Plotting values from multiple columns using ggplot

Question

I have data.frame as follows

test=data.frame(start=rep("0",10),end=rep("100",10),Typ1=c("530","630","500","400","350","600","1032","378","430","567"),Type2=c("100","70","50","120","130","50","75","86","90","95"),Type3=c("10","50","40","22","13","45","15","36","19","20"))
>test
start end Type1 Type2 Type3
0     100  530   100    10
0     100  630    70    50
0     100  500    50    40
0     100  400   120    22
0     100  350   130    13
0     100  600    50    45
0     100 1032    75    15
0     100  378    86    36
0     100  430    90    19
0     100  567    95    20

All I want is to plot the above data frame with x-axis denoting the start and end and Y-axis denoting the Type1, Type2 and Type3. I tried the following code but it throwed me error

ggplot(test,aes(x=c(start,end)),y=c(Type1,Type2,Type3)) +geom_density()

Kindly guide me. Thanks in advance.

Answer 1

I'm not sure if I understand your questions correctly, but I assume that you want a density plot of the three variables Type1, Type2, Type3 and the values indicate the hights of the line. Then you could do something like:

test = data.frame(x = seq(0, 100, length.out = 10),
                  Type1 = c(530, 630, 500, 400, 350, 600, 1032, 378, 430, 567),
                  Type2 = c(100, 70, 50, 120, 130, 50, 75, 86, 90, 95),
                  Type3 = c(10, 50, 40, 22, 13, 45, 15, 36, 19, 20))

ggplot(test, aes(x = x)) +
   geom_line(aes(y = Type1, color = "Type 1")) +
   geom_line(aes(y = Type2, color = "Type 2")) +
   geom_line(aes(y = Type3, color = "Type 3"))

What you have to do is use an own layer for every column of your data.frame.

Answer 2

Your data frame is arranged well, you do not need to specify the start and end in the data frame.

ggplot expect that each row in your df is an observation and each column is a variable. That means we need to reshape the data

library(tidyverse)
test <- data.frame(start=rep("0",10), end=rep("100",10), 
               Type1=c("530","630","500","400","350","600","1032","378","430","567"),
               Type2=c("100","70","50","120","130","50","75","86","90","95"),
               Type3=c("10","50","40","22","13","45","15","36","19","20"))

Notice I fixed a typo. We now can transform it. You can read more about it here ( tidy ).

df <- gather(test, key = 'Type', value = 'Value', contains('type'))

The contains indicates which variables to use to fill the value column. key and value are just to indicate which names to give the columns.

> head(df)
  start end  Type Value
1     0 100 Type1   530
2     0 100 Type1   630
3     0 100 Type1   500
4     0 100 Type1   400
5     0 100 Type1   350
6     0 100 Type1   600

We can now easily plot it using ggplot.

ggplot(df, aes(x = Value, group = Type, fill = Type, color = Type)) + 
  geom_density(alpha = 0.3)

Answer 3

First, cast your data to long format (works better for ggplot), then plot

I also created some x-values...

library(data.table)
library(ggplot2)
plotdata <- setDT(test)[, x := seq(0,100,length.out = 10)]
plotdata <- melt( setDT(test), id.vars = c("x"), measure.vars = patterns("^Typ"), value.factor = FALSE )

ggplot( data = plotdata, 
    aes( x = value, 
         color = variable,
         fill = variable)
    ) + 
  geom_density()