.3
## 4:   Male  7.8
## 5: Female  7.8
## 6:   Both  7.8

which is equivalent to

head(fread("happiness.csv", drop = c("Country","N=")))

In short, fread() saves you work by automatically guessing the delimiter, whether or not the file has a header, how many lines to skip by default, providing an easy way to select variables and more. Nevertheless, if you wish to specify them, you can do it, along with other arguments. Check the documentation.

Comment You might have noticed by now that the fread() function produces data frames that look slightly different when you print them out. That’s because another class is assigned to the resulting data frames, namely data.table and data.frame. read_delim() creates an object with three classes: tbl_df, tbl and data.frame. The printout of such data.table objects is different. Well, it allows for a different treatment of the printouts, for example.

## When to use read.table(), read_delim() or fread()

In a nutshell, the main differences between these functions are : the read_ functions from the readr package have more consistent naming scheme for the parameters (e.g. col_names and col_types) than read. and all functions work exactly the same way regardless of the current locale (to override the US-centric defaults, use locale()). It is also faster! But fread() is even faster! And it saves you work by automatically guessing parameters. This README goes in more detail.

Speed Auto-detection Locale
read.table() fast NO YES
read_delim() faster NO NO
fread() fastest YES NO

With some loss of generality a few suggestions are:

• for large files (many MB-GB) fread() will be the fastest (with a few exceptions)
• the consistency and independence of the actual locale makes readr a good candidate for everyday use
• if you are new to all these using read.table() will allow you to develop intuition on how R works.