Load the data into R, select useful nutritional variables, and transform the dataset into a simpler format for future analysis.
Dataset source: https://github.com/rfordatascience/tidytuesday/tree/main/data/2018/2018-09-04
library(tidyverse)The original dataset is loaded directly from its online source
fastfood <- read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2018/2018-09-04/fastfood_calories.csv"
)## New names:
## Rows: 515 Columns: 18
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): restaurant, item, salad dbl (15): ...1, calories, cal_fat, total_fat,
## sat_fat, trans_fat, cholestero...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`head(fastfood)## # A tibble: 6 × 18
## ...1 restaurant item calories cal_fat total_fat sat_fat trans_fat
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Mcdonalds Artisan Grilled… 380 60 7 2 0
## 2 2 Mcdonalds Single Bacon Sm… 840 410 45 17 1.5
## 3 3 Mcdonalds Double Bacon Sm… 1130 600 67 27 3
## 4 4 Mcdonalds Grilled Bacon S… 750 280 31 10 0.5
## 5 5 Mcdonalds Crispy Bacon Sm… 920 410 45 12 0.5
## 6 6 Mcdonalds Big Mac 540 250 28 10 1
## # ℹ 10 more variables: cholesterol <dbl>, sodium <dbl>, total_carb <dbl>,
## # fiber <dbl>, sugar <dbl>, protein <dbl>, vit_a <dbl>, vit_c <dbl>,
## # calcium <dbl>, salad <chr>Inspected original data structure and column names.
dim(fastfood)## [1] 515 18names(fastfood)## [1] "...1" "restaurant" "item" "calories" "cal_fat"
## [6] "total_fat" "sat_fat" "trans_fat" "cholesterol" "sodium"
## [11] "total_carb" "fiber" "sugar" "protein" "vit_a"
## [16] "vit_c" "calcium" "salad"glimpse(fastfood)## Rows: 515
## Columns: 18
## $ ...1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
## $ restaurant <chr> "Mcdonalds", "Mcdonalds", "Mcdonalds", "Mcdonalds", "Mcdon…
## $ item <chr> "Artisan Grilled Chicken Sandwich", "Single Bacon Smokehou…
## $ calories <dbl> 380, 840, 1130, 750, 920, 540, 300, 510, 430, 770, 380, 62…
## $ cal_fat <dbl> 60, 410, 600, 280, 410, 250, 100, 210, 190, 400, 170, 300,…
## $ total_fat <dbl> 7, 45, 67, 31, 45, 28, 12, 24, 21, 45, 18, 34, 20, 34, 8, …
## $ sat_fat <dbl> 2.0, 17.0, 27.0, 10.0, 12.0, 10.0, 5.0, 4.0, 11.0, 21.0, 4…
## $ trans_fat <dbl> 0.0, 1.5, 3.0, 0.5, 0.5, 1.0, 0.5, 0.0, 1.0, 2.5, 0.0, 1.5…
## $ cholesterol <dbl> 95, 130, 220, 155, 120, 80, 40, 65, 85, 175, 40, 95, 125, …
## $ sodium <dbl> 1110, 1580, 1920, 1940, 1980, 950, 680, 1040, 1040, 1290, …
## $ total_carb <dbl> 44, 62, 63, 62, 81, 46, 33, 49, 35, 42, 38, 48, 48, 67, 31…
## $ fiber <dbl> 3, 2, 3, 2, 4, 3, 2, 3, 2, 3, 2, 3, 3, 5, 2, 2, 3, 3, 5, 2…
## $ sugar <dbl> 11, 18, 18, 18, 18, 9, 7, 6, 7, 10, 5, 11, 11, 11, 6, 3, 1…
## $ protein <dbl> 37, 46, 70, 55, 46, 25, 15, 25, 25, 51, 15, 32, 42, 33, 13…
## $ vit_a <dbl> 4, 6, 10, 6, 6, 10, 10, 0, 20, 20, 2, 10, 10, 10, 2, 4, 6,…
## $ vit_c <dbl> 20, 20, 20, 25, 20, 2, 2, 4, 4, 6, 0, 10, 20, 15, 2, 6, 15…
## $ calcium <dbl> 20, 20, 50, 20, 20, 15, 10, 2, 15, 20, 15, 35, 35, 35, 4, …
## $ salad <chr> "Other", "Other", "Other", "Other", "Other", "Other", "Oth…For this analysis, I selected variables related to the restaurant, menu item, calories, fat, sodium, carbohydrates, sugar, and protein. I also renamed several columns to make their meanings easier to understand.
fastfood_clean <- fastfood %>%
select(
restaurant,
item,
calories,
total_fat,
sodium,
total_carb,
sugar,
protein
) %>%
rename(
menu_item = item,
total_fat_grams = total_fat,
sodium_mg = sodium,
carbohydrates_grams = total_carb,
sugar_grams = sugar,
protein_grams = protein
)
head(fastfood_clean)## # A tibble: 6 × 8
## restaurant menu_item calories total_fat_grams sodium_mg carbohydrates_grams
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Mcdonalds Artisan Gri… 380 7 1110 44
## 2 Mcdonalds Single Baco… 840 45 1580 62
## 3 Mcdonalds Double Baco… 1130 67 1920 63
## 4 Mcdonalds Grilled Bac… 750 31 1940 62
## 5 Mcdonalds Crispy Baco… 920 45 1980 81
## 6 Mcdonalds Big Mac 540 28 950 46
## # ℹ 2 more variables: sugar_grams <dbl>, protein_grams <dbl>The resulting dataframe contains a smaller set of clearly labeled variables that are useful for comparing nutritional information across fast-food menu items.
dim(fastfood_clean)## [1] 515 8names(fastfood_clean)## [1] "restaurant" "menu_item" "calories"
## [4] "total_fat_grams" "sodium_mg" "carbohydrates_grams"
## [7] "sugar_grams" "protein_grams"head(fastfood_clean)## # A tibble: 6 × 8
## restaurant menu_item calories total_fat_grams sodium_mg carbohydrates_grams
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Mcdonalds Artisan Gri… 380 7 1110 44
## 2 Mcdonalds Single Baco… 840 45 1580 62
## 3 Mcdonalds Double Baco… 1130 67 1920 63
## 4 Mcdonalds Grilled Bac… 750 31 1940 62
## 5 Mcdonalds Crispy Baco… 920 45 1980 81
## 6 Mcdonalds Big Mac 540 28 950 46
## # ℹ 2 more variables: sugar_grams <dbl>, protein_grams <dbl>The dataset was cleaned to focus on key nutritional variables such as calories, fat, sodium, sugar, and protein. Future analysis could compare restaurants more closely or use newer nutrition data to see how fast-food menus have changed over time.