Category: 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.

John Mount points out that there is a built-in function to mutate data frames in R:

The notation we used above is the “explicit argument” variation we recommend for readability. What a lot of dplyr users do not seem to know is: base-R already has this functionality. The function is called transform().

To demonstrate this, let’s first detach dplyr to show that we are not using functions from dplyr.

detach("package:dplyr", unload = TRUE)

Now let’s write the equivalent pipeline using exclusively base-R.

Click through for the way to do this as a pipeline operation.

Dave Mason continues his look at lists in R:

Subsetting the list with single brackets [] for the first element returns “Atlantic”. But if we take a closer look using the str() function, we see R returned the data as a class of type list:

> #Appears to return "Atlantic" as a character class.
> division[1]
$Name
[1] "Atlantic"

> #str shows us the return is actually a list of 1 element.
> str(division[1])
List of 1
 $ Name: chr "Atlantic"

Dave also explains the difference between single brackets and double brackets for list elements.

John Mount has a new R tip, this time around passing a formula and seeing that formula later:

This works, and the paste() pattern is so useful we suggest researching and memorizing it.

However the “call” portion of the model is reported as “formula = f” (the name of the variable carrying the formula) instead of something more detailed. Frankly this printing issue never bothered us. None of our tools or workflows currently use the model call item, and for a very large number of variables formatting the call contents in the model report becomes unweildy. We also already have the formula in a variable, so if we need it we can save it or pass it along.

There is a much better place on many models to get model structure information from than the model call item: the model terms item. This item carries a lot of information and formats up quite nicely:

format(terms(model))
# [1] "mpg ~ cyl + disp + hp + carb"

Be sure to check out the comments too, as there are several solutions to this problem.