Project 37
AlearnsR
2025-09-22
#Questions #Which authors are most successful: who is most prolific, who has the highest average ratings or popularity, and do top authors specialize by cuisine, ingredient, or recipe length? #Is there a relationship between prep/cook time and average rating? #Which recipe categories or cuisines tend to have the highest average ratings and review counts? #Which recipes are the most “actionable” — high rating with low total time?
#Install data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidytuesdayR)
tuesdata <- tidytuesdayR::tt_load(2025, week = 37)## ---- Compiling #TidyTuesday Information for 2025-09-16 ----
## --- There are 2 files available ---
##
##
## ── Downloading files ───────────────────────────────────────────────────────────
##
## 1 of 2: "all_recipes.csv"
## 2 of 2: "cuisines.csv"#two data sets available; using cuisines.
cuisines <- tuesdata$cuisines#To understand the data set
view(cuisines)#Who is most prolific?
cuisines |>
count(author, sort = TRUE)## # A tibble: 1,635 × 2
## author n
## <chr> <int>
## 1 John Mitzewich 130
## 2 Allrecipes Member 35
## 3 Nicole McLaughlin 22
## 4 lola 20
## 5 Diana Moutsopoulos 19
## 6 Brenda Venable 17
## 7 Allrecipes 16
## 8 Buckwheat Queen 12
## 9 Soup Loving Nicole 12
## 10 Pat Bernitt 10
## # ℹ 1,625 more rows#Who has the highest average ratings or popularity?
cuisines |>
group_by(author) |>
summarise(review = mean(avg_rating)) |>
arrange(desc(review))## # A tibble: 1,635 × 2
## author review
## <chr> <dbl>
## 1 2CHAE 5
## 2 Andrew Currin-Chodur 5
## 3 Andrew Holness 5
## 4 Aron Bartram 5
## 5 Ashley B 5
## 6 Avon- status quo PRO 5
## 7 Barbara Kahian 5
## 8 Bevin 5
## 9 BigDaddy 5
## 10 Boitumelo 5
## # ℹ 1,625 more rows#Which recipe has highest reviews? #Which recipe categories or cuisines tend to have the highest average ratings and review counts?
cuisines |>
arrange(desc(reviews)) |>
relocate(reviews, .after = author)## # A tibble: 2,218 × 17
## name country url author reviews date_published ingredients calories fat
## <chr> <chr> <chr> <chr> <dbl> <date> <chr> <dbl> <dbl>
## 1 Garli… Soul F… http… TANAQ… 975 2022-11-10 2 teaspoon… 391 11
## 2 Easy … Indian http… mn 967 2024-05-06 1 cup butt… 880 82
## 3 Old C… Southe… http… bersk… 965 2024-11-14 3 cups wat… 618 44
## 4 Russi… Russian http… DTERE… 963 2024-11-09 5 tablespo… 167 8
## 5 Best … Cajun … http… Terri 950 2025-02-23 2 tablespo… 465 20
## 6 Real … Greek http… ROYHO… 932 2025-01-28 2 cloves g… 54 3
## 7 South… Southe… http… QUEEN… 928 2024-10-11 2 cups shr… 208 20
## 8 Sweet… Chinese http… PAM_1 915 2024-11-12 1 pound po… 663 35
## 9 Black… Cajun … http… JEFF … 893 2025-03-05 2 tablespo… 511 38
## 10 Cajun… Cajun … http… Star … 890 2025-02-03 1 pound dr… 695 37
## # ℹ 2,208 more rows
## # ℹ 8 more variables: carbs <dbl>, protein <dbl>, avg_rating <dbl>,
## # total_ratings <dbl>, prep_time <dbl>, cook_time <dbl>, total_time <dbl>,
## # servings <dbl>#Do top authors specialize by cuisine?
cuisines |>
distinct(author,country)## # A tibble: 1,921 × 2
## author country
## <chr> <chr>
## 1 John Mitzewich Greek
## 2 John Mitzewich Jewish
## 3 CHIPPENDALE Australian and New Zealander
## 4 Heidi Chilean
## 5 Ann Tex-Mex
## 6 MomWhoCooks Canadian
## 7 Buckwheat Queen Italian
## 8 TheOtherJuliaGulia Danish
## 9 Laura Sandahl Amish and Mennonite
## 10 Luis Luna Spanish
## # ℹ 1,911 more rows#Is there a relationship between prep/cook time and average rating?
cuisines |>
ggplot(aes(x = avg_rating, y = total_time))+
geom_point()+
labs(x = "avg_rating", y = "total_time")## Warning: Removed 97 rows containing missing values or values outside the scale range
## (`geom_point()`).
#Which recipes are the most “actionable” — high rating with low total time?
cuisines |>
arrange(desc(avg_rating), total_time) |>
relocate(avg_rating, .after = author) |>
relocate(total_time, .after = avg_rating) |>
head()## # A tibble: 6 × 17
## name country url author avg_rating total_time date_published ingredients
## <chr> <chr> <chr> <chr> <dbl> <dbl> <date> <chr>
## 1 Kaisers… Austri… http… John … 5 0 2023-04-24 2 large eg…
## 2 Vietnam… Vietna… http… Yoly 5 5 2021-02-09 1 large eg…
## 3 Coconut… Brazil… http… Nicol… 5 5 2024-06-13 2 limes, 2…
## 4 Italian… Italian http… Nicol… 5 5 2025-07-26 2 fresh ch…
## 5 Lebanes… Lebane… http… BigDa… 5 5 2018-11-30 1 tablespo…
## 6 Easy Mo… Cuban http… Jenni… 5 5 2025-05-12 12 leaves …
## # ℹ 9 more variables: calories <dbl>, fat <dbl>, carbs <dbl>, protein <dbl>,
## # total_ratings <dbl>, reviews <dbl>, prep_time <dbl>, cook_time <dbl>,
## # servings <dbl>#Not satisfied with this work. Need to improve the graph. And I might be missing other ways of looking at the data.