tidytuesday
2025-11-18
# Using R
# Option 1: tidytuesdayR R package
all_recipes <- readr::read_csv(
"https://raw.githubusercontent.com/owlzyseyes/tastyR/refs/heads/main/data-raw/allrecipes.csv"
)
cuisines <- readr::read_csv(
"https://raw.githubusercontent.com/owlzyseyes/tastyR/refs/heads/main/data-raw/cuisines.csv"
)
viz1_df <- all_recipes %>%
mutate(actionable = total_time <= 45 & avg_rating >= 4.5)
all_recipes <- readr::read_csv("https://raw.githubusercontent.com/owlzyseyes/tastyR/refs/heads/main/data-raw/allrecipes.csv")
cuisines <- readr::read_csv("https://raw.githubusercontent.com/owlzyseyes/tastyR/refs/heads/main/data-raw/cuisines.csv")
all_recipes## # A tibble: 14,426 × 16
## name url author date_published ingredients calories fat carbs protein
## <chr> <chr> <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chewy W… http… DMOMMY 2020-06-18 ⅓ cup marg… 222 13 24 6
## 2 Pumpkin… http… Bobbi… 2022-09-26 12 egg yo… 477 31 43 8
## 3 Eggs Po… http… Bren 2018-06-08 2 tablespo… 354 18 32 20
## 4 Minestr… http… Sarah… 2025-03-03 4 cups dri… 356 9 53 19
## 5 Yummy S… http… Procr… 2024-12-11 4 green be… 366 22 23 19
## 6 Prime R… http… Cajun… 2019-04-03 1 (8 pound… 709 47 31 37
## 7 Parmesa… http… Anika 2023-01-04 1 large eg… 466 27 1 52
## 8 Chicken… http… Bob C… 2022-07-14 12 cups wa… 782 61 19 40
## 9 Sweet P… http… Dean 2023-01-19 3 pounds p… 355 15 33 23
## 10 Quick B… http… Chris… 2024-11-14 canola oil… 395 12 33 37
## # ℹ 14,416 more rows
## # ℹ 7 more variables: avg_rating <dbl>, total_ratings <dbl>, reviews <dbl>,
## # prep_time <dbl>, cook_time <dbl>, total_time <dbl>, servings <dbl>cuisines## # A tibble: 2,218 × 17
## name country url author date_published ingredients calories fat carbs
## <chr> <chr> <chr> <chr> <date> <chr> <dbl> <dbl> <dbl>
## 1 Saganak… Greek http… John … 2024-02-07 1 (4 ounce… 391 25 15
## 2 Coney I… Jewish http… John … 2024-11-26 2 ¾ cups a… 301 17 31
## 3 Diana's… Austra… http… CHIPP… 2022-07-14 1 ½ cups w… 64 3 9
## 4 Chilean… Chilean http… Heidi 2025-01-31 ½ cup chop… 106 9 7
## 5 Tex-Mex… Tex-Mex http… Ann 2025-02-18 2 cups all… 449 23 58
## 6 Newfoun… Canadi… http… MomWh… 2022-08-12 1 (3 pound… 958 24 144
## 7 Pasta e… Italian http… Buckw… 2023-12-12 1 cup dry … 378 10 59
## 8 Danish … Danish http… TheOt… 2020-06-19 4 cups all… 90 5 10
## 9 Lemon P… Amish … http… Laura… 2025-01-21 2 cups all… 157 6 25
## 10 Pan con… Spanish http… Luis … 2025-06-02 1 large to… 322 16 39
## # ℹ 2,208 more rows
## # ℹ 8 more variables: protein <dbl>, avg_rating <dbl>, total_ratings <dbl>,
## # reviews <dbl>, prep_time <dbl>, cook_time <dbl>, total_time <dbl>,
## # servings <dbl>The Tidy Tuesday dataset for the week of September 16, 2025 contains recipe information scraped from Allrecipes.com. The dataset includes two tables: all_recipes, which provides detailed information on more than 5,000 individual dishes, and cuisines, which links each recipe to a country or cultural cuisine type.
viz1 <- ggplot(viz1_df, aes(x = total_time, y = avg_rating)) +
geom_point(alpha = 0.15, size = 1) +
geom_point(
data = viz1_df %>% filter(actionable),
aes(x = total_time, y = avg_rating),
color = "gold",
alpha = 0.7,
size = 1.5
) +
geom_smooth(
method = "loess",
se = FALSE,
linewidth = 1.1,
color = "blue"
) +
scale_x_continuous(
breaks = c(15, 30, 45, 60, 90, 120, 180, 240)
) +
scale_y_continuous(
limits = c(0, 5),
breaks = 0:5
) +
labs(
title = "Are efficient recipes also highly rated?",
subtitle = "Highly rated Allrecipes dishes tend to cluster in the under 1 hour range.\ngold points highlight 'efficient' recipes (< 45 min, rating > 4.5).",
x = "Total time in minutes",
y = "Average rating from 0–5 stars",
caption = "Data: TidyTuesday 2025-09-16 • Allrecipes.com"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold"),
plot.subtitle = element_text(size = 10),
panel.grid.minor = element_blank()
)
viz1
Why These Visualizations Are Effective Visualization 1: Do We Really
Love Time-Intensive Recipes?
This scatter plot explores the relationship between total cooking time and average user ratings across more than 5,000 Allrecipes dishes. By displaying every recipe as part of a semi-transparent point cloud, the visualization makes it easy to see overall density patterns—specifically, that the vast majority of highly rated dishes cluster under the one-hour mark. The addition of a LOESS smoothing line emphasizes the broader negative trend: as recipe time increases, ratings tend to decline.
I added a gold overlay for recipes that are both fast (< 45 minutes) and highly rated (> 4.5 stars), to add a more distinct insight. These points stand out immediately and help answer the key question: Are efficient recipes also highly rated? The contrast created by the gold layer against the blue smoothing line brings attention to this subset without overwhelming the overall distribution. The axis choices, minimal grid, and clear caption make the plot clean, readable, and purposeful. Overall, this visualization is effective because it reveals both the general trend and a meaningful subgroup in a way that is intuitive and visually compelling.
library(tidyverse)
library(maps)
library(countrycode)
cuisine_world <- cuisines %>%
filter(!is.na(country)) %>%
group_by(country) %>%
summarise(
n_recipes = n(),
total_reviews = sum(reviews, na.rm = TRUE),
mean_rating = mean(avg_rating, na.rm = TRUE),
.groups = "drop"
) %>%
filter(n_recipes >= 5)
cuisine_world$map_country <- countrycode(cuisine_world$country,
origin = "country.name",
destination = "country.name")
world_map <- map_data("world")
country_centroids <- world_map %>%
group_by(region) %>%
summarise(
long = mean(range(long)),
lat = mean(range(lat))
)
cuisine_map <- cuisine_world %>%
inner_join(country_centroids, by = c("map_country" = "region"))
viz_map <- ggplot() +
geom_map(
data = world_map,
map = world_map,
aes(x = long, y = lat, map_id = region),
fill = "white",
color = "gray",
size = 0.2
) +
geom_point(
data = cuisine_map,
aes(x = long, y = lat,
size = total_reviews,
color = mean_rating),
alpha = 0.8
) +
scale_color_gradient(
low = "lightblue",
high = "darkblue"
) +
scale_size(range = c(2, 12), guide = "none") +
coord_fixed(1.3) +
labs(
title = "Global Popularity of Recipe Cuisines",
subtitle = "Bubble size = total reviews Bubble color = avg rating",
color = "Avg Rating",
caption = "Data: TidyTuesday 2025-09-16 • Allrecipes.com"
) +
theme_minimal(base_size = 12) +
theme(
panel.grid = element_blank(),
plot.title = element_text(face = "bold", size = 18),
plot.subtitle = element_text(size = 10)
)
viz_map
Visualization 2: Global Popularity of Recipe Cuisines
This world map visualization highlights the geographic distribution of cuisines represented on Allrecipes, summarizing popularity using two intuitive visual encodings: bubble size (total number of reviews received) and bubble color (average rating). Countries with at least five dishes appear on the map, ensuring that each point represents a meaningful presence rather than noise. Placing cuisine summaries at approximate country centroids works well because it avoids clutter while still preserving geographic context. The soft gray basemap allows the review and rating information to stand out clearly, and the blue gradient color scale provides an immediate sense of which cuisines, on average, are rated more favorably. This visualization effectively conveys global differences in user engagement with cuisine types while maintaining simplicity and interpretability.