Importing Data Into R – Part Two

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(This article was first published on The DataCamp Blog » R, and kindly contributed to R-bloggers)

In this follow-up tutorial of This R Data Import Tutorial Is Everything You Need-Part One, DataCamp continues with its comprehensive, yet easy tutorial to quickly import data into R, going from simple, flat text files to the more advanced SPSS and SAS files.

As a lot of our readers noticed correctly from the first post, some great packages to import data into R haven’t yet received any attention, nor did the post cover explicitly the distinction between working with normal data sets and large data sets. That is why this will be the focus of today’s post.

Keep on reading to discover other and new ways to import your specific file into R, and feel free to reach out if you have additional questions or spot an error we should correct.

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Getting Data From Common Sources into R

Firstly, this post will go deeper into the ways of getting data from common sources, which is often spreadsheet-like data, into R. Just like with the previous post, the focus will be on reading data into R that is different from Excel or any other type of files.

Next, the data from other sources like statistical software, databases, webscraping, etc. will be discussed.

If you want to know more about the possible steps that you might need to undertake before importing your data, go to our first post, which explains how you can prepare your data and workspace before getting your data into R.

Reading in Flat Files Into R with scan()

Besides read.table(), which was mentioned in the first post of the R data import tutorial, the scan() function can also work when handling data that is stored in simple delimited text files. Unlike the read.table() function, the scan() function returns a list or a vector, not a dataframe.

Suppose you have the following .txt document:

24 1991
21 1993
53 1962

You can read in the data (which you can download here) with the following command:

data <- scan("birth.txt")

Note that your file can also be an online data set. In that case, you just pass the URL as the first argument of the scan() function.

Alternatively, you could also read in the data into a matrix:

data <- matrix(scan("birth.txt"), 
               nrow=2, 
               byrow=TRUE)

Tip: if you want to do this thoroughly, you might need to specify additional arguments to make the matrix just the way you want it to be. Go to this page for more information on the matrix() function.

You can also read the columns of the input file into separate vectors:

data <- scan("age.txt", 
             what = list(Age = 0, 
                         Birthyear= 0),
             skip=1,
             quiet=TRUE)

Note how you first pass the (path to the) file with its extension as an argument (depending on whether you set the working directory to the folder that holds your dataset or not) and then specify the type of data to be read in, whether or not you want to skip the first line of your dataset, which character delimits the fields and if you want to print a line that says how many items have been read.

If your data can also contain other data types, you should tweak the scan() function a little bit, just like in this example:

data <- scan("age.csv", 
             what = list(Age = 0, 
                         Name= "", 
                         Birthyear= 0),
             skip=1,
             sep=";",
             quiet=TRUE)

Tip: you can do this yourself, too! Download the text file that was used above here.

And then you can also read in the data in a data frame:

data <- data.frame(scan("age.csv", 
                        what = list(Age = 0, 
                                    Name = "", 
                                    Birthyear= 0),
                        skip=1,
                        sep=";",
                        quiet=TRUE)

Tip: a lot of the arguments that the scan() function can take are the same as the ones that you can use with the read.table() function. That is why it’s always handy to check out the documentation! Go here if you want to read up on the scan() function’s arguments.

Remember that you can get the working directory and set it with the following commands, respectively:

getwd()
setwd("<path to your folder>")

Getting Fixed Column Data Into R with read.fwf()

To read a table of “fixed width formatted data” into a data frame in R, you can use the read.fwf() function from the utils package.

You use this function when your data file has columns containing spaces, or columns with no spaces to separate them.

   Phys / 00 / 1:    M  abadda
   Math / 00 / 2:    F  bcdccb
   Lang / 00 / 3:    F  abcdab
   Chem / 00 / 4:    M  cdabaa

Here, you do know that, for example, the subject values always reside in the first 7 characters of each line and the sex values are always at 22, and the scores start from character 25 to 30.

If you want to try out loading these data into R, you can easily download the text file here.

You would need to execute the following command to get the data from above correctly into R:

read.fwf("scores.txt", 
         widths= c(7,-14,1,-2,1,1,1,1,1,1), 
         col.names=c("subject","sex","s1","s2","s3","s4","s5","s6"),
         strip.white=TRUE)   

Note that the widths argument gives the widths of the fixed-width fields. In this case, the first seven characters in the file are reserved for the course names; Then, you don’t want the next fourteen characters to be read in: you pass -14. Next, you need one character to represent the sex, but you don’t want the two following characters, so you pass -2. All following characters need to be read in into separate columns, so you split them by passing 1,1,1,1,1,1 to the argument. Of course these values can and will differ, depending on what colums you want to import.

There are a number of extra arguments that you can pass to the read.fwf()function. Click here to read up on them.

Note that if you want to load in a file using Fortran-style format specifications, you can use the read.fortran() function:

data <- tempfile()
cat(file = data, "345678", "654321", sep = "n")
read.fortran(data, c("F2.1","F2.0","I2"))

As you can see from the small example above, you use Fortran-style format specifications as a second argument to the read.fortran() function. The arguments that you could possibly pass are in the style of: “rFl.d”, “rDl.d”, “rXl”, “rAl” or “rIl”, where “l” is the number of columns, “d” is the number of decimal places, and “r” is the number of repeats. In this case, you see 2.1, 2.0 and 2 listed by means of the c() function, which means that you have three columns with two rows. In the first column, you have values with one decimal place, in the second and the third also contain values with no decimal place.

For what concerns the type of values that the columns contain, you can have:

  • “F” and “D” for numeric formats;
  • “A” if you have character values;
  • “I” for integer values;
  • And “X” to indicate columns that can be skipped skipped.

In this case, the first and second columns will contain numeric formats, while the third column contains integer values.

Note that the repeat code “r” and decimal place code “d” are always optional. The length code “l” is required except for “X” formats when “r” is present.

Getting Your (Google) Spreadsheets Into R

Spreadsheets can be imported into R in various ways, as you might have already read in our tutorial on reading and importing Excel files into R or our first This R Data Import Tutorial Is Everything You Need post. This section will elaborate on that and go even further, also including Google spreadsheets and DIF files!

Scroll further to find out more on how to import your spreadsheets into R.

Importing Excel Spreadsheets Into R

Apart from the xlsx package, you also have a number of other options to read spreadsheets into R:

1. Reading Excel Spreadsheets into R From The Clipboard

If you have a spreadsheet open, you can actually copy the contents to your clipboard and import them quickly into R. To do this, you can either use the readClipboard() or read.table() functions:

readClipboard() #Only on Windows
read.table(file="clipboard")`

As you will see if you try this out, the first approach works well for vector data, but it gets pretty complicated if you have tabular data in your clipboard. If you want to know more about read.table(), you should definitely go to the first part of the R data import tutorial or our tutorial on reading and importing Excel files into R.

2. Reading Excel Spreadsheets into R Through The RODBC Package

The second way to get your Excel spreadsheets into R is through the RODBC package:

  • A first way to use this package is like this:
library(RODBC)
connection <- odbcConnect("<DSN>")

Note that the argument that you pass to odbcConnect() is actually a DSN. For a complete guide on how to set up your DSN, on how to set up a connection, etc., go to this page an extensive, yet easily accessible tutorial!

  • Once you have set up your connection, you could also use the sqlQuery() function to get data from .xls spreadsheets:
query <- "<SQL Query>"
data <- sqlQuery(connection, 
                 query)
str(data)

Big tip: go to this page an extensive, yet easily accessible tutorial!

At the end of an R session, don’t forget to close the connections:

odbcCloseAll()

Tip: If you want to know more about importing spreadsheets or Excel files into R, definitely go to our first tutorial on importing data into R or consider reading our tutorial on reading and importing Excel files into R, which deals with the readxl and XLConnect packages, among others.

Importing Google Spreadsheets Into R

The googlesheets package with its gs_read() function allows you to read in Google spreadsheets into R.

Start by executing the following line of code:

gs_ls()

Let the browser start up and complete the authentication process. Then, if you want to read in the data or edit it, you have to register it. You can do this by specifying your spreadsheet by title or by key:

data <- gs_title("<your spreadsheet>")
data <- gs_key()

Next, you can read in the data:

gs_read(data)

This is only a short overview of what you do with the googlesheets package. Definitely read up on all details here, and make sure to also check out this page.

Reading in Data Interchange Format (DIF) Files Into R

Use the read.DIF() function to get your DIF files into R:

data <- read.DIF("<your spreadsheet>",
                 header=FALSE,
                 as.is = !stringsAsFactors)

Note that you can specify whether your spreadsheet has a header or not and whether you want to import the data “as is”, that is, whether you want to convert character variables to convert to factors. In this case, you didn’t want to have this, so you gave in !stringsAsFactors.

For more information on this function or its arguments, go to this page.

Getting Excel Files Into R

Besides spreadsheets, you might also be interested in getting your actual Excel files into R. Look no further and keep on reading to find out how you can do this!

Note that this post only elaborates on what has been described in our tutorial on reading and importing Excel files into R and our first This R Data Import Tutorial Is Everything You Need post!

Importing Excel Files Into R With readxl

Even though this package is still under active development, it’s really worth to check it out, because it offers you a pretty easy way to read in Excel files:

library(readxl)
read_excel("<path to file")

Remember that you can just type the file’s name, together with its extension if your folder is in your working directory. Get and set your working directory through the following lines of code:

getwd()
setwd("<Path to your folder>")

Note that you can specify the sheet to read, the column names and types, the missing values and the number of rows to skip before reading any data with the sheet, col_names, col_types, na and skip arguments, respectively. Read up on them here.

Reading In Excel Files Into R With openxlsx

The openxlsx package also provides you with a simple way to read Excel .xlsx files into R:

library(openxlsx)
read.xlsx("<path to your file>")

If you want to know more details on this package or on the arguments that you can pass to the read.xlsx() function, definitely click here.

Tip: If you want to know more about importing Excel files into R, definitely go to our first tutorial on “importing data into R” or consider reading our extensive tutorial on reading and importing Excel files into R, which also deals with the XLConnect package, amongst others.

Getting OpenDocument Spreadsheets Into R

Use the read.ods() function from the readODS package to read in your OpenDocument spreadsheets into R and put them into data frames:

library(readODS)
read.ods("<path to your file>",
         sheet = 1,
         formulaAsFormula = FALSE)

Note that, apart from the file that you want to get into R, you can specify the sheet that you want and that you have the possibility to display formulas as formulas (for example, “SUM(B1:B3)” or the resulting values).

Importing JavaScript Object Notation (JSON) Files Into R

In our first post on importing data into R, the rjson package was mentioned to get JSON files into R.

Nevertheless, there are also other packages that you can use to import JSON files into R. Keep on reading to find out more!

Importing JSON Files Into R With The jsonlite Package

Recently ranked in the top 25 of most downloaded R packages with 66952 downloads, the jsonlite package is definitely one of the favorite packages of R users.

You import JSON files with the fromJSON() function:

library(jsonlite)
data <- fromJSON("<Path to your JSON file>")

For a well-explained quickstart with the jsonlite package, go here.

Importing JSON Files Into R With The RJSONIO package

The third, well-known package to get JSON files into R is RJSONIO. Just like the jsonlite and the jsonlite packages, you use the fromJSON() function:

library(RJSONIO)
data <- fromJSON("<Path to your JSON file")

The Best JSON Package?

There has been considerable discussion about this topic. If you want to know more, you should definitely check out the following pages and posts:

  • this page offers mainly illustrations through code examples which provide you with more insight into the behavior and performance of JSON packages in R.
  • Definitely read this blogpost, which tries to figure out which package handles JSON data best in R.

Getting Data From Statistical Software Packages into R

If your data is not really spreadsheet-like and isn’t an Excel or JSON file, it might just be one that is made with one of the many statistical software packages.

This section will provide you with more ways to read in your SPSS, Stata or SAS files, while also giving an overview of importing files that come from S-plus and Epi Info. Definitely make sure to go back to our first post or to the links that are provided below if you want to have more information!

Importing SPSS Files into R

Instead of using the foreign package, you can also resort to the haven package to get your SPSS files into R.

Remember to make sure to install and activate it in your workspace before starting!

The haven package offers the read_spss() function to read SPSS files into R:

library(haven)
data <- read_spss("<path to your SPSS file>")

Importing Stata Files into R

Similar to the foreign package, the haven package also provides a function to read in Stata files into R, namely read_dta():

data <- read_dta("<path to your STATA file>")

Remember to always install your packages if necessary and to activate them in your workspace. For example, you can install and activate the haven package in your workspace with the following commands:

install.packages("haven")
library(haven)

Importing SAS Files into R

Since the sas7bdat package was cited in last post, this follow-up tutorial will focus on other ways to read in SAS files:

1. How To Import SAS XPORT Files into R With The foreign package

The foreign package with the read.xport() function also allows you to get your SAS XPORT files into R:

library(foreign)
data <- read.xport("<path to your SAS file>")

2. How To Import SAS XPORT Files into R With The SASxport Package

The sasXPORT package also allows to read in SAS XPORT files with the read.xport() function:

library(SASxport)
data <- read.xport("<path to your SAS file>")

3. How To Import SAS Files into R With The haven Package

Just like the foreign and the sas7bdat packages, the haven package also allows you to read in b7dat files into R with the read_sas() function:

library(haven)
data <- read_sas("<path to your SAS file>")

Getting S-plus Files Into R

For old S-plus datasets, namely those that were produced on either Windows versions 3.x, 4.x or 2000 or Unix, version 3.x with 4 byte integers, you can use the read.S() function from the foreign package:

library(foreign)
data <- read.S("<Path to your file>")

Reading In Epi Info Files Into R

As you may have read in our previous tutorial or in this one, the foreign package offers many functions to read in specific files into R, and Epi Info is one of them. You can just use the read.epiinfo() function to get your data into R:

library(foreign)
data <- read.epiinfo("<Path to your file>")

For more information on Epi Info, click here.

Getting Data From Other Sources Into R

Next to the common sources and the statistical software, there are also many other sources from which you can have data that you want to read into R.

A few are listed below. Keep on reading!

Importing MATLAB Files Into R

You can use the R.matlab package with its readMat() function to import MATLAB files into R.

You can either pass a character string as a first argument to this function or you can pass a raw vector. In the first case, your input would be interpreted as a filename, while in the second case it will be considered a raw binary connection:

library(R.matlab)
data <- readMat("<Path to your file>")

The readMat() function will return a named list structure that contains all variables from the MAT file that you imported.

Reading In Octave Files Into R

The foreign package is here again! Use the read.octave() function to import Octave text data into R:

library(foreign)
data <- read.octave("<Path to your file>")

Getting FitbitScraper Data Into R

You can use the fitbitScraper package to get data from fitbit.

(For those who aren’t familiar with the company: the company offers products such as activity trackers and other technology devices that measure personal data such as the number of steps walked or the quality of sleep.)

Go here for a short and practical tutorial on how you can use the fitbitScraper package.

Importing Quantmod Data Into R

you can use this package to extracting financial data from an Internet source with R. The function that you use to get your data into R is getSymbols(), like in this example:

library(quantmod)
data <- getSymbols("YHOO", src="google")

Note that first you specify a character vector with the names of each symbol to be loaded. In this case, that is "YHOO". Then, you define a sourcing method. The sourcing methods that are available at this point in time are yahoo, google, MySQL, FRED, csv, RData, and oanda.

Next, you specify the lookup parameters and save them for future sessions:

setSymbolLookup(YHOO='google',GOOG='yahoo') 
saveSymbolLookup(file="mysymbols.rda") 

The new sessions then call

loadSymbolLookup(file="mysymbols.rda")
getSymbols(c("YHOO","GOOG")) 

If you want more information on quantitative finance applications in R, click here or go to this page for a detailed tutorial for beginners on working with quantmod.

Getting ARFF Files Into R

Data from Weka Attribute-Relation File Format (ARFF) files can be read in with the read.arff() function:

library(foreign)
data <- read.arff("<Path to your file>")

For more information on this function, go to this page.

Note that the RWeka package also offers the same function to import ARFF files. Go here if you want to know more!

Importing Data From Databases Into R

Besides MonetDB.R, rmongodb and RMySQL, which were covered in the previous post, you also have other packages to connect with your databases in R.

you also have mongolite, RMongo, RODBC, ROracle, RPostgreSQL, RSQLite, RJDBC.

For tutorials on these packages, check out the following list:

Note that there is also a database interface package DBI which allows communication between R and relational database management systems. For more information, click here.

Some explanations on how to work with this package can be found here.

Getting Binary Files Into R

Binary data files contain information that is stored in groups of binary digits. Each binary digit is a zero or one. Eight binary digits that are grouped together form a byte. You can read in binary data with the readBin() function:

connection <- file("<path to your file>", "rb") #You open the connection as "reading binary"(rb)
data <- readBin(connection,
                what="numeric") #Mode of the vector to be read

For a more detailed example, go to this page. For more information on the readBin() function, click here.

Reading In Binary Data Formats Into R

The packages hdf5, h5r, rhdf5, RNetCDF, ncdf and ncdf4 provide interfaces to NASA’s HDF5 and to UCAR’s netCDF data files.

For those of you who are interested in some tutorials on how to work with HDF5 or netCDF files in R, consider checking out the following resources:

  • You can find a nice tutorial on working with HDF files in R, also using the pathfinder package here;
  • An easily accessible tutorial for beginners on netCDF in R can be found on this page.

Getting Your DBF Files Into R

A DBF or DataBase File is the underlying format of dBase. You can read in DBF files with the use of the foreign package, which offers the read.dbf() function:

library(foreign)
data <- read.dbf("<Path to your file>")

Note that if you’re using Windows, you can also use the RODBC package with the odbcConnectDbase() function to read DBF files via Microsoft’s dBase ODBC driver.

Importing Flat Contingency Tables Into R

The foreign package allows you to read multiple file formats; ‘Flat’ contingency tables are no exception. You can use the read.ftable() function to accomplish this:

library(foreign)
data <- read.ftable("<Path to your file>")

Remember that “flat” contingency tables are very similar to the “normal” contingency tables: they contain
the counts of each combination of the levels of the variables (factors) involved. However, this information is re-arranged as a matrix whose rows and columns correspond to the unique combinations of the levels of the row and column variables. “Flat” contingency tables are therefore often preferred to represent higher-dimensional contingency tables.

Reading in Geographical Information System (GIS) Files Into R

You can use the rgdal and raster packages, amongst others, to get your GIS files into R.

If you’re not sure how to start using the rgdal package, consider checking out this nice blog post, which introduces you to working with geospatial data in R.
You can also check out this tutorial, which works with rgdal as well as with raster.

Importing Integrated Taxonomical Information (ITIS) Tables Into R

You can import ITIS tables with the read.table() function:

data <- read.table("<Path to your file>")

For more information on ITIS, click here.

Importing Large Data Sets Into R

Importing large data sets often causes discussion amongst R users. Besides the packages that are meant to connect with databases, there are also some others that stand out when working with big data.

Importing Large Data Sets Into R With the data.table Package

Described as the “fast and friendly file finagler”, the popular data.table package is an extremely useful and easy to use. Its fread() function is meant to import data from regular delimited files directly into R, without any detours or nonsense.

Note that “regular” in this case means that every row of your data needs to have the same number of columns. An example:

  V1 V2 V3
1  1  6  a
2  2  7  b
3  3  8  c
4  4  9  d
5  5 10  e

One of the great things about this function is that all controls, expressed in arguments such as sep, colClasses and nrows are automatically detected. Also, bit64::integer64 types are also detected and read directly without needing to read as character before converting.

Remember that bit64::integer64 types are 64 bit integers: these numbers are stored in the computer as being 64 bits long. By default, these are 32 bits only. Because the bit64::integer64 types are detected, the system knows it’s a number and it’s not being read in as a character to then be converted into an integer.

An example of the fread() function is:

library(data.table)
data <- fread("http://assets.datacamp.com/blog_assets/chol.txt")
data
##      AGE HEIGHT WEIGHT CHOL  SMOKE BLOOD  MORT
##   1:  20    176     77  195 nonsmo     b alive
##   2:  53    167     56  250 sigare     o  dead
##   3:  44    170     80  304 sigare     a  dead
##   4:  37    173     89  178 nonsmo     o alive
##   5:  26    170     71  206 sigare     o alive
##  ---                                          
## 196:  35    174     57  222   pipe     a alive
## 197:  38    172     91  227 nonsmo     b alive
## 198:  26    170     60  167 sigare     a alive
## 199:  39    165     74  259 sigare     o alive
## 200:  49    178     81  275   pipe     b alive

Note that reading in your data with the fread() function returns you a data table:

str(data)
## Classes 'data.table' and 'data.frame':   200 obs. of  7 variables:
##  $ AGE   : int  20 53 44 37 26 41 39 28 33 39 ...
##  $ HEIGHT: int  176 167 170 173 170 165 174 171 180 166 ...
##  $ WEIGHT: int  77 56 80 89 71 62 75 68 100 74 ...
##  $ CHOL  : int  195 250 304 178 206 284 232 152 209 150 ...
##  $ SMOKE : chr  "nonsmo" "sigare" "sigare" "nonsmo" ...
##  $ BLOOD : chr  "b" "o" "a" "o" ...
##  $ MORT  : chr  "alive" "dead" "dead" "alive" ...
##  - attr(*, ".internal.selfref")=<externalptr>

This is different from the read.table(), which creates a data frame of your data.

You can find more on the differences between data frames and data tables are explained here. In short, the most important thing is to know that all data.tables are also data.frames: data.tables are data.frames, too. A data.table can be passed to any package that only accepts data.frame and that package can use the [.data.frame syntax on the data.table. Read more on data.table here.

library(data.table)
data <- fread("http://assets.datacamp.com/blog_assets/chol.txt",
              sep=auto,
              nrows = -1,
              na.strings = c("NA","N/A",""),
              stringsAsFactors=FALSE
              )

Note that the input may also be a file that you want to read in and doesn’t always need to be a URL. Also, note how many of the arguments are the same as the ones that you use in read.table(), for example.

Tip: want to know more about data.table? Maybe our course on Data Analysis In R, The data.table Way can interest you! With the guidance of Matt Dowle and Arun Srinivasan you will go from being a data.table novice to data.table expert in no time.

Getting Large Data Sets Into R With The ff Package

The ff package allows for the “memory-efficient storage of large data on disk and fast access functions”. It’s one of the solutions that frequently pops up when you’re looking into discussions that deal with reading in big data as data frames, like here.

If you want to import separated flat files into ff data frames, you can just use the read.table.ffdf(), read.csv.ffdf(), read.csv2.ffdf(), read.delim.ffdf() or read.delim2.ffdf() functions, much like the read.table() function and its variants or convenience wrappers, which are described in one of our previous posts:

bigdata <- read.table.ffdf(file="<Path to file>",
                           nrows=n)

Note that your first argument can be NULL (like in this case) or can designate an optional ffdf object to which the read records are appended. If you want to know more, please go here. Then, you name the file from which the data are read with the argument file. You can also specify a maximum number of rows to be read in with nrows (which is the same as you would do with read.table()!).

You can also go further and specify the file encoding, the levels or the name of a function that is called for reading each chunk:

library(ff)
bigdata <- read.table.ffdf(file="<Path to file>",
                           nrows=n, 
                           fileEncoding="", 
                           levels=NULL,
                           FUN="read.table")

Tip more arguments that you can add to the read.table.ffdf(), read.csv.ffdf(), read.csv2.ffdf(), read.delim.ffdf() or read.delim2.ffdf() functions can be found here.

Importing Large Data Sets Into R With bigmemory

Another package that frequently pops up in the search results for any query related to large data sets in R is the bigmemory package. This package allows you to “manage massive matrices with shared memory and memory-mapped files”.

Note that you can not use this package on Windows: there are no Windows binaries available.

library(bigmemory)
bigdata <- read.big.matrix(filename="<File name>",
                           sep="/",
                           header=TRUE,
                           skip=2)

As usual, you first give the file name to the function, and then you can begin to specify other things, like the separator symbol, the header or the number of lines to skip before starting to read in your file with the arguments sep, header and skip respectively.

Note that these are only a few examples! You can pass a lot more arguments to the read.big.matrix() function! Consider reading the documentation if you want to know more.

Reading in Large Data Sets Into R With The sqldf Package

The sqldf package is also one of the packages that you might consider using when you’re working with large data sets. This package allows you to “perform SQL selects on R”, and especially its read.csv.sql() function is very handy if you want to read a file into R, filtering it with an SQL statement. Then, only a portion of the data is processed by R:

library(sqldf)
bigdata <- read.csv.sql(file="<Path to your file>",
                        sql="select * from file where ...",
                        colClasses=c("character", 
                                     rep("numeric",10)))

Note that the example above is very similar to other functions that allow you to import large data sets into R, with the sole exception that the second argument that you pass to read.csv.sql() function is an SQL statement. The tables to which you refer in your SQL query are part of the file that you mention in the file argument of read.csv.sql().

Tip: for more information on how to work with sqldf, you can go here for a video tutorial or here for a written overview of the basics.

Importing Large Data Sets Into R With The read.table() Function

You can use the “standard” read.table() function to import your data, but this will probably take more time than other packages that are especially designed to work better with bigger data sets. To see how the read.table() function works, go back to our first post.

To make this function go a little bit faster, you could tweak it yourself to get an optimized read.table() function. This tweaking actually only consists of adding arguments to the usual read.table() function, just like this:

df <- read.table("<Path to your file>", 
                 header = FALSE, 
                 sep="/", 
                 quote = "",
                 na.strings = "EMPTY", 
                 colClasses = c("character", "numeric", "factor"),
                 strip.white = TRUE,                 
                 comment.char="", 
                 stringsAsFactors = FALSE,
                 nrows = n
                 )

Note that

  • you first pass the (path to your) file, depending on whether you have set your working directory to the folder in which the file is located or not.
  • Then, you use the header argument to indicate whether the file contains the names of the variables as its first line. This is not the case in the example above.
  • The field separator character is set as / with the argument sep; This means that the values on each line of the file are separated by this character.
  • Next, you can also choose to disable or enable quoting. In this case, since quote="", you disable quoting.
  • You also define that the string “EMPTY” in your dataset is to be interpreted as an NA value.
  • Then, you also define the classes of your columns: in this case, you indicate that the first column is character column, the second a numeric one and the last a factor.
  • With strip.white you allow the stripping of leading and trailing white space from unquoted character fields; This is only applicable when you have used the sep argument!
  • When comment.char is set as "", you turn off the interpretation of comments.
  • You don’t want characters to be converted to factors! That is why you have also defined colClasses. You confirm this by setting stringsAsFactors to FALSE.
    Tip: this argument is, together with colClasses and comment.char, probably one of the more important ones if you want to import your data smoothly!
  • Lastly, you put a maximum number of rows to read in.

Tip: if you want to have more information on all arguments that you can pass to the read.table() function, you should definitely consider reading our post on reading Excel files into R.

Getting Large Data Sets Into R With The readr Package

One of the faster packages that you may use to import your big data set into R could be the readr package, which allows you to read tabular text data, just like read.table. Nevertheless, the readr package offers “a number of replacement functions that provide additional functionality and are much faster” (see here).

df <- read_table("<Path to your file>",
                 col_names=TRUE)

Note that the readr package also offers the functions read_csv(), read_csv2(), read_delim(), read_fwf(), read_tsv() and many other functions that go faster than their original ones! Details can be found here.

Tip: more information on this package can be found on this GitHub page.

Some Remarks On Handling Big Data In R

For further tips on handling big data in R, you should probably take a look at this StackOverflow discussion, which deals with packages but also with tips such as storing your data in binary formats and the usage of saveRDS/readRDS or the rhdf5 package for the HDF5 format.

Note that this last file format has been covered above and that many other packages exist besides the ones that have been covered above. For example, the packages that are used to connect with databases, such as RODBC and MonetDB.R, can also easily be used to handle larger data sets and the dplyr package also proves its value when you want to work directly with data stored in several types of database.

Tip: interested in manipulating data in R? Then our interactive course on dplyr might be something for you! With the guidance of Garrett Grolemund, you will get to learn how to perform sophisticated data manipulation tasks using dplyr.

Make sure to also check out this interesting post, which tests the load performance of some of the packages listed above!

Getting Your Data Into R With The rio Package

This “Swiss-army knife for data Input/Output” makes the input and output of data into R easier for you! You can in- or output data from almost any file format: when you install the rio package, you pull a lot of separate data-reading packages into one. If you then want to input or output data, you just need to remember two functions: import() and export(): rio will rely on the separate data-reading packages to infer the data structure from the file extension, to natively read web-based data sources and to set reasonable defaults for import and export.

In short, rio supports a broad set of commonly used file types for import and export.

Importing your files with rio happens in the following way:

library(rio)
data <- import("<Path to your file>")

If you want to see exactly which file formats are supported by rio, visit this page.

On An Endnote

If you’re interested in learning more about working with big data in R, make sure to check out the How To Work With Quandl in R and the “Big Data Analysis With Revolution R Enterprise” courses at DataCamp!

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