6 The most important question: how do I import data?

When you are an applied researcher or a student learning statistics, you have a question in the back of your head: how do I import data in R? After all, you will not program in R, you will be an R user.

Importing data in R is simple but, you have multiple options to read in data. R also opens all sorts of files, it is almost universal in the sense that you can open almost any file.

Before explaining how to import a data set, I need to introduce the concept of working directory.

6.1 Working directory

Programming languages and software always need a working space. The working space is a folder in your computer where R will try to find files needed to be opened. The working folder or directory will also be the place where R saves any information or file that you request to save from R.

In the next example I will specify my working directory, using the function setwd(), in this example the working folder is “labBook”:

setwd("C:/Users/emontenegro1/Documents/MEGA/stanStateDocuments/PSYC3000/labBook")

Important remarks: I specify the whole path to the folder where my files will be located. Also, after copying the path in Windows, I had to change the back slash \ for slash /. This is a step that you have to do if you are using a Windows computer. Mac users don’t need to change the path.

If you need to check the path to your working directory, you can run the following code:

getwd()

When you are using RStudio you can set your working directory using the user interface. You can click on Session –> Set Working Directory –> Choose Directory.

If you don’t set your working directory manually, R will set your working director at your Documents folder in Windows and Mac. If you are using RStudio, the working directory by default is where you opened your code. For instance, if you open your code from the folder PSYC3000_homework. The default working directory will be PSYC3000_homework.

6.2 Import a data file

After setting the working directory, you are now ready to import the data you need to analyze. There are multiple functions to open specific file formats. In this course we will use mostly read.csv() function.

life_expectancy <- read.csv("lifeExpect.csv")

I’m importing the file named lifeExpect.csv, this file is located in my local folder. The same folder that I specify as my working directory in the previous step.

You might wonder, what is a .csv file? CSV stands for “comma separated values”, it is a light format to save information. It is also easy to open in any operated system. I use this format frequently because I can send the file to any student and I’m sure the file opens in any computer, tablet or cellphone.

The file lifeExpect is now inside our virtual space in R. It is now represented by the object life_expectancy. From now on, we need to refer to the object to get access to the information inside the external file.

For example, we can print the first five values of each column using the function head():

head(life_expectancy)

Country.Name	Country.Code	X1960	X1961	X1962	X1963	X1964	X1965	X1966	X1967	X1968	X1969	X1970	X1971	X1972	X1973	X1974	X1975	X1976	X1977	X1978	X1979	X1980	X1981	X1982	X1983	X1984	X1985	X1986	X1987	X1988	X1989	X1990	X1991	X1992	X1993	X1994	X1995	X1996	X1997	X1998	X1999	X2000	X2001	X2002	X2003	X2004	X2005	X2006	X2007	X2008	X2009	X2010	X2011	X2012	X2013	X2014	X2015	X2016	X2017	X2018	X2019	X2020
Aruba	ABW	65.66200	66.07400	66.44400	66.78700	67.11300	67.43500	67.76200	68.09500	68.43600	68.78400	69.14000	69.49800	69.85100	70.19100	70.51900	70.83300	71.14000	71.44100	71.73600	72.02300	72.29300	72.53800	72.75100	72.92900	73.07100	73.18100	73.26200	73.32500	73.37800	73.42500	73.46800	73.50900	73.54400	73.57300	73.59800	73.62200	73.64600	73.67100	73.70000	73.73800	73.78700	73.85300	73.93700	74.03800	74.15600	74.28700	74.42900	74.57600	74.7250	74.87200	75.01700	75.15800	75.29900	75.44100	75.58300	75.72500	75.86800	76.01000	76.15200	76.29300	76.43400
Africa Eastern and Southern	AFE	42.71605	43.16694	43.60399	44.02562	44.43272	44.82692	45.21305	45.59429	45.97406	46.35240	46.72880	47.10287	47.47117	47.82940	48.17500	48.50331	48.81084	49.09785	49.36633	49.62000	49.87069	50.11589	50.36346	50.61038	50.84833	51.05806	51.21428	51.29978	51.30860	51.25176	51.15411	51.04841	50.95726	50.89025	50.84186	50.80848	50.79616	50.82061	50.89761	51.04419	51.27613	51.60646	52.04315	52.58585	53.22891	53.96655	54.79171	55.68234	56.6098	57.54877	58.47070	59.35359	60.18556	60.95336	61.64737	62.25929	62.78768	63.24626	63.64899	64.00521	64.32570
Afghanistan	AFG	32.44600	32.96200	33.47100	33.97100	34.46300	34.94800	35.43000	35.91400	36.40300	36.90000	37.40900	37.93000	38.46100	39.00300	39.55800	40.12800	40.71500	41.32000	41.94400	42.58500	43.24400	43.92300	44.61700	45.32400	46.04000	46.76100	47.48600	48.21100	48.93000	49.64000	50.33100	50.99900	51.64100	52.25600	52.84200	53.39800	53.92400	54.42400	54.90600	55.37600	55.84100	56.30800	56.78400	57.27100	57.77200	58.29000	58.82600	59.37500	59.9300	60.48400	61.02800	61.55300	62.05400	62.52500	62.96600	63.37700	63.76300	64.13000	64.48600	64.83300	65.17300
Africa Western and Central	AFW	37.20538	37.63255	38.05261	38.46375	38.86707	39.26484	39.66276	40.06641	40.48283	40.91421	41.36512	41.83721	42.32704	42.82908	43.33922	43.85504	44.37397	44.89223	45.40268	45.89748	46.36610	46.79829	47.18862	47.53399	47.83054	48.07938	48.28458	48.45413	48.59742	48.71923	48.81700	48.88593	48.92342	48.93371	48.92551	48.90994	48.89988	48.90913	48.95534	49.05264	49.21971	49.47514	49.81693	50.23943	50.73342	51.28349	51.86820	52.46396	53.0495	53.61209	54.14431	54.65000	55.13894	55.61899	56.08827	56.54201	56.97476	57.38236	57.76235	58.11572	58.44595
Angola	AGO	37.52400	37.81100	38.11300	38.43000	38.76000	39.10200	39.45400	39.81300	40.17800	40.54600	40.91400	41.28200	41.65000	42.01600	42.37400	42.72100	43.05300	43.36700	43.66000	43.93100	44.17800	44.40400	44.61100	44.79900	44.96600	45.10700	45.21300	45.28300	45.31700	45.32400	45.30600	45.27100	45.23000	45.20100	45.20100	45.24600	45.35000	45.51900	45.76300	46.09300	46.52200	47.05900	47.70200	48.44000	49.26300	50.16500	51.14300	52.17700	53.2430	54.31100	55.35000	56.33000	57.23600	58.05400	58.77600	59.39800	59.92500	60.37900	60.78200	61.14700	61.48700
Albania	ALB	62.28300	63.30100	64.19000	64.91400	65.46300	65.85000	66.11000	66.30400	66.48700	66.68900	66.93500	67.23700	67.58200	67.95300	68.34300	68.73600	69.11000	69.44800	69.74200	69.99100	70.20800	70.41600	70.63500	70.87600	71.13400	71.38800	71.60500	71.76000	71.84300	71.86000	71.83600	71.80300	71.80200	71.86000	71.99200	72.20500	72.49500	72.83800	73.20800	73.58700	73.95500	74.28800	74.57900	74.82800	75.03900	75.22800	75.42300	75.64600	75.9120	76.22100	76.56200	76.91400	77.25200	77.55400	77.81300	78.02500	78.19400	78.33300	78.45800	78.57300	78.68600

There is another option to import your data set in R, you may use the following code:

life_expectancy <- read.csv(file.choose())

This options includes the function file.choose() inside the function read.csv(), yes that’s correct. Functions can be nested inside other functions.

file.choose() will open a new window where you can select the file you need to import: