Topic 5: Data Cleaning and filtering

Keep and remove variables

Keeping and Removing Variables in R Programming

In R, you can selectively keep or remove variables (columns) from a data frame using various functions and techniques. This allows you to manipulate and subset data to work with only the variables you need for analysis or visualization.

Keeping Variables:

Using $ Operator: You can keep specific variables by using the $ operator to select them from the data frame.

Example: Keeping Variables using $ Operator

# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Height = c(170, 180, 165, 175, 190),
  Weight = c(70, 80, 60, 75, 85)
)
print(data)

##   ID Age Height Weight
## 1  1  25    170     70
## 2  2  30    180     80
## 3  3  22    165     60
## 4  4  28    175     75
## 5  5  35    190     85

# Keep only the Age and Height variables using $
a<-data$Age
a

## [1] 25 30 22 28 35

# Keep only the Age and Height variables []
selected_data <- data[c("Age", "Height")]
print(selected_data)

##   Age Height
## 1  25    170
## 2  30    180
## 3  22    165
## 4  28    175
## 5  35    190

Using Square Brackets: You can use square brackets with column names to keep specific variables from the data frame.

Example: Keeping Variables using Square Brackets

# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Height = c(170, 180, 165, 175, 190),
  Weight = c(70, 80, 60, 75, 85)
)

# Keep only the Age and Weight variables
selected_data <- data[, c("Age", "Weight")]
print(selected_data)

##   Age Weight
## 1  25     70
## 2  30     80
## 3  22     60
## 4  28     75
## 5  35     85

Removing Variables:

Using $ Operator or Square Brackets with Negation: You can remove specific variables by using the $ operator or square brackets with a negation sign (-) in front of the column names.

Example: Removing Variables using $ Operator and Square Brackets with Negation

# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Height = c(170, 180, 165, 175, 190),
  Weight = c(70, 80, 60, 75, 85)
)

# Remove the ID variable
selected_data <- data[c("Age", "Height", "Weight")]
print(selected_data)

##   Age Height Weight
## 1  25    170     70
## 2  30    180     80
## 3  22    165     60
## 4  28    175     75
## 5  35    190     85

# Alternative method: Remove the ID variable
# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Height = c(170, 180, 165, 175, 190),
  Weight = c(70, 80, 60, 75, 85)
)
selected_data <- data[-1]
print(selected_data)

##   Age Height Weight
## 1  25    170     70
## 2  30    180     80
## 3  22    165     60
## 4  28    175     75
## 5  35    190     85

Using select() from dplyr: If you are using the dplyr package, you can use the select() function to remove variables.

Example: Removing Variables using select() from dplyr

# Load dplyr library
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Height = c(170, 180, 165, 175, 190),
  Weight = c(70, 80, 60, 75, 85)
)

# Remove the ID variable
selected_data <- select(data, -ID)
print(selected_data)

##   Age Height Weight
## 1  25    170     70
## 2  30    180     80
## 3  22    165     60
## 4  28    175     75
## 5  35    190     85

Summary:

You can keep specific variables using the $ operator, square brackets, or the select() function from dplyr.
To remove variables, use the negation sign with the $ operator, square brackets, or the select() function from dplyr.
Choose the method that best suits your data manipulation needs and the packages you are using.

Filtering data and variables

Filtering Data and Variables in R Programming

Filtering data and variables in R involves selecting specific rows or columns from a data frame based on certain conditions or criteria. This process allows you to extract subsets of the data for further analysis or visualization.

Filtering Data (Rows):

Using Logical Indexing: You can use logical indexing to filter rows based on specific conditions.

Example: Filtering Data using Logical Indexing

# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Gender = c("Male", "Female", "Male", "Male", "Female")
)

# Filter rows where Age is greater than 25
filtered_data <- data[data$Age > 25, ]
print(filtered_data)

##   ID Age Gender
## 2  2  30 Female
## 4  4  28   Male
## 5  5  35 Female

Filtering Variables (Columns):

Using $ Operator or Square Brackets: You can use the $ operator or square brackets to select specific columns from a data frame.

Example: Filtering Variables using $ Operator and Square Brackets

# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Gender = c("Male", "Female", "Male", "Male", "Female")
)

# Select only the Age and Gender columns
selected_variables <- data[c("Age", "Gender")]
print(selected_variables)

##   Age Gender
## 1  25   Male
## 2  30 Female
## 3  22   Male
## 4  28   Male
## 5  35 Female

Using select() from dplyr: If you are using the dplyr package, you can use the select() function to filter columns.

Example: Filtering Variables using select() from dplyr

# Load dplyr library
library(dplyr)

# Sample data frame
data <- data.frame(
  ID = 1:5,
  Age = c(25, 30, 22, 28, 35),
  Gender = c("Male", "Female", "Male", "Male", "Female")
)
print(data)

##   ID Age Gender
## 1  1  25   Male
## 2  2  30 Female
## 3  3  22   Male
## 4  4  28   Male
## 5  5  35 Female

# Select only the Age and Gender columns
selected_variables <- select(data, Age, Gender)
print(selected_variables)

##   Age Gender
## 1  25   Male
## 2  30 Female
## 3  22   Male
## 4  28   Male
## 5  35 Female