Following is a list of command summary for creating data frames by extracting multiple columns from existing data frame based on following criteria, whose sample is provided later in this article:
Following commands have been based on diamonds data frame which is loaded as part of loading ggplot2 library.
Following is how the diamonds data frame looks like:
#1: Create data frame with selected columns using column indices
# Displays column carat, cut, depth
dfnew1 <- diamonds[,c(1,2,5)]
#2: Create data frame with selected columns using column indices with sequences
# Displays column carat, cut, color, depth, price, x
dfnew2 <- diamonds[, c(1:3, 5, 7:8)]
#3: Create data frame with selected columns using data.frame command
# Displays column carat, cut, color
dfnew3 <- data.frame(diamonds$carat, diamonds$cut, diamonds$color)
names(dfnew3) <- c("Carat", "Cut", "Color")
#4: Create data frame using selected columns and column names
# Displays column carat, depth, price
dfnew4 <- diamonds[,c("carat", "depth", "price")]
#5: Create data frame using subset command and column names
# Displays column color, carat, price
dfnew5 <- subset(diamonds, select=c("color", "carat", "price"))
#6: Create data frame using subset command and column indices
# Displays column carat, cut, color, depth
dfnew6 <- subset(diamonds, select=c(1:3, 5))
In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…
Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…
With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…
Anxiety is a common mental health condition that affects millions of people around the world.…
In machine learning, confounder features or variables can significantly affect the accuracy and validity of…
Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…