Examine a Data Frame in R with 7 Basic Functions

When I first started learning R, it seemed way more complicated than what I was used to with looking at spreadsheets in Microsoft Excel. When I started working with data frames in R, it didn’t seem quite as easy to know what I was looking at.

I’ve since come to see the light. While there is a bit of a learning curve to get a handle on it, viewing data in R is infinitely more flexible than doing so in Excel. In this post, I’ll cover the most basic R functions for examining a data set and explain why they’re important.

Understanding how to get a simple overview of the data set has become a huge time saver for me. If you aren’t familiar with these functions, you need to be. If you’re anything like me, you’ll use them first for every single data set you consider.

All of the functions I’m discussing here come in the base R Utils package, so there’s no need to install any additional packages. Here are the functions, with links to their documentation:

1. dim(): shows the dimensions of the data frame by row and column
2. str(): shows the structure of the data frame
3. summary(): provides summary statistics on the columns of the data frame
4. colnames(): shows the name of each column in the data frame
5. head(): shows the first 6 rows of the data frame
6. tail(): shows the last 6 rows of the data frame
7. View(): shows a spreadsheet-like display of the entire data frame

Now, let’s import a data set see how each of these functions works. First, here’s the code:

### Import a data set on violent crime by state and assign it to the data frame "crime"
crime <- read.csv("http://vincentarelbundock.github.io/Rdatasets/csv/datasets/USArrests.csv", stringsAsFactors = FALSE)

### Call the functions on crime to examine the data frame
dim(crime)
str(crime)
summary(crime)
colnames(crime)

### The head() and tail() functions default to 6 rows, but we can adjust the number of rows using the "n = " argument
head(crime, n = 10)
tail(crime, n = 5)

### While the first 6 functions are printed to the console, the View() function opens a table in another window
View(crime)

Now, let’s take a look at the output, so we can see what happens when the code is run.

First, we’ll look at the dim(), str(), summary(), and colnames()  functions:

• dim(): In the crime data set, we can see immediately that there are only 50 rows and 5 columns. This function is useful, because it tells us whether it would be okay to print the entire data frame to the console. With this data set, it’s probably okay. If, however, there were 5,000 rows and 50 columns, we’d definitely want to view the data frame in smaller chunks.
• str(): The structure of the crime data set also tells us the number of rows (observations) and columns (variables), but it provides even more information. It tells us the column names, the class of each column (what kind of data is stored in it), and the first few observations of each variable.
• summary(): The summary provides descriptive statistics including the min, max, mean, median, and quartiles of each column. For example, we can see in the crime data set that the average murder rate across all states is 7.8 for every 100k people.
• colnames(): This function prints a vector of the column names, which can be useful if you’re trying to reference a particular column. For the crime data set, we can see that the state column has no name. Knowing this, we may want to assign it a name before going forward in our analysis.

Now, let’s take a look at the head() and tail() functions:

• head(): This function defaults to printing the first 6 rows, but we’ve decided to call the first 10. In the crime data set, this gives us the data on states Alabama through Georgia.
• tail(): The same as head(), except this function prints the end of the data frame. In this case, we’ve called the last 5 observations, so we can see the data on Virginia through Wyoming.

Finally, let’s take a look at the window that appears when we call the View() function:

• View(): This window provides vertical and horizontal (if enough columns to justify) scroll bars for you to browse the entire data set. It looks exactly like an Excel spreadsheet–you just can’t manipulate any of the data. (Note: make sure you use a capital “V” when calling this function; it’s case sensitive).

That’s it! Getting comfortable with these functions should make it easier for you to work with data frames in a more logical and efficient manner. 

Happy viewing!