how to obtain a dodged geom_bar (ggplot2) from different columns of a df-CodePudding

Dataframe df is shown:

     ID               gene1                   gene2
    4602              TET2                    TET2
    4602              TP53                    TP53
    4602              TET2                    TET2
    5095             ASXL1                   ASXL1
    5095            DNMT3A                  DNMT3A
    5095              NPM1                    <NA>

I have been trying to obtain a matching barplot displaying a count (bars) for both the column gene1 and gene2. the gene1 method is the standard, while gene2 is another mutation detector that should be compared to gene1. As you see, in sample 5095 only 2 mutations were detected, while the 3rd was not reproduced.

How do i make a barplot with two bars for each ID showing the count in gene1 and gene2?

here dput()

structure(list(ID = c(4602, 4602, 4602, 5095, 5095, 5095, 5095, 
4649, 4649, 4649, 5069, 5069, 5069, 5146, 5132, 5132, 5132, 5132, 
5132, 5132, 4297, 4297, 4297, 4297, 4297, 4345, 4345, 4345, 4345, 
4345, 4356, 4356, 4356, 4356, 4385, 4385, 4385, 4385, 4385, 4385, 
4437, 4437, 4437, 4437, 4437, 4437, 4442, 4442, 4442, 4442, 4442, 
4479, 4479, 4479, 4479, 4479, 4479, 4479, 4479, 4479, 4479, 4479, 
4479, 4479, 4479, 4479, 4487, 4487, 4487, 4487, 4487, 4487, 4537, 
4537, 4537, 4537, 4537, 4537, 4621, 4621, 4621, 4621, 4621, 4621, 
4621, 4624, 4624, 4624, 4624, 4624, 4665, 4736, 4736, 4736, 4736, 
4736, 4895, 4895, 4895, 4895, 4895, 4903, 4903, 4903, 4903, 4691, 
4691, 4691, 4691, 4261, 4261, 4261, 4261, 4394, 4394, 4394, 4394, 
4424, 4424, 4424, 4424, 4943, 4943, 4943, 5073, 5169, 5169), 
    gene1 = c("TET2", "TP53", "TET2", "ASXL1", "DNMT3A", "NPM1", 
    "PTPN11", "TP53", "TP53", "TET2", "DNMT3A", "TET2", "TET2", 
    "negative", "JAK2", "ASXL1", "BRAF", "CBL", "TET2", "TET2", 
    "DNMT3A", "IDH1", "NPM1", "CREBBP", "FLT3", "DNMT3A", "FLT3", 
    "NPM1", "BCOR", "KIT", "DNMT3A", "IDH1", "NRAS", "BCOR", 
    "KRAS", "NPM1", "PTPN11", "ETV6", "PHF6", "TET2", "DNMT3A", 
    "KRAS", "NPM1", "WT1", "TET2", "WT1", "DNMT3A", "FLT3", "NPM1", 
    "NRAS", "WT1", "DNMT3A", "IDH2", "NPM1", "SRSF2", "ATRX", 
    "CUX1", "CUX1", "FLT3", "GNAS", "PHF6", "PIGA", "PIGA", "PRPF40B", 
    "PTPN11", "TET2", "IDH1", "IDH2", "RUNX1", "U2AF1", "TET2", 
    "TP53", "DNMT3A", "IDH2", "ATRX", "GATA2", "STAG2", "TP53", 
    "IDH2", "SRSF2", "ASXL1", "GATA1", "KDM6A", "STAG2", "TP53", 
    "IDH2", "JAK2", "SRSF2", "ASXL1", "RIT1", "KRAS", "NPM1", 
    "NRAS", "NRAS", "BCOR", "MYD88", "FLT3", "NPM1", "NRAS", 
    "TET2", "TET2", "DNMT3A", "IDH1", "NPM1", "CREBBP", "DNMT3A", 
    "IDH1", "IDH2", "NPM1", "FLT3", "FLT3", "GATA2", "SH2B3", 
    "FLT3", "NPM1", "KDM6A", "SMC1A", "IDH2", "SRSF2", "ASXL2", 
    "RUNX1", "IDH2", "JAK2", "NPM1", "JAK2", "SRSF2", "STAG2"
    ), gene2 = c("TET2", "TP53", "TET2", "ASXL1", "DNMT3A", NA, 
    "PTPN11", "TP53", "TP53", "TET2", "DNMT3A", NA, "TET2", "PTEN", 
    NA, NA, "BRAF", "CBL", "TET2", "TET2", "JAK2", "SRSF2", NA, 
    "DNMT3A", "IDH1", "NPM1", NA, "FLT3", "DNMT3A", "FLT3", "NPM1", 
    NA, NA, "DNMT3A", "IDH1", "NRAS", "BCOR", "KRAS", "NPM1", 
    "PTPN11", "ETV6", "PHF6", "TET2", "DNMT3A", "KRAS", "NPM1", 
    NA, "TET2", NA, "DNMT3A", "FLT3", "NPM1", "NRAS", NA, NA, 
    "IDH2", "NPM1", "SRSF2", NA, "CALR", NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "IDH1", "IDH2", "RUNX1", "U2AF1", "TET2", 
    NA, "DNMT3A", "IDH2", NA, NA, NA, NA, "IDH2", "SRSF2", "ASXL1", 
    NA, NA, "KMT2D", "TP53", "IDH2", "JAK2", "SRSF2", "ASXL1", 
    NA, "KRAS", "NPM1", "NRAS", "NRAS", NA, NA, "FLT3", "NPM1", 
    "NRAS", "TET2", "TET2", "DNMT3A", "IDH1", "NPM1", "CREBBP", 
    "DNMT3A", "IDH1", "IDH2", "NPM1", "FLT3", "FLT3", NA, NA, 
    "FLT3", "NPM1", NA, "SMC1A", "IDH2", "SRSF2", NA, "RUNX1", 
    "IDH2", "JAK2", "NPM1")), class = "data.frame", row.names = c(NA, 
-127L))

CodePudding user response：

One more:

df %>%  
  group_by(ID) %>% 
  add_count(gene1, gene2) %>% 
  pivot_longer(
    cols = contains("gene")
  ) %>% 
  ungroup() %>% 
  ggplot(aes(factor(name), n, fill=value, group=value, label=value))   
  geom_col()  
  facet_wrap(.~ID, scales = "free_y")  
  geom_text(size = 3, position = position_stack(vjust = 0.5))  
  theme_classic() 
  xlab("")  
  guides(fill=FALSE)

Last try, now it should work. Now we count before pivoting:

df %>%  
  group_by(ID) %>% 
  add_count(gene1, gene2) %>% 
  pivot_longer(
    cols = contains("gene")
  ) %>% 
  ungroup() %>% 
  ggplot(aes(factor(name), n, fill=value, group=value))   
  geom_col()  
  facet_wrap(.~ID, scales = "free_y")  
  theme(legend.position = "bottom") 
  guides(fill=guide_legend(nrow=2))