Tidy Data Rule #2: Every Row is an Observation
This lesson is called Tidy Data Rule #2: Every Row is an Observation, part of the Going Deeper with R course. This lesson is called Tidy Data Rule #2: Every Row is an Observation, part of the Going Deeper with R course.
Transcript
Click on the transcript to go to that point in the video. Please note that transcripts are auto generated and may contain minor inaccuracies.
Loading transcript...
Your Turn
Run the following code to view the built-in gss_cat data frame.
library(tidyverse)
gss_cat |>
view()Then, write code to count the number of unique responses in the partyid variable.
You’ll need to use the separate_longer_delim() and count() functions to do this.
Learn More
This lesson from the R for Social Scientists course materials covers separate_longer_delim().
Have any questions? Put them below and we will help you out!
Course Content
44 Lessons
1
Downloading and Importing Data
08:13
2
Overview of Tidy Data
05:03
3
Tidy Data Rule #1: Every Column is a Variable
06:26
4
Tidy Data Rule #3: Every Cell is a Single Value
09:27
5
Tidy Data Rule #2: Every Row is an Observation
04:05
6
Changing Variable Types
05:13
7
Dealing With Missing Data
04:41
8
Advanced Summarizing
07:52
9
Binding Data Frames
06:56
10
Functions
11:59
11
Data Merging
09:24
12
Exporting Data
04:20
13
Bring It All Together (Advanced Data Wrangling)
14:22
1
Best Practices in Data Visualization
03:38
2
Tidy Data
02:25
3
Pipe Data in ggplot
08:18
4
Reorder Plots to Highlight Findings
03:50
5
Line Charts
04:13
6
Use Color to Highlight Findings
08:23
7
Declutter
07:53
8
Add Descriptive Labels to Your Plots
09:18
9
Use Titles to Highlight Findings
08:30
10
Use Annotations to Explain
06:35
11
Tweak Spacing
05:36
12
Create a Custom Theme
03:20
13
Customize Your Fonts
04:42
14
Try New Plot Types
03:24
15
Bring it All Together (Advanced Data Visualization)
11:04
1
Advanced Markdown
07:10
2
Tables
15:48
3
Advanced YAML and Code Chunk Options
05:42
4
Inline R Code
03:42
5
Making Your Reports Shine: Word Edition
05:08
6
Making Your Reports Shine: PDF Edition
07:37
7
Making Your Reports Shine: HTML Edition
06:08
8
Presentations
11:12
9
Dashboards
06:20
10
Websites
08:11
11
Publishing Your Work
02:37
12
Quarto Extensions
06:38
13
Parameterized Reporting, Part 1
07:02
14
Parameterized Reporting, Part 2
04:03
15
Parameterized Reporting, Part 3
06:22
16
Wrapping up Going Deeper with R
You need to be signed-in to comment on this post. Login.
Mallik Janthak • November 1, 2026
In gss_cats, partyid is shown to be of the type
<fct>. Can you refresh my memory on this type and where it is used?Gracielle Higino Coach • November 3, 2026
Hi Mallik! Factors are categorical variables that can be ordered by a defined order other than alphabetical or numerical. We have a special lesson about this here. We use factors pretty much everytime we need a categorical variable that has an intrinsic order of values (like "big", "medium" and "small"), instead of a character variable (you can think of those as plain text variables that are not ordered, like species names). I hope this helps! We'll have a moment to discuss that in the live session this week.
Lilly Yu • November 5, 2026
I noticed that in the solution, there is a select(partyid) |>, whereas the code I modeled mine off in the video omitted this. I got the same result using both code. What does select(partidyid) do here exactly?
Gracielle Higino Coach • November 5, 2026
Hi Lilly! You can get the same result without the select() in this case. It's here for clarity and pedagogical purposes only, just so we focus on this variable specifically. As always, there are many ways to do the same thing in R!