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@naomispence
Created November 2, 2022 16:22
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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"))
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