分组条形图：ggplot

debugcn 发表于 Dev

用户113156

我有以下数据。

我所试图做的是第一排序和匹配列Feature1，Feature2以及Feature3遵循相同的顺序为第一列Feature，及其对应的编号。

Feature 对应于 LastYear

Feature1 对应于 OneYear

Feature2 对应于 TwoYear

Feature3 对应于 ThreeYear

因此，采取了Feature1和其对应的OneYear列中，值logTA = 0.32627....将下降到row 2因为logTA是row 2在Feature列。在CA.CL = 0.16196....将下降到row 6等

这同样适用于Feature 2和Feature 3。所有根据与Feature列匹配进行排序。

****** 也许上面的部分是不需要的。

第二，我想melt数据帧，通过分组LastYear，OneYear，TwoYear和ThreeYear。

所以我们的想法是绘制类似于以下内容的内容；

其中，Food，Music和People将被替换LastYear，OneYear，TwoYear和ThreeYear。另外，酒吧将对应CA.CL，logTA等等。

structure(list(Feature = structure(c(6L, 8L, 5L, 11L, 4L, 1L, 
3L, 2L, 7L, 10L, 9L), .Label = c("CA.CL", "CA.TA", "CF.NCL", 
"CL.FinExp", "DailySALES.EBIT", "EBIT.FinExp", "EQ.Turnover", 
"logTA", "SALES.WC", "TL.EQ", "TL.TA"), class = "factor"), LastYear = structure(c(11L, 
10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L), .Label = c("0.0322326139141556", 
"0.0418476895487213", "0.0432506289654195", "0.0504153839825875", 
"0.0546743268879608", "0.0549979876321639", "0.0577181189006888", 
"0.107473282590142", "0.112929456881545", "0.139817111427972", 
"0.304643399268643"), class = "factor"), Feature1 = structure(c(8L, 
6L, 1L, 3L, 11L, 9L, 4L, 10L, 5L, 7L, 2L), .Label = c("CA.CL", 
"CA.TA", "CF.NCL", "CL.FinExp", "DailySALES.EBIT", "EBIT.FinExp", 
"EQ.Turnover", "logTA", "SALES.WC", "TL.EQ", "TL.TA"), class = "factor"), 
    OneYear = structure(c(11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 
    2L, 1L), .Label = c("0.0241399538457295", "0.025216904130219", 
    "0.0288943827773218", "0.0290134083108585", "0.0393919110672302", 
    "0.0484816627329215", "0.0660812827117713", "0.0728943625765924", 
    "0.161968277822423", "0.177638448005797", "0.326279406019136"
    ), class = "factor"), Feature2 = structure(c(8L, 1L, 6L, 
    9L, 11L, 3L, 2L, 5L, 4L, 10L, 7L), .Label = c("CA.CL", "CA.TA", 
    "CF.NCL", "CL.FinExp", "DailySALES.EBIT", "EBIT.FinExp", 
    "EQ.Turnover", "logTA", "SALES.WC", "TL.EQ", "TL.TA"), class = "factor"), 
    TwoYear = structure(c(11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 
    2L, 1L), .Label = c("0.0179871842234001", "0.0245082857218191", 
    "0.0276514285623367", "0.0359182021377123", "0.0461243809893583", 
    "0.046996298679094", "0.0566018025811507", "0.0648203522637183", 
    "0.0815346014308433", "0.210073355633034", "0.387784107777533"
    ), class = "factor"), Feature3 = structure(c(8L, 1L, 11L, 
    7L, 9L, 5L, 2L, 6L, 3L, 4L, 10L), .Label = c("CA.CL", "CA.TA", 
    "CF.NCL", "CL.FinExp", "DailySALES.EBIT", "EBIT.FinExp", 
    "EQ.Turnover", "logTA", "SALES.WC", "TL.EQ", "TL.TA"), class = "factor"), 
    ThreeYear = structure(c(11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 
    3L, 2L, 1L), .Label = c("0.0275302883400183", "0.0282746857626618", 
    "0.0403110592712779", "0.0409053619122674", "0.0514576931772448", 
    "0.0570216362435987", "0.076967996046118", "0.0831531609222676", 
    "0.0904194376665785", "0.139457271733071", "0.364501408924896"
    ), class = "factor")), .Names = c("Feature", "LastYear", 
"Feature1", "OneYear", "Feature2", "TwoYear", "Feature3", "ThreeYear"
), row.names = c(NA, -11L), class = "data.frame")

编辑：

feature_suffix <- c("", "1", "2", "3")
year_prefix <- c("Last", "One", "Two", "Three")

x <- map2(feature_suffix, year_prefix,
     ~ df %>% 
       select(feature = paste0("Feature", .x), value = paste0(.y, "Year")) %>%
       mutate(year = paste0(.y, "Year"))
) %>%
  bind_rows(.) %>%
  mutate(value = as.numeric(value))

xy <- x %>% 
  group_by(year) %>% 
  arrange(year, desc(value)) 

ggplot(data = xy, aes(year, value, fill=feature)) +
geom_bar(stat="summary", fun.y=mean, position = position_dodge(.9))

安德鲁里斯

如果您重新排列为长格式，则绘图很简单。
这是使用以下方法执行此操作的方法purrr:map2()：

library(tidyverse)

feature_suffix <- c("", "1", "2", "3")
year_prefix <- c("Last", "One", "Two", "Three")

map2(feature_suffix, year_prefix,
     ~ df %>% 
       select(feature = paste0("Feature", .x), value = paste0(.y, "Year")) %>%
       mutate(year = paste0(.y, "Year"))
) %>%
  bind_rows(.) %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(aes(year, value, fill=feature)) +
  geom_bar(stat="summary", fun.y=mean, position = position_dodge(.9))