使用 ggplot2 facet_grid 优化分类变量的绘图 - 二分变量的两个值中只有一个的绘图比例

Question

我有一个大型数据集，其中包含 150 多个分类变量和连续变量。每个观察值（行）属于 A 组或 B 组。例如：

set.seed(16)
mydf <- data.frame(ID = 1:500, group = sample(c("A", "B", "B", "B"), 500, replace = TRUE), 
length = rnorm(n = 500, mean = 0, sd = 1), 
weight = runif(500, min=0, max=1), 
color = sample(c("red", "orange", "yellow", "green", "blue"), 500,  replace = TRUE), 
size = sample(c("big", "small"), 500, replace = TRUE), 
age = sample(c("old", "young"), 500, replace = T))

我正在努力优化绘图布局，以可视化分类变量的组计数和比例计数之间的关系。到目前为止，在之前 post () 的一些帮助下，我使用 ggplot2 facet-grid 进行了绘图，但遇到了两个问题。

问题 A：条形图按值（例如大、老、小、年轻）的字母顺序排列，而不是按类别分组（年龄：年轻紧挨着老；大小：大紧挨着小， ETC）。问题 B：对于只有两个可能值的分类变量，我只想绘制 A 组与 B 组中其中一个值的比例。例如，仅绘制 "old" 的 A 组与 B 组的比例，因为 "young" 的比例图不会提供任何新信息。其他分类变量，如具有多个值的颜色，应该为每种可能性绘制一个条形图。

我已经通过使用 " mutate(value = factor(value, levels=c("big", "small", "young", "old", "red", "orange", "yellow", "green", "blue")))" 现在绘图顺序按指定显示，年龄组彼此相邻，颜色彼此相邻，等等

data_cat <- 
  mydf %>% select(-ID) %>%
  mutate_if(.predicate = is.factor, .funs = as.character) %>%
  mutate(group = factor(group)) %>%
  pivot_longer(cols = which(sapply(., is.character)), names_to = 'key', values_to =  'value')%>%
  count(group, key, value) %>%
  group_by(group, key) %>%
  mutate(percent =  n/ sum(n)) %>%
  mutate(value = factor(value, levels=c("big", "small", "young", "old", "red", "orange", "yellow", "green", "blue"))) %>%

ggplot(data_cat) +
  geom_col(aes(group, percent, fill = key)) +
  facet_grid(~ value)

我仍然有问题 B，抑制二分分类变量的两个结果之一的绘图。我想我必须找到一种方法从每个变量中提取 "factor levels"，然后使用该值为 == 2 的子集，已搜索但尚未找到执行此操作的方法。

Answer 1

这是您正在寻找的问题 B 的解决方案吗？我在您的数据管理步骤结束时添加了一个过滤器，删除了等于 "young" 和 "small" 的值，这两个二分法。该图现在显示 "big"、"old" 和 5 个颜色类别的条形图。

set.seed(16)
mydf <- data.frame(ID = 1:500, group = sample(c("A", "B", "B", "B"), 500, replace = TRUE), 
                   length = rnorm(n = 500, mean = 0, sd = 1), 
                   weight = runif(500, min=0, max=1), 
                   color = sample(c("red", "orange", "yellow", "green", "blue"), 500,  replace = TRUE), 
                   size = sample(c("big", "small"), 500, replace = TRUE), 
                   age = sample(c("old", "young"), 500, replace = T))

key <- lapply(mydf, function(x){ifelse(length(levels(x))==2, 1, 0)})
dichotomous <- names(which(key == 1))[-1]

mydf %>% select(-ID) %>%
  mutate_if(.predicate = is.factor, .funs = as.character) %>%
  mutate_at(.vars = vars(dichotomous), .funs = function(x){ifelse(x == unique(x)[2], NA, x)}) %>%
  mutate(group = factor(group)) %>%
  pivot_longer(cols = which(sapply(., is.character)), names_to = 'key', values_to =  'value')%>%
  count(group, key, value) %>%
  group_by(group, key) %>%
  mutate(percent =  n/ sum(n)) %>%
  mutate(value = factor(value, levels=c("big", "small", "young", "old", "red", "orange", "yellow", "green", "blue"))) %>%
  na.omit() -> data_cat

ggplot(data_cat) +
  geom_col(aes(group, percent, fill = key)) +
  facet_grid(~ value)

使用 ggplot2 facet_grid 优化分类变量的绘图 - 二分变量的两个值中只有一个的绘图比例

Optimize plotting of categorical variables using ggplot2 facet_grid - plot proportion of only one of two values for dichotomous variables

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