Working With Data Frames In R

Kevin Feasel

2018-09-24

R

Dave Mason has a couple of blog posts on data frames.  First, the basics:

Conceptually, a dataset is a grid or table of data elements. It consists of rows, which we specifically call “observations”, and of columns , which are called “variables”. (Observations may also be referred to as “instances”. Variables may also be referred to as “properties”.) The data frame in R is designed for data sets. As the R documentation tells us, data frames are “used as the fundamental data structure by most of R’s modeling software”.

The function we’ll be working with primarily in this post is the data.frame() function. I have read that in R programming, creating data frames with this function is rather uncommon. Most of the time, data frames are created by invoking other functions that read data from an external data source (like a file or a database table) with a data frame as the return type. But for simplicity, data.frame() will serve our purposes.

Then, subsetting data frames:

Adding columns to a data frame is easy–easy compared to adding rows. We’ll get to that. To add a column, first create a vector. The class doesn’t matter. But the number of elements does–it has to match the number of observations in the data frame. Now that we have our vector, here are some options to add it as a new column to a data frame: use the $ shortcut, use double brackets with the new column name, bind the vector to the dataframe with cbind().

The data frame (or tibble, if using the tidyverse version) is probably the single most important data type in R for getting work done.

Related Posts

Defining Tidy Data

John Mount shares thoughts about the concept of tidy data: A question is: is such a data set “tidy”? The paper itself claims the above definitions are “Codd’s 3rd normal form.” So, no the above table is not “tidy” under that paper’s definition. The the winner’s date of birth is a fact about the winner […]

Read More

Visualizing Earthquake Data

Giorgio Garziano continues a series on analyzing earthquake data: This is the third part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span. In this post, we are going to show static, interactive and animated earthquakes maps of different flavors by […]

Read More

Categories

September 2018
MTWTFSS
« Aug Oct »
 12
3456789
10111213141516
17181920212223
24252627282930