Bring It All Together (Advanced Data Wrangling)
This lesson is called Bring It All Together (Advanced Data Wrangling), part of the Going Deeper with R course. This lesson is called Bring It All Together (Advanced Data Wrangling), part of the Going Deeper with R course.
Transcript
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View code shown in video
# Load Packages -----------------------------------------------------------
library(tidyverse)
library(janitor)
# Import Data -------------------------------------------------------------
survey_data_raw <- read_tsv("data-raw/2020-combined-survey-final.tsv") |>
clean_names() |>
mutate(id = row_number())
# Exploration -------------------------------------------------------------
survey_data_raw |>
glimpse()
survey_data_raw
count(qr_learning_path) |>
arrange(desc(n))
# Tidying -----------------------------------------------------------------
other_coding_languages <-
survey_data_raw |>
select(id, qcoding_languages) |>
separate_longer_delim(qcoding_languages,
delim = ", ")
demographics <- survey_data_raw |>
select(id, qyear_born:qcountry)
# Export ------------------------------------------------------------------
other_coding_languages |>
write_rds("data/other_coding_languages.rds")
demographics |>
write_rds("data/demographics.rds")Learn More
For your reference:
Bring it All Together (Data Wrangling) [video]
Bring it All Together (Data Visualization) [video]
Bring it all Together Quarto [video]
Have any questions? Put them below and we will help you out!
Course Content
44 Lessons
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
Changing Variable Types
04:51
7
Dealing with Missing Data
04:55
8
Advanced Summarizing
06:25
9
Binding Data Frames
07:17
10
Functions
15:06
11
Data Merging
09:27
12
Exporting Data
04:38
13
Bring It All Together (Advanced Data Wrangling)
13:03
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
Tweak Spacing
05:11
12
Create a Custom Theme
03:47
13
Customize Your Fonts
08:32
14
Try New Plot Types
03:24
15
Bring it All Together (Advanced Data Visualization)
14:30
1
Advanced Markdown
06:43
2
Advanced YAML and Code Chunk Options
05:53
3
Tables
18:36
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
Wrapping up Going Deeper with R
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Marina Gimenez • April 4, 2025
I do not understand by instead of working with the whole file you first make 2 files out of it to bring them back together? Or was it just for the sake of practicing? Or because the file was so big that indeed you just only picked what you needed, but then still you could have picked it to make a single file?
Gracielle Higino Coach • April 4, 2025
Hi Marina! All of these are good reasons to split your dataset! Very often, splitting a dataset makes it clearer and easier to transpose the parts, when you need to pivot longer or wider. Then you can use joins to combine the pivoted datasets, using only the variables you want.
That's what David is demonstrating here: notice how the raw data in this example contains 53 variables, while the selected and cleaned datasets only have 6 and 2 variables each. He cleaned and selected the data he'd need, and then used them separately and joined as he needed.