How to plot 2 categorical variables on the X axis and two continuous variables as "fill" using the ggplot2 package?
I have a dataset that has two categorical variables Year
and Category
and two continuous variables TotalSales
and AverageCount
.
Year Category TotalSales AverageCount
1 2013 Beverages 102074.29 22190.06
2 2013 Condiments 55277.56 14173.73
3 2013 Confections 36415.75 12138.58
4 2013 Dairy Products 30337.39 24400.00
5 2013 Seafood 53019.98 27905.25
6 2014 Beverages 81338.06 35400.00
7 2014 Condiments 55948.82 19981.72
8 2014 Confections 44478.36 24710.00
9 2014 Dairy Products 84412.36 32466.00
10 2014 Seafood 65544.19 14565.37
In MS Excel we can happily get a pivot graph for the same table: Year and Category as AXIS, TotalSales and AverageCount as sigma values.
Using R, how do I draw a graph like the one shown in the image where the categorical variables are shown as multiple layers on the same graph?
PS One option I could see is to split the dataframe into two separate dataframes (one for the year 2013 and the other for the year 2014 in our case) and draw two graphs on one separate graph, spread over multiple rows to get the same effect. But is there a way to do this as shown above?
Examples of data used above
dat <- structure(list(Year = c(2013L, 2013L, 2013L, 2013L, 2013L, 2014L,
2014L, 2014L, 2014L, 2014L), Category = structure(c(1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("Beverages", "Condiments",
"Confections", "Dairy Products", "Seafood"), class = "factor"),
TotalSales = c(102074.29, 55277.56, 36415.75, 30337.39, 53019.98,
81338.06, 55948.82, 44478.36, 84412.36, 65544.19), AverageCount = c(22190.06,
14173.73, 12138.58, 24400, 27905.25, 35400, 19981.72, 24710,
32466, 14565.37)), .Names = c("Year", "Category", "TotalSales",
"AverageCount"), class = "data.frame", row.names = c(NA, -10L
)
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You need to reformat your data first, as @EDi showed you how in one of your old questions ( ggplot: multi-line (multiple continuous variable) plot ) and @docendo discimus suggested in the comments.
library(reshape2)
dat_l <- melt(dat, id.vars = c("Year", "Category"))
Then you can use the cut like this:
library(ggplot2)
p <- ggplot(data = dat_l, aes(x = Category, y = value, group = variable, fill = variable))
p <- p + geom_bar(stat = "identity", width = 0.5, position = "dodge")
p <- p + facet_grid(. ~ Year)
p <- p + theme_bw()
p <- p + theme(axis.text.x = element_text(angle = 90))
p
If you are particularly interested in making the plot more consistent with the Excel-look, there are several strategies in this answer: How do I plot plots with nested axis categories? ...
Your original data in a more embeddable format:
dat <- structure(list(Year = c(2013L, 2013L, 2013L, 2013L, 2013L, 2014L,
2014L, 2014L, 2014L, 2014L), Category = structure(c(1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("Beverages", "Condiments",
"Confections", "Dairy Products", "Seafood"), class = "factor"),
TotalSales = c(102074.29, 55277.56, 36415.75, 30337.39, 53019.98,
81338.06, 55948.82, 44478.36, 84412.36, 65544.19), AverageCount = c(22190.06,
14173.73, 12138.58, 24400, 27905.25, 35400, 19981.72, 24710,
32466, 14565.37)), .Names = c("Year", "Category", "TotalSales",
"AverageCount"), class = "data.frame", row.names = c(NA, -10L
))
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