Formatting a data table for normality analysis and plotting-CodePudding

I have the following data in a table that I have inputted into R:

        Group Light Dark
1         One    29   29
2         Two    72   54
3       Three    54   39
4        Four    17    7
5        Five    14    6
6         Six     3    9
7       Seven     8   17

What I want to do next is plot it out onto a graph, with a numerical value on the y axis and on the x axis have two bars for each "Group", one for Light and for Dark, per group. The problem I am having at the moment is I obviously cannot do plot (Group~Light) or plot (Group~Dark) as I have no numerical value on the y axis. Is it possible just to have some advice on how I should format my data and then input it into R, so that I can plot it as mentioned and then carrying tests of statistical significance that will allow me to compare the various groups? Thanks.

CodePudding user response：

To plot your data, you can mix you can tidyr::pivot_longer() them, and then use ggplot2:

library(ggplot2)
library(tidyr)

df %>%
  pivot_longer(!Group, names_to = "color") %>%
  ggplot(aes(x = Group, group = color, fill = color, y = value))  
  geom_bar(stat = 'identity', position = 'dodge')

CodePudding user response：

You're probably looking for barplot.

barplot(t(d[-1]), beside=T, names.arg=d$Group, col=2:3, legend.text=names(d)[-1])

Data:

d <- structure(list(Group = c("One", "Two", "Three", "Four", "Five", 
"Six", "Seven"), Light = c(29L, 72L, 54L, 17L, 14L, 3L, 8L), 
    Dark = c(29L, 54L, 39L, 7L, 6L, 9L, 17L)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7"))