Examine Data
This lesson is called Examine Data, part of the R in 3 Months (Spring 2026) course. This lesson is called Examine Data, part of the R in 3 Months (Spring 2026) course.
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library(tidyverse)
coffee_ratings <- read_csv(file = "coffee_ratings.csv")
coffee_ratings
glimpse(coffee_ratings)
library(skimr)
skim(coffee_ratings)
library(pointblank)
scan_data(coffee_ratings)Your Turn
View your data in the following ways:
In the console by typing
coffee_ratingsUsing the
glimpse()function (make sure you runlibrary(tidyverse)first)With the Positron Data Explorer
With
scan_data()from the {pointblank} package
Have any questions? Put them below and we will help you out!
Course Content
148 Lessons
1
Welcome to Fundamentals of R
01:20
2
Update Everything
02:26
3
Start a New Project
02:38
4
The Tidyverse
03:24
5
Pipes
03:52
6
select()
04:43
7
mutate()
03:22
8
filter()
10:18
9
Quiz
10
summarize()
05:38
11
Grouped Summaries
04:24
12
arrange()
02:50
13
Create a New Data Frame
03:30
14
Quiz
15
Bring it All Together (Data Wrangling)
07:09
16
Week 2 Project Assignment
13:10
17
Week 2 Coworking Session (Spring 2026)
18
Week 2 Live Session (Spring 2026)
59:16
1
The Grammar of Graphics
04:36
2
Scatterplots
03:40
3
Histograms
04:51
4
Bar Charts
04:53
5
Quiz
6
Setting color and fill Aesthetic Properties
02:43
7
Setting color and fill Scales
05:12
8
Quiz
9
Setting x and y Scales
02:58
10
Adding Text to Plots
05:50
11
Plot Labels
02:59
12
Themes
02:10
13
Facets
02:56
14
Save Plots
02:49
15
Bring it All Together (Data Visualization)
06:14
16
Week 3 Project Assignment
06:02
17
Week 3 Coworking Session (Spring 2026)
18
Week 3 Live Session (Spring 2026)
1:00:46
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
Quiz
7
Week 6 Coworking Session (Spring 2026)
8
Week 6 Live Session (Spring 2026)
59:31
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
Quiz
12
Week 9 Coworking Session (Spring 2026)
13
Week 9 Live Session (Spring 2026)
59:10
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
Quiz
17
Week 12 Coworking Session (Spring 2026)
18
Week 12 Live Session (Spring 2026)
57:26
1
Working with labelled data
05:35
2
Understanding Documentation Pages
05:20
3
Factors in R
11:18
4
Add citations to Quarto documents
10:42
5
Change titles of facet plots
08:16
6
Population pyramid plot
04:24
7
Why use Git - example case
06:13
8
How to access data not on GitHub
13:46
9
Dealing with merge conflicts in GitHub Desktop
03:22
10
Crosstabs
06:58
11
Difference between == and %in%
03:17
12
Quarto - rendering and working directories
13:05
13
Using Function Arguments
13:06
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Corbin Campbell • March 18, 2026
When I ran Data Scan, I received an error in "ggplot2..." "problem while computing stat" "error occurred in the 3rd layer" "argument must be coercible to non-negative integer"
Gracielle Higino Coach • March 18, 2026
Hi Corbin! Can you share the exact code you used and the version of the pointblank package you're using?
Gwynne Morrissey • September 24, 2026
I installed the skimr package! It said it was successfully unpacked and MD5 sums checked. When I look in the Packages pane and search for skimr, though, it says "no packages found". And when I tried to use skim(coffee_ratings), I got 'could not find function "skim"'.
Gracielle Higino Coach • September 24, 2026
Hi Gwynne! That's odd... Have you tried refreshing the package pane? There's a little arrow in a circle on the top right of this panel. Restarting your R session might help, too. If you try installing again and it doesn't work, please copy here the whole message you get when the package stops installing.
Maia Werner-Avidon • September 24, 2026
Just curious why sometimes things are in quotation marks and sometimes not. Are the ones in quotation marks being brought in from outside R and then once they are in R, you don't need the quotation marks any more?
Gracielle Higino Coach • September 24, 2026
Hi Maia! We get this question a lot and the truth is that we don't exactly know a rule that works in absolutely all the cases. But in general, we use quotation marks when we're dealing with characters and literal text in general (like when we're adding a title to a plot or mentioning a specific character value in our dataset), which also includes file paths and packages names when we're installing them. For the packages case, you can think of it like we're looking for a package with a name that matches what's between the quotation marks, so it's a search string. When things have names (like columns and objects), we don't need the quotation marks.
text values in cells -> they don't have names -> we use "" (e.g.,
"Adelie"in the penguins dataset)columns -> have names -> we don't use "" (e.g.,
harvest_yearin the coffee ratings dataset)objects -> have names -> we don't use "" (e.g., your
coffee_ratingsdataset object)plot titles -> are literal texts -> don't have names -> we use "" (e.g.,
title = "This plot is awesome!")Tricky thing: when you have column names with spaces, you must use backticks (
`) (e.g.,`Body Mass`). Sometimes using quotation marks in these cases also works, but these cases are exceptions.