5

我的数据：

structure(list(LoB = c("C", "C", "C", "A", 
"A", "B", "C", "A", "A", "C", 
"A", "B", "C", "B", "A", "C", "B", 
"A", "B", "C", "A", "B", "B", "A", 
"B", "C", "A", "B", "C", "B"), word = c("speed", 
"connection", "call", "bt", "reliable", "reliable", "reliable", 
"expensive", "cheaper", "uk", "customer", "customer", "customer", 
"network", "broadband", "broadband", "signal", "price", "price", 
"price", "poor", "poor", "ee", "service", "service", "service", 
"excellent", "excellent", "excellent", "coverage"), word_total = c(68L, 
46L, 44L, 3138L, 3479L, 906L, 71L, 6096L, 2967L, 39L, 10405L, 
1429L, 113L, 676L, 5193L, 73L, 868L, 8763L, 814L, 139L, 4708L, 
659L, 530L, 19185L, 2253L, 136L, 7180L, 1227L, 69L, 1453L), word_prop_by_total_feedbacks = c(0.0656370656370656, 
0.0444015444015444, 0.0424710424710425, 0.0343378635677237, 0.0380692885124636, 
0.101603678367164, 0.0685328185328185, 0.0667060600091918, 0.0324666797977808, 
0.0376447876447876, 0.113857702492723, 0.160255691376023, 0.109073359073359, 
0.075810250084109, 0.0568248965924759, 0.0704633204633205, 0.0973421554334417, 
0.0958899612632132, 0.0912863070539419, 0.134169884169884, 0.0515177379467314, 
0.0739037792979702, 0.0594370303913872, 0.209933687873416, 0.252663451833576, 
0.131274131274131, 0.0785678331473092, 0.137602332623079, 0.0666023166023166, 
0.16294717954469)), class = c("grouped_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -30L), vars = "LoB", drop = TRUE, indices = list(
    c(3L, 4L, 7L, 8L, 10L, 14L, 17L, 20L, 23L, 26L), c(5L, 11L, 
    13L, 16L, 18L, 21L, 22L, 24L, 27L, 29L), c(0L, 1L, 2L, 6L, 
    9L, 12L, 15L, 19L, 25L, 28L)), group_sizes = c(10L, 10L, 
10L), biggest_group_size = 10L, labels = structure(list(LoB = c("A", 
"B", "C")), class = "data.frame", row.names = c(NA, -3L
), vars = "LoB", drop = TRUE, .Names = "LoB"), .Names = c("LoB", 
"word", "word_total", "word_prop_by_total_feedbacks"))

我正在尝试使用ggplot2但不工作fct_reorder并drlib::reorder_within(word, word_total, LoB)给我一条警告信息：Unequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vector

这是代码：

   mutate(word = drlib::reorder_within(word, word_total, LoB)) %>%

或者

  mutate(word = forcats::fct_reorder(word, word_total)) %>%
  ggplot(aes(word, word_prop_by_total_feedbacks, fill = LoB)) +
  geom_col() +
#  drlib::scale_x_reordered()+
  facet_wrap(~ LoB, scales = "free") +
  coord_flip()

它不是按降序绘制的。我错过了什么？

更新：ungroup()之前失踪了mutate()。谢谢大家

4

3 回答 3

4

与@austensen 类似，但使用了不同的方法factor。

您可以相应地创建group_by和索引。这样您就不必担心使用，但您必须在.arrangeungroupfactorbreakslabelsscale_x_continuous

library(ggplot2)
library(dplyr)

plot_data <- df %>% 
  group_by(LoB) %>% 
  arrange(word_total) %>% 
  ungroup() %>% 
  mutate(order = row_number())

ggplot(plot_data, aes(order, word_prop_by_total_feedbacks, fill = LoB)) +
  geom_col() +
  facet_wrap(~ LoB, scales = "free") +
  scale_x_continuous(breaks = plot_data$order, labels = plot_data$word) +
  coord_flip()

在此处输入图像描述

于 2017-10-27T15:19:31.733 回答
1

这是一个有点尴尬的解决方法，因为您可以根据LoB. Tidy Text Mining一书中有一个很好的例子（参见8.4.3本章的部分）。我按照它创建了下面的内容。

本质上，在图表上显示单词标签时，您必须将其word作为连接进行排序word，LoB然后将其剥离。LoB


library(tidyverse)
library(forcats)

df %>% 
  group_by(LoB) %>% 
  arrange(desc(word_total)) %>% 
  ungroup() %>% 
  mutate(word = factor(paste(word, LoB, sep = "__"), 
                       levels = rev(paste(word, LoB, sep = "__")))) %>%
  ggplot(aes(word, word_prop_by_total_feedbacks, fill = LoB)) +
  geom_col() +
  scale_x_discrete(labels = function(x) gsub("__.+$", "", x)) +
  facet_wrap(~ LoB, scales = "free") +
  coord_flip()

于 2017-10-27T15:01:37.323 回答
0

你可能会考虑tidytext::reorder_within

df %>% 
  group_by(LoB) %>% 
  arrange(desc(word_total)) %>% 
  ungroup() %>% 
  ggplot(aes(word_prop_by_total_feedbacks, tidytext::reorder_within(word, word_prop_by_total_feedbacks, LoB), fill = LoB)) +
  geom_col() +
  facet_wrap(~ LoB, scales = "free") +
  scale_y_reordered()
于 2021-05-20T17:07:14.700 回答