WTC Wave 5 Dataset

setwd("~/R_Program_Folder")

library(readr)
Wave_5_Dataset <- read_csv("Wave_5_Dataset.csv")
#load(Wave_5_Dataset)
library(Hmisc)
library(descr)


freq(Wave_5_Dataset$work_pile)

freq(Wave_5_Dataset$sad_w5)
freq(Wave_5_Dataset$nervous_w5)
freq(Wave_5_Dataset$restless_w5)
freq(Wave_5_Dataset$hopeless_w5)
freq(Wave_5_Dataset$lethargic_w5)
freq(Wave_5_Dataset$worthless_w5)
freq(Wave_5_Dataset$depression_w5)
freq(Wave_5_Dataset$nerves_w5)

Wave_5_Dataset$Psychological_Symptoms <- ((Wave_5_Dataset$sad_w5 + Wave_5_Dataset$nervous_w5 +
                                             Wave_5_Dataset$restless_w5 + Wave_5_Dataset$hopeless_w5 + Wave_5_Dataset$lethargic_w5 + Wave_5_Dataset$worthless_w5 + Wave_5_Dataset$depression_w5 + Wave_5_Dataset$nerves_w5) /8)
freq(Wave_5_Dataset$Psychological_Symptoms)

summary(Wave_5_Dataset$Psychological_Symptoms, na.rm = T)
sd(Wave_5_Dataset$Psychological_Symptoms, na.rm = T)

data.plot.bivariate10<-na.omit(Wave_5_Dataset[,c("work_pile", "Psychological_Symptoms")])
data.plot.bivariate10$work_pile<-as.factor(data.plot.bivariate1$work_pile)
levels(data.plot.bivariate10$work_pile)<-c("Yes", "No")
ggplot(data=data.plot.bivariate10)+
  stat_summary(aes(x=work_pile, y=Psychological_Symptoms, geom="bar"))