Histograms are also a quick way to visually inspect and compare outcome variables.
Here is another example of using the Spark histogram function to contrast the mean values of body mass index for diabetic versus non-diabetic patients in the study. For the first bar chart, we can see a peak bar of about 38.9 BMI, versus a peak bar of 29.8 for non-diabetic patients. This suggests that BMI will be an important variable in any model we develop:
This code uses the SparkR histogram function to compute a histogram with 10 bins. The centroids gives the center value for each of the 10 bins. The most frequently occurring bar is the bar with a center value of 38.9 with a count of about 50,000. This type of histogram is useful for quickly getting a sense of the distribution of variables, but is somewhat lacking in labeling, and controlling various elements since as scales and ranges. If you wish to fine tune some of the elements you may want to start by using a collect() function...