Projects
Hands-on analysis of actual data is the best way to learn R programming. This page contains some data sets that you can use to explore what you have learned in this course. For each data set, a brief description as well as download instructions are provided.
Try to focus on using the tools from the course to explore the data, rather than worrying about producing a perfect report with a coherent analysis workflow.
On the last day you will present your Rmd file (or rather, the resulting html report) and share with the class what your data was about.
Palmer penguins 🐧
- This is a data set containing a series of measurements for three species of penguins collected in the Palmer station in Antarctica.
- Data description: https://vincentarelbundock.github.io/Rdatasets/doc/heplots/peng.html
Download instructions
penguins <- read.table("https://vincentarelbundock.github.io/Rdatasets/csv/heplots/peng.csv", header = T, sep = ",")
str(penguins)## 'data.frame': 333 obs. of 9 variables:
## $ rownames : int 1 2 3 4 5 6 7 8 9 10 ...
## $ species : chr "Adelie" "Adelie" "Adelie" "Adelie" ...
## $ island : chr "Torgersen" "Torgersen" "Torgersen" "Torgersen" ...
## $ bill_length : num 39.1 39.5 40.3 36.7 39.3 38.9 39.2 41.1 38.6 34.6 ...
## $ bill_depth : num 18.7 17.4 18 19.3 20.6 17.8 19.6 17.6 21.2 21.1 ...
## $ flipper_length: int 181 186 195 193 190 181 195 182 191 198 ...
## $ body_mass : int 3750 3800 3250 3450 3650 3625 4675 3200 3800 4400 ...
## $ sex : chr "m" "f" "f" "f" ...
## $ year : int 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...Drinking habits 🍷
- Data from a national survey on the drinking habits of american citizens in 2001 and 2002.
- Data description: https://vincentarelbundock.github.io/Rdatasets/doc/stevedata/nesarc_drinkspd.html
Download instructions
library(dplyr)
# this will download the csv file directly from the web
drinks <- read.table("https://vincentarelbundock.github.io/Rdatasets/csv/stevedata/nesarc_drinkspd.csv", header = T, sep = ",")
# the lines below will take a sample from the full data set
set.seed(seed = 2)
drinks <- sample_n(drinks, size = 3000, replace = F)
# and here we check the structure of the data
str(drinks)## 'data.frame': 3000 obs. of 9 variables:
## $ rownames : int 11014 36044 15657 11851 14800 4914 25399 9864 32033 23401 ...
## $ idnum : int 11014 36044 15657 11851 14800 4914 25399 9864 32033 23401 ...
## $ ethrace2a: int 5 1 1 1 1 2 1 5 1 1 ...
## $ region : int 3 1 2 1 2 3 3 2 4 1 ...
## $ age : int 56 55 42 84 18 42 72 39 23 61 ...
## $ sex : int 1 1 1 1 1 1 1 1 0 1 ...
## $ marital : int 1 4 4 3 6 1 3 6 1 5 ...
## $ educ : int 4 4 3 1 2 2 2 2 2 3 ...
## $ s2aq8b : int NA 1 3 NA NA NA NA NA 4 NA ...Car crashes 🚗
- Data from car accidents in the US between 1997-2002.
- Data description: https://vincentarelbundock.github.io/Rdatasets/doc/DAAG/nassCDS.html
Download instructions
library(dplyr)
# this will download the csv file directly from the web
crashes <- read.table("https://vincentarelbundock.github.io/Rdatasets/csv/DAAG/nassCDS.csv", header = T, sep = ",")
# the lines below will take a sample from the full data set
set.seed(seed = 2)
crashes <- sample_n(crashes, size = 3000, replace = F)
# and here we check the structure of the data
str(crashes)## 'data.frame': 3000 obs. of 16 variables:
## $ rownames : int 12117 13263 4806 11014 8465 21853 3276 3453 15657 17074 ...
## $ dvcat : chr "10-24" "25-39" "10-24" "10-24" ...
## $ weight : num 5363.9 29.5 107.4 194.2 98.9 ...
## $ dead : chr "alive" "alive" "alive" "alive" ...
## $ airbag : chr "none" "none" "none" "none" ...
## $ seatbelt : chr "belted" "belted" "none" "belted" ...
## $ frontal : int 0 1 0 0 1 1 1 1 0 0 ...
## $ sex : chr "f" "m" "f" "f" ...
## $ ageOFocc : int 20 38 20 59 40 19 30 43 39 31 ...
## $ yearacc : int 1999 2000 1998 1999 1999 2002 1997 1997 2000 2000 ...
## $ yearVeh : int 1984 1990 1991 1985 1996 2001 1989 1994 1990 1983 ...
## $ abcat : chr "unavail" "unavail" "unavail" "unavail" ...
## $ occRole : chr "driver" "driver" "pass" "driver" ...
## $ deploy : int 0 0 0 0 1 1 0 1 0 0 ...
## $ injSeverity: int 0 2 0 3 1 0 3 2 3 3 ...
## $ caseid : chr "75:85:2" "4:115:1" "9:1:2" "48:61:1" ...Gapminder health and wealth 📈
- This is a collection of country indicators from the Gapminder dataset for the years 2000-2016.
- Data description: https://vincentarelbundock.github.io/Rdatasets/doc/dslabs/gapminder.html
Download instructions
library(dplyr)
# this will download the csv file directly from the web
gapminder <- read.table("https://vincentarelbundock.github.io/Rdatasets/csv/dslabs/gapminder.csv", header = T, sep = ",")
# here we filter the data to remove anything before the year 2000
gapminder <- gapminder |> filter(year >= 2000)
# and here we check the structure of the data
str(gapminder)## 'data.frame': 1520 obs. of 10 variables:
## $ rownames : int 7401 7402 7403 7404 7405 7406 7407 7408 7409 7410 ...
## $ country : chr "Albania" "Algeria" "Angola" "Antigua and Barbuda" ...
## $ year : int 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
## $ infant_mortality: num 23.2 33.9 128.3 13.8 18 ...
## $ life_expectancy : num 74.7 73.3 52.3 73.8 74.2 ...
## $ fertility : num 2.38 2.51 6.84 2.32 2.48 1.3 1.87 1.76 1.37 2.05 ...
## $ population : int 3121965 31183658 15058638 77648 37057453 3076098 90858 19107251 8050884 8117742 ...
## $ gdp : num 3.69e+09 5.48e+10 9.13e+09 8.03e+08 2.84e+11 ...
## $ continent : chr "Europe" "Africa" "Africa" "Americas" ...
## $ region : chr "Southern Europe" "Northern Africa" "Middle Africa" "Caribbean" ...StackOverflow survey 🖥️
- This is a downsampled and modified version of one of StackOverflow’s annual surveys where users respond to a series of questions related to careers in technology and coding.
- Data description: https://vincentarelbundock.github.io/Rdatasets/doc/modeldata/stackoverflow.html
Download instructions
library(dplyr)
# this will download the csv file directly from the web
stackoverflow <- read.table("https://vincentarelbundock.github.io/Rdatasets/csv/modeldata/stackoverflow.csv", header = T, sep = ",")
# the lines below will take a sample from the full data set
set.seed(2)
stackoverflow <- sample_n(stackoverflow, size = 3000)
# and here we check the structure of the data
str(stackoverflow)## 'data.frame': 3000 obs. of 22 variables:
## $ rownames : int 3925 5071 4806 2822 4512 4488 273 5469 3276 3453 ...
## $ Country : chr "Germany" "United States" "United States" "United States" ...
## $ Salary : num 80645 135000 85000 127000 4405 ...
## $ YearsCodedJob : int 19 20 5 20 2 4 3 3 5 3 ...
## $ OpenSource : int 1 1 1 1 0 0 0 1 0 0 ...
## $ Hobby : int 1 1 1 1 0 1 1 1 0 1 ...
## $ CompanySizeNumber : int 10000 10000 5000 20 1 100 1000 20 1000 10000 ...
## $ Remote : chr "Not remote" "Not remote" "Not remote" "Remote" ...
## $ CareerSatisfaction : int 10 8 7 7 8 6 8 5 8 8 ...
## $ Data_scientist : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Database_administrator : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Desktop_applications_developer : int 0 0 0 0 0 0 0 0 1 1 ...
## $ Developer_with_stats_math_background: int 1 0 0 0 0 0 0 0 0 0 ...
## $ DevOps : int 0 1 0 0 0 0 0 0 0 0 ...
## $ Embedded_developer : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Graphic_designer : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Graphics_programming : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Machine_learning_specialist : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Mobile_developer : int 1 0 0 0 1 0 0 0 0 0 ...
## $ Quality_assurance_engineer : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Systems_administrator : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Web_developer : int 1 1 0 0 1 1 1 1 1 0 ...Doctor visits 🤒
- Data on the frequency of doctor visits in the past two weeks in Australia for the years 1977 and 1978.
- Data description: https://vincentarelbundock.github.io/Rdatasets/doc/AER/DoctorVisits.html
Download instructions
library(dplyr)
# this will download the csv file directly from the web
doctor <- read.table("https://vincentarelbundock.github.io/Rdatasets/csv/AER/DoctorVisits.csv", header = T, sep = ",")
# the lines below will take a sample from the full data set
set.seed(2)
doctor <- sample_n(doctor, size = 3000)
# and here we check the structure of the data
str(doctor)## 'data.frame': 3000 obs. of 13 variables:
## $ rownames : int 3925 5071 4806 2822 4512 4488 273 3276 3453 690 ...
## $ visits : int 0 0 0 0 0 0 1 0 0 1 ...
## $ gender : chr "male" "female" "male" "male" ...
## $ age : num 0.27 0.67 0.32 0.19 0.22 0.22 0.22 0.37 0.67 0.62 ...
## $ income : num 0.15 0.25 0.9 0.65 0.35 0.75 0.75 0.25 0.35 0.25 ...
## $ illness : int 0 1 0 1 0 1 0 1 1 5 ...
## $ reduced : int 0 0 0 0 0 0 0 14 0 2 ...
## $ health : int 0 0 0 0 0 0 1 4 0 2 ...
## $ private : chr "no" "no" "yes" "yes" ...
## $ freepoor : chr "no" "no" "no" "no" ...
## $ freerepat: chr "no" "yes" "no" "no" ...
## $ nchronic : chr "yes" "yes" "no" "no" ...
## $ lchronic : chr "no" "no" "no" "no" ...Video Game Sales 🎮
- This data set contains sales figures for video games titles released in 2001 and 2002.
- Data description: https://mavenanalytics.io/data-playground?order=date_added%2Cdesc&search=Video%20Game%20Sales
- Click on “Preview Data” and “VG Data Dictionary” to see the description for each column.
Download instructions
library(dplyr)
library(lubridate)
# this will download the file to your working directory
download.file(url = "https://maven-datasets.s3.amazonaws.com/Video+Game+Sales/Video+Game+Sales.zip", destfile = "video_game_sales.zip")
# this will unzip the file and read it into R
videogames <- read.table(unz(filename = "vgchartz-2024.csv", "video_game_sales.zip"), header = T, sep = ",", quote = "\"", fill = T)
# this will select rows corresponding to years 2001 and 2002
videogames <- filter(videogames, year(as_date(release_date)) %in% c(2001,2002))
# and here we check the structure of the data
str(videogames)## 'data.frame': 3201 obs. of 14 variables:
## $ img : chr "/games/boxart/827563ccc.jpg" "/games/boxart/3570928ccc.jpg" "/games/boxart/7583871ccc.jpg" "/games/boxart/9261584ccc.jpg" ...
## $ title : chr "Grand Theft Auto: Vice City" "Grand Theft Auto III" "Medal of Honor: Frontline" "Crash Bandicoot: The Wrath of Cortex" ...
## $ console : chr "PS2" "PS2" "PS2" "PS2" ...
## $ genre : chr "Action" "Action" "Shooter" "Platform" ...
## $ publisher : chr "Rockstar Games" "Rockstar Games" "Electronic Arts" "Universal Interactive" ...
## $ developer : chr "Rockstar North" "DMA Design" "EA Los Angeles" "Traveller's Tales" ...
## $ critic_score: num 9.6 9.5 9 6.9 8.3 8.2 9.1 NA 9.4 7.3 ...
## $ total_sales : num 16.15 13.1 6.83 5.42 4.67 ...
## $ na_sales : num 8.41 6.99 2.93 2.07 1.94 2.71 2.66 3 3.36 2.03 ...
## $ jp_sales : num 0.47 0.3 0.17 0.24 0.08 0.03 0.01 0.05 0.01 NA ...
## $ pal_sales : num 5.49 4.51 2.75 2.29 1.95 1.51 1.29 1.11 0.21 1.56 ...
## $ other_sales : num 1.78 1.3 0.99 0.82 0.7 0.23 0.46 0.07 0.56 0.17 ...
## $ release_date: chr "2002-10-28" "2001-10-23" "2002-05-28" "2001-10-29" ...
## $ last_update : chr "" "" "" "" ...LEGO Sets 🏗️
- This data set contains the description of all LEGO sets released from 2000 to 2009.
- Data description: https://mavenanalytics.io/data-playground?order=date_added%2Cdesc&search=lego
- Click on “Preview Data” and “VG Data Dictionary” to see the description for each column.
Download instructions
library(dplyr)
# this will download the file to your working directory
download.file(url = "https://maven-datasets.s3.amazonaws.com/LEGO+Sets/LEGO+Sets.zip", destfile = "lego.csv.zip")
# this will unzip the file and read it into R
lego <- read.table(unz(filename = "lego_sets.csv", "lego.csv.zip"), header = T, sep = ",", quote = "\"", fill = T)
# this will select rows corresponding to years 2000-2009
lego <- filter(lego, year %in% seq(2000,2009,1))
# and here we check the structure of the data
str(lego)## 'data.frame': 4304 obs. of 14 variables:
## $ set_id : chr "1086-1" "1177-1" "1196-1" "1197-1" ...
## $ name : chr "Bulk Bucket" "Santa's Truck" "Telekom Race Cyclist" "Telekom Race Cyclist and Television Motorbike" ...
## $ year : int 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
## $ theme : chr "Duplo" "Town" "Town" "Town" ...
## $ subtheme : chr "" "Special" "Telekom" "Telekom" ...
## $ themeGroup : chr "Pre-school" "Modern day" "Modern day" "Modern day" ...
## $ category : chr "Normal" "Normal" "Normal" "Normal" ...
## $ pieces : int 48 27 7 26 81 129 10 27 26 23 ...
## $ minifigs : int NA 1 1 3 3 8 2 NA NA 1 ...
## $ agerange_min : int NA NA NA NA NA NA NA NA NA NA ...
## $ US_retailPrice: num NA NA NA NA NA NA NA NA NA NA ...
## $ bricksetURL : chr "https://brickset.com/sets/1086-1" "https://brickset.com/sets/1177-1" "https://brickset.com/sets/1196-1" "https://brickset.com/sets/1197-1" ...
## $ thumbnailURL : chr "https://images.brickset.com/sets/small/1086-1.jpg" "https://images.brickset.com/sets/small/1177-1.jpg" "https://images.brickset.com/sets/small/1196-1.jpg" "https://images.brickset.com/sets/small/1197-1.jpg" ...
## $ imageURL : chr "https://images.brickset.com/sets/images/1086-1.jpg" "https://images.brickset.com/sets/images/1177-1.jpg" "https://images.brickset.com/sets/images/1196-1.jpg" "https://images.brickset.com/sets/images/1197-1.jpg" ...