CODEWITHSUNDEEP
rggplot2
Ankit
Ankit

Reputation: 6980

Creating grouped bar-plot of multi-column data in R

I have the following data

       Input Rtime Rcost Rsolutions  Btime Bcost 
1   12 proc.     1    36     614425     40    36 
2   15 proc.     1    51     534037     50    51 
3    18-proc     5    62    1843820     66    66 
4    20-proc     4    68    1645581 104400    73 
5 20-proc(l)     4    64    1658509  14400    65 
6    21-proc    10    78    3923623 453600    82

I want to create a grouped bar chart from this data such that x-axis contains Input field (as groups) and y axis represent the log scale for the Rtime and Btime fields (the two bars).

All solutions/examples I checked online had similar data put into a three column layout. I do not know how to use the data I have to generate the grouped bar-chart. Or if there is a way to convert this data (manually converting is not an options because it is a huge file with a lot of rows) into a R and ggplot compatible data format.

Edit :

Graph generated using gncs solution

enter image description here

Upvotes: 15

Views: 100407

Answers (5)

Pipas
Pipas

Reputation: 91

Following : Fusion Multiple ggplots graphs

The database d1:

id = c(1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), 
observations = c("vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis", "vmbis"), 
reponse = c("18_vsys", "18_vsys", "20_vsys", "23_vsys", "15_vsys", "14_vsys", "17_vsys", "14_vsys", "17_vsys", "17_vsys", "23_vsys", "24_vsys", "24_vsys", 
"17_vsys", "14_vsys", "16_vsys", "12_vsys", "12_vsys", "14_vsys", "14_vsys", "18_vsys"), 
inf_palu2 = c("2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2"), inf_sympto2 = c("1", "1", "0", "1", "1", "1", "1", "1", "0", "1", "1", "1", "0", "0", "1", "1", "1", "1", "1", 
"0", "1"))

d1 <- data.frame(id, observations, reponse, inf_palu2, inf_sympto2)

Attached code graph :

library(dplyr)
library(tidyr)
library(ggplot2)

df <- d1 |>
  pivot_longer(-c(id, reponse))

ggplot() + 
  geom_bar(data = df, aes(x = reponse, fill = name),  colour = "#006ddb", position = "dodge") +
    scale_fill_manual(values = c("#DF536B", "#F0E442","#E69F00" )) +
    theme(axis.text.x = element_text(angle = 90, vjust = 1)) +
    ggtitle("Nb observations, infections palustres, infections symptomatiques / visite")

Graph : enter image description here

Upvotes: 0

M.Viking
M.Viking

Reputation: 5398

As requested, a ggplot2 solution that also uses pivot_longer() https://tidyr.tidyverse.org/reference/pivot_longer.html to transform the data into a format that geom_bar() or geom_col() can easily plot. position = "dodge" means make the multi-column style (not stacked-bar). geom_bar(stat = "identity") is the same as geom_col().

Update with cleaner code:

library(tidyverse)
df %>% 
  pivot_longer(-Input) %>% 
  ggplot(aes(x = Input, y = value, fill = name)) + 
  geom_col(position = "dodge") + 
  # geom_bar(stat = "identity", position = "dodge") + 
  scale_y_log10()

enter image description here

Original answer:

library(dplyr)
library(ggplot2)

df <- read.table(text = "Input Rtime Rcost Rsolutions  Btime Bcost 
                         1   12-proc.     1    36     614425     40    36 
                         2   15-proc.     1    51     534037     50    51 
                         3    18-proc     5    62    1843820     66    66 
                         4    20-proc     4    68    1645581 104400    73 
                         5 20-proc(l)     4    64    1658509  14400    65 
                         6    21-proc    10    78    3923623 453600    82", 
                         header = TRUE, sep = "")

dfm <- pivot_longer(df, -Input, names_to="variable", values_to="value")

## pivot_longer takes the input data frame, excludes the Input field from the transformation, turns the remaining column names into the variable "variable" (often called the "key"), and assigns the values to the variable "value". 

ggplot(dfm, aes(x = Input,y = value, fill = variable)) + 
    geom_bar(stat = "identity", position = "dodge") + 
    scale_y_log10()

Upvotes: 9

joran
joran

Reputation: 173517

As requested, a ggplot2 solution that also uses reshape2:

library(reshape2)

df <- read.table(text = "       Input Rtime Rcost Rsolutions  Btime Bcost 
1   12-proc.     1    36     614425     40    36 
2   15-proc.     1    51     534037     50    51 
3    18-proc     5    62    1843820     66    66 
4    20-proc     4    68    1645581 104400    73 
5 20-proc(l)     4    64    1658509  14400    65 
6    21-proc    10    78    3923623 453600    82",header = TRUE,sep = "")

dfm <- melt(df[,c('Input','Rtime','Btime')],id.vars = 1)

ggplot(dfm,aes(x = Input,y = value)) + 
    geom_bar(aes(fill = variable),stat = "identity",position = "dodge") + 
    scale_y_log10()

enter image description here

Note a style difference here, where since log(1) = 0, ggplot2 treats that as a bar of zero height and doesn't plot anything, whereas barplot plots a little stub (which in my opinion is a little misleading).

Upvotes: 39

gncs
gncs

Reputation: 480

I think I understand the problem and this is what I would suggest (short run - option):

data <- read.table("data.txt", header=TRUE)
subset <- t(data.frame(data$Rtime, data$Btime))
barplot(subset, legend = c("Rtime", "Btime"), names.arg=data$Input, log="y", beside=TRUE)

Is that what you want? It is kind of dirty, but it does the job.

Update: code corrected.

Upvotes: 7

vtenhunen
vtenhunen

Reputation: 49

joran's answer helped me a lot, but I had to use stat="identity" in the ggplot statement like that:

ggplot(dfm, aes(x = Input,y = value)) + 
geom_bar(aes(fill = variable), position = "dodge", stat="identity") + 
scale_y_log10()

My version of R is 3.2.2 and ggplot2 version 1.0.1

Thanks.

Upvotes: 2

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