Introduction

Here we will demonstrate how to load data into R. There are two ways to do this. Either you have data already in a file on your computer and then you load that file into R, or you directly link into a database from R. We will discuss both these techniques here.

File upload

When you are working with an existing datafile on your computer, then you should first ensure that you have set your working directory such that it points to the folder from which you are working and where you can find your datafile. Let’s assume that you have saved mroz.csv to your working directory: C:\Rwork. Then the command below should read setwd("C:/Rwork"). Note that all backward slashes (\) have to be replaced by forward slashes (/).

setwd("XXXX:/XXXX")   # replace the XXXX with your drive and path

The datafile we practice with here is a comma separated values file (csv). To upload such a file we use the read.csv function as follows, assuming that the data file is in the working directory:

mydata <- read.csv("mroz.csv")

This loads the data (753 observations and 22 variables), as a new object called mydata into your environment. Check in your environment window to confirm that it is there.

A typical erorr message at this stage is “Error in file(file,”rt”) : cannot open the connection”. If you get that error message this is an indicatin that the datafile you are intending to upload is not in the working directory. You can check what the current working directory is with getwd() in the console. Make sure that file location and working directory are synchronised.

If you use the help function (?read.csv in the console) you will find some guidance in the use of this function.

read.csv help
read.csv help

As you can see there are a lot of options you can set. One which is often important to use is the na.strings option. Here you are telling R how missing values are coded up in the spreadsheet you are uploading.

Let’s see why this is important. You can get a first glimpse at the data using the str() function. This is useful as it gives you the datatypes.

str(mydata)
## 'data.frame':    753 obs. of  22 variables:
##  $ inlf    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hours   : int  1610 1656 1980 456 1568 2032 1440 1020 1458 1600 ...
##  $ kidslt6 : int  1 0 1 0 1 0 0 0 0 0 ...
##  $ kidsge6 : int  0 2 3 3 2 0 2 0 2 2 ...
##  $ age     : int  32 30 35 34 31 54 37 54 48 39 ...
##  $ educ    : int  12 12 12 12 14 12 16 12 12 12 ...
##  $ wage    : chr  "3.354" "1.3889" "4.5455" "1.0965" ...
##  $ repwage : num  2.65 2.65 4.04 3.25 3.6 4.7 5.95 9.98 0 4.15 ...
##  $ hushrs  : int  2708 2310 3072 1920 2000 1040 2670 4120 1995 2100 ...
##  $ husage  : int  34 30 40 53 32 57 37 53 52 43 ...
##  $ huseduc : int  12 9 12 10 12 11 12 8 4 12 ...
##  $ huswage : num  4.03 8.44 3.58 3.54 10 ...
##  $ faminc  : int  16310 21800 21040 7300 27300 19495 21152 18900 20405 20425 ...
##  $ mtr     : num  0.722 0.661 0.692 0.781 0.622 ...
##  $ motheduc: int  12 7 12 7 12 14 14 3 7 7 ...
##  $ fatheduc: int  7 7 7 7 14 7 7 3 7 7 ...
##  $ unem    : num  5 11 5 5 9.5 7.5 5 5 3 5 ...
##  $ city    : int  0 1 0 0 1 1 0 0 0 0 ...
##  $ exper   : int  14 5 15 6 7 33 11 35 24 21 ...
##  $ nwifeinc: num  10.9 19.5 12 6.8 20.1 ...
##  $ lwage   : chr  "1.210154" "0.3285121" "1.514138" "0.0921233" ...
##  $ expersq : int  196 25 225 36 49 1089 121 1225 576 441 ...

Most of the data are of the num (numeric) and int (integer) type, but two variables (wage and lwage) come as character/text (chr) variables. This is awkwar as we are likely to want to use these data (here wages) for some numerical analysis. Why did R not recognise these numbers as numbers?

To see that, here is an excerpt of the file we just uploaded:

Example for missing data
Example for missing data

You can see that in the wage column there are some observations which do not have a number but rather a “.”. This is this spreadsheet’s way of telling you that for these observations there is no wage information. The information is missing. Different spreadsheets code missing values in different ways. Sometimes it will just be empty cells, sometimes it will say “NA” or “na”. You need to help R to recognise missing values. That is what the na.strings option in the read.csv function does.

mydata <- read.csv("mroz.csv", na.strings = ".")

You can now test with str(mydata) to confirm that the wage and lwage data are now recognised as numeric data.

If your data come as an excel file (mroz.xlsx), then we need to use a different function. There are, as always, different functions which do the same job. We recommend read_excel which comes from the readxl package (needs loading!). Note that the option to indicate how missing values are coded is here called na.

library(readxl)
mydata <- read_excel("Mroz.xlsx", na = ".")

Summary

In this workthrough you learned how to make data available to your code. The two techniques were to either load the data from a file on your computer or to load them directly from a database.