Importing Data in R

Comma-Seperated Values (CSV)

Warning

This tutorial assumes the data set is in working condition. That is we assume the default settings for read.csv. In some cases we may need to change the header, specify the field separator and more. See ?read.csv for further details and examples.

We will now import a csv file, to do this we will use the read.csv function. A simple template to follow is

read.csv(file = 'path where csv is located in your computer')

An easy way to find the location of your data (or any file) is using the file.choose() function in R. file.choose() will bring up a file explorer window that allows you to interactively choose a file path to work with.

In your console, run the following command

file.choose()

For example, after running the above command the births dataset is located in

[1] "/Users/jtoledo/Desktop/Projects/uci-stats8/data/births.csv"

Warning

Depending on your location and operating system the directory will be different

Therefore, to read the births data set I would copy/paste the directory of the csv location and run the following command

birth_dat <- read.csv(file = "/Users/jtoledo/Desktop/Projects/uci-stats8/data/births.csv")

  fage mage      mature weeks    premie visits     marital gained weight
1   NA   13 younger mom    39 full term     10 not married     38   7.63
2   NA   14 younger mom    42 full term     15 not married     20   7.88
3   19   15 younger mom    37 full term     11 not married     38   6.63
4   21   15 younger mom    41 full term      6 not married     34   8.00
5   NA   15 younger mom    39 full term      9 not married     27   6.38
6   NA   15 younger mom    38 full term     19 not married     22   5.38
  lowbirthweight gender     habit  whitemom
1        not low   male nonsmoker not white
2        not low   male nonsmoker not white
3        not low female nonsmoker     white
4        not low   male nonsmoker     white
5        not low female nonsmoker not white
6            low   male nonsmoker not white

We are not just limited to csv files, we can import data from Excel (in csv, XLSX, or txt format), SAS, Stata, SPSS, or others. A good reference to import various data formats can be found on datacamp r-data-import tutorial

Text File (TXT)

Next, we consider importing a .txt file. To do so we will use the read.table function instead of the read.csv function. For this example, we consider the ozone.txt file from our course website

A simple template to follow is

read.table(file = 'path where txt file is located in your computer')

After running file.choose() on our console and locating the path in which we stored our data

file.choose()

[1] "/Users/jtoledo/Desktop/Projects/uci-stats8/data/ozone.txt"

we can copy/paste the path as follows

ozone_dat <- read.table(file = "/Users/jtoledo/Desktop/Projects/uci-stats8/data/ozone.txt", 
                        header =TRUE)

ozone_dat

          x       y    o3
1 -120.0258 34.4622 0.044
2 -119.7413 36.7055 0.081
3 -121.7333 36.4819 0.035
4 -119.2908 36.3325 0.080
5 -117.1289 32.8364 0.053

You will notice we now used an additional argument header = TRUE in our read.table function. We use header=TRUE, whenever the text tile contains names of the variables as its first line.

If we forget to use header=TRUE, the first line of the text file will be treated as a row of the dataset and read.table will automatically create the variable names for us

wrong_ozone_dat <- read.table(file = "/Users/jtoledo/Desktop/Projects/uci-stats8/data/ozone.txt")

wrong_ozone_dat

         V1      V2    V3
1         x       y    o3
2 -120.0258 34.4622 0.044
3 -119.7413 36.7055 0.081
4 -121.7333 36.4819 0.035
5 -119.2908 36.3325  0.08

In the above example, read.table automatically create the variable names V1,V2,V3 for each column and the first row has values x,y,o3 (which is incorrect).

In conclusion, some text files do not have variable names in the first row and only contain the actual data. As a result, it is our responsibility to import the data in a suitable manner.