After imputing the missing values, one should perform an exploratory analysis, which involves using a visualization plot and an aggregation method to summarize the data characteristics. The result helps the user gain a better understanding of the data in use. The following recipe will introduce how to use basic plotting techniques with a view to help the user with exploratory analysis.
This recipe needs the previous recipe to be completed by imputing the missing value in the Ozone
and Solar.R
attribute.
Perform the following steps to explore and visualize data:
- First, you can use a bar plot and histogram to generate descriptive statistics for each attribute, starting with Ozone. The following code gives us a bar plot for Ozone Observations:
> barplot(table(mydata$Ozone), main="Ozone Observations", xlab="O bservations", ylab="Frequency")
Ozone observation
- We can generate the bar plot of
Temp
using the following code:
...