目前所學(xué)知識(shí)里，主要需要用到factor因子的地方就是繪圖時(shí)（例如箱線圖）用于調(diào)整順序。forcats提供了一些便捷函數(shù)用于調(diào)整factor的levels順序

1、基礎(chǔ)函數(shù)

• factor() 將非因子類向量轉(zhuǎn)為因子類向量；
  默認(rèn)順序?yàn)樽址鬃帜赶群箜樞颍蛘邤?shù)值從小到大順序。
  可通過levels=參數(shù)自定義 因子順序
• levels() 用于查看 levels組成、及順序

test = factor(c("c","b","c"))
test
# [1] c b c
# Levels: b c
levels(test)
# [1] "b" "c"

factor(c("c","b","c"), levels = c("c","b"))
# [1] c b c
# Levels: c b

2、forcats包調(diào)整因子順序

首先forcats包本身提供了關(guān)于factor多種多樣的處理技巧，這里僅提及學(xué)習(xí)其中關(guān)于因子順序的相關(guān)函數(shù)。

2.1 根據(jù)向量本身特點(diǎn)修改levels

• fct_relevel() 自定義更新levels順序

f <- factor(c("a", "c", "b", "a"),
        levels = c("a", "b", "c"))
fct_relevel(f, c("b", "c", "a"))
# [1] a c b a
# Levels: b c a

• fct_infreq 根據(jù)頻率修改levels順序

f <- factor(c("b", "c", "c", "b","a","b"))
fct_infreq(f)
# [1] b c c b a b
# Levels: b c a

• fct_inorder()根據(jù)出現(xiàn)順序修改levels順序

f <- factor(c("b", "c", "c", "a","b","a"))
fct_inorder(f)
# [1] b c c a b a
# Levels: b c a

• fct_rev() 逆序

f <- factor(c("b", "c", "c", "a","b","a"))
fct_rev(f)
# [1] b c c a b a
# Levels: c b a

2.2 根據(jù)向量相關(guān)屬性(data.frame)指標(biāo)修改levels

以iris示例數(shù)據(jù)為例

data("iris")

summary(iris)
# Sepal.Length    Sepal.Width     Petal.Length    Petal.Width          Species  
# Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100   setosa    :50  
# 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300   versicolor:50  
# Median :5.800   Median :3.000   Median :4.350   Median :1.300   virginica :50  
# Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199                  
# 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800                  
# Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500        

str(iris)
# 'data.frame': 150 obs. of  5 variables:
#   $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
# $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
# $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
# $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
# $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

levels(iris$Species)
# [1] "setosa"     "versicolor" "virginica"         

fct_reorder()

• 第一個(gè)參數(shù)為待修改的factor，第二個(gè)參數(shù)為相關(guān)屬性信息，第三個(gè)參數(shù)為計(jì)算方法
• 例如我想按照每種花的花萼的平均寬度由低到高 設(shè)置Species的順序

fct_reorder(iris$Species, iris$Sepal.Width) %>% levels()
# [1] "versicolor" "virginica"  "setosa"

aggregate(iris$Sepal.Width, by=list(iris$Species), mean)
#     Group.1     x
# 1     setosa 3.428
# 2 versicolor 2.770
# 3  virginica 2.974

• fct_reorder()默認(rèn)計(jì)算方法為mean均值，默認(rèn)排序?yàn)樯?code>.desc=F。在實(shí)際使用使可按需修改

#aggregate(iris$Sepal.Width, by=list(iris$Species), median)
fct_reorder(iris$Species, iris$Sepal.Width, median, .desc=T) %>% levels()

• 下面以ggplot繪圖演示fct_reorder的用法

p1=ggplot(iris, aes(x=Species, y=Sepal.Width)) +
    geom_boxplot() +
    ggtitle("default factor levels")
p2=ggplot(iris, aes(x=fct_reorder(Species, Sepal.Width), y=Sepal.Width)) +
    geom_boxplot() +
    ggtitle("fct_reorder default levels")
p3=ggplot(iris, aes(x=fct_reorder(Species, Sepal.Width, .desc=T), y=Sepal.Width)) +
    geom_boxplot() +
    ggtitle("fct_reorder descent levels")
library(patchwork)
p1 | p2 | p3