Dynamically Create the new column name using the date_col variable and value

It is possible to simplify the code, especially the lines where the cyl_ # columns are created. This is an example code. But the real need is to create 12 columns. And make the boxplot graphs for each column. It is a priority to use the data.table package

my code:

library(vioplot)
library(data.table)
dt=setDT(copy(mtcars))[,.(mpg,cyl)][]
dt[,mpg,keyby=cyl]

dt[cyl == 4,cyl_4:=mpg]
dt[cyl == 6,cyl_6:=mpg]
dt[cyl == 8,cyl_8:=mpg]
dt[,.SD,.SDcols=patterns("cyl_")]
> dt[,vioplot(.SD,col="gold"),.SDcols=patterns("cyl_")]
    upper  lower median    q1    q3
1: 33.900 21.400   26.0 22.80 30.40
2: 21.400 17.800   19.7 18.65 21.00
3: 19.025 11.625   15.2 14.40 16.25

CodePudding user response：

Using data.table for dynamical columns creation (which is possibly not necessary):

cyls <- sort(unique(dt$cyl))
dt[,(paste0("cyl_",cyls)):=lapply(cyls,function(x) fifelse(x == cyl,mpg,NA))]
dt[,vioplot(.SD,col="gold"),.SDcols=patterns("cyl_")]

Or with ggplot2:

library(ggplot2)
ggplot(dt)   geom_violin(aes(x=paste0('cyl_',cyl),y=mpg),fill='gold')   
             xlab('cylinders')

CodePudding user response：

I am not sure why the OP is asking to create individual columns for each value of cyl (with many NA values in each column) but if the goal is to create a violin plot for each individual value of cyl this is what I would do:

library(vioplot)
library(data.table)
as.data.table(mtcars)[, vioplot(mpg ~ cyl, col="gold")]

    upper  lower median    q1    q3
    <num>  <num>  <num> <num> <num>
1: 33.900 21.400   26.0 22.80 30.40
2: 21.400 17.800   19.7 18.65 21.00
3: 19.025 11.625   15.2 14.40 16.25

This approach uses the formula parameter of vioplot(). There is no need for filtering or manipulating the mtcars data (except for coercing to class data.table).

If it is required to rename the labels on the x-axis this can be achieved by

as.data.table(mtcars)[, vioplot(mpg ~ paste0("cyl_", cyl), xlab = NULL, col = "gold")]

The formula parameter is quite handy as it allows for switching easily between different variables, e.g.,

as.data.table(mtcars)[, vioplot(hp ~ gear, col = "lightblue")]

   upper lower median     q1    q3
   <num> <num>  <num>  <num> <num>
1:   245    97    180 150.00   210
2:   123    52     94  65.75   110
3:   335    91    175 113.00   264