根据它们的常见程度更改 ggplot2 热图 y 轴上的因子顺序

Question

我有一个包含数千行的 data.frame，看起来像这样

rep = c("rep1", "rep1", "rep1","rep2", "rep2", "rep2","rep3", "rep3", "rep3")
species = c("a","b","d","b","e","f","b","f","h")
value=c(sample.int(100, 9))

df <- data.frame(rep,species,value)
df2 <- df %>% 
  mutate(species=factor(species)) %>% 
  arrange(species,rep)

ggplot(df2,aes(rep, species, fill=value))+
  geom_tile()

我想改变 y 轴的顺序，使所有重复中存在的物种首先出现，然后出现在两个重复中的物种在最后，最后出现在一个重复中的物种。

例如，我想先有物种 b，然后是 f，然后是其余的。 我的因素太多了，不可能一一改变。

如果有任何建议或指导，我将不胜感激。 提前谢谢你

更新解决方案： 我通过使用 fct_infreq() https://cran.r-project.org/web/packages/forcats/vignettes/forcats.html 找到了另一个解决方案。请检查一下

ggplot(df2,aes(rep, fct_infreq(species), fill=value))+
  geom_tile()

Answer 1

这可以通过首先添加一个包含每个物种的重复次数的列来实现，例如dplyr::add_count 并根据计数对species 重新排序：

library(ggplot2)
library(dplyr)

set.seed(42)

rep = c("rep1", "rep1", "rep1","rep2", "rep2", "rep2","rep3", "rep3", "rep3")
species = c("a","b","d","b","e","f","b","f","h")
value=c(sample.int(100, 9))

df <- data.frame(rep,species,value)
df2 <- df %>% 
  add_count(species) %>% 
  mutate(species=reorder(species, n)) %>% 
  arrange(species,rep)

ggplot(df2,aes(rep, species, fill=value))+
  geom_tile()

Answer 2

不是最干净的

library(tidyverse)

rep = c("rep1", "rep1", "rep1","rep2", "rep2", "rep2","rep3", "rep3", "rep3")
species = c("a","b","d","b","e","f","b","f","h")
value=c(sample.int(100, 9))

df <- data.frame(rep,species,value)
df2 <- df %>% 
  mutate(species=factor(species)) %>% 
  arrange(species,rep)

df3 <- df2 |>
  add_count(species,name = "order") 

ggplot(df3,aes(x = rep,y = fct_reorder(species,order,.desc   = T), fill=value))+
  geom_tile()

^{由reprex package (v2.0.0) 于 2021-06-05 创建}

会话信息

sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 21390)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=Portuguese_Brazil.1252  LC_CTYPE=Portuguese_Brazil.1252   
#> [3] LC_MONETARY=Portuguese_Brazil.1252 LC_NUMERIC=C                      
#> [5] LC_TIME=Portuguese_Brazil.1252    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.6     purrr_0.3.4    
#> [5] readr_1.4.0     tidyr_1.1.3     tibble_3.1.2    ggplot2_3.3.3  
#> [9] tidyverse_1.3.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.1.1  xfun_0.23         haven_2.4.1       colorspace_2.0-1 
#>  [5] vctrs_0.3.8       generics_0.1.0    htmltools_0.5.1.1 yaml_2.2.1       
#>  [9] utf8_1.2.1        rlang_0.4.11      pillar_1.6.1      glue_1.4.2       
#> [13] withr_2.4.2       DBI_1.1.1         dbplyr_2.1.1      modelr_0.1.8     
#> [17] readxl_1.3.1      lifecycle_1.0.0   munsell_0.5.0     gtable_0.3.0     
#> [21] cellranger_1.1.0  rvest_1.0.0       evaluate_0.14     labeling_0.4.2   
#> [25] knitr_1.33        ps_1.6.0          curl_4.3.1        fansi_0.5.0      
#> [29] highr_0.9         broom_0.7.6       Rcpp_1.0.6        backports_1.2.1  
#> [33] scales_1.1.1      jsonlite_1.7.2    mime_0.10         farver_2.1.0     
#> [37] fs_1.5.0          hms_1.1.0         digest_0.6.27     stringi_1.6.2    
#> [41] grid_4.1.0        cli_2.5.0         tools_4.1.0       magrittr_2.0.1   
#> [45] crayon_1.4.1      pkgconfig_2.0.3   ellipsis_0.3.2    xml2_1.3.2       
#> [49] reprex_2.0.0      lubridate_1.7.10  assertthat_0.2.1  rmarkdown_2.8    
#> [53] httr_1.4.2        rstudioapi_0.13   R6_2.5.0          compiler_4.1.0

Answer 3

一种方法是按如下方式计算因子的水平：

df2 <- df %>%
  mutate(species = factor(species,
                          levels = df %>%
                                     count(~ species) %>%
                                     arrange(freq) %>%
                                     .$species))

ggplot(df2, aes(rep, species, fill = value)) +
  geom_tile()

Answer 4

rep = c("rep1", "rep1", "rep1","rep2", "rep2", "rep2","rep3", "rep3", "rep3")
species = c("a","b","d","b","e","f","b","f","h")
value=c(sample.int(100, 9))

df <- data.frame(rep,species,value)
library(tidyverse)
df %>% add_count(species) %>% arrange(-n) %>% filter(!duplicated(species)) %>% pull(species) -> vec

df2 <- df %>% 
  mutate(species=factor(species, levels = vec,ordered = T)) %>% 
  arrange(species,rep)

ggplot(df2,aes(rep, species, fill=value))+
  geom_tile()

^{由reprex package (v2.0.0) 于 2021-06-05 创建}