r - How to subset with ggplot based on facet aggregates?

Question

I have a dataset with 16 groups available for facets—however, that is too many, and I'd like to keep only the most important groups (determined by what percentage of a certain total falls in that group). For example, I'd like to keep only groups that represent 30% or more of the total of Var1.

To illustrate, if I run the following code, R correctly outputs the two species whose Petal.length sum represents more than 30% of the total Petal.length in the dataset (ignore that it's a meaningless statistic in this case).

library(tidyverse)

iris %>% 
  group_by(Species) %>% 
  summarise(t_length = sum(Petal.Length),
            p_length = round(100*t_length/sum(.$Petal.Length))) %>% 
  filter(p_length >=30)

So, what I'd like to do is have ggplot facet by all groups that meet the specified condition. In my dataset, only 5 out of the 16 groups capture over 90% of the interesting observations, so, I don't need the other 11 groups in the facet grid.

This is my attempt, and the output is all 3 species, where it should only be the same 2 from the table above:

iris.sub <- ggplot(subset(iris, round(100*sum(Petal.Length)/sum(iris$Petal.Length)) >= 30), aes(x = ' ', y = Petal.Length)) +
  geom_point(stat = 'summary', fun.y = 'mean') +
  geom_errorbar(stat = 'summary', fun.data = 'mean_se', 
                width=0, fun.args = list(mult = 1.96)) +
  facet_grid( . ~ Species ) +
  theme_bw()
iris.sub

标签： rggplot2subsetfacet

Answer 1

filter不会受到影响group_by。例如，如果您有一个按列分组的数据框，var1并且您想过滤列x> 50 的行，则观察值在某个组中的事实不会影响数字是否存在这一事实大于 50。

dplyr以下是使用某些功能的两种方法。第一个计算每个组对总花瓣长度的贡献，提取这些物种，并将其保留为向量。然后，您过滤数据框以仅对其中一种物种进行观察，然后进行绘图。

第二个在一个块中完成这些计算和绘图。这样做的好处是您不必为您所保留的物种保存变量；缺点是在mutate调用中做总结数学而不是summarise混乱，如果你不小心你需要加起来的确切内容（根据经验说），可能会导致错误。

library(tidyverse)

major_categories <- iris %>%
  group_by(Species) %>%
  summarise(group_Petal.Length = sum(Petal.Length)) %>%
  mutate(share_Petal.Length = group_Petal.Length / sum(group_Petal.Length)) %>%
  filter(share_Petal.Length >= 0.3) %>%
  pull(Species)

iris %>%
  filter(Species %in% major_categories) %>%
  ggplot(aes(x = 1, y = Petal.Length)) +
    geom_point(stat = "summary", fun.y = "mean") +
    geom_errorbar(stat = "summary", fun.data = "mean_se", width = 0, fun.args = list(mult = 1.96)) +
    facet_grid(. ~ Species) +
    theme_bw()

iris %>%
  group_by(Species) %>%
  mutate(group_Petal.Length = sum(Petal.Length)) %>%
  ungroup() %>%
  mutate(share_Petal.Length = group_Petal.Length / sum(unique(group_Petal.Length))) %>%
  filter(share_Petal.Length >= 0.3) %>%
  ggplot(aes(x = 1, y = Petal.Length)) +
    geom_point(stat = "summary", fun.y = "mean") +
    geom_errorbar(stat = "summary", fun.data = "mean_se", width = 0, fun.args = list(mult = 1.96)) +
    facet_grid(. ~ Species) +
    theme_bw()

还要注意的是，如果您在 x 轴上没有任何值（这里只是一个虚拟值），您不妨跳过刻面并放在Speciesx 轴上。不确定这是否仍适用于您更大的数据集。