R

I am using the mpg dataset included in the ggplot2 package. #load packages library(tidyverse) library(tidytext) #view data glimpse(mpg) ## Rows: 234 ## Columns: 11 ## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "… ## $ model <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "… ## $ displ <dbl> 1.

Please check McNeish (2018) for details. Here, I am using the NSCH dataset as an example. #load packages library(haven) library(tidyverse) library(userfriendlyscience) #import data data<-read_sav("nsch.sav") #clear the current graphics frame and get ready for the next plot plot.

Load Packages library(fastDummies) library(tidyverse) library(psych) Create a DataSet # Create a vector of race scores race <- c("White", "Black", "Asian", "Hispanic", "Other") # Generate random income values for each race (100 cases) set.

Introduction In this post, I am going to demonstrate how to display Chinese characters in ggplot2 #loading necessary packages library(rvest) library(xml2) library(tidyverse) library(tidytext) library(knitr) Let’s get our text data by scraping Chien-Ming Wang’s page on Wikipedia.

Introduction Recoding values is one of the most common tasks a researcher needs to do before data analysis. For me, often I need to prepare my data in R first before using it for advanced statistical analyses in Mplus.

Introduction In this post, I am going to demonstrate how to compute composite scores or means aggregated over multiple items. There are at least two approaches to achieve the goal.