A Fast Food Solution
Fast Food Data
These data came care of a Tidy Tuesday a while ago. The data consist of Fast Food menu items for a selection of fast food chains. The units are menu items. We have the chain [restaurant], item [the item name], and a series of variables (columns) representing sodium, cholesterol, fat, calories, and other information. Some is missing. The data can be imported from the tidytuesday website on github as .csv. It is worth noting that the code in the setup chunk is not echoed.
library(plotly); library(widgetframe)
FFood <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-09-04/fastfood_calories.csv")Basic Summary
A basic summary of the data.
| Name | FFood |
| Number of rows | 515 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 15 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| restaurant | 0 | 1 | 5 | 11 | 0 | 8 | 0 |
| item | 0 | 1 | 5 | 63 | 0 | 505 | 0 |
| salad | 0 | 1 | 5 | 5 | 0 | 1 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| X | 0 | 1.00 | 258.00 | 148.81 | 1 | 129.5 | 258.0 | 386.5 | 515 | ▇▇▇▇▇ |
| calories | 0 | 1.00 | 530.91 | 282.44 | 20 | 330.0 | 490.0 | 690.0 | 2430 | ▇▆▁▁▁ |
| cal_fat | 0 | 1.00 | 238.81 | 166.41 | 0 | 120.0 | 210.0 | 310.0 | 1270 | ▇▃▁▁▁ |
| total_fat | 0 | 1.00 | 26.59 | 18.41 | 0 | 14.0 | 23.0 | 35.0 | 141 | ▇▃▁▁▁ |
| sat_fat | 0 | 1.00 | 8.15 | 6.42 | 0 | 4.0 | 7.0 | 11.0 | 47 | ▇▃▁▁▁ |
| trans_fat | 0 | 1.00 | 0.47 | 0.84 | 0 | 0.0 | 0.0 | 1.0 | 8 | ▇▁▁▁▁ |
| cholesterol | 0 | 1.00 | 72.46 | 63.16 | 0 | 35.0 | 60.0 | 95.0 | 805 | ▇▁▁▁▁ |
| sodium | 0 | 1.00 | 1246.74 | 689.95 | 15 | 800.0 | 1110.0 | 1550.0 | 6080 | ▇▆▁▁▁ |
| total_carb | 0 | 1.00 | 45.66 | 24.88 | 0 | 28.5 | 44.0 | 57.0 | 156 | ▅▇▂▁▁ |
| fiber | 12 | 0.98 | 4.14 | 3.04 | 0 | 2.0 | 3.0 | 5.0 | 17 | ▇▅▂▁▁ |
| sugar | 0 | 1.00 | 7.26 | 6.76 | 0 | 3.0 | 6.0 | 9.0 | 87 | ▇▁▁▁▁ |
| protein | 1 | 1.00 | 27.89 | 17.68 | 1 | 16.0 | 24.5 | 36.0 | 186 | ▇▂▁▁▁ |
| vit_a | 214 | 0.58 | 18.86 | 31.38 | 0 | 4.0 | 10.0 | 20.0 | 180 | ▇▁▁▁▁ |
| vit_c | 210 | 0.59 | 20.17 | 30.59 | 0 | 4.0 | 10.0 | 30.0 | 400 | ▇▁▁▁▁ |
| calcium | 210 | 0.59 | 24.85 | 25.52 | 0 | 8.0 | 20.0 | 30.0 | 290 | ▇▁▁▁▁ |
A Cautionary Note
The NA….
For Next Time
- In Excel, construct a pivot table of the data by fast food en terprise that displays five key quantities: the count of the number of menu i tems, the mean and sample standard deviation of calories, and the mean and sample standard deviation of sodium.
- Which chain has the highest average calories? Mcdonalds
- Which chain has the lower average calories? Chick Fil-A
- Which chain has the highest average sodium content? Arbys
** The pivot table is the first sheet in the Excel file.**
To answer them in R:
FFood %>% group_by(restaurant) %>% skim(calories,sodium)| Name | Piped data |
| Number of rows | 515 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | restaurant |
Variable type: numeric
| skim_variable | restaurant | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| calories | Arbys | 0 | 1 | 532.73 | 210.34 | 70 | 360.0 | 550 | 690.0 | 1030 | ▃▆▇▇▂ |
| calories | Burger King | 0 | 1 | 608.57 | 290.42 | 190 | 365.0 | 555 | 760.0 | 1550 | ▇▇▃▂▁ |
| calories | Chick Fil-A | 0 | 1 | 384.44 | 220.49 | 70 | 220.0 | 390 | 480.0 | 970 | ▇▇▇▁▂ |
| calories | Dairy Queen | 0 | 1 | 520.24 | 259.34 | 20 | 350.0 | 485 | 630.0 | 1260 | ▂▇▆▂▁ |
| calories | Mcdonalds | 0 | 1 | 640.35 | 410.70 | 140 | 380.0 | 540 | 740.0 | 2430 | ▇▅▁▁▁ |
| calories | Sonic | 0 | 1 | 631.70 | 300.88 | 100 | 410.0 | 570 | 740.0 | 1350 | ▃▇▆▂▃ |
| calories | Subway | 0 | 1 | 503.02 | 282.22 | 50 | 287.5 | 460 | 740.0 | 1160 | ▅▇▃▃▂ |
| calories | Taco Bell | 0 | 1 | 443.65 | 184.34 | 140 | 320.0 | 420 | 575.0 | 880 | ▆▇▇▃▂ |
| sodium | Arbys | 0 | 1 | 1515.27 | 663.67 | 100 | 960.0 | 1480 | 2020.0 | 3350 | ▂▇▇▃▁ |
| sodium | Burger King | 0 | 1 | 1223.57 | 499.88 | 310 | 850.0 | 1150 | 1635.0 | 2310 | ▅▇▅▆▂ |
| sodium | Chick Fil-A | 0 | 1 | 1151.48 | 726.92 | 220 | 700.0 | 1000 | 1405.0 | 3660 | ▇▇▂▁▁ |
| sodium | Dairy Queen | 0 | 1 | 1181.79 | 609.94 | 15 | 847.5 | 1030 | 1362.5 | 3500 | ▂▇▂▁▁ |
| sodium | Mcdonalds | 0 | 1 | 1437.89 | 1036.17 | 20 | 870.0 | 1120 | 1780.0 | 6080 | ▇▅▁▁▁ |
| sodium | Sonic | 0 | 1 | 1350.75 | 665.13 | 470 | 900.0 | 1250 | 1550.0 | 4520 | ▇▆▂▁▁ |
| sodium | Subway | 0 | 1 | 1272.97 | 743.63 | 65 | 697.5 | 1130 | 1605.0 | 3540 | ▅▇▃▁▂ |
| sodium | Taco Bell | 0 | 1 | 1013.91 | 474.05 | 290 | 615.0 | 960 | 1300.0 | 2260 | ▇▇▆▂▂ |
- In R, construct the same pivot table equivalent using the tidyverse,the piping operator, and the skim function from the skim library
FFood %>% group_by(restaurant) %>% summarise(Median.Calories = median(calories)) %>% arrange(Median.Calories)## # A tibble: 8 x 2
## restaurant Median.Calories
## <chr> <dbl>
## 1 Chick Fil-A 390
## 2 Taco Bell 420
## 3 Subway 460
## 4 Dairy Queen 485
## 5 Mcdonalds 540
## 6 Arbys 550
## 7 Burger King 555
## 8 Sonic 570- Which chain has the highest median calories? Sonic
- Which chain has the lowest median calories? Chick Fil-A
FFood %>% group_by(restaurant) %>% summarise(Median.Sodium = median(sodium))## # A tibble: 8 x 2
## restaurant Median.Sodium
## <chr> <dbl>
## 1 Arbys 1480
## 2 Burger King 1150
## 3 Chick Fil-A 1000
## 4 Dairy Queen 1030
## 5 Mcdonalds 1120
## 6 Sonic 1250
## 7 Subway 1130
## 8 Taco Bell 960- Which chain has the highest median sodium? Arbys
FFood %>% group_by(restaurant) %>% summarise(Q3.Calories = quantile(calories, 0.75))## # A tibble: 8 x 2
## restaurant Q3.Calories
## <chr> <dbl>
## 1 Arbys 690
## 2 Burger King 760
## 3 Chick Fil-A 480
## 4 Dairy Queen 630
## 5 Mcdonalds 740
## 6 Sonic 740
## 7 Subway 740
## 8 Taco Bell 575- Which chain has the lowest 3rd quartile of calories? Chick Fil-A
FFood %>% group_by(restaurant) %>% summarise(Q3.Protein = quantile(protein, 0.75, na.rm = TRUE))## # A tibble: 8 x 2
## restaurant Q3.Protein
## <chr> <dbl>
## 1 Arbys 38
## 2 Burger King 36
## 3 Chick Fil-A 37
## 4 Dairy Queen 34
## 5 Mcdonalds 46
## 6 Sonic 35
## 7 Subway 40
## 8 Taco Bell 22- Which chain has the highest 3rd quartile of protein? Taco Bell
- In R, provide two graphics – a boxplot and something resembling a density plot or histogram – that shows the distributions of sodium and cholesterol.
boxplot(FFood$sodium)
hist(FFood$sodium)
boxplot(FFood$cholesterol)
hist(FFood$cholesterol)
- In R, provide two graphics – a boxplot and something resembling a density plot – that compares the distributions of total fat and cholesterol by chain. ** I will use an R package called
patchworkto create multipane graphics with add (+).
install.packages("patchwork")The structure of each graphic is identical with only the y changing or the geom changing amogst violin and boxplot.
ggplot(FFood) + aes(x=restaurant, y=total_fat, fill=restaurant) + geom_boxplot() + coord_flip() + guides(fill=FALSE)library(patchwork)
ggplot(FFood) + aes(x=restaurant, y=total_fat, fill=restaurant) + geom_boxplot() + coord_flip() + guides(fill=FALSE) +
ggplot(FFood) + aes(x=restaurant, y=total_fat, fill=restaurant) + geom_violin() + coord_flip() + guides(fill=FALSE) +
ggplot(FFood) + aes(x=restaurant, y=cholesterol, fill=restaurant) + geom_boxplot() + coord_flip() + guides(fill=FALSE) +
ggplot(FFood) + aes(x=restaurant, y=cholesterol, fill=restaurant) + geom_violin() + coord_flip() + guides(fill=FALSE)
- In R, let’s construct some summary visualizations. We will use the piping operator, group_by() and summarise, to plot by chain: (a) average calories and (b) average sodium.
library(tidyverse)
( My.Plot <- FFood %>% group_by(restaurant) %>% summarise(calories = mean(calories, na.rm=TRUE)) %>% ggplot(.) + aes(x=reorder(x=restaurant, calories), y=calories) + geom_bar(stat="identity", fill="magenta") + labs(title="Average Calories for Fast Food Chains", x="Chain", y="Average Calories") + theme_light() + coord_flip() )
Ok. Now, something quite fancy.
MP <- ggplotly(My.Plot)
frameWidget(MP)A Great Feature, filter grepl
# Let's Fix the Chicken Variable
FFood <- FFood %>% mutate(Chicken = grepl('Chicken|Chick-n',item))
FFood$Chicken[FFood$restaurant=="Chick Fil-A"] <- TRUEGraphing Just Chicken
FFood %>% filter(Chicken==TRUE) %>% ggplot(.) + aes(x=restaurant, y=calories, fill=restaurant, color=restaurant) + geom_violin(alpha=0.3) + scale_fill_viridis_d() + scale_color_viridis_d() + geom_jitter(alpha=0.2) + coord_flip() + guides(fill=FALSE, color=FALSE) + labs(x="Chain", y="Calories", main="Calories of Chicken Items by Chain")
Robert W. Walker
Associate Professor of Quantitative Methods
My research interests include causal inference, statistical computation and data visualization.