bibliometrix
bibloshiny
bibentry()
bibentry()
library(grid)
library(tools)
library(Matrix)
library(boot)
library(class)
library(cluster)
library(codetools)
library(compiler)
library(grid)
library(lattice)
library(MASS)
library(KernSmooth)
library(mgcv)
detach("package:nlme", unload = TRUE)
library(nlme)
library(nnet)
library(parallel)
library(rpart)
library(spatial)
detach("package:spatial", unload = TRUE)
library(spatial)
library(splines)
library(stats4)
library(survival)
library(tcltk)
library(tools)
install.packages("bibliometrix")
library(bibliometrix)
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
install.packages("shiny")
package_version("shiny")
packageVersion("shiny")
install.packages("bibliometrix")
library(bibliometrix)
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
install.packages(c("bibliometrix", "bit", "bit64", "bookdown", "bslib", "chromote", "class", "cli", "cluster", "cpp11", "curl", "data.table", "evaluate", "foreign", "graphlayouts", "igraph", "KernSmooth", "later", "MASS", "Matrix", "nlme", "nnet", "openssl", "openxlsx", "pagedown", "pillar", "processx", "promises", "purrr", "R6", "Rcpp", "RcppArmadillo", "rlang", "rpart", "shiny", "spatial", "stringdist", "survival", "systemfonts", "webshot2", "xfun", "XML", "zip"))
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
bibliometrix::biblioshiny()
install.packages("ggplot2")
library(ggplot2)
ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point()
install.packages("dplyr")
library(dplyr)
head(starwars)
library(dplyr)
head(starwars)
install.packages("dplyr")
install.packages("ggplot2")
library(dplyr)
library(ggplot2)
four_cyl_cars <- filter(mtcars, cyl == 4)
ggplot(data = four_cyl_cars, aes(x = wt, y = mpg)) +
geom_point(color = "red", size = 3)
four_cyl_cars <- filter(mtcars, cyl == 4)
# ====================================================
# STUDENT TASK: INSTALL, LOAD, AND RUN YOUR FIRST CODE
# ====================================================
# Step 1: Install the package from CRAN (App Store)
install.packages("ggplot2")
# Step 2: Load the package into memory
library(ggplot2)
# Step 3: Run a simple plot using R's built-in 'mpg' dataset
# (This creates a scatter plot of Engine Size vs Highway Mileage)
ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point()
install.packages("dplyr")
install.packages("ggplot2")
library(dplyr)
library(ggplot2)
library(dplyr)
library(ggplot2)
ggplot(data = four_cyl_cars, aes(x = wt, y = mpg)) +
geom_point(color = "red", size = 3)
employees <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eva"),
Department = c("HR", "IT", "IT", "Sales", "Sales"),
Salary = c(50000, 70000, 85000, 60000, 65000),
Experience_Years = c(2, 5, 8, 3, 4)
library(dplyr)
employees <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eva"),
Department = c("HR", "IT", "IT", "Sales", "Sales"),
Salary = c(50000, 70000, 85000, 60000, 65000),
Experience_Years = c(2, 5, 8, 3, 4)
)
employees
library(dplyr)
library(ggplot2)
high_earners <- filter(employees, Salary > 60000)
high_earners
ggplot(data = employees, aes(x = Name, y = Salary, fill = Department)) +
geom_col()
students <- data.frame(
Name = c("John", "Sara", "Mike", "Emma", "Alex"),
Study_Hours = c(2, 5, 8, 4, 7),
Exam_Score = c(55, 72, 95, 68, 88)
)
students
top_students <- filter(students, Study_Hours > 4)
top_students
ggplot(data = students, aes(x = Study_Hours, y = Exam_Score)) +
geom_point(color = "purple", size = 5)
ggplot(data = students, aes(x = Study_Hours, y = Exam_Score)) +
geom_point(color = "red", size = 5)
install.packages("dplyr")
install.packages("ggplot2")
library(dplyr)
library(ggplot2)
employees <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eva"),
Department = c("HR", "IT", "Marketing", "Sales", "Sales"),
Salary = c(50000, 70000, 85000, 60000, 65000),
Experience_Years = c(2, 5, 8, 3, 4,1,9)
)
employees <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eva"),
Department = c("HR", "IT", "IT", "Sales", "Sales"),
Salary = c(50000, 70000, 85000, 60000, 65000),
Experience_Years = c(2, 5, 8, 3, 4,)
)
employees <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eva"),
Department = c("HR", "IT", "IT", "Sales", "Sales"),
Salary = c(50000, 70000, 85000, 60000, 65000),
Experience_Years = c(2, 5, 8, 3, 4)
)
employees
high_earners <- filter(employees, Salary > 60000)
high_earners
ggplot(data = employees, aes(x = Name, y = Salary, fill = Department)) +
geom_col()
install.packages(c("psych", "readxl"))
setwd("~/Desktop/hssc")
file.choose()
setwd("/Users/ushashreerathod/Desktop/AnalysisScripts_HSSC")
source("Stage1_Reliability.R")
source("Stage1_Reliability.R")
library(psych)
library(readxl)
data <- read_excel("Data set.xlsx", sheet = "Raw Data")
head(data)
items <- list(
AI_Anxiety = c("AIA1","AIA2","AIA3","AIA4","AIA5","AIA6"),
Digital_Overload = c("DO1","DO2","DO3","DO4","DO5","DO6"),
Technostress = c("TS1","TS2","TS3","TS4","TS5","TS6"),
Emot_Exhaustion = c("EE1","EE2","EE3","EE4","EE5"),
PsyCap = c("PC1","PC2","PC3","PC4","PC5","PC6","PC7","PC8"),
PWB = c("PWB1","PWB2","PWB3","PWB4","PWB5","PWB6","PWB7")
)
for(name in names(items)){
cat("\n===", name, "===\n")
a <- alpha(data[, items[[name]]])
cat("Cronbach alpha:", round(a$total$raw_alpha, 3), "\n")
o <- omega(data[, items[[name]]], plot=FALSE)
cat("McDonald omega:", round(o$omega.tot, 3), "\n")
}
# Harman's test
all_items <- unlist(items)
d_all <- data[, all_items]
pca_result <- principal(d_all, nfactors=1, rotate="none")
var_explained <- pca_result$values[1] / sum(pca_result$values)
cat("\n=== Harman's Single-Factor Test ===\n")
cat("First factor variance explained:", round(var_explained*100, 1), "%\n")
cat("Threshold: < 50% (CMB unlikely if below threshold)\n")
