# How to Extract Factor Variables from DataFrame in R

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:

1. `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.
2. `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.
3. `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.

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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