Create a missing value ratio filter

The house_sales_df data frame contains a target variable price and a variety of predictors that describe individual houses and determine their selling prices. Several of the features have a varying number of missing values. If the missing value ratio is too high, then the feature will not be very informative in predicting the price of the house. These features can be removed. In this exercise, you will calculate the missing value ratio for each column. This will help you think about an appropriate threshold for each column.

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This exercise is part of the course

Dimensionality Reduction in R

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Exercise instructions

Store the total number of rows in house_sales_df into n.
Calculate the missing value ratios for each column in house_sales_df and store them in missing_vals_df.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate total rows
___ <-  ___(___)

# Calculate missing value ratios
___ <- ___ %>% 
  ___(___(___(), ~ ___(___(.)))) %>% 
  pivot_longer(everything(), names_to = "feature", values_to = "num_missing_values") %>% 
  mutate(missing_val_ratio = ___ / ___)

# Display missing value ratios
missing_vals_df

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