In R, boxplot()) (and whisker plot) is created using the
boxplot() function takes in any number of numeric
vectors, drawing a boxplot for each
You can also pass in a list (or
data frame) with numeric vectors as
its components. Let us use the built-in dataset
has "Daily air quality measurements in New York, May to September 1973."-R
> str(airquality) 'data.frame': 153 obs. of 6 variables: $ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ... $ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ... $ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ... $ Temp : int 67 72 74 62 56 66 65 59 61 69 ... $ Month : int 5 5 5 5 5 5 5 5 5 5 ... $ Day : int 1 2 3 4 5 6 7 8 9 10 ...
Let us make a boxplot for the ozone readings.
We can see that data above the median is more dispersed. We can also notice two outliers at the higher extreme.
We can pass in additional parameters to control the way our plot looks. You
can read about them in the help section
Some of the frequently used ones are,
main-to give the title,
ylab-to provide labels for the axes,
col to define color etc.
Additionally, with the argument
horizontal = TRUE we can plot it
horizontally and with
notch = TRUE we can add a notch to the box.
boxplot(airquality$Ozone, main = "Mean ozone in parts per billion at Roosevelt Island", xlab = "Parts Per Billion", ylab = "Ozone", col = "orange", border = "brown", horizontal = TRUE, notch = TRUE )
Return Value of boxplot()
boxplot() function returns a list with 6 components shown as
> b <- boxplot(airquality$Ozone) > b $stats [,1] [1,] 1.0 [2,] 18.0 [3,] 31.5 [4,] 63.5 [5,] 122.0 attr(,"class") 1 "integer" $n  116 $conf [,1] [1,] 24.82518 [2,] 38.17482 $out  135 168 $group  1 1 $names  "1"
As we can see above, a list is returned which has
the position of the upper/lower extremes of the whiskers and box along with
n-the number of observation the boxplot is drawn with (notice that
NA's are not taken into account)
conf-upper/lower extremes of the notch,
out-value of the outliers
group-a vector of the same length as out whose elements indicate to which group the outlier belongs and
names-a vector of names for the groups.
We can draw multiple boxplots in a single plot, by passing in a list, data frame or multiple vectors.
Let us consider the
Temp field of
airquality dataset. Let us also generate normal distribution with
the same mean and standard deviation and plot them side by side for
# prepare the data ozone <- airquality$Ozone temp <- airquality$Temp # gererate normal distribution with same mean and sd ozone_norm <- rnorm(200,mean=mean(ozone, na.rm=TRUE), sd=sd(ozone, na.rm=TRUE)) temp_norm <- rnorm(200,mean=mean(temp, na.rm=TRUE), sd=sd(temp, na.rm=TRUE))
Now we us make 4 boxplots with this data. We use the arguments
names to denote the place and label.
boxplot(ozone, ozone_norm, temp, temp_norm, main = "Multiple boxplots for comparision", at = c(1,2,4,5), names = c("ozone", "normal", "temp", "normal"), las = 2, col = c("orange","red"), border = "brown", horizontal = TRUE, notch = TRUE )
Boxplot form Formula
boxplot() can also take in formulas of the form
y is a numeric vector which is grouped
according to the value of
For example, in our dataset
be our numeric vector. Month can be our grouping variable, so that we get the
boxplot for each month separately. In our dataset, month is in the form of
number (1=January, 2-Febuary and so on).
boxplot(Temp~Month, data=airquality, main="Different boxplots for each month", xlab="Month Number", ylab="Degree Fahrenheit", col="orange", border="brown" )
It is clear from the above figure that the month number 7 (July) is relatively hotter than the rest.