How to delete columns in a data frame in r with more than x% of missing values?

This is a common question when we are programming in R. Delete Columns is one of the most common tasks when dealing with data wrangling in any programming language. Then, here is how you can delete columns in a data frame using R with more than x% of missing values:

# Create a sample data frame
df <- data.frame(
  v1 = c(1, 2, 3, NA, 5),
  v2 = c(1, NA, 3, 4, 5),
  v3 = c(NA, NA, NA, 4, 5),
  v4 = c(1, 2, 3, 4, 5)
)

# Calculate the percentage of missing values in each column
missing_values <- colMeans(is.na(df))

# Identify the columns with more than x% of missing values
threshold <- 0.3
keep_cols <- which(missing_values <= threshold)

# Keep only the columns with less than x% of missing values
df <- df[, keep_cols]

This code will first create a sample data frame with some missing values. Then, it will calculate the percentage of missing values in each column. Finally, it will identify the columns with more than x% of missing values and keep only the columns with less than x% of missing values.

Here is an explanation of the code:

• The colMeans() function calculates the mean of a vector of logical values. In this case, the vector of logical values is is.na(df), which indicates whether each value in the data frame is missing. The colMeans() function will return a vector of the mean percentage of missing values in each column.
• The which() function returns the indices of the elements in a vector that meet a certain condition. In this case, the condition is that the percentage of missing values in a column is less than or equal to the threshold.
• The keep_cols variable will contain the indices of the columns that we want to keep.
• The df[, keep_cols] expression will select the columns from the data frame that we want to keep.

That is for today. Any doubt you can ask in the comments below.