绘制两个变量的箱线图，根据第三个变量的平均值为它们着色
Boxplot two variables, color them based on mean of a third variable
我正在尝试绘制一个箱线图，其中我的 MFR（制造商）显示在 x 轴上，评级显示在 y 轴上。但是，我想根据平均货架价值为不同的箱线图着色。 (shelf是1到3之间的值)
我试过这段代码：
boxplot(rating ~ mfr, col= brewer.pal(nrow(aggregate(shelf ~ mfr, FUN=mean, data=Cereals)), "Blues"),
names =c("American Home Food Products", "General Mills", "Kellogs", "Nabisco", "Post", "Quaker Oats", "Ralston Purina"),
data = Cereals)
我的箱线图是彩色的，但它从左到右用不同种类的蓝色着色，而不是基于每个制造商的最高平均值。我该如何解决这个问题？我需要更改什么？
我的数据是：dput(Cereals.csv)
structure(list(name = c("100% Bran", "100% Natural Bran", "All-Bran",
"All-Bran with Extra Fiber", "Almond Delight", "Apple Cinnamon Cheerios",
"Apple Jacks", "Basic 4", "Bran Chex", "Bran Flakes", "Cap'n'Crunch",
"Cheerios", "Cinnamon Toast Crunch", "Clusters", "Cocoa Puffs",
"Corn Chex", "Corn Flakes", "Corn Pops", "Count Chocula", "Cracklin' Oat Bran",
"Cream of Wheat (Quick)", "Crispix", "Crispy Wheat & Raisins",
"Double Chex", "Froot Loops", "Frosted Flakes", "Frosted Mini-Wheats",
"Fruit & Fibre Dates; Walnuts; and Oats", "Fruitful Bran", "Fruity Pebbles",
"Golden Crisp", "Golden Grahams", "Grape Nuts Flakes", "Grape-Nuts",
"Great Grains Pecan", "Honey Graham Ohs", "Honey Nut Cheerios",
"Honey-comb", "Just Right Crunchy Nuggets", "Just Right Fruit & Nut",
"Kix", "Life", "Lucky Charms", "Maypo", "Muesli Raisins; Dates; & Almonds",
"Muesli Raisins; Peaches; & Pecans", "Mueslix Crispy Blend",
"Multi-Grain Cheerios", "Nut&Honey Crunch", "Nutri-Grain Almond-Raisin",
"Nutri-grain Wheat", "Oatmeal Raisin Crisp", "Post Nat. Raisin Bran",
"Product 19", "Puffed Rice", "Puffed Wheat", "Quaker Oat Squares",
"Quaker Oatmeal", "Raisin Bran", "Raisin Nut Bran", "Raisin Squares",
"Rice Chex", "Rice Krispies", "Shredded Wheat", "Shredded Wheat 'n'Bran",
"Shredded Wheat spoon size", "Smacks", "Special K", "Strawberry Fruit Wheats",
"Total Corn Flakes", "Total Raisin Bran", "Total Whole Grain",
"Triples", "Trix", "Wheat Chex", "Wheaties", "Wheaties Honey Gold"
), mfr = c("N", "Q", "K", "K", "R", "G", "K", "G", "R", "P",
"Q", "G", "G", "G", "G", "R", "K", "K", "G", "K", "N", "K", "G",
"R", "K", "K", "K", "P", "K", "P", "P", "G", "P", "P", "P", "Q",
"G", "P", "K", "K", "G", "Q", "G", "A", "R", "R", "K", "G", "K",
"K", "K", "G", "P", "K", "Q", "Q", "Q", "Q", "K", "G", "K", "R",
"K", "N", "N", "N", "K", "K", "N", "G", "G", "G", "G", "G", "R",
"G", "G"), type = c("C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "H", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "H", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "H", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C"), calories = c(70L, 120L, 70L, 50L, 110L, 110L,
110L, 130L, 90L, 90L, 120L, 110L, 120L, 110L, 110L, 110L, 100L,
110L, 110L, 110L, 100L, 110L, 100L, 100L, 110L, 110L, 100L, 120L,
120L, 110L, 100L, 110L, 100L, 110L, 120L, 120L, 110L, 110L, 110L,
140L, 110L, 100L, 110L, 100L, 150L, 150L, 160L, 100L, 120L, 140L,
90L, 130L, 120L, 100L, 50L, 50L, 100L, 100L, 120L, 100L, 90L,
110L, 110L, 80L, 90L, 90L, 110L, 110L, 90L, 110L, 140L, 100L,
110L, 110L, 100L, 100L, 110L), protein = c(4L, 3L, 4L, 4L, 2L,
2L, 2L, 3L, 2L, 3L, 1L, 6L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 3L,
2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 3L, 1L, 3L,
1L, 2L, 3L, 2L, 4L, 2L, 4L, 4L, 4L, 3L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 4L, 5L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 6L, 2L,
2L, 3L, 3L, 2L, 1L, 3L, 3L, 2L), fat = c(1L, 5L, 1L, 0L, 2L,
2L, 0L, 2L, 1L, 0L, 2L, 2L, 3L, 2L, 1L, 0L, 0L, 0L, 1L, 3L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 2L, 0L, 1L, 0L, 1L, 1L, 0L, 3L, 2L, 1L,
0L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 2L, 0L, 2L, 1L,
0L, 0L, 0L, 1L, 2L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), sodium = c(130L, 15L, 260L,
140L, 200L, 180L, 125L, 210L, 200L, 210L, 220L, 290L, 210L, 140L,
180L, 280L, 290L, 90L, 180L, 140L, 80L, 220L, 140L, 190L, 125L,
200L, 0L, 160L, 240L, 135L, 45L, 280L, 140L, 170L, 75L, 220L,
250L, 180L, 170L, 170L, 260L, 150L, 180L, 0L, 95L, 150L, 150L,
220L, 190L, 220L, 170L, 170L, 200L, 320L, 0L, 0L, 135L, 0L, 210L,
140L, 0L, 240L, 290L, 0L, 0L, 0L, 70L, 230L, 15L, 200L, 190L,
200L, 250L, 140L, 230L, 200L, 200L), fiber = c(10, 2, 9, 14,
1, 1.5, 1, 2, 4, 5, 0, 2, 0, 2, 0, 0, 1, 1, 0, 4, 1, 1, 2, 1,
1, 1, 3, 5, 5, 0, 0, 0, 3, 3, 3, 1, 1.5, 0, 1, 2, 0, 2, 0, 0,
3, 3, 3, 2, 0, 3, 3, 1.5, 6, 1, 0, 1, 2, 2.7, 5, 2.5, 2, 0, 0,
3, 4, 3, 1, 1, 3, 0, 4, 3, 0, 0, 3, 3, 1), carbo = c(5, 8, 7,
8, 14, 10.5, 11, 18, 15, 13, 12, 17, 13, 13, 12, 22, 21, 13,
12, 10, 21, 21, 11, 18, 11, 14, 14, 12, 14, 13, 11, 15, 15, 17,
13, 12, 11.5, 14, 17, 20, 21, 12, 12, 16, 16, 16, 17, 15, 15,
21, 18, 13.5, 11, 20, 13, 10, 14, -1, 14, 10.5, 15, 23, 22, 16,
19, 20, 9, 16, 15, 21, 15, 16, 21, 13, 17, 17, 16), sugars = c(6L,
8L, 5L, 0L, 8L, 10L, 14L, 8L, 6L, 5L, 12L, 1L, 9L, 7L, 13L, 3L,
2L, 12L, 13L, 7L, 0L, 3L, 10L, 5L, 13L, 11L, 7L, 10L, 12L, 12L,
15L, 9L, 5L, 3L, 4L, 11L, 10L, 11L, 6L, 9L, 3L, 6L, 12L, 3L,
11L, 11L, 13L, 6L, 9L, 7L, 2L, 10L, 14L, 3L, 0L, 0L, 6L, -1L,
12L, 8L, 6L, 2L, 3L, 0L, 0L, 0L, 15L, 3L, 5L, 3L, 14L, 3L, 3L,
12L, 3L, 3L, 8L), potass = c(280L, 135L, 320L, 330L, -1L, 70L,
30L, 100L, 125L, 190L, 35L, 105L, 45L, 105L, 55L, 25L, 35L, 20L,
65L, 160L, -1L, 30L, 120L, 80L, 30L, 25L, 100L, 200L, 190L, 25L,
40L, 45L, 85L, 90L, 100L, 45L, 90L, 35L, 60L, 95L, 40L, 95L,
55L, 95L, 170L, 170L, 160L, 90L, 40L, 130L, 90L, 120L, 260L,
45L, 15L, 50L, 110L, 110L, 240L, 140L, 110L, 30L, 35L, 95L, 140L,
120L, 40L, 55L, 90L, 35L, 230L, 110L, 60L, 25L, 115L, 110L, 60L
), vitamins = c(25L, 0L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 0L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 100L, 100L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 100L, 0L, 0L, 25L, 0L, 25L, 25L, 25L,
25L, 25L, 0L, 0L, 0L, 25L, 25L, 25L, 100L, 100L, 100L, 25L, 25L,
25L, 25L, 25L), shelf = c(3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 1L,
3L, 2L, 1L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L,
1L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 3L, 3L,
2L, 1L, 1L, 1L), weight = c(1, 1, 1, 1, 1, 1, 1, 1.33, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.25, 1.33,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.3, 1, 1, 1, 1, 1, 1, 1.5, 1,
1, 1.33, 1, 1.25, 1.33, 1, 0.5, 0.5, 1, 1, 1.33, 1, 1, 1, 1,
0.83, 1, 1, 1, 1, 1, 1, 1.5, 1, 1, 1, 1, 1, 1), cups = c(0.33,
1, 0.33, 0.5, 0.75, 0.75, 1, 0.75, 0.67, 0.67, 0.75, 1.25, 0.75,
0.5, 1, 1, 1, 1, 1, 0.5, 1, 1, 0.75, 0.75, 1, 0.75, 0.8, 0.67,
0.67, 0.75, 0.88, 0.75, 0.88, 0.25, 0.33, 1, 0.75, 1.33, 1, 0.75,
1.5, 0.67, 1, 1, 1, 1, 0.67, 1, 0.67, 0.67, 1, 0.5, 0.67, 1,
1, 1, 0.5, 0.67, 0.75, 0.5, 0.5, 1.13, 1, 1, 0.67, 0.67, 0.75,
1, 1, 1, 1, 1, 0.75, 1, 0.67, 1, 0.75), rating = c(68.402973,
33.983679, 59.425505, 93.704912, 34.384843, 29.509541, 33.174094,
37.038562, 49.120253, 53.313813, 18.042851, 50.764999, 19.823573,
40.400208, 22.736446, 41.445019, 45.863324, 35.782791, 22.396513,
40.448772, 64.533816, 46.895644, 36.176196, 44.330856, 32.207582,
31.435973, 58.345141, 40.917047, 41.015492, 28.025765, 35.252444,
23.804043, 52.076897, 53.371007, 45.811716, 21.871292, 31.072217,
28.742414, 36.523683, 36.471512, 39.241114, 45.328074, 26.734515,
54.850917, 37.136863, 34.139765, 30.313351, 40.105965, 29.924285,
40.69232, 59.642837, 30.450843, 37.840594, 41.50354, 60.756112,
63.005645, 49.511874, 50.828392, 39.259197, 39.7034, 55.333142,
41.998933, 40.560159, 68.235885, 74.472949, 72.801787, 31.230054,
53.131324, 59.363993, 38.839746, 28.592785, 46.658844, 39.106174,
27.753301, 49.787445, 51.592193, 36.187559), sugars1 = c(6, 8,
5, 0, 8, 10, 14, 8, 6, 5, 12, 1, 9, 7, 13, 3, 2, 12, 13, 7, 0,
3, 10, 5, 13, 11, 7, 10, 12, 12, 15, 9, 5, 3, 4, 11, 10, 11,
6, 9, 3, 6, 12, 3, 11, 11, 13, 6, 9, 7, 2, 10, 14, 3, 0, 0, 6,
6.92207792207792, 12, 8, 6, 2, 3, 0, 0, 0, 15, 3, 5, 3, 14, 3,
3, 12, 3, 3, 8), carbo1 = c(5, 8, 7, 8, 14, 10.5, 11, 18, 15,
13, 12, 17, 13, 13, 12, 22, 21, 13, 12, 10, 21, 21, 11, 18, 11,
14, 14, 12, 14, 13, 11, 15, 15, 17, 13, 12, 11.5, 14, 17, 20,
21, 12, 12, 16, 16, 16, 17, 15, 15, 21, 18, 13.5, 11, 20, 13,
10, 14, 14.5974025974026, 14, 10.5, 15, 23, 22, 16, 19, 20, 9,
16, 15, 21, 15, 16, 21, 13, 17, 17, 16), sugars_per = c(6, 8,
5, 0, 8, 10, 14, 6.01503759398496, 6, 5, 12, 1, 9, 7, 13, 3,
2, 12, 13, 7, 0, 3, 10, 5, 13, 11, 7, 8, 9.02255639097744, 12,
15, 9, 5, 3, 4, 11, 10, 11, 6, 6.92307692307692, 3, 6, 12, 3,
11, 11, 8.66666666666667, 6, 9, 5.26315789473684, 2, 8, 10.5263157894737,
3, 0, 0, 6, 6.92207792207792, 9.02255639097744, 8, 6, 2, 3, 0,
0, 0, 15, 3, 5, 3, 9.33333333333333, 3, 3, 12, 3, 3, 8), protein_per = c(4,
3, 4, 4, 2, 2, 2, 2.25563909774436, 2, 3, 1, 6, 1, 3, 1, 2, 2,
1, 1, 3, 3, 2, 2, 2, 2, 1, 3, 2.4, 2.25563909774436, 1, 2, 1,
3, 3, 3, 1, 3, 1, 2, 2.30769230769231, 2, 4, 2, 4, 4, 4, 2, 2,
2, 2.25563909774436, 3, 2.4, 2.25563909774436, 3, 2, 4, 4, 5,
2.25563909774436, 3, 2, 1, 2, 2.40963855421687, 3, 3, 2, 6, 2,
2, 2, 3, 2, 1, 3, 3, 2), fiber_per = c(10, 2, 9, 14, 1, 1.5,
1, 1.50375939849624, 4, 5, 0, 2, 0, 2, 0, 0, 1, 1, 0, 4, 1, 1,
2, 1, 1, 1, 3, 4, 3.7593984962406, 0, 0, 0, 3, 3, 3, 1, 1.5,
0, 1, 1.53846153846154, 0, 2, 0, 0, 3, 3, 2, 2, 0, 2.25563909774436,
3, 1.2, 4.51127819548872, 1, 0, 2, 2, 2.7, 3.7593984962406, 2.5,
2, 0, 0, 3.6144578313253, 4, 3, 1, 1, 3, 0, 2.66666666666667,
3, 0, 0, 3, 3, 1), fat_per = c(1, 5, 1, 0, 2, 2, 0, 1.50375939849624,
1, 0, 2, 2, 3, 2, 1, 0, 0, 0, 1, 3, 0, 0, 1, 0, 1, 0, 0, 1.6,
0, 1, 0, 1, 1, 0, 3, 2, 1, 0, 1, 0.769230769230769, 1, 2, 1,
1, 3, 3, 1.33333333333333, 1, 1, 1.50375939849624, 0, 1.6, 0.75187969924812,
0, 0, 0, 1, 2, 0.75187969924812, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0.666666666666667, 1, 1, 1, 1, 1, 1), carbo_per = c(5, 8,
7, 8, 14, 10.5, 11, 13.5338345864662, 15, 13, 12, 17, 13, 13,
12, 22, 21, 13, 12, 10, 21, 21, 11, 18, 11, 14, 14, 9.6, 10.5263157894737,
13, 11, 15, 15, 17, 13, 12, 11.5, 14, 17, 15.3846153846154, 21,
12, 12, 16, 16, 16, 11.3333333333333, 15, 15, 15.7894736842105,
18, 10.8, 8.27067669172932, 20, 26, 20, 14, 14.5974025974026,
10.5263157894737, 10.5, 15, 23, 22, 19.2771084337349, 19, 20,
9, 16, 15, 21, 10, 16, 21, 13, 17, 17, 16)), row.names = c(NA,
-77L), class = "data.frame")
使用ggplot2
library(dplyr)
library(ggplot2)
dummy <- Cereals %>%
select(mfr, shelf) %>%
group_by(mfr) %>%
summarise(colo = mean(shelf))
Cereals %>%
select(mfr, shelf,rating) %>%
full_join(dummy, by = "mfr") %>%
ggplot()+
geom_boxplot(aes(x = mfr, y = rating, group = mfr, fill = colo))
基础R方式
dummy <- Cereals %>%
select(mfr, shelf) %>%
group_by(mfr) %>%
summarise(colo = mean(shelf))
boxplot(rating ~ mfr, data = Cereals, col = brewer.pal(dummy$colo, "Blues"))
我正在尝试绘制一个箱线图，其中我的 MFR（制造商）显示在 x 轴上，评级显示在 y 轴上。但是，我想根据平均货架价值为不同的箱线图着色。 (shelf是1到3之间的值)
我试过这段代码：
boxplot(rating ~ mfr, col= brewer.pal(nrow(aggregate(shelf ~ mfr, FUN=mean, data=Cereals)), "Blues"),
names =c("American Home Food Products", "General Mills", "Kellogs", "Nabisco", "Post", "Quaker Oats", "Ralston Purina"),
data = Cereals)
我的箱线图是彩色的，但它从左到右用不同种类的蓝色着色，而不是基于每个制造商的最高平均值。我该如何解决这个问题？我需要更改什么？
我的数据是：dput(Cereals.csv)
structure(list(name = c("100% Bran", "100% Natural Bran", "All-Bran",
"All-Bran with Extra Fiber", "Almond Delight", "Apple Cinnamon Cheerios",
"Apple Jacks", "Basic 4", "Bran Chex", "Bran Flakes", "Cap'n'Crunch",
"Cheerios", "Cinnamon Toast Crunch", "Clusters", "Cocoa Puffs",
"Corn Chex", "Corn Flakes", "Corn Pops", "Count Chocula", "Cracklin' Oat Bran",
"Cream of Wheat (Quick)", "Crispix", "Crispy Wheat & Raisins",
"Double Chex", "Froot Loops", "Frosted Flakes", "Frosted Mini-Wheats",
"Fruit & Fibre Dates; Walnuts; and Oats", "Fruitful Bran", "Fruity Pebbles",
"Golden Crisp", "Golden Grahams", "Grape Nuts Flakes", "Grape-Nuts",
"Great Grains Pecan", "Honey Graham Ohs", "Honey Nut Cheerios",
"Honey-comb", "Just Right Crunchy Nuggets", "Just Right Fruit & Nut",
"Kix", "Life", "Lucky Charms", "Maypo", "Muesli Raisins; Dates; & Almonds",
"Muesli Raisins; Peaches; & Pecans", "Mueslix Crispy Blend",
"Multi-Grain Cheerios", "Nut&Honey Crunch", "Nutri-Grain Almond-Raisin",
"Nutri-grain Wheat", "Oatmeal Raisin Crisp", "Post Nat. Raisin Bran",
"Product 19", "Puffed Rice", "Puffed Wheat", "Quaker Oat Squares",
"Quaker Oatmeal", "Raisin Bran", "Raisin Nut Bran", "Raisin Squares",
"Rice Chex", "Rice Krispies", "Shredded Wheat", "Shredded Wheat 'n'Bran",
"Shredded Wheat spoon size", "Smacks", "Special K", "Strawberry Fruit Wheats",
"Total Corn Flakes", "Total Raisin Bran", "Total Whole Grain",
"Triples", "Trix", "Wheat Chex", "Wheaties", "Wheaties Honey Gold"
), mfr = c("N", "Q", "K", "K", "R", "G", "K", "G", "R", "P",
"Q", "G", "G", "G", "G", "R", "K", "K", "G", "K", "N", "K", "G",
"R", "K", "K", "K", "P", "K", "P", "P", "G", "P", "P", "P", "Q",
"G", "P", "K", "K", "G", "Q", "G", "A", "R", "R", "K", "G", "K",
"K", "K", "G", "P", "K", "Q", "Q", "Q", "Q", "K", "G", "K", "R",
"K", "N", "N", "N", "K", "K", "N", "G", "G", "G", "G", "G", "R",
"G", "G"), type = c("C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "H", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "H", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "H", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C"), calories = c(70L, 120L, 70L, 50L, 110L, 110L,
110L, 130L, 90L, 90L, 120L, 110L, 120L, 110L, 110L, 110L, 100L,
110L, 110L, 110L, 100L, 110L, 100L, 100L, 110L, 110L, 100L, 120L,
120L, 110L, 100L, 110L, 100L, 110L, 120L, 120L, 110L, 110L, 110L,
140L, 110L, 100L, 110L, 100L, 150L, 150L, 160L, 100L, 120L, 140L,
90L, 130L, 120L, 100L, 50L, 50L, 100L, 100L, 120L, 100L, 90L,
110L, 110L, 80L, 90L, 90L, 110L, 110L, 90L, 110L, 140L, 100L,
110L, 110L, 100L, 100L, 110L), protein = c(4L, 3L, 4L, 4L, 2L,
2L, 2L, 3L, 2L, 3L, 1L, 6L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 3L,
2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 3L, 1L, 3L,
1L, 2L, 3L, 2L, 4L, 2L, 4L, 4L, 4L, 3L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 4L, 5L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 6L, 2L,
2L, 3L, 3L, 2L, 1L, 3L, 3L, 2L), fat = c(1L, 5L, 1L, 0L, 2L,
2L, 0L, 2L, 1L, 0L, 2L, 2L, 3L, 2L, 1L, 0L, 0L, 0L, 1L, 3L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 2L, 0L, 1L, 0L, 1L, 1L, 0L, 3L, 2L, 1L,
0L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 2L, 0L, 2L, 1L,
0L, 0L, 0L, 1L, 2L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), sodium = c(130L, 15L, 260L,
140L, 200L, 180L, 125L, 210L, 200L, 210L, 220L, 290L, 210L, 140L,
180L, 280L, 290L, 90L, 180L, 140L, 80L, 220L, 140L, 190L, 125L,
200L, 0L, 160L, 240L, 135L, 45L, 280L, 140L, 170L, 75L, 220L,
250L, 180L, 170L, 170L, 260L, 150L, 180L, 0L, 95L, 150L, 150L,
220L, 190L, 220L, 170L, 170L, 200L, 320L, 0L, 0L, 135L, 0L, 210L,
140L, 0L, 240L, 290L, 0L, 0L, 0L, 70L, 230L, 15L, 200L, 190L,
200L, 250L, 140L, 230L, 200L, 200L), fiber = c(10, 2, 9, 14,
1, 1.5, 1, 2, 4, 5, 0, 2, 0, 2, 0, 0, 1, 1, 0, 4, 1, 1, 2, 1,
1, 1, 3, 5, 5, 0, 0, 0, 3, 3, 3, 1, 1.5, 0, 1, 2, 0, 2, 0, 0,
3, 3, 3, 2, 0, 3, 3, 1.5, 6, 1, 0, 1, 2, 2.7, 5, 2.5, 2, 0, 0,
3, 4, 3, 1, 1, 3, 0, 4, 3, 0, 0, 3, 3, 1), carbo = c(5, 8, 7,
8, 14, 10.5, 11, 18, 15, 13, 12, 17, 13, 13, 12, 22, 21, 13,
12, 10, 21, 21, 11, 18, 11, 14, 14, 12, 14, 13, 11, 15, 15, 17,
13, 12, 11.5, 14, 17, 20, 21, 12, 12, 16, 16, 16, 17, 15, 15,
21, 18, 13.5, 11, 20, 13, 10, 14, -1, 14, 10.5, 15, 23, 22, 16,
19, 20, 9, 16, 15, 21, 15, 16, 21, 13, 17, 17, 16), sugars = c(6L,
8L, 5L, 0L, 8L, 10L, 14L, 8L, 6L, 5L, 12L, 1L, 9L, 7L, 13L, 3L,
2L, 12L, 13L, 7L, 0L, 3L, 10L, 5L, 13L, 11L, 7L, 10L, 12L, 12L,
15L, 9L, 5L, 3L, 4L, 11L, 10L, 11L, 6L, 9L, 3L, 6L, 12L, 3L,
11L, 11L, 13L, 6L, 9L, 7L, 2L, 10L, 14L, 3L, 0L, 0L, 6L, -1L,
12L, 8L, 6L, 2L, 3L, 0L, 0L, 0L, 15L, 3L, 5L, 3L, 14L, 3L, 3L,
12L, 3L, 3L, 8L), potass = c(280L, 135L, 320L, 330L, -1L, 70L,
30L, 100L, 125L, 190L, 35L, 105L, 45L, 105L, 55L, 25L, 35L, 20L,
65L, 160L, -1L, 30L, 120L, 80L, 30L, 25L, 100L, 200L, 190L, 25L,
40L, 45L, 85L, 90L, 100L, 45L, 90L, 35L, 60L, 95L, 40L, 95L,
55L, 95L, 170L, 170L, 160L, 90L, 40L, 130L, 90L, 120L, 260L,
45L, 15L, 50L, 110L, 110L, 240L, 140L, 110L, 30L, 35L, 95L, 140L,
120L, 40L, 55L, 90L, 35L, 230L, 110L, 60L, 25L, 115L, 110L, 60L
), vitamins = c(25L, 0L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 0L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 100L, 100L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 100L, 0L, 0L, 25L, 0L, 25L, 25L, 25L,
25L, 25L, 0L, 0L, 0L, 25L, 25L, 25L, 100L, 100L, 100L, 25L, 25L,
25L, 25L, 25L), shelf = c(3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 1L,
3L, 2L, 1L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L,
1L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 3L, 3L,
2L, 1L, 1L, 1L), weight = c(1, 1, 1, 1, 1, 1, 1, 1.33, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.25, 1.33,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.3, 1, 1, 1, 1, 1, 1, 1.5, 1,
1, 1.33, 1, 1.25, 1.33, 1, 0.5, 0.5, 1, 1, 1.33, 1, 1, 1, 1,
0.83, 1, 1, 1, 1, 1, 1, 1.5, 1, 1, 1, 1, 1, 1), cups = c(0.33,
1, 0.33, 0.5, 0.75, 0.75, 1, 0.75, 0.67, 0.67, 0.75, 1.25, 0.75,
0.5, 1, 1, 1, 1, 1, 0.5, 1, 1, 0.75, 0.75, 1, 0.75, 0.8, 0.67,
0.67, 0.75, 0.88, 0.75, 0.88, 0.25, 0.33, 1, 0.75, 1.33, 1, 0.75,
1.5, 0.67, 1, 1, 1, 1, 0.67, 1, 0.67, 0.67, 1, 0.5, 0.67, 1,
1, 1, 0.5, 0.67, 0.75, 0.5, 0.5, 1.13, 1, 1, 0.67, 0.67, 0.75,
1, 1, 1, 1, 1, 0.75, 1, 0.67, 1, 0.75), rating = c(68.402973,
33.983679, 59.425505, 93.704912, 34.384843, 29.509541, 33.174094,
37.038562, 49.120253, 53.313813, 18.042851, 50.764999, 19.823573,
40.400208, 22.736446, 41.445019, 45.863324, 35.782791, 22.396513,
40.448772, 64.533816, 46.895644, 36.176196, 44.330856, 32.207582,
31.435973, 58.345141, 40.917047, 41.015492, 28.025765, 35.252444,
23.804043, 52.076897, 53.371007, 45.811716, 21.871292, 31.072217,
28.742414, 36.523683, 36.471512, 39.241114, 45.328074, 26.734515,
54.850917, 37.136863, 34.139765, 30.313351, 40.105965, 29.924285,
40.69232, 59.642837, 30.450843, 37.840594, 41.50354, 60.756112,
63.005645, 49.511874, 50.828392, 39.259197, 39.7034, 55.333142,
41.998933, 40.560159, 68.235885, 74.472949, 72.801787, 31.230054,
53.131324, 59.363993, 38.839746, 28.592785, 46.658844, 39.106174,
27.753301, 49.787445, 51.592193, 36.187559), sugars1 = c(6, 8,
5, 0, 8, 10, 14, 8, 6, 5, 12, 1, 9, 7, 13, 3, 2, 12, 13, 7, 0,
3, 10, 5, 13, 11, 7, 10, 12, 12, 15, 9, 5, 3, 4, 11, 10, 11,
6, 9, 3, 6, 12, 3, 11, 11, 13, 6, 9, 7, 2, 10, 14, 3, 0, 0, 6,
6.92207792207792, 12, 8, 6, 2, 3, 0, 0, 0, 15, 3, 5, 3, 14, 3,
3, 12, 3, 3, 8), carbo1 = c(5, 8, 7, 8, 14, 10.5, 11, 18, 15,
13, 12, 17, 13, 13, 12, 22, 21, 13, 12, 10, 21, 21, 11, 18, 11,
14, 14, 12, 14, 13, 11, 15, 15, 17, 13, 12, 11.5, 14, 17, 20,
21, 12, 12, 16, 16, 16, 17, 15, 15, 21, 18, 13.5, 11, 20, 13,
10, 14, 14.5974025974026, 14, 10.5, 15, 23, 22, 16, 19, 20, 9,
16, 15, 21, 15, 16, 21, 13, 17, 17, 16), sugars_per = c(6, 8,
5, 0, 8, 10, 14, 6.01503759398496, 6, 5, 12, 1, 9, 7, 13, 3,
2, 12, 13, 7, 0, 3, 10, 5, 13, 11, 7, 8, 9.02255639097744, 12,
15, 9, 5, 3, 4, 11, 10, 11, 6, 6.92307692307692, 3, 6, 12, 3,
11, 11, 8.66666666666667, 6, 9, 5.26315789473684, 2, 8, 10.5263157894737,
3, 0, 0, 6, 6.92207792207792, 9.02255639097744, 8, 6, 2, 3, 0,
0, 0, 15, 3, 5, 3, 9.33333333333333, 3, 3, 12, 3, 3, 8), protein_per = c(4,
3, 4, 4, 2, 2, 2, 2.25563909774436, 2, 3, 1, 6, 1, 3, 1, 2, 2,
1, 1, 3, 3, 2, 2, 2, 2, 1, 3, 2.4, 2.25563909774436, 1, 2, 1,
3, 3, 3, 1, 3, 1, 2, 2.30769230769231, 2, 4, 2, 4, 4, 4, 2, 2,
2, 2.25563909774436, 3, 2.4, 2.25563909774436, 3, 2, 4, 4, 5,
2.25563909774436, 3, 2, 1, 2, 2.40963855421687, 3, 3, 2, 6, 2,
2, 2, 3, 2, 1, 3, 3, 2), fiber_per = c(10, 2, 9, 14, 1, 1.5,
1, 1.50375939849624, 4, 5, 0, 2, 0, 2, 0, 0, 1, 1, 0, 4, 1, 1,
2, 1, 1, 1, 3, 4, 3.7593984962406, 0, 0, 0, 3, 3, 3, 1, 1.5,
0, 1, 1.53846153846154, 0, 2, 0, 0, 3, 3, 2, 2, 0, 2.25563909774436,
3, 1.2, 4.51127819548872, 1, 0, 2, 2, 2.7, 3.7593984962406, 2.5,
2, 0, 0, 3.6144578313253, 4, 3, 1, 1, 3, 0, 2.66666666666667,
3, 0, 0, 3, 3, 1), fat_per = c(1, 5, 1, 0, 2, 2, 0, 1.50375939849624,
1, 0, 2, 2, 3, 2, 1, 0, 0, 0, 1, 3, 0, 0, 1, 0, 1, 0, 0, 1.6,
0, 1, 0, 1, 1, 0, 3, 2, 1, 0, 1, 0.769230769230769, 1, 2, 1,
1, 3, 3, 1.33333333333333, 1, 1, 1.50375939849624, 0, 1.6, 0.75187969924812,
0, 0, 0, 1, 2, 0.75187969924812, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0.666666666666667, 1, 1, 1, 1, 1, 1), carbo_per = c(5, 8,
7, 8, 14, 10.5, 11, 13.5338345864662, 15, 13, 12, 17, 13, 13,
12, 22, 21, 13, 12, 10, 21, 21, 11, 18, 11, 14, 14, 9.6, 10.5263157894737,
13, 11, 15, 15, 17, 13, 12, 11.5, 14, 17, 15.3846153846154, 21,
12, 12, 16, 16, 16, 11.3333333333333, 15, 15, 15.7894736842105,
18, 10.8, 8.27067669172932, 20, 26, 20, 14, 14.5974025974026,
10.5263157894737, 10.5, 15, 23, 22, 19.2771084337349, 19, 20,
9, 16, 15, 21, 10, 16, 21, 13, 17, 17, 16)), row.names = c(NA,
-77L), class = "data.frame")
使用ggplot2
library(dplyr)
library(ggplot2)
dummy <- Cereals %>%
select(mfr, shelf) %>%
group_by(mfr) %>%
summarise(colo = mean(shelf))
Cereals %>%
select(mfr, shelf,rating) %>%
full_join(dummy, by = "mfr") %>%
ggplot()+
geom_boxplot(aes(x = mfr, y = rating, group = mfr, fill = colo))
基础R方式
dummy <- Cereals %>%
select(mfr, shelf) %>%
group_by(mfr) %>%
summarise(colo = mean(shelf))
boxplot(rating ~ mfr, data = Cereals, col = brewer.pal(dummy$colo, "Blues"))