Apply a missing value ratio filter
Now that you have calculated the missing value ratios, you can create a filter using a missing value threshold. In this exercise, we will select an arbitrary, but reasonable, missing value ratio threshold and apply it to all the columns. In the real world, you will think critically and customize the threshold to each feature.
The missing_vals_df
which contains the ratios you calculated in the last exercise and the house_sales_df
data frame are both available for your use. The tidyverse
package has also been loaded for you.
This exercise is part of the course
Dimensionality Reduction in R
Exercise instructions
- Use
missing_vals_df
and a threshold of 0.5 to create a missing values ratio filter and store it inmissing_vals_filter
. - Apply
missing_vals_df
tohouse_sales_df
to reduce its dimensionality and store the new data frame infiltered_house_sales_df
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create the missing values filter
___ <- ___ %>%
___(___ <= ___) %>%
___(___)
# Apply the missing values filter
filtered_house_sales_df <- ___ %>%
___(___)
# Display the first five rows of data
___ %>% ___(___)