How to Extract Numeric Variables from Dataframe in R

In R, you can extract numeric columns from a data frame using various methods. Here are a few common ways to achieve this:

Let's create a sample data frame called mydata having 3 variables (name, age, height).

# Create a sample data frame
mydata <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  age = c(25, 30, 28),
  height = c(165.5, 180.0, 172.3)
)

How to Extract all Numeric Variables in R

In the dataframe named "mydata", we have two numeric columns "age" and "height". When we have multiple variables in a dataframe, we don't know the name of the numeric columns in advance.

In base R, you can extract multiple numeric columns (variables) using sapply function. The sapply function is a part of apply family of functions. They perform multiple iterations (loops) in R.

In dplyr package, the select_if function is used to select columns based on a condition. In this case, is.numeric selects only the numeric columns.

Base R

numeric_columns <- mydata[sapply(mydata, is.numeric)]
print(numeric_columns)

dplyr

library(dplyr)

# Select numeric columns using select_if()
numeric_columns <- mydata %>% select_if(is.numeric)
print(numeric_columns)

Extracting Numeric Variables with No Missing Values in R

Let's say you want to keep numeric columns that have no missing values in R.

# Create a sample data frame
mydata <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Dave"),
  age = c(25, 30, 28, NA),
  height = c(165.5, 180.0, 172.3, 189),
  weight = c(NA, NA, 72, 74)
)

Base R

numeric_cols <- sapply(mydata, is.numeric)
numeric_no_missing <- colSums(is.na(mydata[numeric_cols])) == 0
numeric_no_missing_cols <- mydata[numeric_cols] [numeric_no_missing]

Here's a step-by-step breakdown of the code:

1. numeric_cols <- sapply(mydata, is.numeric):
   • This line creates a logical vector numeric_cols where each element corresponds to a column in the dataframe mydata.
   • It checks whether each column is numeric using the is.numeric() function.
2. numeric_no_missing <- colSums(is.na(mydata[numeric_cols])) == 0:
   • This line calculates a logical vector numeric_no_missing which indicates for each numeric column whether it has no missing values (NA).
   • mydata[numeric_cols] subsets the original dataframe to include only the numeric columns.
   • is.na(mydata[numeric_cols]) creates a logical dataframe with TRUE where there are missing values and FALSE otherwise.
   • colSums(is.na(mydata[numeric_cols])) calculates the count of missing values in each numeric column.
   • colSums(is.na(mydata[numeric_cols])) == 0 checks whether the count of missing values in each column is equal to zero.
3. numeric_no_missing_cols <- mydata[numeric_cols][numeric_no_missing]:
   • This line creates a new dataframe numeric_no_missing_cols.
   • mydata[numeric_cols] subsets the original dataframe to include only the numeric columns.
   • [numeric_no_missing] then further subsets these numeric columns using the numeric_no_missing logical vector.
   • This subset operation effectively keeps only the columns that are both numeric and have no missing values.

dplyr

If you want to keep columns that have no missing values, you can use the select() function with where() in dplyr. select(where(is.numeric)) selects only the numeric columns. select(where(~ all(!is.na(.)))) selects columns where all values are not missing (NA).

library(dplyr)

numeric_no_missing_cols <- mydata %>%
  select(where(is.numeric)) %>%
  select(where(~ all(!is.na(.))))

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About Author:

Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.

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