Question

所以我有一个名为Carbon的数据集（Carbon $ Graphite确实包含一些头部看不见的值）：

       Mesa  TIC   TOC Graphite    TC
Kx V17   Ja 5.26 15.64       NA 20.90
Mu V17  Nej 4.08 11.32       NA 15.40
Ob V17  Nej 5.22 12.68       NA 17.90
Vä V17   Ja 6.45  6.35       NA 12.80
Ös V17  Nej 3.90  2.54       NA  6.44
Ig V17   Ja 8.20  3.20       NA 11.40

我想要一个箱形图，该箱形图显示8个框，一个框仅包含可满足Carbon $ TIC [Carbon $ Mesa ==“ Ja，]的值，一个等于Carbon $ TIC [Carbon $ Mesa ==” Ja， ]，等于Carbon $ TOC [Carbon $ Mesa ==“ Ja，]，然后设堡垒。盒子的颜色（填充）是Carbon $ Mesa，所以“ Ja” =红色盒子和“ Nej” =蓝色盒子。我已经设法不使用ggpplot来做到这一点，但是我需要使用ggplot来做到这一点（因此我的所有图形看起来都一样，其余的抓图都由ggplot完成）。

我以前没有ggplot的代码（这是我想要的，但像往常一样在ggplot的一面带有图例）：

MesaJa <-Carbon[Carbon$Mesa=="Ja", ]
MesaNej <-Carbon[Carbon$Mesa=="Nej", ]

col.box<- c( rep("red", 3),   rep("blue", 3))

boxplot( list(MesaJa [, "TIC"], MesaJa [, "TOC"], MesaJa [, "TC"], 
              MesaNej[, "TIC"], MesaNej[, "TOC"], MesaNej[, "TC"] ),
         names=c("TIC", "TOC", "TC","TIC", "TOC", "TC") , 
         col=col.box
)  
legend("topleft", legend= c("Lime mud", "No lime mud"), pch=19, col=c("red","blue"), cex=0.7)

我尝试了几种不同的方法，但是仍然没有奏效。我最接近的是：

Carbon$TIC_Ja <- ifelse(Carbon$Mesa=="Ja",Carbon$TIC, NA)
Carbon$TIC_Nej <- ifelse(Carbon$Mesa=="Nej",Carbon$TIC, NA)
Carbon$TOC_Ja <- ifelse(Carbon$Mesa=="Ja",Carbon$TOC, NA)
Carbon$TOC_Nej <- ifelse(Carbon$Mesa=="Nej",Carbon$TOC, NA)
Carbon$TC_Ja <- ifelse(Carbon$Mesa=="Ja",Carbon$TC, NA)
Carbon$TC_Nej <- ifelse(Carbon$Mesa=="Nej",Carbon$TC, NA)

Carbon.plot<-Carbon[ , c(1, 6:11)]
Carbon.key <- colnames(Carbon)  

ggplot(
  gather(Carbon.plot, key=Carbon.key, value="value", -"Mesa"),
  aes(x=factor(Carbon.key), y=as.numeric(value), fill= Carbon.key) 
  ) + 
  geom_boxplot() +
  scale_fill_manual(values=c("red", "blue", "red", "blue", "red", "blue"),
                    labels=c("Lime mud added", "No lime mud")
                    )

但是它仍然不是很好，因为我只想要图例中的上两个帖子，并且想要删除“ NA”。而且我认为必须有一种不涉及使用ifelse对数据帧进行排序的简便方法。我已经搜索过SO，但到目前为止还没有看到任何类似的示例。所以，请帮忙？

编辑：添加了关于df和会话信息的信息。但是在发布问题之后，我开始更新一些软件包，但是进展并不顺利，所以目前我什至没有ggplot工作。

> str(Carbon)
'data.frame':   70 obs. of  5 variables:
 $ Mesa    : chr  "Ja" "Nej" "Nej" "Ja" ...
 $ TIC     : num  5.26 4.08 5.22 6.45 3.9 ...
 $ TOC     : num  15.64 11.32 12.68 6.35 2.54 ...
 $ Graphite: num  NA NA NA NA NA NA NA NA NA NA ...
 $ TC      : num  20.9 15.4 17.9 12.8 6.44 11.4 12.9 21.6 11.8 15.3 ...

> dput(Carbon)
structure(list(Mesa = c("Ja", "Nej", "Nej", "Ja", "Nej", "Ja", 
"Ja", "Ja", "Ja", "Ja", "Nej", "Nej", "Nej", "Ja", "Ja", "Nej", 
"Nej", "Nej", "Nej", "Ja", "Ja", "Ja", "Ja", "Nej", "Nej", "Nej", 
"Ja", "Ja", "Ja", "Nej", "Nej", "Nej", "Nej", "Ja", "Ja", "Ja", 
"Ja", "Nej", "Nej", "Nej", "Ja", "Ja", "Ja", "Nej", "Nej", "Nej", 
"Ja", "Nej", "Ja", "Ja", "Ja", "Ja", "Nej", "Nej", "Nej", "Ja", 
"Ja", "Ja", "Nej", "Nej", "Nej", "Nej", "Ja", "Ja", "Ja", "Ja", 
"Nej", "Nej", "Nej", "Ja"), TIC = c(5.26, 4.08, 5.22, 6.45, 3.9, 
8.2, 10.67, 7.43, 9.55, 8.19, 7.83, 4.04, 2.66, 4.93, 7.41, 3.25, 
4.47, 4.385, 3.48, 8.01, 9.49, 8.93, 6.03, 7.32, 3.84, 2.42, 
5.01, 3.87, 7, 4.8, 5.64, 5.76, 5.69, 8.7, 10.2, 9.78, 6.1, 8.07, 
4.33, 3.98, 7.39, 9.68, 9.67, 3.75, 5.07, 4.7, 4.86, 2.98, 8.05, 
10.29, 9.99, 6.65, 8.85, 4.82, 3.84, 8.49, 3.86, 6.63, 3.49, 
3.01, 4.83, 3.78, 8.95, 10.1, 8.15, 6.16, 8.15, 4.27, 3.96, 4.96
), TOC = c(15.64, 11.32, 12.68, 6.35, 2.54, 3.2, 2.23, 14.17, 
2.25, 7.11, 2.37, 17.16, 36.14, 13.47, 5.29, 17.95, 14.63, 3.85, 
6.31, 3.19, 2.81, 7.27, 23.07, 1.94, 26.19, 36.63, 23.19, 11.37, 
5.1, 2.39, 18.46, 2.17, 2.45, 1.7, 4.2, 4.92, 20.2, 0.86, 20.67, 
33.32, 5.11, 2.01, 0.53, 6.48, 29.51, 2.5, 9.41, 3.42, 3.04, 
4, 4.1, 11.15, 1.94, 20.66, 31.73, 1.81, 16.39, 20.75, 6.61, 
33.98, 2.48, 3.15, 1.65, 2.4, 12.63, 17.33, 0.99, 25.62, 38.63, 
17.93), Graphite = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, 0.13, NA, NA, NA, NA, NA, 0.07, 
0.05, NA, 0.06, NA, 0.03, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 0.07, 0.02, 0.08, 0.03, 0.33, NA, NA, NA, NA, 
NA, 0.02, 0.13, NA, 0.05, 0.02, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA), TC = c(20.9, 15.4, 17.9, 12.8, 6.44, 11.4, 12.9, 
21.6, 11.8, 15.3, 10.2, 21.2, 38.8, 18.4, 12.7, 21.2, 19.1, 8.235, 
9.92, 11.2, 12.3, 16.2, 29.1, 9.26, 30.1, 39.1, 28.2, 15.3, 12.1, 
7.22, 24.1, 7.93, 8.14, 10.4, 14.4, 14.7, 26.3, 8.93, 25, 37.3, 
12.5, 11.7, 10.2, 10.3, 34.6, 7.28, 14.3, 6.73, 11.1, 14.3, 14.1, 
17.8, 10.8, 25.5, 35.7, 10.3, 20.3, 27.4, 10.1, 37, 7.33, 6.93, 
10.6, 12.5, 20.8, 23.5, 9.14, 29.9, 42.6, 22.9)), class = "data.frame", row.names = c("Kx V17", 
"Mu V17", "Ob V17", "Vä V17", "Ös V17", "Ig V17", "Va V17", "Gä V17", 
"Sk V17", "Fr V17", "Gr V17", "Bi V17", "As V17", "Kx H17", "Pi H17", 
"Mu H17", "Ob H17", "Do H17", "Ös H17", "Ig H17", "Va H17", "Gä H17", 
"Fr H17", "Gr H17", "Bi H17", "As H17", "So H17", "Kx V18", "Pi V18", 
"Mu V18", "Ob V18", "Do V18", "Ös V18", "Ig V18", "Va V18", "Gä V18", 
"Fr V18", "Gr V18", "Bi V18", "As V18", "So V18", "Kx H18", "Pi H18", 
"Mu H18", "Ob H18", "Do H18", "Vä H18", "Ös H18", "Ig H18", "Va H18", 
"Gä H18", "Fr H18", "Gr H18", "Bi H18", "As H18", "So H18", "Kx V19", 
"Pi V19", "Mu V19", "Ob V19", "Do V19", "Ös V19", "Ig V19", "Va V19", 
"Gä V19", "Fr V19", "Gr V19", "Bi V19", "As V19", "So V19"))

> sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

Matrix products: default

locale:
[1] LC_COLLATE=Swedish_Sweden.1252  LC_CTYPE=Swedish_Sweden.1252   
[3] LC_MONETARY=Swedish_Sweden.1252 LC_NUMERIC=C                   
[5] LC_TIME=Swedish_Sweden.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] crayon_1.3.4     grid_3.5.3       R6_2.4.1         lifecycle_0.2.0  gtable_0.3.0    
 [6] magrittr_1.5     scales_1.1.0     rlang_0.4.5      rstudioapi_0.11  ellipsis_0.3.0  
[11] tools_3.5.3      glue_1.4.0       munsell_0.5.0    compiler_3.5.3   colorspace_1.4-1

Answer 1

这里有一种可能的方法，例如使用pivot_longer中的tidyr函数将数据框转换为更长的格式（pivot_longer tidyr中有1.0.0 ）：

library(tidyr)
library(dplyr)

Carbon %>% pivot_longer(cols = c(TIC, TOC, TC), names_to = "var",values_to = "val") %>%
  mutate(var = factor(var,levels = c("TIC","TOC","TC"))) 

# A tibble: 210 x 4
   Mesa  Graphite var     val
   <chr>    <dbl> <fct> <dbl>
 1 Ja          NA TIC    5.26
 2 Ja          NA TOC   15.6 
 3 Ja          NA TC    20.9 
 4 Nej         NA TIC    4.08
 5 Nej         NA TOC   11.3 
 6 Nej         NA TC    15.4 
 7 Nej         NA TIC    5.22
 8 Nej         NA TOC   12.7 
 9 Nej         NA TC    17.9 
10 Ja          NA TIC    6.45
# … with 200 more rows

然后，您可以使用interaction将“ Mesa”和“ var”分组为x值，并在boxplot中获得以下ggplot2：

library(tidyr)
library(dplyr)
library(ggplot)

Carbon %>% pivot_longer(cols = c(TIC, TOC, TC), names_to = "var",values_to = "val") %>%
  mutate(var = factor(var,levels = c("TIC","TOC","TC"))) %>%
  ggplot(aes(x = interaction(var, Mesa), y = val, fill = Mesa))+
  geom_boxplot()+
  scale_x_discrete(labels = rep(c("TIC","TOC","TC"),2))+
  scale_fill_manual(values = c("red","blue"), labels = c("Lime mud", "No lime mud"))

它回答了您的问题吗？

根据数据集的条件设置颜色和框（箱形图）

1 个答案: