rm(list=ls())
setwd("C:/Pandemic_2020/analysis/data")
library(dplyr)
library(tidyverse)
library(ggpubr)
library(openintro)
library(rstatix)
pandemic <- read.csv("pandemic.csv",
stringsAsFactors = FALSE)
pandemic$Period <- factor(pandemic$Period, levels = c("Pre-lockdown", "Lockdown", "Reopening"))
pandemic.cleaned <- pandemic[-c(1:72),]
pandemic.df <- data.frame(id = 1:nrow(pandemic.cleaned),
period = pandemic.cleaned$Period,
paranoia = pandemic.cleaned$Paranoia.score.normalized,
group = pandemic.cleaned$Paranoia.group)
pandemic.pda.df <- data.frame(id = 1:nrow(pandemic.cleaned),
date.group = pandemic.cleaned$date.group,
period = pandemic.cleaned$Period,
paranoia = as.numeric(pandemic.cleaned$Paranoia.score.normalized),
depression = as.numeric(pandemic.cleaned$BDI.score.normalized),
anxiety = as.numeric(pandemic.cleaned$BAI.score.normalized))
pda <- pandemic.pda.df %>%
gather(key = "condition", value = "score", paranoia, depression, anxiety) %>%
convert_as_factor(id,condition)
# anxiety
boxplot(pandemic.cleaned$BAI.score ~ pandemic.cleaned$Period)
ANOVA1 <- aov(pandemic.cleaned$BAI.score ~ pandemic.cleaned$Period)
summary(ANOVA1)
# depression
boxplot(pandemic.cleaned$BDI.score ~ pandemic.cleaned$Period)
ANOVA2 <- aov(pandemic.cleaned$BDI.score ~ pandemic.cleaned$Period)
summary(ANOVA2)
# paranoia
boxplot(pandemic.cleaned$Paranoia.score ~ pandemic.cleaned$Period)
ANOVA3 <- aov(pandemic.cleaned$Paranoia.score ~ pandemic.cleaned$Period)
summary(ANOVA3)
pandemic.nonormalized <- data.frame(id = 1:nrow(pandemic.cleaned),
date.group = pandemic.cleaned$date.group,
period = pandemic.cleaned$Period,
paranoia = as.numeric(pandemic.cleaned$Paranoia.score),
depression = as.numeric(pandemic.cleaned$BDI.score),
anxiety = as.numeric(pandemic.cleaned$BAI.score))
pda.nonnormalized <- pandemic.nonormalized %>%
gather(key = "condition", value = "score", paranoia, depression, anxiety) %>%
convert_as_factor(id,condition)
pda.df <- pda.nonnormalized %>%
group_by(date.group,period, condition) %>%
summarise(mean = mean(score, na.rm = TRUE),
sem = std.error(score, na.rm = TRUE))
# covid cases
cases.deaths <- read.csv('confirmed_covid_cases_matchedDate.csv')
cases.deaths$state <- state2abbr(cases.deaths$state)
df3 <- cases.deaths %>%
group_by(date.group) %>%
summarise(num.cases = sum(cases),
mean = mean(cases),
sd = std.error(cases))
df3$normalized.mean = (df3$mean-min(df3$mean))/(max(df3$mean)-min(df3$mean))
df3$normalized.sd = (df3$sd-min(df3$sd))/(max(df3$sd)-min(df3$sd))
df3$log.mean = log(df3$mean)
df3$log.sd = log(df3$sd)
df3$colorGroup = c("darkorchid")
plot1 <- ggplot(pda.df, aes(x=date.group, y=mean)) +
geom_bar(aes(fill=condition, alpha=period), stat="identity", position=position_dodge(width=0.9)) +
geom_errorbar(aes(fill=condition, alpha=period, ymin=mean - sem, ymax=mean + sem),
width=0.2,
position=position_dodge(width=0.9),
width=0.4,
colour="black",
alpha=0.9,
size=1.3) +
geom_point(data=pda,aes(x=date.group,y=score),position=position_jitter(width =.15),
shape=20, color="black", size=1)+
geom_line(data=df3,aes(x=date.group, y = normalized.mean, color="colorGroup"),size=1.5) +
geom_point(data=df3,aes(x=date.group, y = normalized.mean)) +
scale_color_manual(name="", labels = "COVID-19", values=c("#696969")) +
scale_alpha_manual(values = c(0.6, 0.8, 1)) +
scale_fill_manual(values = c("#0f7718","#1c1189","#E74C3C")) +
scale_x_continuous(breaks = c(0,1,2)) +
scale_y_continuous(sec.axis = dup_axis(name = ""))+
labs(x="",
y="") + theme(panel.background = element_rect(fill = "white"),
#panel.border = element_rect(colour = "black", fill = NA, size = 1),
axis.line = element_line( colour = "black"),
legend.position = "none",
#legend.box = "vertical",
axis.text.x=element_blank(),
axis.text.y = element_blank(),
aspect.ratio = 1)