In R, you can extract factor variables from a dataframe using various methods. Here are a few common ways to achieve this:

Let's create a sample data frame called `mydata`

having 4 variables (ID, Gender, Region, Grade).

# Create a sample data frame mydata <- data.frame( ID = 1:5, Gender = c("Male", "Female", "Male", "Male", "Female"), Region = c("North", "South", "East", "West", "North"), Grade = factor(c("A", "B", "A", "C", "B")) )

You can use the `str()`

function to see the structure or data type of a data frame. As shown in the result below, the variable "Grade" is a factor variable.

str(mydata)

'data.frame': 5 obs. of 4 variables: $ ID : int 1 2 3 4 5 $ Gender: chr "Male" "Female" "Male" "Male" ... $ Region: chr "North" "South" "East" "West" ... $ Grade : Factor w/ 3 levels "A","B","C": 1 2 1 3 2

## How to Extract all Factor Variables in R

In the dataframe named "mydata", we know we have a factor variable "Grade". When we have multiple variables in a dataframe, we don't know the name of the factor variables in advance.

In **base R**, you can extract multiple factor 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.factor`

selects only the factor columns.

Base R

factor_columns <- mydata[sapply(mydata, is.factor)] print(factor_columns)

dplyr

library(dplyr) # Select factor columns using select_if() factor_columns <- mydata %>% select_if(is.factor) print(factor_columns)

## Extract Factor Variables with more than 2 Unique Levels in R

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

mydata <- data.frame( ID = 1:5, Gender = c("Male", "Female", "Male", "Male", "Female"), Region = factor(c("North", "South", "East", "West", "North")), Grade = factor(c("A", "B", "A", "B", "B")) )

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 factor data type (is.factor(col)) and if it has more than 2 unique levels (nlevels(col) > 2).

# Extract factor columns with more than 2 unique categories factor_cols0 <- sapply(mydata, function(col) is.factor(col) && nlevels(col) > 2) # Select columns based on the extracted factor column indicators factor_cols <- mydata[factor_cols0] print(factor_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 factor data type (is.factor(col)) and have more than 2 unique categories (nlevels(col) > 2).

library(dplyr) factor_cols <- mydata %>% select_if(function(col) is.factor(col) && nlevels(col) > 2) print(factor_cols)

## Extracting Factor Variables with No Missing Values in R

Let's say you want to keep factor 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

factor_cols <- sapply(mydata, is.factor) factor_no_missing <- colSums(is.na(mydata[factor_cols])) == 0 factor_no_missing_cols <- mydata[factor_cols] [factor_no_missing]

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

`factor_cols <- sapply(mydata, is.factor):`

- This line creates a logical vector
`factor_cols`

where each element corresponds to a column in the dataframe`mydata`

. - It checks whether each column is factor using the
`is.factor()`

function.

- This line creates a logical vector
`factor_no_missing <- colSums(is.na(mydata[factor_cols])) == 0:`

- This line calculates a logical vector
`factor_no_missing`

which indicates for each factor column whether it has no missing values (NA). `mydata[factor_cols]`

subsets the original dataframe to include only the factor columns.`is.na(mydata[factor_cols])`

creates a logical dataframe with`TRUE`

where there are missing values and`FALSE`

otherwise.`colSums(is.na(mydata[factor_cols]))`

calculates the count of missing values in each factor column.`colSums(is.na(mydata[factor_cols])) == 0`

checks whether the count of missing values in each column is equal to zero.

- This line calculates a logical vector
`factor_no_missing_cols <- mydata[factor_cols][factor_no_missing]:`

- This line creates a new dataframe
`factor_no_missing_cols`

. `mydata[factor_cols]`

subsets the original dataframe to include only the factor columns.`[factor_no_missing]`

then further subsets these factor columns using the`factor_no_missing`

logical vector.- This subset operation effectively keeps only the columns that are both factor and have no missing values.

- This line creates a new dataframe

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.factor))** selects only the factor columns. **select(where(~ all(!is.na(.))))** selects columns where all values are not missing (NA).

library(dplyr) factor_no_missing_cols <- mydata %>% select(where(is.factor)) %>% select(where(~ all(!is.na(.))))

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