TidyTuesday is a GitHub repo that provides one dataset per week to practice learning data science skills.
For the final project, you will choose one dataset from TidyTuesday and use it to make at least two plots.
This will show you how to find and load a dataset from TidyTuesday.
In your browser, navigate to https://github.com/rfordatascience/tidytuesday
Scroll down until you reach the section labeled “DataSets”.
Once you find a dataset that sounds interesting, click on its link in the “Data” column.
For example, I think “The History of Himalayan Mountaineering Expeditions” dataset sounds interesting, so I’ll click on that.
This will take us to the README file (a file that explains basic info about a piece of software, dataset, or analysis) for the “History of Himalayan Mountaineering Expeditions” dataset.
The first few paragraphs give you an overview of the dataset.
The section called of the README file called “The Data” tells you how to load the data into R.
This is very important, because you need to load the data to analyze it.
Note that there are usually two options given for loading the data.
You only need to use one of them; option 2 is probably easier.
The first option involves installing the “tidytuesdayR” package, which you can do with the command install.packages("tidytuesdayR").
Once you’ve installed the package, you can use it to load the data by specifying the date the data were published on TidyTuesday.
library(tidytuesdayR)
tuesdata <- tt_load("2025-01-21")---- Compiling #TidyTuesday Information for 2025-01-21 ----
--- There are 2 files available ---
── Downloading files ───────────────────────────────────────────────────────────
1 of 2: "exped_tidy.csv"
2 of 2: "peaks_tidy.csv"Note that the TidyTuesday webpage shows tidytuesdayR::tt_load("2025-01-21"), but this does the same thing as running library(tidytuesdayR) followed by tt_load("2025-01-21").
Also, when you use Option 1, the data are loaded as a list, so we need to extract each dataframe from the list. For the himalaya data, there are two dataframes. It shows us how to extract them from the list using the $ symbol:
exped_tidy <- tuesdata$exped_tidy
peaks_tidy <- tuesdata$peaks_tidyThe second option is simpler. You can use the read_csv() function to load the data directly without installing another package.
library(tidyverse)
exped_tidy <- read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-01-21/exped_tidy.csv"
)
peaks_tidy <- read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-01-21/peaks_tidy.csv"
)Now that you’ve loaded the data, have a look at it:
exped_tidy# A tibble: 882 × 69
EXPID PEAKID YEAR SEASON SEASON_FACTOR HOST HOST_FACTOR ROUTE1 ROUTE2
<chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr>
1 EVER20101 EVER 2020 1 Spring 2 China N Col-N… <NA>
2 EVER20102 EVER 2020 1 Spring 2 China N Col-N… <NA>
3 EVER20103 EVER 2020 1 Spring 2 China N Col-N… <NA>
4 AMAD20301 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
5 AMAD20302 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
6 AMAD20303 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
7 AMAD20304 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
8 AMAD20305 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
9 AMAD20306 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
10 AMAD20307 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
# ℹ 872 more rows
# ℹ 60 more variables: ROUTE3 <lgl>, ROUTE4 <lgl>, NATION <chr>, LEADERS <chr>,
# SPONSOR <chr>, SUCCESS1 <lgl>, SUCCESS2 <lgl>, SUCCESS3 <lgl>,
# SUCCESS4 <lgl>, ASCENT1 <chr>, ASCENT2 <chr>, ASCENT3 <lgl>, ASCENT4 <lgl>,
# CLAIMED <lgl>, DISPUTED <lgl>, COUNTRIES <chr>, APPROACH <chr>,
# BCDATE <date>, SMTDATE <date>, SMTTIME <chr>, SMTDAYS <dbl>, TOTDAYS <dbl>,
# TERMDATE <date>, TERMREASON <dbl>, TERMREASON_FACTOR <chr>, …peaks_tidy# A tibble: 480 × 29
PEAKID PKNAME PKNAME2 LOCATION HEIGHTM HEIGHTF HIMAL HIMAL_FACTOR REGION
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
1 AMAD Ama Dablam Amai D… Khumbu … 6814 22356 12 Khumbu 2
2 AMPG Amphu Gyab… Amphu … Khumbu … 5630 18471 12 Khumbu 2
3 ANN1 Annapurna I <NA> Annapur… 8091 26545 1 Annapurna 5
4 ANN2 Annapurna … <NA> Annapur… 7937 26040 1 Annapurna 5
5 ANN3 Annapurna … <NA> Annapur… 7555 24787 1 Annapurna 5
6 ANN4 Annapurna … <NA> Annapur… 7525 24688 1 Annapurna 5
7 ANNE Annapurna … <NA> Annapur… 8026 26332 1 Annapurna 5
8 ANNM Annapurna … <NA> Annapur… 8051 26414 1 Annapurna 5
9 ANNS Annapurna … Annapu… Annapur… 7219 23684 1 Annapurna 5
10 APIM Api Main <NA> Api Him… 7132 23399 2 Api/Byas Ri… 7
# ℹ 470 more rows
# ℹ 20 more variables: REGION_FACTOR <chr>, OPEN <lgl>, UNLISTED <lgl>,
# TREKKING <lgl>, TREKYEAR <dbl>, RESTRICT <chr>, PHOST <dbl>,
# PHOST_FACTOR <chr>, PSTATUS <dbl>, PSTATUS_FACTOR <chr>, PEAKMEMO <dbl>,
# PYEAR <dbl>, PSEASON <dbl>, PEXPID <chr>, PSMTDATE <chr>, PCOUNTRY <chr>,
# PSUMMITERS <chr>, PSMTNOTE <chr>, REFERMEMO <dbl>, PHOTOMEMO <dbl>Note that since there are two dataframes in this dataset, you may need to join them in order to conduct the data analysis.
Every dataset in TidyTuesday includes a “Data Dictionary” that explains what each column in the data means. Scroll down the README file a bit further to find it. This is very important for understanding and analyzing your data.
For example, let’s count the names of the mountain peaks included in the peaks_tidy data:
peaks_tidy |>
count(PKNAME)# A tibble: 480 × 2
PKNAME n
<chr> <int>
1 Aichyn 1
2 Ama Dablam 1
3 Amotsang 1
4 Amphu Gyabjen 1
5 Amphu I 1
6 Amphu Middle 1
7 Anidesh Chuli 1
8 Annapurna I 1
9 Annapurna I East 1
10 Annapurna I Middle 1
# ℹ 470 more rowsOnce you get a feel for the columns in the dataset, try making some plots.
Your goal is to create a plot that tells the story hidden in the data. Good luck!