Bring it All Together (Data Visualization)
This lesson is called Bring it All Together (Data Visualization), part of the R in 3 Months (Spring 2025) course. This lesson is called Bring it All Together (Data Visualization), part of the R in 3 Months (Spring 2025) course.
Transcript
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View code shown in video
# Load Packages -----------------------------------------------------------
library(tidyverse)
library(janitor)
library(scales)
# Import Data -------------------------------------------------------------
survey_data <- read_tsv("2020-combined-survey-final.tsv") |>
clean_names()
survey_data |>
select(contains("enjoy"))
survey_data |>
filter(is.na(qr_enjoyment)) |>
select(qr_enjoyment)
survey_data |>
glimpse()
avg_r_enjoyment <- survey_data |>
drop_na(qr_enjoyment) |>
group_by(qcountry) |>
summarize(avg_enjoyment = mean(qr_enjoyment),
n = n()) |>
filter(n >= 50) |>
arrange(desc(avg_enjoyment)) |>
drop_na(qcountry) |>
mutate(avg_enjoyment_two_digits = number(avg_enjoyment, accuracy = 0.01))
# Data Visualization ------------------------------------------------------
ggplot(data = avg_r_enjoyment,
mapping = aes(x = avg_enjoyment,
y = qcountry,
label = avg_enjoyment_two_digits)) +
geom_col(fill = "#6cabdd") +
geom_text(hjust = 1.2,
color = "white") +
theme_minimal() +
labs(title = "Average Enjoyment of R on a 5-Point Scale Among Users Around the World",
subtitle = "Only countries with 50 or more responses included",
y = NULL,
x = NULL)
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Course Content
127 Lessons
1
Welcome to Getting Started with R
00:57
2
Install R
02:05
3
Install RStudio
02:14
4
Files in R
04:33
5
Projects
07:54
6
Packages
02:38
7
Import Data
05:24
8
Objects and Functions
03:16
9
Examine our Data
12:50
10
Import Our Data Again
07:11
11
Getting Help
07:46
12
Week 1 Live Session (Spring 2025)
1:03:11
1
Welcome to Fundamentals of R
01:36
2
Update Everything
02:45
3
Start a New Project
02:16
4
The Tidyverse
03:34
5
Pipes
04:15
6
select()
07:25
7
mutate()
04:25
8
filter()
10:05
9
summarize()
05:59
10
group_by() and summarize()
05:54
11
arrange()
02:07
12
Create a New Data Frame
03:58
13
Bring it All Together (Data Wrangling)
07:29
14
Week 2 Project Assignment
09:39
15
Week 2 Coworking Session (Spring 2025)
16
Week 2 Live Session (Spring 2025)
1:03:24
1
The Grammar of Graphics
04:39
2
Scatterplots
03:46
3
Histograms
05:47
4
Bar Charts
06:37
5
Setting color and fill Aesthetic Properties
02:39
6
Setting color and fill Scales
05:40
7
Setting x and y Scales
03:09
8
Adding Text to Plots
07:32
9
Plot Labels
03:57
10
Themes
02:19
11
Facets
03:12
12
Save Plots
02:57
13
Bring it All Together (Data Visualization)
06:42
14
Week 3 Project Assignment
03:30
15
Week 3 Coworking Session (Spring 2025)
16
Week 3 Live Session (Spring 2025)
1:02:31
1
Downloading and Importing Data
10:32
2
Overview of Tidy Data
05:50
3
Tidy Data Rule #1: Every Column is a Variable
07:43
4
Tidy Data Rule #3: Every Cell is a Single Value
10:04
5
Tidy Data Rule #2: Every Row is an Observation
04:42
6
Week 6 Coworking Session (Spring 2025)
7
Week 6 Live Session (Spring 2025)
1:02:38
1
Best Practices in Data Visualization
03:44
2
Tidy Data
02:25
3
Pipe Data into ggplot
09:54
4
Reorder Plots to Highlight Findings
03:37
5
Line Charts
04:17
6
Use Color to Highlight Findings
09:16
7
Declutter
08:29
8
Add Descriptive Labels to Your Plots
09:10
9
Use Titles to Highlight Findings
08:14
10
Use Annotations to Explain
07:09
11
Week 9 Coworking Session (Spring 2025)
12
Week 9 Live Session (Spring 2025)
59:09
1
Advanced Markdown
06:43
2
Tables
18:36
3
Advanced YAML and Code Chunk Options
05:53
4
Inline R Code
04:42
5
Making Your Reports Shine: Word Edition
04:30
6
Making Your Reports Shine: PDF Edition
06:11
7
Making Your Reports Shine: HTML Edition
06:06
8
Presentations
10:12
9
Dashboards
05:38
10
Websites
06:43
11
Publishing Your Work
04:38
12
Quarto Extensions
05:50
13
Parameterized Reporting, Part 1
10:57
14
Parameterized Reporting, Part 2
05:11
15
Parameterized Reporting, Part 3
07:47
16
Week 12 Coworking Session (Spring 2025)
17
Week 12 Live Session (Spring 2025)
57:01
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Claire Hazbun • August 13, 2024
So far I've really struggled, especially from one day to the next, with retaining information about how to do things in R. I did see a tweet the other day that said something like "90% of coding is googling", but it's hard to imagine that my current retention level is sustainable for actually using R with any level of efficiency. I'm just wondering in your experience, what does retention typically look like for students? What kinds of things are they able to retain vs have to google? thanks :)
David Keyes Founder • August 14, 2024
I don't have a specific answer here, but I will say that it often takes longer to memorize code things than I expect. I wouldn't be too hard on yourself if it's not happening right away. The more you code, the easier it will become.
If it helps, one of the R for the Rest of Us R in 3 Months coaches wrote this:
Keep going and I promise it will get easier!
David Keyes Founder • August 27, 2024
Hi again! I recently attended posit::conf where Hadley Wickham said something that I think you might find interesting. He said that sometimes uses AI to help him remember how ggplot works. If the developer of ggplot has to use tools like AI to remember how it works, it's ok if the rest of us have to do the same thing!