Scaling transforms datasets to a common scale, usually to compare datasets with different scoring mechanisms. It allows measurement of relative effects across scales. The scale() function scales and centers data by dividing column values by the standard deviation and subtracting from the column mean. Recent updates to the forecast package allow using ggplot for plotting time series forecasts, with the autoplot() function creating ggplot graphics from time series objects.

1. Scaling in R
&
ggplot + forecast
Ed Goodwin
Houston R Users Group

2. What is scaling?
Scaling is when you need to transform a dataset to
another size scale…usually to analyze with a
another data set
Example: comparing two survey datasets with
different scoring mechanisms
It allows you to measure the relative effects of
datasets on different scales

3. Let’s make some samples

4. How do we common-size our
samples?
• With the scale( ) function
• scale( ) allows you to scale and center the data

5. Scaling
Divide all column values
by the standard deviation
to arrive at a common
scaling

6. Centering
Subtract all column values
from the column mean to
get
a common center value

7. Centered & Scaled

8. You could also do this
yourself (z-transform)

9. Forecast + ggplot
Recent updates to Rob Hyndman’s forecast
package include ability to use ggplot for plotting
time series forecasts.
See http://robjhyndman.com/hyndsight/forecast7-
ggplot2/ for more details

10. Using ggplot with time series
use autoplot( ) function with time series objects to
create ggplot graphics.