How can I make a time-series boxplot in ggplot2?

Question

I am doing a Bayesian analysis. I have monthly posterior means of sales data for 100 stores. I want to create a boxplot using ggplot which visualizes this data. The problem I am facing is I'm not sure how to let ggplot know that there is 100 columns and 12 rows.

Here is a sample of the data. Here is a link to the data It is very small, 13 rows of months and 100 columns representing 100 stores. The 13th row is the means.

> head(t.data)
               1          2         3         4          5           6          7          8          9        10
Month1 -26.25917   1.740833 151.74083 67.740833  21.740833  -42.259167  -1.259167  49.740833  -53.25917 -98.25917
Month2 -27.25917  -9.259167 117.74083 74.740833  -2.259167    5.740833  64.740833  59.740833  -41.25917 -59.25917
Month3 -32.25917 -68.259167  42.74083 79.740833 -61.259167 -113.259167  26.740833 -36.259167  -81.25917 -32.25917
Month4 -37.25917  -9.259167 128.74083 -6.259167 -14.259167  -38.259167  69.740833  28.740833  -46.25917 -16.25917
Month5  15.74083   5.740833 102.74083 89.740833 -57.259167  -60.259167 112.740833 -28.259167  -42.25917 -41.25917
Month6 -22.25917  -9.259167  82.74083 11.740833 -42.259167  -56.259167  42.740833   4.740833 -101.25917 -58.25917
               11        12        13         14        15        16         17        18          19         20
Month1   47.74083 160.74083 -63.25917   8.740833  58.74083  20.74083  64.740833 -13.25917 -35.2591667  -78.25917
Month2   38.74083 153.74083 -36.25917 -57.259167  46.74083 -71.25917  22.740833  23.74083 -29.2591667 -108.25917
Month3   37.74083  77.74083 -29.25917 -49.259167 127.74083 -63.25917 -18.259167 -34.25917 -98.2591667 -143.25917
Month4   49.74083 189.74083 -46.25917 -54.259167  97.74083 -27.25917  55.740833 -43.25917 -54.2591667  -82.25917
Month5  -14.25917  78.74083  18.74083 -16.259167  47.74083 -13.25917  19.740833 -22.25917   0.7408333  -91.25917
Month6 -164.25917  83.74083  10.74083 -22.259167 -14.25917 -33.25917  -4.259167 -16.25917 -34.2591667 -105.25917

Essentially then, how would I replicate the boxplot function of base R using this type (timeseries) of data? Here is a reproducible example of the standard R boxplot. Here is a link to the q.csv data.

t.data <- read.csv("t.data.csv", header=TRUE)  
q <- read.csv("q.csv", header=TRUE)
colnames(t.data) <- paste("", 1:100, sep = "")  
boxplot(t.data,xlab="Store Number",ylab="Effect of Promo on Sales (centered)",outline=FALSE,
            main="Data versus posterior of the spaial random effect")

lines(q[1:100,1],col=2,lty=2)
lines(q[1:100,2],col=2,lty=1)
lines(q[1:100,3],col=2,lty=2)

legend("topright",c("Median","95% interval"),lty=1:2,col=2,bg=gray(1),inset=0.05)

mtoto · Accepted Answer · 2016-05-01 20:49:49Z

1

We need to melt t.data first and then we can use ggplot2.

library(ggplot2)
ggplot(melt(t.data), aes(variable, value)) + 
  geom_boxplot()

answered May 1, 2016 at 20:49

mtoto

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4 Comments

mtoto Over a year ago

lines of what exactly?

Jason Matney Over a year ago

The median and 95% interval lines, shown in the example as lines(q[1:100,1],col=2,lty=2) lines(q[1:100,2],col=2,lty=1) lines(q[1:100,3],col=2,lty=2)

Jason Matney Over a year ago

my apologies, I have added a link to the q data and a line to load it.

Jason Matney Over a year ago

Nevermind, I figured it out! Thank you for your help!

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