Portfolio Project 1

Posing and answering research questions

library(tidyverse)

Overview

In your first portfolio project, you’ll explore a dataset and pose your own research questions. You’ll then apply what you’ve learned about ggplot2 and design principles to answer the questions.

Your final report should include

1. A paragraph introduction to your data
2. A section for Research Question 1 that includes:
   • Your question
   • Why that question is interesting/important
   • A graph that answers the question
   • A written answer to the question based on the graph
3. A section for Research Question 2 that includes:
   • Your question. This should focus on a variable that was not included in Research Question 1.
   • Why that question is interesting/important
   • A graph that answers the question
   • A written answer to the question based on the graph
4. A section describing your design choices
   • This should be about a paragraph telling me why you chose the geometries, aesthetics, scales, themes, etc. that you did

Each of the two questions you come up with should involve more than two variables to answer. You should phrase them in a way that the is within the scope of inference of your data. For example, if you have an observational dataset, you shouldn’t phrase your question in a causal way. Your questions shouldn’t be identical to the example questions in the TidyTuesday repo pages.

Your rendered file should not echo the code used to create the graphs, but the code in your .qmd should be clean and readable. Your report should not be long! If rendering to a PDF, I expect the entire report to be about 3 pages.

Options

Choosing a dataset is something you should do carefully but also relatively quickly. The dataset you choose should allow for two distinct questions to be asked and answered using a not-completely-overlapping set of variables, i.e., Question 1 requires the use of variables x, y, and z and Question 2 requires the use of variables a, b, c, and d or x, a, and b. Some shared variables are ok, but the set of variables should not be completely overlapping, i.e., Question 2 can’t also require the use of variables x, y, and z.

1. Income Inequality before and after taxes

income_inequality_raw <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-05/income_inequality_raw.csv') |>
  filter(Year > 1963)

income_inequality_raw

# A tibble: 15,710 × 7
   Entity      Code   Year gini_disposable__age_total gini_market__age_total
   <chr>       <chr> <dbl>                      <dbl>                  <dbl>
 1 Afghanistan AFG    1964                         NA                     NA
 2 Afghanistan AFG    1965                         NA                     NA
 3 Afghanistan AFG    1966                         NA                     NA
 4 Afghanistan AFG    1967                         NA                     NA
 5 Afghanistan AFG    1968                         NA                     NA
 6 Afghanistan AFG    1969                         NA                     NA
 7 Afghanistan AFG    1970                         NA                     NA
 8 Afghanistan AFG    1971                         NA                     NA
 9 Afghanistan AFG    1972                         NA                     NA
10 Afghanistan AFG    1973                         NA                     NA
# ℹ 15,700 more rows
# ℹ 2 more variables: population_historical <dbl>, owid_region <chr>

For more information about the variables in the dataset, see the TidyTuesday repo page. For more information about the data itself, read the original Our World In Data article.

2. Billboard Hot100 Number Ones

billboard <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-26/billboard.csv')
billboard

# A tibble: 1,177 × 105
   song  artist date                weeks_at_number_one non_consecutive rating_1
   <chr> <chr>  <dttm>                            <dbl>           <dbl>    <dbl>
 1 Poor… Ricky… 1958-08-04 00:00:00                   2               0        4
 2 Nel … Domen… 1958-08-18 00:00:00                   5               1        7
 3 Litt… The E… 1958-08-25 00:00:00                   1               0        5
 4 It's… Tommy… 1958-09-29 00:00:00                   6               0        3
 5 It's… Conwa… 1958-11-10 00:00:00                   2               1        7
 6 Tom … The K… 1958-11-17 00:00:00                   1               0        5
 7 To K… The T… 1958-12-01 00:00:00                   3               0        8
 8 The … The C… 1958-12-22 00:00:00                   4               0        1
 9 Smok… The P… 1959-01-19 00:00:00                   3               0        9
10 Stag… Lloyd… 1959-02-02 00:00:00                   4               0        6
# ℹ 1,167 more rows
# ℹ 99 more variables: rating_2 <dbl>, rating_3 <dbl>, overall_rating <dbl>,
#   divisiveness <dbl>, label <chr>, parent_label <chr>, cdr_genre <chr>,
#   cdr_style <chr>, discogs_genre <chr>, discogs_style <chr>,
#   artist_structure <dbl>, featured_artists <chr>,
#   multiple_lead_vocalists <dbl>, group_named_after_non_lead_singer <dbl>,
#   talent_contestant <chr>, posthumous <dbl>, artist_place_of_origin <chr>, …

For more information about the variables in the dataset, see the TidyTuesday repo page. For more information about the data itself and an example analysis, see the *Can’t get much higher newsletter.

3. NSF grant terminations

nsf_terminations <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-06/nsf_terminations.csv')
nsf_terminations

# A tibble: 1,041 × 21
   grant_number project_title termination_letter_d…¹ org_name org_city org_state
          <dbl> <chr>         <date>                 <chr>    <chr>    <chr>    
 1      2135329 Collaborativ… 2025-04-25             Univers… ALBUQUE… NM       
 2      2342099 MyTurn: An A… 2025-04-25             Univers… CHICAGO  IL       
 3      2201103 Collaborativ… 2025-04-25             America… WASHING… DC       
 4      2215382 Engaging Rur… 2025-04-25             TERC Inc CAMBRID… MA       
 5      2405633 Design Effec… 2025-04-25             San Fra… SAN FRA… CA       
 6      2317714 Collaborativ… 2025-04-25             Occiden… LOS ANG… CA       
 7      2046081 CAREER: SBP:… 2025-04-25             Univers… SANTA B… CA       
 8      2348705 Rural Commun… 2025-04-25             Hamilto… CLINTON  NY       
 9      2404708 Postdoctoral… 2025-04-25             New Yor… New York CA       
10      2215138 BRITE Girls … 2025-04-25             Florida… TALLAHA… FL       
# ℹ 1,031 more rows
# ℹ abbreviated name: ¹​termination_letter_date
# ℹ 15 more variables: org_district <chr>, usaspending_obligated <dbl>,
#   award_type <chr>, directorate_abbrev <chr>, directorate <chr>,
#   division <chr>, nsf_program_name <chr>, nsf_url <chr>,
#   usaspending_url <chr>, nsf_startdate <date>, nsf_expected_end_date <date>,
#   org_zip <chr>, org_uei <chr>, abstract <chr>, in_cruz_list <lgl>

For more information about the variables in the dataset, see the TidyTuesday repo page. For more information about the data itself, see Grant Watch.

4. Water Quality at Sydney Beaches

water_quality <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/water_quality.csv')
water_quality

# A tibble: 123,530 × 10
   region         council       swim_site date       time  enterococci_cfu_100ml
   <chr>          <chr>         <chr>     <date>     <tim>                 <dbl>
 1 Western Sydney Hawkesbury C… Windsor … 2025-04-28 11:00                   620
 2 Sydney Harbour North Sydney… Hayes St… 2025-04-28 11:40                    64
 3 Sydney Harbour Willoughby C… Northbri… 2025-04-28 10:54                   160
 4 Sydney Harbour Northern Bea… Fairligh… 2025-04-28 09:28                    54
 5 Western Sydney Hawkesbury C… Yarramun… 2025-04-28 10:35                   720
 6 Sydney Harbour Northern Bea… Little M… 2025-04-28 09:19                   230
 7 Sydney Harbour City of Cana… Chiswick… 2025-04-28 13:06                   120
 8 Sydney Harbour Inner West C… Dawn Fra… 2025-04-28 08:04                   280
 9 Sydney Harbour Woollahra Mu… Rose Bay… 2025-04-28 08:50                    60
10 Sydney Harbour Woollahra Mu… Camp Cove 2025-04-28 09:09                   100
# ℹ 123,520 more rows
# ℹ 4 more variables: water_temperature_c <dbl>, conductivity_ms_cm <dbl>,
#   latitude <dbl>, longitude <dbl>

For more information about the variables in the dataset, see the TidyTuesday repo page. For more information about the data itself, see BeachWatch.

5. Pokemon

pokemon_df <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-01/pokemon_df.csv')

pokemon_df

# A tibble: 949 × 22
      id pokemon    species_id height weight base_experience type_1 type_2    hp
   <dbl> <chr>           <dbl>  <dbl>  <dbl>           <dbl> <chr>  <chr>  <dbl>
 1     1 bulbasaur           1    0.7    6.9              64 grass  poison    45
 2     2 ivysaur             2    1     13               142 grass  poison    60
 3     3 venusaur            3    2    100               236 grass  poison    80
 4     4 charmander          4    0.6    8.5              62 fire   <NA>      39
 5     5 charmeleon          5    1.1   19               142 fire   <NA>      58
 6     6 charizard           6    1.7   90.5             240 fire   flying    78
 7     7 squirtle            7    0.5    9                63 water  <NA>      44
 8     8 wartortle           8    1     22.5             142 water  <NA>      59
 9     9 blastoise           9    1.6   85.5             239 water  <NA>      79
10    10 caterpie           10    0.3    2.9              39 bug    <NA>      45
# ℹ 939 more rows
# ℹ 13 more variables: attack <dbl>, defense <dbl>, special_attack <dbl>,
#   special_defense <dbl>, speed <dbl>, color_1 <chr>, color_2 <chr>,
#   color_f <chr>, egg_group_1 <chr>, egg_group_2 <chr>, url_icon <chr>,
#   generation_id <dbl>, url_image <chr>

For more information about the variables in the dataset, see the TidyTuesday repo page. For more information about the data itself, see PokeAPI.

6. BYO

If none of the options listed here speak to you, you can also choose your own dataset. The data should have at least two quantitative and two categorical variables, at least eight variables total, and be “complicated” enough to demonstrate an advanced understanding of ggplot and design principles from class. I’d encourage you to run your idea by me before diving too deep!

Submission

Your submission should include two visualizations (replication and improvement), a written overview of your work, and all associated code. You should work in a quarto file. Your rendered file can be either pdf or html. I will distribute GitHub skeleton repos that you should fill in as you work. You should commit your final code and output file to your repo and link your repo to gradescope to submit.

Rubric

A successful project will:

• Include all necessary components pushed to GitHub and linked to gradescope
  • There should be at least two commits with substantial changes between the two
• Contain code, plots, and written summary in a single .qmd document
• The .qmd file contains all necessary code for recreation
• The .qmd file does not include unnecessary code
• Both research questions posed are appropriate, thoughtful, and multivariate (involve at least two variables)
• The graphs are appropriate to answer the research questions
• The two graphs use different geoms and aesthetics
• Meet minimum submission quality standards
  • Very few grammatical mistakes, spelling mistakes, or typos
  • Appropriate labels and font sizes
  • A readable theme
  • The rendered document does not contain any unnecessary content (package loading messages, warnings, etc.)

An excellent project will meet all of the requirements for a successful project, plus

• At least one question uses three variables from the data at once
• At least one graph uses two or more geometry layers (this might be two layers of data, or a map + points, annotations, direct labeling with geom_text, etc.) OR includes a ggplot element that goes beyond what we discussed in class (eg., a new ggplot extension package or a geometry we didn’t discuss or see in homework)
• Graphs use consistent themes that have been customized beyond a full theme (eg theme_minimal or theme_bw)
• Both graphs contain a custom scale_ element (color, x/y axis, size, etc.)
• A clear history of commits that show your progress (e.g. commit 1: load data and make basic graph, commit 2: make graph #2, commit 3: change idea for graph #1, etc.)

This is an individual portfolio project.

FAQ

If you have any questions, please post them to Ed with the “portfolio projects” tag