Finding outliers in Boxplots via Geom_Boxplot in R Studio

In the first boxplot that I created using GA data, it had ggplot2 + geom_boxplot to show google analytics data summarized by day of week. 

While the min/max, median, 50% of values being within the boxes [inter quartile range] were easier to visualize/understand, these two dots stood out in the boxplot.

If the whiskers from the box edges describes the min/max values, what are these two dots doing in the geom_boxplot?

Using a dplyr package to provide summary stats for geom_boxplot

After asking around, I found out a dplyr package that could provide summary stats for the boxplot [while I still haven't figured out how to add the data labels to the boxplot, the summary table seems like a good start].

This bit of the code creates a summary table that provides the min/max and inter-quartile range.

If you download the Xlsx dataset and then filter out the values where dayofWeek =0, we get the below values:

3, 5, 6, 10, 10, 10, 10, 11,12, 14, 14, 15, 16, 20

N = 14

Central values = 10, 11 [50% of values are above/below these numbers]

Median = (10+11)/2 or 10.5 [matches with the table above]

Smallest value = 3

Largest value = 20 

Lower Quartile Value [Q1]: = (7+1)/2 = 4th value [below median range]= 10

Upper Quartile Value [Q3]: (7+1)/2 = 4th value [above median range] = 14

Inter Quartile Range = 14-10 = 4

If we want to know whether the first value [3] is an outlier here,

Lower outlier limit = Q1 - 1.5 * IQR = 10 - 1.5 *4

Lower outlier limit = 4

Upper outlier limit = Q3 + 1.5 *IQR = 14 + 1.5*4

Upper outlier limit = 20

As 3 is below the outlier limit, the min whisker starts at the next value [5],

As all the max value is 20, the whisker reaches 20 and doesn't have any data value above this point.

And there's the geom_boxplot explained. Kinda cool it does all of this automatically!

Hope you found this blog post helpful