Session 2: Data Management in R
Rutgers University Sociology R Bootcamp
Fred Traylor, Lab TA (he/him)
September 1, 2022
Good Afternoon!
This Afternoon’s Goals
Working Directories & Projects
Environments and Packages
Packages
Tidyverse
usdata
Data Management in R
Filtering Rows
Selecting Columns
Directories and Projects
Working Directories
Tomorrow, we’ll be working with data from outside of R. That is,
we’ll be importing our own data files.
Before we can do that, though, we first need to understand where our
data and files are currently being saved.
This is called a “Working Directory.” To find it, we can run
getwd(), short for “get
working directory.”
## [1] "C:/Users/fhtra/Documents/R/ay23_lab_ta/bootcamp"
Projects
If you haven’t already, let’s go ahead and set up a project. We’ll
save everything we do this semester into the project, so it’ll be a
handy place to keep everything together. Projects do two useful
functions:
Let’s create a new project
In the RStudio menu, click File > New Project
In the popup menu, click “New Directory”
Click “New Project”
Give it an appropriate name and choose where it should go in your
files
I named mine “ay23_lab_ta”
Other good options include “soc541”, “sociology_stats_1”,
etc.
- We’ll be making another project for 542 (Stats II) in the
spring.
All of my R files are saved in “Documents > R”, so I made it a
“subdirectory” within that folder.
You should now be looking at a new project.
Your working directory might have changed, too, and we can look at it
with getwd().
## [1] "C:/Users/fhtra/Documents/R/ay23_lab_ta/bootcamp"
Official Question Time 1
Since the last OQT, we’ve done:
Viewing the Working Directory
Creating a New Project
Environments and Packages
Other R Environments
Up to now, we’ve used what is called “Base R.” If you go to the
“Environment” pane of your R window, there is a dropdown menu to see
what is in each environment. Go ahead and click on it and you’ll see a
selection of other “packages.”
No need to do anything inside these packages, but now you know how R
knows what to do. If we look at the documentation for a function, it
gives us the function’s name followed by the name of the package holding
in in curly brackets.
For example, ?table gives us
table {base}, telling us that the function
table comes in the base package built into
R.
If we do it for head() we see that it comes in the
utilities (utils) R package.
- This is still considered part of “Base R” because it comes along
with the default installation.
Generally, we don’t want to mess with the code of any functions or
values that come in our other packages because fixing them involves
reinstalling a lot of things.
Introduction to Packages
The beauty of R is that, because it is open-source, anybody can add
new functions and make them available to anybody. Because these
functions often rely on each other, they get packaged together to make
for easy (and consistent) usage.
These “packages” are what we’re going to be doing today. These will
come in handy during this bootcamp and throughout the entire year as you
take statistics.
I generally group packages into two main categories:
- Quality of life packages make R easier to use or simplify code
- Extension packages add capabilities that would otherwise be
complicated in Base R
As you can guess, there’s a lot of overlap here. We’ll mostly be
working with quality of life packages this fall, but we’ll also add some
extension packages as well.
Preparing to Use Packages
When you want to use a package, there are two things you have to
do:
You have to install it.
There are likely thousands of packages available on CRAN (where
you downloaded R from) and even more available elsewhere online. It’d be
too much for R to install every possible package to your computer when
you downloaded it the first time, so they’re only available as you want
them.
Luckily, packages (like R) are free!
To install a package, you use the
install.packages() function built into
R.
You have to call it from your library.
After it’s been downloaded, it’s saved on your computer.
(YAY!)
But, R doesn’t know which ones you want to use yet, so you will
need to call it into the working library via the
library() function.
You can think of install.packages() as buying a tool and
library() as actually getting it out of the toolbox.
Intro to the Tidyverse
This afternoon, and all throughout this year, we’re also going to use
a popular package (well, set of packages) called the
tidyverse.
Because it’ll take a minute or two to install, go ahead and type
install.packages("tidyverse") into R and run it.
- Remember to include the the quotation marks!
The tidyverse is “an
opinionated collection of R packages designed for data science. All
packages share an underlying design philosophy, grammar, and data
structures.”
Basically, it makes it easier to manage, use, and look at our data.
Today, we’ll be working with the manage and use parts of this. In a few
weeks, we’ll do a little bit with how the tidyverse makes data
visualization better and easier than with Base R.
Hopefully, by now, the tidyverse has been installed, so let’s go
ahead and call it into the library with
library(tidyverse).
- No quotation marks needed this time
## Warning: package 'tidyverse' was built under R version 4.1.3
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## Warning: package 'tidyr' was built under R version 4.1.2
## Warning: package 'readr' was built under R version 4.1.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
You’ll get these warnings every time you load the tidyverse. It’s
telling you which packages have been loaded, if the package(s) was built
using a newer version of R you have downloaded, and if there are any
“conflicting” functions. Generally you don’t need to worry about these.
(We’ll deal with them more in the Spring…)
The County Dataset
This afternoon, we’re going to work with a dataset of US counties.
(In case you don’t know, counties are smaller than state governments but
(generally) larger than cities.)
We’re going to use a package called usdata and a dataset
saved in it called “county.” To download it, go ahead and
type install.packages("usdata") into R and run it.
- You have to have the quotation marks or R won’t know what you
mean.
Once that’s loaded up, run library(usdata), to bring it
into our environment.
- No quotation marks needed this time.
## Warning: package 'usdata' was built under R version 4.1.2
Let’s take a look at the dataframe to see what we’re working
with.
## [1] 3142 15
## tibble [3,142 x 15] (S3: tbl_df/tbl/data.frame)
## $ name : chr [1:3142] "Autauga County" "Baldwin County" "Barbour County" "Bibb County" ...
## $ state : Factor w/ 51 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ pop2000 : num [1:3142] 43671 140415 29038 20826 51024 ...
## $ pop2010 : num [1:3142] 54571 182265 27457 22915 57322 ...
## $ pop2017 : int [1:3142] 55504 212628 25270 22668 58013 10309 19825 114728 33713 25857 ...
## $ pop_change : num [1:3142] 1.48 9.19 -6.22 0.73 0.68 -2.28 -2.69 -1.51 -1.2 -0.6 ...
## $ poverty : num [1:3142] 13.7 11.8 27.2 15.2 15.6 28.5 24.4 18.6 18.8 16.1 ...
## $ homeownership : num [1:3142] 77.5 76.7 68 82.9 82 76.9 69 70.7 71.4 77.5 ...
## $ multi_unit : num [1:3142] 7.2 22.6 11.1 6.6 3.7 9.9 13.7 14.3 8.7 4.3 ...
## $ unemployment_rate: num [1:3142] 3.86 3.99 5.9 4.39 4.02 4.93 5.49 4.93 4.08 4.05 ...
## $ metro : Factor w/ 2 levels "no","yes": 2 2 1 2 2 1 1 2 1 1 ...
## $ median_edu : Factor w/ 4 levels "below_hs","hs_diploma",..: 3 3 2 2 2 2 2 3 2 2 ...
## $ per_capita_income: num [1:3142] 27842 27780 17892 20572 21367 ...
## $ median_hh_income : int [1:3142] 55317 52562 33368 43404 47412 29655 36326 43686 37342 40041 ...
## $ smoking_ban : Factor w/ 3 levels "none","partial",..: 1 1 2 1 1 1 NA NA 1 1 ...
We can see that we have a lot of observations (3,142) and 15
variables.
Fortunately, the names are fairly descriptive, but we should still
look at the documentation (?county) to see what everything
means.
It also tell us that the data is saved as a “tibble.”
In the Packages pane, scroll down to the one that says “tibble.” If
it isn’t checked, go ahead and check it to bring it to the library.
- This does the same thing as
library(tibble), and you’ll
actually see this code run in the console when you check it.
The description for “tibble” is “Simple Data Frames.” Indeed, tibbles
are the same as data frames, but with a few nice features that make it
easier to work with large datasets:
It gives us the data type for each column
When you print to the console, it shortens the output
Smaller storage