library(tidyverse)
library(skimr)
FastFood <- read_csv("https://github.com/rfordatascience/tidytuesday/raw/master/data/2018/2018-09-04/fastfood_calories.csv")
table(FastFood$restaurant)
##
## Arbys Burger King Chick Fil-A Dairy Queen Mcdonalds Sonic
## 55 70 27 42 57 53
## Subway Taco Bell
## 96 115
skim(FastFood) # Summary of the data
## Skim summary statistics
## n obs: 515
## n variables: 18
## group variables:
##
## ── Variable type:character ─────────────────────────────────────────────────────
## variable missing complete n min max empty n_unique
## item 0 515 515 5 63 0 505
## restaurant 0 515 515 5 11 0 8
## salad 0 515 515 5 5 0 1
##
## ── Variable type:integer ───────────────────────────────────────────────────────
## variable missing complete n mean sd p0 p25 p50 p75 p100
## cal_fat 0 515 515 238.81 166.41 0 120 210 310 1270
## calcium 210 305 515 24.85 25.52 0 8 20 30 290
## calories 0 515 515 530.91 282.44 20 330 490 690 2430
## cholesterol 0 515 515 72.46 63.16 0 35 60 95 805
## fiber 12 503 515 4.14 3.04 0 2 3 5 17
## protein 1 514 515 27.89 17.68 1 16 24.5 36 186
## sodium 0 515 515 1246.74 689.95 15 800 1110 1550 6080
## sugar 0 515 515 7.26 6.76 0 3 6 9 87
## total_carb 0 515 515 45.66 24.88 0 28.5 44 57 156
## total_fat 0 515 515 26.59 18.41 0 14 23 35 141
## vit_a 214 301 515 18.86 31.38 0 4 10 20 180
## vit_c 210 305 515 20.17 30.59 0 4 10 30 400
## X1 0 515 515 258 148.81 1 129.5 258 386.5 515
## hist
## ▆▇▃▁▁▁▁▁
## ▇▂▁▁▁▁▁▁
## ▅▇▅▁▁▁▁▁
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## ▇▆▁▁▁▁▁▁
## ▃▇▃▁▁▁▁▁
## ▇▂▁▁▁▁▁▁
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##
## ── Variable type:numeric ───────────────────────────────────────────────────────
## variable missing complete n mean sd p0 p25 p50 p75 p100 hist
## sat_fat 0 515 515 8.15 6.42 0 4 7 11 47 ▇▇▂▁▁▁▁▁
## trans_fat 0 515 515 0.47 0.84 0 0 0 1 8 ▇▁▁▁▁▁▁▁
Filter
- Selecting a set of rows
- can use %in% with a vector c()
FastFood %>% filter(restaurant == "Taco Bell")
## # A tibble: 115 x 18
## X1 restaurant item calories cal_fat total_fat sat_fat trans_fat
## <int> <chr> <chr> <int> <int> <int> <dbl> <dbl>
## 1 401 Taco Bell 1/2 … 540 230 26 7 1
## 2 402 Taco Bell 1/2 … 460 170 18 7 1
## 3 403 Taco Bell 7-La… 510 170 19 7 0
## 4 404 Taco Bell Bean… 370 100 11 4 0
## 5 405 Taco Bell Beef… 550 200 22 8 0
## 6 406 Taco Bell Beef… 440 160 18 5 0
## 7 407 Taco Bell Blac… 410 110 12 4 0
## 8 408 Taco Bell Burr… 420 140 16 7 0
## 9 409 Taco Bell Burr… 390 110 12 5 0
## 10 410 Taco Bell Burr… 390 120 13 5 0
## # … with 105 more rows, and 10 more variables: cholesterol <int>, sodium <int>,
## # total_carb <int>, fiber <int>, sugar <int>, protein <int>, vit_a <int>,
## # vit_c <int>, calcium <int>, salad <chr>
# Give me vectors where we want both Taco bell and Sonic
FastFood %>% filter(restaurant %in% c("Taco Bell", "Sonic"))
## # A tibble: 168 x 18
## X1 restaurant item calories cal_fat total_fat sat_fat trans_fat
## <int> <chr> <chr> <int> <int> <int> <dbl> <dbl>
## 1 85 Sonic Hatc… 710 380 43 17 2
## 2 86 Sonic Jala… 640 330 37 14 2
## 3 87 Sonic Jr. … 340 150 17 6 1
## 4 88 Sonic Jr. … 410 220 24 9 0.5
## 5 89 Sonic Jr. … 380 200 23 6 1
## 6 90 Sonic Jr. … 450 250 28 9 1
## 7 91 Sonic Jr. … 600 350 38 16 2
## 8 92 Sonic Soni… 870 530 59 20 2
## 9 93 Sonic Soni… 640 330 37 14 2
## 10 94 Sonic Soni… 650 340 37 14 2
## # … with 158 more rows, and 10 more variables: cholesterol <int>, sodium <int>,
## # total_carb <int>, fiber <int>, sugar <int>, protein <int>, vit_a <int>,
## # vit_c <int>, calcium <int>, salad <chr>
# Find the item names that start with 'B'
FastFood %>% filter(startsWith(item, "B") == T)
## # A tibble: 48 x 18
## X1 restaurant item calories cal_fat total_fat sat_fat trans_fat
## <int> <chr> <chr> <int> <int> <int> <dbl> <dbl>
## 1 6 Mcdonalds Big … 540 250 28 10 1
## 2 115 Sonic Buff… 1000 550 61 12 0.5
## 3 140 Arbys Beef… 450 180 20 6 1
## 4 141 Arbys Beef… 630 290 32 11 1.5
## 5 142 Arbys Bour… 650 300 33 12 1
## 6 143 Arbys Bour… 690 280 31 9 0
## 7 144 Arbys Bour… 690 280 31 9 0
## 8 145 Arbys Butt… 540 220 24 4.5 0
## 9 146 Arbys Butt… 650 280 31 9 0
## 10 147 Arbys Butt… 690 310 35 10 0
## # … with 38 more rows, and 10 more variables: cholesterol <int>, sodium <int>,
## # total_carb <int>, fiber <int>, sugar <int>, protein <int>, vit_a <int>,
## # vit_c <int>, calcium <int>, salad <chr>
# Resturant names starting with 'S'
FastFood %>%
filter(startsWith(restaurant,"S")==TRUE) %>%
group_by(restaurant) %>%
skim()
## Skim summary statistics
## n obs: 149
## n variables: 18
## group variables: restaurant
##
## ── Variable type:character ─────────────────────────────────────────────────────
## restaurant variable missing complete n min max empty n_unique
## Sonic item 0 53 53 8 48 0 53
## Sonic salad 0 53 53 5 5 0 1
## Subway item 0 96 96 7 58 0 96
## Subway salad 0 96 96 5 5 0 1
##
## ── Variable type:integer ───────────────────────────────────────────────────────
## restaurant variable missing complete n mean sd p0 p25 p50
## Sonic cal_fat 0 53 53 338.3 197.14 100 180 290
## Sonic calcium 4 49 53 17.24 12.07 1 8 15
## Sonic calories 0 53 53 631.7 300.88 100 410 570
## Sonic cholesterol 0 53 53 86.98 63.77 0 40 80
## Sonic fiber 0 53 53 2.66 1.78 0 2 2
## Sonic protein 0 53 53 29.19 14.53 6 18 30
## Sonic sodium 0 53 53 1350.75 665.13 470 900 1250
## Sonic sugar 0 53 53 6.53 3.94 0 4 7
## Sonic total_carb 0 53 53 47.21 21.55 16 33 44
## Sonic total_fat 0 53 53 37.64 21.97 11 20 32
## Sonic vit_a 4 49 53 6.94 5.68 0 2 6
## Sonic vit_c 4 49 53 5.76 4.81 0 2 6
## Sonic X1 0 53 53 111 15.44 85 98 111
## Subway cal_fat 0 96 96 165.1 134.84 10 48.75 137.5
## Subway calcium 0 96 96 39.12 25.13 4 20 35
## Subway calories 0 96 96 503.02 282.22 50 287.5 460
## Subway cholesterol 0 96 96 61.3 40.93 0 40 50
## Subway fiber 0 96 96 6.56 3.24 3 4 5
## Subway protein 0 96 96 30.31 16.14 3 18 26
## Subway sodium 0 96 96 1272.97 743.63 65 697.5 1130
## Subway sugar 0 96 96 10.09 5.61 3 6 8
## Subway total_carb 0 96 96 54.72 33.31 8 25.75 47
## Subway total_fat 0 96 96 18.48 14.61 1 6 15
## Subway vit_a 0 96 96 22.39 15.14 6 10 16
## Subway vit_c 0 96 96 41.97 44.01 4 20 40
## Subway X1 0 96 96 352.5 27.86 305 328.75 352.5
## p75 p100 hist
## 430 900 ▇▅▅▂▂▂▁▁
## 27 40 ▇▇▆▆▂▆▁▃
## 740 1350 ▁▇▇▇▃▁▃▂
## 110 260 ▂▇▅▅▁▁▁▂
## 3 8 ▅▇▂▂▂▁▁▁
## 35 67 ▂▆▃▇▃▁▁▂
## 1550 4520 ▇▇▅▂▁▁▁▁
## 9 17 ▅▃▂▇▃▃▁▁
## 51 126 ▃▇▇▃▁▁▁▁
## 48 100 ▇▆▅▂▂▂▁▁
## 10 20 ▇▂▅▃▁▃▁▁
## 8 25 ▇▅▇▂▁▁▁▁
## 124 137 ▇▇▇▇▇▇▇▇
## 242.5 620 ▇▅▃▂▁▁▁▁
## 60 100 ▆▂▇▂▅▂▂▁
## 740 1160 ▅▇▆▆▅▅▃▂
## 85 190 ▅▇▇▅▃▁▁▁
## 10 16 ▇▇▁▁▆▁▁▁
## 40 78 ▃▇▆▆▃▂▁▁
## 1605 3540 ▃▇▇▆▁▂▂▁
## 14 36 ▇▅▂▃▁▁▁▁
## 92 118 ▇▂▇▂▁▂▇▁
## 26.5 62 ▇▆▂▃▃▁▁▁
## 30 60 ▇▆▅▂▁▁▃▁
## 50 400 ▇▁▁▁▁▁▁▁
## 376.25 400 ▇▇▇▇▇▇▇▇
##
## ── Variable type:numeric ───────────────────────────────────────────────────────
## restaurant variable missing complete n mean sd p0 p25 p50 p75 p100
## Sonic sat_fat 0 53 53 11.42 8.67 2.5 5 8 15 36
## Sonic trans_fat 0 53 53 0.93 1.26 0 0 0 2 4
## Subway sat_fat 0 96 96 6.2 5.24 0 2 4.5 9.25 22
## Subway trans_fat 0 96 96 0.22 0.52 0 0 0 0 2
## hist
## ▇▇▃▂▁▁▁▂
## ▇▂▁▂▁▁▁▁
## ▇▅▃▃▂▁▁▁
## ▇▁▁▁▁▁▁▁
FastFood %>%
filter(startsWith(item, "M") == TRUE | startsWith(item, "A") == TRUE)
## # A tibble: 19 x 18
## X1 restaurant item calories cal_fat total_fat sat_fat trans_fat
## <int> <chr> <chr> <int> <int> <int> <dbl> <dbl>
## 1 1 Mcdonalds "Art… 380 60 7 2 0
## 2 17 Mcdonalds "Map… 640 330 36 14 1.5
## 3 20 Mcdonalds "McC… 350 130 15 3.5 0
## 4 21 Mcdonalds "McD… 380 160 18 8 1
## 5 22 Mcdonalds "McR… 480 200 22 7 0
## 6 129 Sonic "All… 370 160 18 7 0
## 7 130 Sonic "All… 430 180 20 7 0
## 8 131 Sonic "All… 410 230 26 11 0
## 9 132 Sonic "All… 340 170 19 7 0
## 10 133 Sonic "All… 320 160 18 7 0
## 11 138 Arbys "Arb… 330 100 11 4 0
## 12 139 Arbys "Arb… 400 90 10 3 0
## 13 193 Burger Ki… "Ame… 1550 1134 126 47 8
## 14 210 Burger Ki… "Mus… 940 567 63 21 2.5
## 15 369 Subway "Aut… 300 80 9 3 0
## 16 382 Subway "Mea… 310 150 17 7 1
## 17 460 Taco Bell "Mil… 440 270 30 7 0
## 18 500 Taco Bell "Mex… 540 270 31 8 1
## 19 501 Taco Bell "Mex… 270 130 14 7 1
## # … with 10 more variables: cholesterol <int>, sodium <int>, total_carb <int>,
## # fiber <int>, sugar <int>, protein <int>, vit_a <int>, vit_c <int>,
## # calcium <int>, salad <chr>
FastFood %>%
filter(startsWith(item, "B") == TRUE | endsWith(item, "C") == TRUE)
## # A tibble: 48 x 18
## X1 restaurant item calories cal_fat total_fat sat_fat trans_fat
## <int> <chr> <chr> <int> <int> <int> <dbl> <dbl>
## 1 6 Mcdonalds Big … 540 250 28 10 1
## 2 115 Sonic Buff… 1000 550 61 12 0.5
## 3 140 Arbys Beef… 450 180 20 6 1
## 4 141 Arbys Beef… 630 290 32 11 1.5
## 5 142 Arbys Bour… 650 300 33 12 1
## 6 143 Arbys Bour… 690 280 31 9 0
## 7 144 Arbys Bour… 690 280 31 9 0
## 8 145 Arbys Butt… 540 220 24 4.5 0
## 9 146 Arbys Butt… 650 280 31 9 0
## 10 147 Arbys Butt… 690 310 35 10 0
## # … with 38 more rows, and 10 more variables: cholesterol <int>, sodium <int>,
## # total_carb <int>, fiber <int>, sugar <int>, protein <int>, vit_a <int>,
## # vit_c <int>, calcium <int>, salad <chr>
# pause, Not!
FastFood %>%
filter(!(startsWith(restaurant, "S")== TRUE)) %>% group_by(restaurant) %>% skim()
## Skim summary statistics
## n obs: 366
## n variables: 18
## group variables: restaurant
##
## ── Variable type:character ─────────────────────────────────────────────────────
## restaurant variable missing complete n min max empty n_unique
## Arbys item 0 55 55 10 39 0 55
## Arbys salad 0 55 55 5 5 0 1
## Burger King item 0 70 70 9 63 0 70
## Burger King salad 0 70 70 5 5 0 1
## Chick Fil-A item 0 27 27 12 36 0 27
## Chick Fil-A salad 0 27 27 5 5 0 1
## Dairy Queen item 0 42 42 7 45 0 42
## Dairy Queen salad 0 42 42 5 5 0 1
## Mcdonalds item 0 57 57 5 49 0 57
## Mcdonalds salad 0 57 57 5 5 0 1
## Taco Bell item 0 115 115 7 45 0 113
## Taco Bell salad 0 115 115 5 5 0 1
##
## ── Variable type:integer ───────────────────────────────────────────────────────
## restaurant variable missing complete n mean sd p0 p25 p50
## Arbys cal_fat 0 55 55 237.84 113.17 45 135 250
## Arbys calcium 30 25 55 17.36 12.71 2 8 15
## Arbys calories 0 55 55 532.73 210.34 70 360 550
## Arbys cholesterol 0 55 55 70.45 34.31 15 45 65
## Arbys fiber 0 55 55 2.71 1.41 1 2 2
## Arbys protein 0 55 55 29.25 12.39 5 20 29
## Arbys sodium 0 55 55 1515.27 663.67 100 960 1480
## Arbys sugar 0 55 55 7.56 5.86 0 3.5 6
## Arbys total_carb 0 55 55 44.87 19.1 4 34 46
## Arbys total_fat 0 55 55 26.98 13.24 5 15.5 28
## Arbys vit_a 30 25 55 12.56 16.83 0 2 6
## Arbys vit_c 30 25 55 8.4 6.34 0 2 10
## Arbys X1 0 55 55 165 16.02 138 151.5 165
## Burger King cal_fat 0 70 70 333.76 194.5 90 172.5 285
## Burger King calcium 70 0 70 NaN NA NA NA NA
## Burger King calories 0 70 70 608.57 290.42 190 365 555
## Burger King cholesterol 0 70 70 100.86 107.32 5 40 85
## Burger King fiber 10 60 70 2.38 1.35 0 1.75 2
## Burger King protein 1 69 70 30.01 19.47 5 16 29
## Burger King sodium 0 70 70 1223.57 499.88 310 850 1150
## Burger King sugar 0 70 70 8.19 6.19 0 6 7.5
## Burger King total_carb 0 70 70 39.31 15.56 7 28 41
## Burger King total_fat 0 70 70 36.81 21.24 10 19.25 31.5
## Burger King vit_a 70 0 70 NaN NA NA NA NA
## Burger King vit_c 70 0 70 NaN NA NA NA NA
## Burger King X1 0 70 70 227.5 20.35 193 210.25 227.5
## Chick Fil-A cal_fat 0 27 27 145.37 102.36 18 67.5 126
## Chick Fil-A calcium 2 25 27 11.32 10.49 0 2 8
## Chick Fil-A calories 0 27 27 384.44 220.49 70 220 390
## Chick Fil-A cholesterol 0 27 27 79.07 47.31 25 55 70
## Chick Fil-A fiber 2 25 27 2.32 3.05 0 1 1
## Chick Fil-A protein 0 27 27 31.7 16.93 11 23.5 29
## Chick Fil-A sodium 0 27 27 1151.48 726.92 220 700 1000
## Chick Fil-A sugar 0 27 27 4.15 3.67 0 1 4
## Chick Fil-A total_carb 0 27 27 28.63 20.43 1 8 29
## Chick Fil-A total_fat 0 27 27 16.15 11.38 2 7.5 14
## Chick Fil-A vit_a 6 21 27 12.62 18.52 0 0 2
## Chick Fil-A vit_c 2 25 27 14.08 15.26 0 2 8
## Chick Fil-A X1 0 27 27 71 7.94 58 64.5 71
## Dairy Queen cal_fat 0 42 42 260.48 156.49 0 160 220
## Dairy Queen calcium 15 27 42 16.41 19.02 0 6 10
## Dairy Queen calories 0 42 42 520.24 259.34 20 350 485
## Dairy Queen cholesterol 0 42 42 71.55 43.75 0 41.25 60
## Dairy Queen fiber 0 42 42 2.83 2.92 0 1 2
## Dairy Queen protein 0 42 42 24.83 11.54 1 17 23
## Dairy Queen sodium 0 42 42 1181.79 609.94 15 847.5 1030
## Dairy Queen sugar 0 42 42 6.36 5.03 0 3 6
## Dairy Queen total_carb 0 42 42 38.69 23.73 0 25.25 34
## Dairy Queen total_fat 0 42 42 28.86 17.52 0 18 24.5
## Dairy Queen vit_a 15 27 42 14 11.01 0 9 10
## Dairy Queen vit_c 15 27 42 4.37 5.87 0 0 4
## Dairy Queen X1 0 42 42 283.5 12.27 263 273.25 283.5
## Mcdonalds cal_fat 0 57 57 285.61 220.9 50 160 240
## Mcdonalds calcium 0 57 57 20.6 37.93 0 6 15
## Mcdonalds calories 0 57 57 640.35 410.7 140 380 540
## Mcdonalds cholesterol 0 57 57 109.74 79.53 0 70 95
## Mcdonalds fiber 0 57 57 3.23 1.66 0 2 3
## Mcdonalds protein 0 57 57 40.3 29.48 7 25 33
## Mcdonalds sodium 0 57 57 1437.89 1036.17 20 870 1120
## Mcdonalds sugar 0 57 57 11.07 13.35 0 4 9
## Mcdonalds total_carb 0 57 57 48.79 26.44 9 32 46
## Mcdonalds total_fat 0 57 57 31.81 24.52 5 18 27
## Mcdonalds vit_a 0 57 57 33.72 64.16 0 2 6
## Mcdonalds vit_c 0 57 57 18.3 18.08 0 2 15
## Mcdonalds X1 0 57 57 29 16.6 1 15 29
## Taco Bell cal_fat 0 115 115 188 84.81 35 120 180
## Taco Bell calcium 89 26 115 24.81 11.33 6 15 25
## Taco Bell calories 0 115 115 443.65 184.34 140 320 420
## Taco Bell cholesterol 0 115 115 39.04 19.19 0 25 35
## Taco Bell fiber 0 115 115 5.71 3.06 1 3 5
## Taco Bell protein 0 115 115 17.42 7.14 6 12 16
## Taco Bell sodium 0 115 115 1013.91 474.05 290 615 960
## Taco Bell sugar 0 115 115 3.7 1.89 1 2 4
## Taco Bell total_carb 0 115 115 46.63 22.52 12 29 44
## Taco Bell total_fat 0 115 115 20.9 9.41 4 13 20
## Taco Bell vit_a 89 26 115 11.85 3.94 6 8.5 12.5
## Taco Bell vit_c 89 26 115 4.54 2.44 0 2 4
## Taco Bell X1 0 115 115 458 33.34 401 429.5 458
## p75 p100 hist
## 310 495 ▆▃▃▇▇▂▂▂
## 25 45 ▇▇▇▁▃▁▂▂
## 690 1030 ▁▇▅▆▆▇▁▁
## 90 155 ▇▅▇▇▅▃▂▂
## 4 6 ▃▇▁▃▃▁▂▁
## 38 62 ▂▇▇▆▇▅▂▂
## 2020 3350 ▁▆▇▅▇▃▁▁
## 9 23 ▇▇▆▅▁▅▂▁
## 54 83 ▂▃▂▅▇▃▁▃
## 35 59 ▆▅▅▇▆▂▁▂
## 20 60 ▇▃▂▁▁▁▁▁
## 10 20 ▅▁▁▇▁▁▁▂
## 178.5 192 ▇▇▇▇▇▇▇▇
## 431.5 1134 ▇▆▅▂▂▁▁▁
## NA NA
## 760 1550 ▇▇▆▃▃▁▁▁
## 115 805 ▇▂▁▁▁▁▁▁
## 3 7 ▁▅▇▃▁▂▁▁
## 36 134 ▆▇▂▂▁▁▁▁
## 1635 2310 ▅▅▇▅▇▆▃▂
## 10 37 ▃▇▃▁▁▁▁▁
## 52 69 ▂▃▆▅▃▇▅▂
## 48 126 ▇▆▅▃▂▁▁▁
## NA NA
## NA NA
## 244.75 262 ▇▇▇▇▇▇▇▇
## 171 423 ▇▇▆▆▁▁▁▂
## 20 35 ▇▂▂▂▂▂▁▁
## 480 970 ▅▅▃▇▁▁▁▂
## 87.5 285 ▅▇▃▁▁▁▁▁
## 3 15 ▇▅▂▁▁▁▁▁
## 37 103 ▅▇▃▁▁▁▁▁
## 1405 3660 ▆▇▇▃▁▁▁▁
## 7 12 ▇▂▂▃▂▂▂▁
## 42.5 70 ▇▂▂▃▆▃▁▂
## 19 47 ▇▇▆▆▁▁▁▂
## 30 60 ▇▁▁▂▁▁▁▁
## 20 50 ▇▅▁▂▁▁▂▁
## 77.5 84 ▇▆▆▇▆▆▆▇
## 310 670 ▂▅▇▅▁▃▁▂
## 20 100 ▇▅▁▁▁▁▁▁
## 630 1260 ▁▅▇▇▁▁▂▁
## 100 180 ▂▆▇▂▃▂▁▁
## 3 12 ▇▇▁▁▁▁▁▁
## 34 49 ▂▃▇▆▂▆▃▂
## 1362.5 3500 ▁▃▇▃▁▁▁▁
## 8.75 30 ▇▇▅▂▁▁▁▁
## 44.75 121 ▂▆▇▂▁▁▁▁
## 34.75 75 ▂▅▇▅▁▃▁▂
## 20 50 ▅▇▂▆▁▁▁▁
## 6 30 ▇▇▁▁▁▁▁▁
## 293.75 304 ▇▇▇▇▇▇▇▇
## 320 1270 ▇▆▂▁▁▁▁▁
## 20 290 ▇▁▁▁▁▁▁▁
## 740 2430 ▅▇▃▁▁▁▁▁
## 125 475 ▃▇▂▁▁▁▁▁
## 4 8 ▃▇▇▅▃▂▁▁
## 46 186 ▆▇▁▁▁▁▁▁
## 1780 6080 ▃▇▃▁▁▁▁▁
## 13 87 ▇▅▁▁▁▁▁▁
## 62 156 ▅▆▇▂▁▁▁▁
## 36 141 ▇▇▂▁▁▁▁▁
## 20 180 ▇▁▁▁▁▁▁▂
## 25 70 ▇▃▅▁▁▁▁▁
## 43 57 ▇▇▇▇▇▇▇▇
## 250 380 ▂▇▃▅▆▃▂▂
## 35 45 ▃▃▃▂▂▇▁▁
## 575 880 ▇▅▇▇▇▃▅▁
## 55 85 ▂▂▇▅▃▃▂▂
## 7 17 ▇▇▆▃▂▁▁▁
## 22 37 ▆▇▇▆▆▂▂▁
## 1300 2260 ▇▇▇▇▅▂▂▂
## 5 8 ▅▆▇▇▆▂▃▂
## 64 107 ▇▃▇▆▅▂▂▁
## 28 42 ▂▇▇▆▂▆▃▂
## 15 20 ▂▂▃▁▁▇▁▁
## 6 10 ▁▇▁▇▇▁▃▁
## 486.5 515 ▇▇▇▇▇▇▇▇
##
## ── Variable type:numeric ───────────────────────────────────────────────────────
## restaurant variable missing complete n mean sd p0 p25 p50 p75 p100
## Arbys sat_fat 0 55 55 7.97 4.16 1.5 4.5 7 11 17
## Arbys trans_fat 0 55 55 0.42 0.59 0 0 0 1 2
## Burger King sat_fat 0 70 70 11.15 8.77 2 5 8 13.75 47
## Burger King trans_fat 0 70 70 0.86 1.35 0 0 0 1.38 8
## Chick Fil-A sat_fat 0 27 27 4.11 3.9 0 1.5 3 4.75 16
## Chick Fil-A trans_fat 0 27 27 0.037 0.19 0 0 0 0 1
## Dairy Queen sat_fat 0 42 42 10.44 8.25 0 5 9 12.5 43
## Dairy Queen trans_fat 0 42 42 0.68 0.71 0 0 1 1 2
## Mcdonalds sat_fat 0 57 57 8.29 5.53 0.5 4.5 7 11 27
## Mcdonalds trans_fat 0 57 57 0.46 0.72 0 0 0 1 3
## Taco Bell sat_fat 0 115 115 6.59 2.98 1 4 6 9 14
## Taco Bell trans_fat 0 115 115 0.26 0.4 0 0 0 0.5 1
## hist
## ▅▇▆▅▅▃▃▁
## ▇▂▁▂▁▁▁▁
## ▇▅▁▂▁▁▁▁
## ▇▁▁▁▁▁▁▁
## ▇▇▂▂▁▁▁▂
## ▇▁▁▁▁▁▁▁
## ▇▇▆▂▂▁▁▁
## ▇▁▁▇▁▁▁▂
## ▃▇▅▃▂▁▁▁
## ▇▂▁▂▁▁▁▁
## ▂▇▆▆▆▃▃▁
## ▇▁▁▂▁▁▁▂
Select
FastFood %>% select(restaurant, calories)
## # A tibble: 515 x 2
## restaurant calories
## <chr> <int>
## 1 Mcdonalds 380
## 2 Mcdonalds 840
## 3 Mcdonalds 1130
## 4 Mcdonalds 750
## 5 Mcdonalds 920
## 6 Mcdonalds 540
## 7 Mcdonalds 300
## 8 Mcdonalds 510
## 9 Mcdonalds 430
## 10 Mcdonalds 770
## # … with 505 more rows
FastFood %>% select(restaurant, starts_with("vit")) # different starts_with than filter
## # A tibble: 515 x 3
## restaurant vit_a vit_c
## <chr> <int> <int>
## 1 Mcdonalds 4 20
## 2 Mcdonalds 6 20
## 3 Mcdonalds 10 20
## 4 Mcdonalds 6 25
## 5 Mcdonalds 6 20
## 6 Mcdonalds 10 2
## 7 Mcdonalds 10 2
## 8 Mcdonalds 0 4
## 9 Mcdonalds 20 4
## 10 Mcdonalds 20 6
## # … with 505 more rows
FastFood %>% select(-restaurant)
## # A tibble: 515 x 17
## X1 item calories cal_fat total_fat sat_fat trans_fat cholesterol sodium
## <int> <chr> <int> <int> <int> <dbl> <dbl> <int> <int>
## 1 1 Arti… 380 60 7 2 0 95 1110
## 2 2 Sing… 840 410 45 17 1.5 130 1580
## 3 3 Doub… 1130 600 67 27 3 220 1920
## 4 4 Gril… 750 280 31 10 0.5 155 1940
## 5 5 Cris… 920 410 45 12 0.5 120 1980
## 6 6 Big … 540 250 28 10 1 80 950
## 7 7 Chee… 300 100 12 5 0.5 40 680
## 8 8 Clas… 510 210 24 4 0 65 1040
## 9 9 Doub… 430 190 21 11 1 85 1040
## 10 10 Doub… 770 400 45 21 2.5 175 1290
## # … with 505 more rows, and 8 more variables: total_carb <int>, fiber <int>,
## # sugar <int>, protein <int>, vit_a <int>, vit_c <int>, calcium <int>,
## # salad <chr>
Mutate() & Transmute()
Transmute is about to disapear
FastFood<- FastFood %>%
mutate(Sodium.Grams = sodium / 1000) %>%
select(restaurant, Sodium.Grams, sodium, everything())
#FastFood <- FastFood %>%
# transmute(Sodium.Grams = sodium / 1000)
FastFood <- FastFood %>% mutate(Chicken = stringr::str_detect(item, 'Chicken|Chick-n'))
head(FastFood)
## # A tibble: 6 x 20
## restaurant Sodium.Grams sodium X1 item calories cal_fat total_fat sat_fat
## <chr> <dbl> <int> <int> <chr> <int> <int> <int> <dbl>
## 1 Mcdonalds 1.11 1110 1 Arti… 380 60 7 2
## 2 Mcdonalds 1.58 1580 2 Sing… 840 410 45 17
## 3 Mcdonalds 1.92 1920 3 Doub… 1130 600 67 27
## 4 Mcdonalds 1.94 1940 4 Gril… 750 280 31 10
## 5 Mcdonalds 1.98 1980 5 Cris… 920 410 45 12
## 6 Mcdonalds 0.95 950 6 Big … 540 250 28 10
## # … with 11 more variables: trans_fat <dbl>, cholesterol <int>,
## # total_carb <int>, fiber <int>, sugar <int>, protein <int>, vit_a <int>,
## # vit_c <int>, calcium <int>, salad <chr>, Chicken <lgl>
Group_by()
- Great for having aggregate totals
- Good for summaries
FastFood %>%
group_by(restaurant) %>% skim()
## Skim summary statistics
## n obs: 515
## n variables: 20
## group variables: restaurant
##
## ── Variable type:character ─────────────────────────────────────────────────────
## restaurant variable missing complete n min max empty n_unique
## Arbys item 0 55 55 10 39 0 55
## Arbys salad 0 55 55 5 5 0 1
## Burger King item 0 70 70 9 63 0 70
## Burger King salad 0 70 70 5 5 0 1
## Chick Fil-A item 0 27 27 12 36 0 27
## Chick Fil-A salad 0 27 27 5 5 0 1
## Dairy Queen item 0 42 42 7 45 0 42
## Dairy Queen salad 0 42 42 5 5 0 1
## Mcdonalds item 0 57 57 5 49 0 57
## Mcdonalds salad 0 57 57 5 5 0 1
## Sonic item 0 53 53 8 48 0 53
## Sonic salad 0 53 53 5 5 0 1
## Subway item 0 96 96 7 58 0 96
## Subway salad 0 96 96 5 5 0 1
## Taco Bell item 0 115 115 7 45 0 113
## Taco Bell salad 0 115 115 5 5 0 1
##
## ── Variable type:integer ───────────────────────────────────────────────────────
## restaurant variable missing complete n mean sd p0 p25 p50
## Arbys cal_fat 0 55 55 237.84 113.17 45 135 250
## Arbys calcium 30 25 55 17.36 12.71 2 8 15
## Arbys calories 0 55 55 532.73 210.34 70 360 550
## Arbys cholesterol 0 55 55 70.45 34.31 15 45 65
## Arbys fiber 0 55 55 2.71 1.41 1 2 2
## Arbys protein 0 55 55 29.25 12.39 5 20 29
## Arbys sodium 0 55 55 1515.27 663.67 100 960 1480
## Arbys sugar 0 55 55 7.56 5.86 0 3.5 6
## Arbys total_carb 0 55 55 44.87 19.1 4 34 46
## Arbys total_fat 0 55 55 26.98 13.24 5 15.5 28
## Arbys vit_a 30 25 55 12.56 16.83 0 2 6
## Arbys vit_c 30 25 55 8.4 6.34 0 2 10
## Arbys X1 0 55 55 165 16.02 138 151.5 165
## Burger King cal_fat 0 70 70 333.76 194.5 90 172.5 285
## Burger King calcium 70 0 70 NaN NA NA NA NA
## Burger King calories 0 70 70 608.57 290.42 190 365 555
## Burger King cholesterol 0 70 70 100.86 107.32 5 40 85
## Burger King fiber 10 60 70 2.38 1.35 0 1.75 2
## Burger King protein 1 69 70 30.01 19.47 5 16 29
## Burger King sodium 0 70 70 1223.57 499.88 310 850 1150
## Burger King sugar 0 70 70 8.19 6.19 0 6 7.5
## Burger King total_carb 0 70 70 39.31 15.56 7 28 41
## Burger King total_fat 0 70 70 36.81 21.24 10 19.25 31.5
## Burger King vit_a 70 0 70 NaN NA NA NA NA
## Burger King vit_c 70 0 70 NaN NA NA NA NA
## Burger King X1 0 70 70 227.5 20.35 193 210.25 227.5
## Chick Fil-A cal_fat 0 27 27 145.37 102.36 18 67.5 126
## Chick Fil-A calcium 2 25 27 11.32 10.49 0 2 8
## Chick Fil-A calories 0 27 27 384.44 220.49 70 220 390
## Chick Fil-A cholesterol 0 27 27 79.07 47.31 25 55 70
## Chick Fil-A fiber 2 25 27 2.32 3.05 0 1 1
## Chick Fil-A protein 0 27 27 31.7 16.93 11 23.5 29
## Chick Fil-A sodium 0 27 27 1151.48 726.92 220 700 1000
## Chick Fil-A sugar 0 27 27 4.15 3.67 0 1 4
## Chick Fil-A total_carb 0 27 27 28.63 20.43 1 8 29
## Chick Fil-A total_fat 0 27 27 16.15 11.38 2 7.5 14
## Chick Fil-A vit_a 6 21 27 12.62 18.52 0 0 2
## Chick Fil-A vit_c 2 25 27 14.08 15.26 0 2 8
## Chick Fil-A X1 0 27 27 71 7.94 58 64.5 71
## Dairy Queen cal_fat 0 42 42 260.48 156.49 0 160 220
## Dairy Queen calcium 15 27 42 16.41 19.02 0 6 10
## Dairy Queen calories 0 42 42 520.24 259.34 20 350 485
## Dairy Queen cholesterol 0 42 42 71.55 43.75 0 41.25 60
## Dairy Queen fiber 0 42 42 2.83 2.92 0 1 2
## Dairy Queen protein 0 42 42 24.83 11.54 1 17 23
## Dairy Queen sodium 0 42 42 1181.79 609.94 15 847.5 1030
## Dairy Queen sugar 0 42 42 6.36 5.03 0 3 6
## Dairy Queen total_carb 0 42 42 38.69 23.73 0 25.25 34
## Dairy Queen total_fat 0 42 42 28.86 17.52 0 18 24.5
## Dairy Queen vit_a 15 27 42 14 11.01 0 9 10
## Dairy Queen vit_c 15 27 42 4.37 5.87 0 0 4
## Dairy Queen X1 0 42 42 283.5 12.27 263 273.25 283.5
## Mcdonalds cal_fat 0 57 57 285.61 220.9 50 160 240
## Mcdonalds calcium 0 57 57 20.6 37.93 0 6 15
## Mcdonalds calories 0 57 57 640.35 410.7 140 380 540
## Mcdonalds cholesterol 0 57 57 109.74 79.53 0 70 95
## Mcdonalds fiber 0 57 57 3.23 1.66 0 2 3
## Mcdonalds protein 0 57 57 40.3 29.48 7 25 33
## Mcdonalds sodium 0 57 57 1437.89 1036.17 20 870 1120
## Mcdonalds sugar 0 57 57 11.07 13.35 0 4 9
## Mcdonalds total_carb 0 57 57 48.79 26.44 9 32 46
## Mcdonalds total_fat 0 57 57 31.81 24.52 5 18 27
## Mcdonalds vit_a 0 57 57 33.72 64.16 0 2 6
## Mcdonalds vit_c 0 57 57 18.3 18.08 0 2 15
## Mcdonalds X1 0 57 57 29 16.6 1 15 29
## Sonic cal_fat 0 53 53 338.3 197.14 100 180 290
## Sonic calcium 4 49 53 17.24 12.07 1 8 15
## Sonic calories 0 53 53 631.7 300.88 100 410 570
## Sonic cholesterol 0 53 53 86.98 63.77 0 40 80
## Sonic fiber 0 53 53 2.66 1.78 0 2 2
## Sonic protein 0 53 53 29.19 14.53 6 18 30
## Sonic sodium 0 53 53 1350.75 665.13 470 900 1250
## Sonic sugar 0 53 53 6.53 3.94 0 4 7
## Sonic total_carb 0 53 53 47.21 21.55 16 33 44
## Sonic total_fat 0 53 53 37.64 21.97 11 20 32
## Sonic vit_a 4 49 53 6.94 5.68 0 2 6
## Sonic vit_c 4 49 53 5.76 4.81 0 2 6
## Sonic X1 0 53 53 111 15.44 85 98 111
## Subway cal_fat 0 96 96 165.1 134.84 10 48.75 137.5
## Subway calcium 0 96 96 39.12 25.13 4 20 35
## Subway calories 0 96 96 503.02 282.22 50 287.5 460
## Subway cholesterol 0 96 96 61.3 40.93 0 40 50
## Subway fiber 0 96 96 6.56 3.24 3 4 5
## Subway protein 0 96 96 30.31 16.14 3 18 26
## Subway sodium 0 96 96 1272.97 743.63 65 697.5 1130
## Subway sugar 0 96 96 10.09 5.61 3 6 8
## Subway total_carb 0 96 96 54.72 33.31 8 25.75 47
## Subway total_fat 0 96 96 18.48 14.61 1 6 15
## Subway vit_a 0 96 96 22.39 15.14 6 10 16
## Subway vit_c 0 96 96 41.97 44.01 4 20 40
## Subway X1 0 96 96 352.5 27.86 305 328.75 352.5
## Taco Bell cal_fat 0 115 115 188 84.81 35 120 180
## Taco Bell calcium 89 26 115 24.81 11.33 6 15 25
## Taco Bell calories 0 115 115 443.65 184.34 140 320 420
## Taco Bell cholesterol 0 115 115 39.04 19.19 0 25 35
## Taco Bell fiber 0 115 115 5.71 3.06 1 3 5
## Taco Bell protein 0 115 115 17.42 7.14 6 12 16
## Taco Bell sodium 0 115 115 1013.91 474.05 290 615 960
## Taco Bell sugar 0 115 115 3.7 1.89 1 2 4
## Taco Bell total_carb 0 115 115 46.63 22.52 12 29 44
## Taco Bell total_fat 0 115 115 20.9 9.41 4 13 20
## Taco Bell vit_a 89 26 115 11.85 3.94 6 8.5 12.5
## Taco Bell vit_c 89 26 115 4.54 2.44 0 2 4
## Taco Bell X1 0 115 115 458 33.34 401 429.5 458
## p75 p100 hist
## 310 495 ▆▃▃▇▇▂▂▂
## 25 45 ▇▇▇▁▃▁▂▂
## 690 1030 ▁▇▅▆▆▇▁▁
## 90 155 ▇▅▇▇▅▃▂▂
## 4 6 ▃▇▁▃▃▁▂▁
## 38 62 ▂▇▇▆▇▅▂▂
## 2020 3350 ▁▆▇▅▇▃▁▁
## 9 23 ▇▇▆▅▁▅▂▁
## 54 83 ▂▃▂▅▇▃▁▃
## 35 59 ▆▅▅▇▆▂▁▂
## 20 60 ▇▃▂▁▁▁▁▁
## 10 20 ▅▁▁▇▁▁▁▂
## 178.5 192 ▇▇▇▇▇▇▇▇
## 431.5 1134 ▇▆▅▂▂▁▁▁
## NA NA
## 760 1550 ▇▇▆▃▃▁▁▁
## 115 805 ▇▂▁▁▁▁▁▁
## 3 7 ▁▅▇▃▁▂▁▁
## 36 134 ▆▇▂▂▁▁▁▁
## 1635 2310 ▅▅▇▅▇▆▃▂
## 10 37 ▃▇▃▁▁▁▁▁
## 52 69 ▂▃▆▅▃▇▅▂
## 48 126 ▇▆▅▃▂▁▁▁
## NA NA
## NA NA
## 244.75 262 ▇▇▇▇▇▇▇▇
## 171 423 ▇▇▆▆▁▁▁▂
## 20 35 ▇▂▂▂▂▂▁▁
## 480 970 ▅▅▃▇▁▁▁▂
## 87.5 285 ▅▇▃▁▁▁▁▁
## 3 15 ▇▅▂▁▁▁▁▁
## 37 103 ▅▇▃▁▁▁▁▁
## 1405 3660 ▆▇▇▃▁▁▁▁
## 7 12 ▇▂▂▃▂▂▂▁
## 42.5 70 ▇▂▂▃▆▃▁▂
## 19 47 ▇▇▆▆▁▁▁▂
## 30 60 ▇▁▁▂▁▁▁▁
## 20 50 ▇▅▁▂▁▁▂▁
## 77.5 84 ▇▆▆▇▆▆▆▇
## 310 670 ▂▅▇▅▁▃▁▂
## 20 100 ▇▅▁▁▁▁▁▁
## 630 1260 ▁▅▇▇▁▁▂▁
## 100 180 ▂▆▇▂▃▂▁▁
## 3 12 ▇▇▁▁▁▁▁▁
## 34 49 ▂▃▇▆▂▆▃▂
## 1362.5 3500 ▁▃▇▃▁▁▁▁
## 8.75 30 ▇▇▅▂▁▁▁▁
## 44.75 121 ▂▆▇▂▁▁▁▁
## 34.75 75 ▂▅▇▅▁▃▁▂
## 20 50 ▅▇▂▆▁▁▁▁
## 6 30 ▇▇▁▁▁▁▁▁
## 293.75 304 ▇▇▇▇▇▇▇▇
## 320 1270 ▇▆▂▁▁▁▁▁
## 20 290 ▇▁▁▁▁▁▁▁
## 740 2430 ▅▇▃▁▁▁▁▁
## 125 475 ▃▇▂▁▁▁▁▁
## 4 8 ▃▇▇▅▃▂▁▁
## 46 186 ▆▇▁▁▁▁▁▁
## 1780 6080 ▃▇▃▁▁▁▁▁
## 13 87 ▇▅▁▁▁▁▁▁
## 62 156 ▅▆▇▂▁▁▁▁
## 36 141 ▇▇▂▁▁▁▁▁
## 20 180 ▇▁▁▁▁▁▁▂
## 25 70 ▇▃▅▁▁▁▁▁
## 43 57 ▇▇▇▇▇▇▇▇
## 430 900 ▇▅▅▂▂▂▁▁
## 27 40 ▇▇▆▆▂▆▁▃
## 740 1350 ▁▇▇▇▃▁▃▂
## 110 260 ▂▇▅▅▁▁▁▂
## 3 8 ▅▇▂▂▂▁▁▁
## 35 67 ▂▆▃▇▃▁▁▂
## 1550 4520 ▇▇▅▂▁▁▁▁
## 9 17 ▅▃▂▇▃▃▁▁
## 51 126 ▃▇▇▃▁▁▁▁
## 48 100 ▇▆▅▂▂▂▁▁
## 10 20 ▇▂▅▃▁▃▁▁
## 8 25 ▇▅▇▂▁▁▁▁
## 124 137 ▇▇▇▇▇▇▇▇
## 242.5 620 ▇▅▃▂▁▁▁▁
## 60 100 ▆▂▇▂▅▂▂▁
## 740 1160 ▅▇▆▆▅▅▃▂
## 85 190 ▅▇▇▅▃▁▁▁
## 10 16 ▇▇▁▁▆▁▁▁
## 40 78 ▃▇▆▆▃▂▁▁
## 1605 3540 ▃▇▇▆▁▂▂▁
## 14 36 ▇▅▂▃▁▁▁▁
## 92 118 ▇▂▇▂▁▂▇▁
## 26.5 62 ▇▆▂▃▃▁▁▁
## 30 60 ▇▆▅▂▁▁▃▁
## 50 400 ▇▁▁▁▁▁▁▁
## 376.25 400 ▇▇▇▇▇▇▇▇
## 250 380 ▂▇▃▅▆▃▂▂
## 35 45 ▃▃▃▂▂▇▁▁
## 575 880 ▇▅▇▇▇▃▅▁
## 55 85 ▂▂▇▅▃▃▂▂
## 7 17 ▇▇▆▃▂▁▁▁
## 22 37 ▆▇▇▆▆▂▂▁
## 1300 2260 ▇▇▇▇▅▂▂▂
## 5 8 ▅▆▇▇▆▂▃▂
## 64 107 ▇▃▇▆▅▂▂▁
## 28 42 ▂▇▇▆▂▆▃▂
## 15 20 ▂▂▃▁▁▇▁▁
## 6 10 ▁▇▁▇▇▁▃▁
## 486.5 515 ▇▇▇▇▇▇▇▇
##
## ── Variable type:logical ───────────────────────────────────────────────────────
## restaurant variable missing complete n mean count
## Arbys Chicken 0 55 55 0.24 FAL: 42, TRU: 13, NA: 0
## Burger King Chicken 0 70 70 0.49 FAL: 36, TRU: 34, NA: 0
## Chick Fil-A Chicken 0 27 27 0.93 TRU: 25, FAL: 2, NA: 0
## Dairy Queen Chicken 0 42 42 0.31 FAL: 29, TRU: 13, NA: 0
## Mcdonalds Chicken 0 57 57 0.65 TRU: 37, FAL: 20, NA: 0
## Sonic Chicken 0 53 53 0.42 FAL: 31, TRU: 22, NA: 0
## Subway Chicken 0 96 96 0.17 FAL: 80, TRU: 16, NA: 0
## Taco Bell Chicken 0 115 115 0.2 FAL: 92, TRU: 23, NA: 0
##
## ── Variable type:numeric ───────────────────────────────────────────────────────
## restaurant variable missing complete n mean sd p0 p25 p50
## Arbys sat_fat 0 55 55 7.97 4.16 1.5 4.5 7
## Arbys Sodium.Grams 0 55 55 1.52 0.66 0.1 0.96 1.48
## Arbys trans_fat 0 55 55 0.42 0.59 0 0 0
## Burger King sat_fat 0 70 70 11.15 8.77 2 5 8
## Burger King Sodium.Grams 0 70 70 1.22 0.5 0.31 0.85 1.15
## Burger King trans_fat 0 70 70 0.86 1.35 0 0 0
## Chick Fil-A sat_fat 0 27 27 4.11 3.9 0 1.5 3
## Chick Fil-A Sodium.Grams 0 27 27 1.15 0.73 0.22 0.7 1
## Chick Fil-A trans_fat 0 27 27 0.037 0.19 0 0 0
## Dairy Queen sat_fat 0 42 42 10.44 8.25 0 5 9
## Dairy Queen Sodium.Grams 0 42 42 1.18 0.61 0.015 0.85 1.03
## Dairy Queen trans_fat 0 42 42 0.68 0.71 0 0 1
## Mcdonalds sat_fat 0 57 57 8.29 5.53 0.5 4.5 7
## Mcdonalds Sodium.Grams 0 57 57 1.44 1.04 0.02 0.87 1.12
## Mcdonalds trans_fat 0 57 57 0.46 0.72 0 0 0
## Sonic sat_fat 0 53 53 11.42 8.67 2.5 5 8
## Sonic Sodium.Grams 0 53 53 1.35 0.67 0.47 0.9 1.25
## Sonic trans_fat 0 53 53 0.93 1.26 0 0 0
## Subway sat_fat 0 96 96 6.2 5.24 0 2 4.5
## Subway Sodium.Grams 0 96 96 1.27 0.74 0.065 0.7 1.13
## Subway trans_fat 0 96 96 0.22 0.52 0 0 0
## Taco Bell sat_fat 0 115 115 6.59 2.98 1 4 6
## Taco Bell Sodium.Grams 0 115 115 1.01 0.47 0.29 0.61 0.96
## Taco Bell trans_fat 0 115 115 0.26 0.4 0 0 0
## p75 p100 hist
## 11 17 ▅▇▆▅▅▃▃▁
## 2.02 3.35 ▁▆▇▅▇▃▁▁
## 1 2 ▇▂▁▂▁▁▁▁
## 13.75 47 ▇▅▁▂▁▁▁▁
## 1.64 2.31 ▅▅▇▅▇▆▃▂
## 1.38 8 ▇▁▁▁▁▁▁▁
## 4.75 16 ▇▇▂▂▁▁▁▂
## 1.41 3.66 ▆▇▇▃▁▁▁▁
## 0 1 ▇▁▁▁▁▁▁▁
## 12.5 43 ▇▇▆▂▂▁▁▁
## 1.36 3.5 ▁▃▇▃▁▁▁▁
## 1 2 ▇▁▁▇▁▁▁▂
## 11 27 ▃▇▅▃▂▁▁▁
## 1.78 6.08 ▃▇▃▁▁▁▁▁
## 1 3 ▇▂▁▂▁▁▁▁
## 15 36 ▇▇▃▂▁▁▁▂
## 1.55 4.52 ▇▇▅▂▁▁▁▁
## 2 4 ▇▂▁▂▁▁▁▁
## 9.25 22 ▇▅▃▃▂▁▁▁
## 1.61 3.54 ▃▇▇▆▁▂▂▁
## 0 2 ▇▁▁▁▁▁▁▁
## 9 14 ▂▇▆▆▆▃▃▁
## 1.3 2.26 ▇▇▇▇▅▂▂▂
## 0.5 1 ▇▁▁▂▁▁▁▂
FastFood %>% group_by(restaurant) %>% summarise(Mean.Prot = mean(protein), Mean.Protein.NA = mean(protein, na.rm=TRUE))
## # A tibble: 8 x 3
## restaurant Mean.Prot Mean.Protein.NA
## * <chr> <dbl> <dbl>
## 1 Arbys 29.3 29.3
## 2 Burger King NA 30.0
## 3 Chick Fil-A 31.7 31.7
## 4 Dairy Queen 24.8 24.8
## 5 Mcdonalds 40.3 40.3
## 6 Sonic 29.2 29.2
## 7 Subway 30.3 30.3
## 8 Taco Bell 17.4 17.4
# Helpful for specific skim() on functions
FastFood %>% group_by(restaurant, Chicken) %>% skim(Sodium.Grams)
## Skim summary statistics
## n obs: 515
## n variables: 20
## group variables: restaurant, Chicken
##
## ── Variable type:numeric ───────────────────────────────────────────────────────
## restaurant Chicken variable missing complete n mean sd p0 p25 p50
## Arbys FALSE Sodium.Grams 0 42 42 1.57 0.7 0.1 0.99 1.52
## Arbys TRUE Sodium.Grams 0 13 13 1.33 0.52 0.64 0.95 1.21
## Burger King FALSE Sodium.Grams 0 36 36 1.21 0.5 0.45 0.8 1.16
## Burger King TRUE Sodium.Grams 0 34 34 1.24 0.5 0.31 0.85 1.15
## Chick Fil-A FALSE Sodium.Grams 0 2 2 1.48 0.4 1.2 1.34 1.48
## Chick Fil-A TRUE Sodium.Grams 0 25 25 1.13 0.75 0.22 0.67 0.99
## Dairy Queen FALSE Sodium.Grams 0 29 29 1.08 0.46 0.015 0.9 1
## Dairy Queen TRUE Sodium.Grams 0 13 13 1.41 0.84 0.67 0.82 1.19
## Mcdonalds FALSE Sodium.Grams 0 20 20 1.31 0.9 0.48 0.83 1.02
## Mcdonalds TRUE Sodium.Grams 0 37 37 1.51 1.11 0.02 0.96 1.19
## Sonic FALSE Sodium.Grams 0 31 31 1.19 0.42 0.47 0.86 1.18
## Sonic TRUE Sodium.Grams 0 22 22 1.58 0.87 0.67 0.97 1.42
## Subway FALSE Sodium.Grams 0 80 80 1.3 0.77 0.065 0.72 1.19
## Subway TRUE Sodium.Grams 0 16 16 1.15 0.6 0.28 0.66 1.08
## Taco Bell FALSE Sodium.Grams 0 92 92 0.98 0.46 0.29 0.59 0.93
## Taco Bell TRUE Sodium.Grams 0 23 23 1.15 0.52 0.46 0.74 1.07
## p75 p100 hist
## 2.09 3.35 ▁▆▇▆▇▅▁▁
## 1.75 2.11 ▃▇▁▂▂▂▂▆
## 1.58 2.27 ▇▅▇▆▆▃▅▂
## 1.64 2.31 ▂▇▇▇▇▇▂▂
## 1.62 1.76 ▇▁▁▁▁▁▁▇
## 1.35 3.66 ▆▇▆▃▁▁▁▁
## 1.25 2.21 ▁▂▃▇▅▃▁▂
## 1.53 3.5 ▇▅▅▁▁▂▁▂
## 1.38 4.45 ▇▅▂▁▁▁▁▁
## 1.82 6.08 ▃▇▅▁▂▁▁▁
## 1.41 2.31 ▃▇▆▇▆▂▁▁
## 2.02 4.52 ▇▅▂▂▁▁▁▁
## 1.61 3.54 ▃▇▇▆▁▂▂▁
## 1.41 2.28 ▃▅▂▇▂▁▃▃
## 1.27 2.26 ▇▇▇▇▅▂▁▂
## 1.5 2.23 ▇▂▇▂▅▁▂▁
Summarize
FastFood %>%
group_by(restaurant, Chicken) %>%
summarise(Mean.Protein = mean(protein),
Mean.Protein.NA = mean(protein, na.rm=TRUE))
## # A tibble: 16 x 4
## # Groups: restaurant [8]
## restaurant Chicken Mean.Protein Mean.Protein.NA
## <chr> <lgl> <dbl> <dbl>
## 1 Arbys FALSE 29.6 29.6
## 2 Arbys TRUE 28 28
## 3 Burger King FALSE NA 34.1
## 4 Burger King TRUE 25.8 25.8
## 5 Chick Fil-A FALSE 33.5 33.5
## 6 Chick Fil-A TRUE 31.6 31.6
## 7 Dairy Queen FALSE 23.3 23.3
## 8 Dairy Queen TRUE 28.3 28.3
## 9 Mcdonalds FALSE 36.2 36.2
## 10 Mcdonalds TRUE 42.5 42.5
## 11 Sonic FALSE 28.9 28.9
## 12 Sonic TRUE 29.5 29.5
## 13 Subway FALSE 28.7 28.7
## 14 Subway TRUE 38.4 38.4
## 15 Taco Bell FALSE 16.4 16.4
## 16 Taco Bell TRUE 21.3 21.3
Ungroup
FastFood %>%
group_by(restaurant) %>%
mutate(Avg.Protein = mean(protein, na.rm=TRUE),
Protein.Dev = protein - Avg.Protein)
## # A tibble: 515 x 22
## # Groups: restaurant [8]
## restaurant Sodium.Grams sodium X1 item calories cal_fat total_fat sat_fat
## <chr> <dbl> <int> <int> <chr> <int> <int> <int> <dbl>
## 1 Mcdonalds 1.11 1110 1 Arti… 380 60 7 2
## 2 Mcdonalds 1.58 1580 2 Sing… 840 410 45 17
## 3 Mcdonalds 1.92 1920 3 Doub… 1130 600 67 27
## 4 Mcdonalds 1.94 1940 4 Gril… 750 280 31 10
## 5 Mcdonalds 1.98 1980 5 Cris… 920 410 45 12
## 6 Mcdonalds 0.95 950 6 Big … 540 250 28 10
## 7 Mcdonalds 0.68 680 7 Chee… 300 100 12 5
## 8 Mcdonalds 1.04 1040 8 Clas… 510 210 24 4
## 9 Mcdonalds 1.04 1040 9 Doub… 430 190 21 11
## 10 Mcdonalds 1.29 1290 10 Doub… 770 400 45 21
## # … with 505 more rows, and 13 more variables: trans_fat <dbl>,
## # cholesterol <int>, total_carb <int>, fiber <int>, sugar <int>,
## # protein <int>, vit_a <int>, vit_c <int>, calcium <int>, salad <chr>,
## # Chicken <lgl>, Avg.Protein <dbl>, Protein.Dev <dbl>
FastFood %>%
group_by(restaurant) %>%
mutate(Avg.Protein = mean(protein, na.rm=TRUE),
Protein.Dev = protein - Avg.Protein) %>%
ungroup()
## # A tibble: 515 x 22
## restaurant Sodium.Grams sodium X1 item calories cal_fat total_fat sat_fat
## <chr> <dbl> <int> <int> <chr> <int> <int> <int> <dbl>
## 1 Mcdonalds 1.11 1110 1 Arti… 380 60 7 2
## 2 Mcdonalds 1.58 1580 2 Sing… 840 410 45 17
## 3 Mcdonalds 1.92 1920 3 Doub… 1130 600 67 27
## 4 Mcdonalds 1.94 1940 4 Gril… 750 280 31 10
## 5 Mcdonalds 1.98 1980 5 Cris… 920 410 45 12
## 6 Mcdonalds 0.95 950 6 Big … 540 250 28 10
## 7 Mcdonalds 0.68 680 7 Chee… 300 100 12 5
## 8 Mcdonalds 1.04 1040 8 Clas… 510 210 24 4
## 9 Mcdonalds 1.04 1040 9 Doub… 430 190 21 11
## 10 Mcdonalds 1.29 1290 10 Doub… 770 400 45 21
## # … with 505 more rows, and 13 more variables: trans_fat <dbl>,
## # cholesterol <int>, total_carb <int>, fiber <int>, sugar <int>,
## # protein <int>, vit_a <int>, vit_c <int>, calcium <int>, salad <chr>,
## # Chicken <lgl>, Avg.Protein <dbl>, Protein.Dev <dbl>
Counts
( Restaurant.Table <- FastFood %>% group_by(restaurant) %>% summarise(Count = n()) %>% arrange(Count) )
## # A tibble: 8 x 2
## restaurant Count
## <chr> <int>
## 1 Chick Fil-A 27
## 2 Dairy Queen 42
## 3 Sonic 53
## 4 Arbys 55
## 5 Mcdonalds 57
## 6 Burger King 70
## 7 Subway 96
## 8 Taco Bell 115