How to Extract Character Variables from DataFrame in R

In R, you can extract character 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, city, age).

# Create a sample data frame
mydata <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  city = c("Los Angeles", "New York", "Dallas"),
  height = c(165.5, 180.0, 172.3)
)

How to Extract all Character Variables in R

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

In base R, you can extract multiple character 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.character selects only the character columns.

Base R

character_columns <- mydata[sapply(mydata, is.character)]
print(character_columns)

dplyr

library(dplyr)

# Select character columns using select_if()
character_columns <- mydata %>% select_if(is.character)
print(character_columns)

Extract Character Variables with more than 2 Unique Categories in R

Let's modify the "mydata" dataframe by adding one more character variable for demonstration purpose.

mydata <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Jon"),
  product = c("A", "A", "A", "B"),
  sales = c(21, 32, 45, 36)
)

Base R

In this code, we're using the sapply function to iterate through each column of the "mydata" data frame. For each column, we check if it's of character data type (is.character(col)) and if it has more than 2 unique categories (length(unique(col)) > 2).

# Extract character columns with more than 2 unique categories
character_cols0 <- sapply(mydata, function(col) is.character(col) && length(unique(col)) > 2)

# Select columns based on the extracted character column indicators
character_cols <- mydata[character_cols0]
print(character_cols)

dplyr

In this code, we're using the dplyr package to work with data frames. The select_if function is used to select columns based on a condition. In this case, we're selecting columns that are of character data type (is.character(col)) and have more than 2 unique categories (length(unique(col)) > 2).

library(dplyr)
character_cols <- mydata %>%
  select_if(function(col) is.character(col) && length(unique(col)) > 2)

print(character_cols)

Extracting Character Variables with No Missing Values in R

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

# Create a sample data frame
mydata <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Jon"),
  city = c("Los Angeles", "New York", "Dallas", NA),
  height = c(165.5, 180.0, 172.3, 181)
)

Base R

character_cols <- sapply(mydata, is.character)
character_no_missing <- colSums(is.na(mydata[character_cols])) == 0
character_no_missing_cols <- mydata[character_cols] [character_no_missing]

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

1. character_cols <- sapply(mydata, is.character):
   • This line creates a logical vector character_cols where each element corresponds to a column in the dataframe mydata.
   • It checks whether each column is character using the is.character() function.
2. character_no_missing <- colSums(is.na(mydata[character_cols])) == 0:
   • This line calculates a logical vector character_no_missing which indicates for each character column whether it has no missing values (NA).
   • mydata[character_cols] subsets the original dataframe to include only the character columns.
   • is.na(mydata[character_cols]) creates a logical dataframe with TRUE where there are missing values and FALSE otherwise.
   • colSums(is.na(mydata[character_cols])) calculates the count of missing values in each character column.
   • colSums(is.na(mydata[character_cols])) == 0 checks whether the count of missing values in each column is equal to zero.
3. character_no_missing_cols <- mydata[character_cols][character_no_missing]:
   • This line creates a new dataframe character_no_missing_cols.
   • mydata[character_cols] subsets the original dataframe to include only the character columns.
   • [character_no_missing] then further subsets these character columns using the character_no_missing logical vector.
   • This subset operation effectively keeps only the columns that are both character 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.character)) selects only the character columns. select(where(~ all(!is.na(.)))) selects columns where all values are not missing (NA).

library(dplyr)

character_no_missing_cols <- mydata %>%
  select(where(is.character)) %>%
  select(where(~ all(!is.na(.))))

Related Posts

R Tutorials : Top 100 R Tutorials

Spread the Word!
Share Share Tweet

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.

While I love having friends who agree, I only learn from those who don't
Let's Get Connected Email LinkedIn