我希望这个例子能够让人清楚。我想要堆叠条形，其中中间条形跨越“0”，因为它代表中性值。这与李克特量表一起使用。为了重现性，我使用钻石数据集。
以下示例与我的用例足够接近，并演示了我很难以正确的顺序获取“好”或“正”数据（因此中性最接近 0）。
这是我的代码：
require(tidyverse)
diamonds_new <- diamonds %>%
mutate(quality = fct_recode(cut, "Very poor" = "Fair", "Poor" = "Good", "Neutral" = "Very Good", "Good" = "Premium", "Excellent" = "Ideal")) %>%
select(color, clarity, quality) %>%
group_by(color, clarity, quality) %>% count()
diamonds_bad <-
diamonds_new %>% filter(quality %in% c("Very poor", "Poor", "Neutral")) %>%
mutate(n = ifelse(quality == "Neutral", -n/2, -n))
diamonds_good <-
diamonds_new %>% filter(quality %in% c("Neutral", "Good", "Excellent")) %>%
mutate(n = ifelse(quality == "Neutral", n/2, n)) # %>%
# arrange(color, clarity, desc(quality)) # this doesn't seem to make a difference
ggplot() + geom_col(data = diamonds_bad, aes(x=color, y = n, fill = quality)) +
geom_col(data = diamonds_good, aes(x=color, y = n, fill = quality)) +
facet_grid(. ~ clarity, scales = "free") +
coord_flip()
I have also tried using scale_fill_manual() but haven't found a way for that to work, either.
我相信这比一些现有的例子更复杂，这些例子没有负值的复杂性或需要span 0。使用当前版本的 ggplot，我缺少什么？
另外，我是否正确地认为正集和负集需要分开，或者至少这样做更容易？
创建的列geom_col是使用形成的position_stack它分别堆叠正值和负值，其中正值向上堆叠，负值向下堆叠。中心组，Neutral在此示例中，通过将其设置为等于其原始值的一半，然后将其绘制为正值和负值，使其跨越 0。此外，对于正值，组的顺序需要颠倒。
这种方法有助于呈现我所参与的一些调查的结果，因此我将其变成了一个函数以使其更加通用。
library(tidyverse)
#
# summarize groups and save counts in variable quality_cnt
#
diamonds_cnt <- diamonds %>%
mutate(quality = fct_recode(cut, "Very_Poor" = "Fair", "Poor" = "Good",
"Neutral" = "Very Good", "Good" = "Premium", "Excellent" = "Ideal")) %>%
select(color, clarity, quality) %>%
group_by(color, clarity, quality) %>% summarize(quality_cnt = n())
# make function to plot counts
plot_ratings <- function(survey, rated_item, rating_cnt, rating, rating_cat, facet = "wrap") {
#
# Input:
# rated_item = unquoted variable name of rated items
# rating = unquoted variable name of ratings for each rated_items;
# variable should be a factor ordered from lowest to highest
# rating_cnt = unquoted variable name of counts or frequencies for each rated_item
# rated_cat = unquoted variable name of categories of rated items
# facet = "grid" for all panels on one row or
# "wrap" to spread panels across multiple rows
#
# make arguments quosures
#
rated_item <- enquo(rated_item)
rating_cnt <- enquo(rating_cnt)
rating <- enquo(rating)
rating_cat <- enquo(rating_cat)
#
# If number of rating levels is odd, find middle rating
#
rating_levels <- levels(pull(survey, !!rating))
mid_level <- ceiling(length(rating_levels)/2)
mid_rating <- ifelse(length(rating_levels)%%2 == 1, rating_levels[mid_level], NA_character_)
#
# make local variabels for use with aes
# plot positive and negative columns separately
#
survey <- survey %>% mutate( rating_plt = !!rating, rating_cnt_plt = !!rating_cnt)
sp <- ggplot(survey, aes_(x = rated_item, fill = rating)) +
geom_col(data=filter(survey, !!rating %in% tail(rating_levels, mid_level)),
aes( y = ifelse(rating_plt == mid_rating, .5*rating_cnt_plt, rating_cnt_plt)),
position = position_stack(reverse = TRUE )) +
geom_col(data=filter(survey, !!rating %in% head(rating_levels, mid_level)),
aes( y = ifelse(rating_plt == mid_rating, -.5*rating_cnt_plt, -rating_cnt_plt)),
position = "stack") +
labs(y = rating_cnt) +
scale_fill_brewer(palette = "RdYlGn", direction = -1) +
coord_flip() +
switch(facet,
grid = facet_grid( facets=rating_cat, scales = "free_x"),
wrap = facet_wrap( facets=rating_cat, scales = "free_x"))
plot(sp)
}
#
# Use function to make charts
#
plot_ratings(diamonds_cnt, rated_item = color, rating_cnt = quality_cnt,
rating = quality, rating_cat = clarity, facet = "wrap")
给出了图表