GGPlot在方面

Question

我有一个数据框（长格式），其中包含来自个性自我报告的数据。我使用ggplot2绘制了我之前运行的探索性因子分析。下面，我创建了原始数据框的子集。

Here，Dan Mirman博士提出了一种绘制这类数据的好方法，我想这样做。所以，我已经调整了他的代码，但正如你从图像中看到的那样，情节有一个问题。 y轴上的刻度按字母顺序反向排序。但是，我想根据每个因素的负荷来订购这些尺度。例如，我认为最好的订单可能是第一个＆＃34; A＆＃34;和＆＃34; IdP＆＃34;因为它们强烈地加载在Factor1上，然后＆＃34; R＆＃34;和＆＃34; Ca＆＃34;因为他们加载了因子2，＆＃34; InP＆＃34;因为它加载了因子3，并且＆＃34; Co＆＃34;因为它加载了因子4.在底部，＆＃34; S＆＃34;因为它在多个尺度上显示出相似的载荷。

任何人都可以帮助我吗？我真的很感激！

library(ggplot2)
Scale <- c("A", "IdP", "R", "Ca", "S", "InP", "Co", "A", "IdP", "R", "Ca", "S", "InP", "Co", "A", "IdP", "R", "Ca", "S", "InP", "Co", "A", "IdP", "R", "Ca", "S", "InP", "Co")
Factor <- c("Factor1", "Factor1", "Factor1", "Factor1", "Factor1", "Factor1", "Factor1", "Factor2", "Factor2", "Factor2", "Factor2", "Factor2", "Factor2", "Factor2", "Factor3", "Factor3", "Factor3", "Factor3", "Factor3", "Factor3", "Factor3", "Factor4", "Factor4", "Factor4", "Factor4", "Factor4", "Factor4", "Factor4")
loading.db <- c(0.93, 0.71, -0.15,  0.00, 0.34, 0.08, 0.04, 0.02, 0.13, 0.79, 0.74, 0.43, 0.03, 0.06, -0.02, 0.30, -0.06, 0.25, 0.08, 0.66, -0.03, 0.09, -0.03, 0.18, -0.01, 0.37, -0.06, 0.62)

db <- data.frame(Scale, Factor, loading.db)

ggplot(db, aes(Scale, abs(loading.db), fill = loading.db)) + 
  facet_wrap(~ Factor, nrow = 1) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_gradient2(name = "Loading", 
                       high = "green", mid = "white", low = "yellow", 
                       midpoint = 0, guide = F) +
  xlab("Test scales") + 
  ylab("Loading Strength") +
  theme_bw(base_size = 10)

Answer 1

你只需要重新排序你的因子水平（用levels(db$Scale)看它们。例如，在这里我通过查看哪些大于0.5来排序它们（按因子1，...，4的顺序排序） ），然后添加末尾缺少的那些（在这种情况下为S）。

lvls <- as.character(unique(db$Scale[db$loading.db > 0.5]))
lvls <- c(lvls, unique(levels(db$Scale)[!(levels(db$Scale) %in% lvls)]))

db$Scale <- factor(db$Scale, rev(lvls))

ggplot(db, aes(Scale, abs(loading.db), fill = loading.db)) + 
  facet_wrap(~ Factor, nrow = 1) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_gradient2(name = "Loading", 
                       high = "green", mid = "white", low = "yellow", 
                       midpoint = 0, guide = F) +
  xlab("Test scales") + 
  ylab("Loading Strength") +
  theme_bw(base_size = 10)

给出