Solved – Making boxplots of hourly data in R

boxplotdata visualizationr

I have air temperature measurements from two fixed locations measured at hourly intervals. The code below is a random set of numbers used to represent the format of my data:

set.seed(1)
RandData <- rnorm(8760*2,sd=10)
Locations <- rep(c('UK','France'),each=8760)

Date = seq(from=as.POSIXct("1991-01-01 00:00"), 
              to=as.POSIXct("1991-12-31 23:00"), length=8760)

Final <- data.frame(Loc = Locations,
                    Doy = as.numeric(format(Date,format = "%j")),
                    Tod = as.numeric(format(Date,format = "%H")),
                    Temp = RandData)

I can plot the variation in temperature as a funtion of day of year with the following code:

require(lattice)
xyplot(Temp~Doy | Loc, data = Final, col = "black", type = "l")

This would show the annual pattern of the data. However, what I would like to do is to produce boxplots of the variation in temperature for different times of the day. So, for the example above I would like two figures, one for each country and each figure should be composed of box plots showing the variation in temperature at 00:00, 01:00… and so on, referring to Final$Tod. How can this be achieved?

Many thanks for your help.

Best Answer

Something like this?

library(ggplot2)
ggplot(Final, aes(x = as.factor(Tod), y = Temp)) + geom_boxplot()  + facet_wrap(~ Loc)

enter image description here

Related Solutions

Solved – Combine multiple boxplots in a lattice

Sam,

I think I understood what you are after, so let me know if I've misinterpreted anything:

You want a separate box_plot for the ratio of each pairs of columns. There are 15 ratios we are interested in...(column 6 / column 7, column 8 / column 9, etc.)
This plot should have a separate "window" or facet for each annotation, for which there are 23 different annotations.

Assuming both of those are right, I think this will give you what you are after. First, we will make the 15 new ratio columns with a for-loop and some indexing. After we make these 15 new columns, we will melt the data into long format for easy plotting with ggplot2. Since we are only interested in the columns annotation and the new ratio columns, we'll specify those in the call to melt. Then it is a relatively straight forward call to ggplot to specify the axes and faceting variable.

These plots don't make much sense with 10 rows of data, but I think it will look better with your full dataset.

library(ggplot2)

#EDIT: this removes the call to cbind which should improve performance.
for (i in seq(6, ncol(df), by = 2)) {
    df[,  paste(i, i+1, sep = "_", collapse = "")] <- df[, i ] / df[, i + 1 ]

}

df.m <- melt(df, id.vars = "annotation", measure.vars = 36:ncol(df))
#Note that we use the column name for the id.vars and the column order for
#the measure.vars. In the case of the latter, this is simply to save on 
#typing.

ggplot(data = df.m, aes(x = variable, y = value)) + 
    geom_boxplot() + 
    facet_wrap(~ annotation) +
    coord_flip()

Solved – Labeling boxplots in R

I think you will find this produces something like your hand-drawn diagram.

data    <- c(0.4, 0.7, 0.75, 0.82, 0.9)
endaxis <- c(0, 1)  # endpoints of axis
datamm  <- c(min(data), max(data))
boxplot(data, horizontal = TRUE, range = 0, ylim = endaxis,
                    axes = FALSE, col = "grey", add = FALSE)
arrows(endaxis, 1,  datamm, 1,  code = 1, angle = 90, length = 0.1)
valuelabels <- c(endaxis[1], round(fivenum(data)[2], digits = 2) ,
                 round(fivenum(data)[4], digits = 2), endaxis[2]  ) 
text(x = valuelabels, y = c(1.05, 1.25, 1.25, 1.05), labels = valuelabels)

R boxplot with value labels

There are probably better ways of doing it. You may need to adapt it to fit your ROC plot, including changing add = FALSE

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