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03_covid_i_stress.2.Rmd
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---
title: "Technical Appendix: Welfare State and Risk Perceptions, Part III"
author: "Nate Breznau"
date: "5/18/2020"
output:
html_document: default
pdf_document: default
---
This is an HTML output of an analysis of public concern about the Covid-19 pandemic and various socioeconomic factors at the country-level. The statistical analysis is performed in R Studio and this is an R Markdown file that it produces.
You will find all of the data files plus the original R code on the Open Science Framework: https://osf.io/muhdz/
```{r setup, warning=F, message=F}
rm(list = ls(all = TRUE))
# this is a function to remove missing data
completeFun <- function(data, desiredCols) {
completeVec <- complete.cases(data[, desiredCols])
return(data[completeVec, ])
}
# need pacman package installed to run this
# install.packages("pacman")
pacman::p_load("dplyr","countrycode","car","ggplot2","jtools","sjPlot","sjmisc","sjlabelled","tidyverse","psych","lavaan","kable","kableExtra","ggrepel","stringi","margins", "ragg")
```
The files
cis_clean.Rmd
covid_i_stress.Rmd
produce the cis2.Rdata file for analysis here.
Figure 1 and the correlation tables are produced in 01_covid_i_stress.Rmd.
```{r round2load, warning=F, message=F, echo=T}
load(file = here::here("data/cis2.Rdata"))
```
### Figure 1. Outbreak Severity by Country and Day
```{r fi1_import}
knitr::include_graphics(here::here("results/Fig1.png"))
```
### Table 1. Descriptive Correlations and Model Indices
Taken from 01_covid_i_stress.Rmd https://osf.io/cbep2/
### Fig A. Covid-19 Two Types of Concern, by Country
```{r figA, echo=T}
ggplot(data=finaldf_C, aes(x=concern_society , y=concern_self)) +
geom_point() +
geom_text_repel(aes(label = iso)) +
geom_smooth(method=lm, se=FALSE) +
labs(x= "Coronavirus Concerns, Societal", y = "Coronavirus Concerns, Societal")
```
### Fig B. Days Since Peak and Self Concern by Country
```{r figB, warning = F, message=F, echo=T}
mid <- 0
ggplot(data=finaldf_C, aes(x=days_since_peak, y=concern_self, color = conf_delta)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, color = "gray50", linetype = "dashed") +
geom_text_repel(aes(label = iso), size = 2.5) +
scale_color_gradient2(midpoint=mid, low="blue", mid="gray55", high="red", space="Lab") +
labs(x= "Days Since Outbreak Curve Inflection", y = "Coronavirus Concerns, Personal", color = "Weekly New Cases\n(rate of change)") +
theme(panel.background = element_rect(fill = "white", colour = "grey50"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_text(size = 9))
```
### Fig C. Days Since Peak and Society Concern by Country
```{r figC, warning = F, message=F, echo=T}
mid <- 0
ggplot(data=finaldf_C, aes(x=days_since_peak, y=concern_society, color = conf_delta)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, color = "gray50", linetype = "dashed") +
geom_text_repel(aes(label = iso), size = 2.5) +
scale_color_gradient2(midpoint=mid, low="blue", mid="gray55", high="red", space="Lab") +
labs(x= "Days Since Outbreak Curve Inflection", y = "Coronavirus Concerns, Societal", color = "Weekly New Cases\n(rate of change)") +
theme(panel.background = element_rect(fill = "white", colour = "grey50"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_text(size = 9))
```
### Fig 2. Welfare State Strength and Covid Concern Society by Country
```{r fig2, warning = F, message=F, echo=T}
agg_png(file = here::here("results/Fig2.png"), width = 1000, height = 700, res = 144)
mid <- 34
ggplot(data=finaldf_C, aes(x=socpolicy, y=concern_self, color=days_since_peak)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, color = "gray50", linetype = "dashed") +
geom_text_repel(aes(label = iso), size = 3.5) +
scale_color_gradient2(midpoint=mid, low="red", mid="gray55", high="blue", space="Lab") +
labs(x= "Welfare State Strength, spending and coverage", y = "Coronavirus Concerns, Personal", color = "Days Since\nCurve Inflection") +
theme(panel.background = element_rect(fill = "white", colour = "grey50"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_text(size = 14),
axis.title = element_text(size = 14),
legend.text = element_text(size = 14))
dev.off()
knitr::include_graphics(here::here("results/Fig2.png"))
```
### Fig 3. Welfare State Strength and Covid Concern Society by Country
```{r fig3, warning = F, message=F, echo=T}
mid <- 34
ggplot(data=finaldf_C, aes(x=socpolicy, y=concern_society, color=days_since_peak)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, color = "gray50", linetype = "dashed") +
geom_text_repel(aes(label = iso), size = 2.5) +
scale_color_gradient2(midpoint=mid, low="red", mid="gray55", high="blue", space="Lab") +
labs(x= "Welfare State Strength, spending and coverage", y = "Coronavirus Concerns, Societal", color = "Days Since\nCurve Inflection") +
theme(panel.background = element_rect(fill = "white", colour = "grey50"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_text(size = 9))
```
#### Fig D. Government Intervention and Personal Concerns
```{r figD, warning = F, message=F, echo=T}
mid <- 0.7
ggplot(data=finaldf_C, aes(x=intervention, y=concern_self, color=socpolicy)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, color = "gray50", linetype = "dashed") +
geom_text_repel(aes(label = iso), size = 2.5) +
scale_color_gradient2(midpoint=mid, low="blue", mid="gray55", high="red", space="Lab") +
labs(x= "Government Intervention, days before 1st death", y = "Personal Coronavirus Concerns", color = "Welfare State\nStrength") +
theme(panel.background = element_rect(fill = "white", colour = "grey50"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_text(size = 9))
```
#### Fig E. Government Intervention and Societal Concerns
```{r figE, warning = F, message=F, echo=T}
mid <- 0.7
ggplot(data=finaldf_C, aes(x=intervention, y=concern_society, color=socpolicy)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, color = "gray50", linetype = "dashed") +
geom_text_repel(aes(label = iso), size = 2.5) +
scale_color_gradient2(midpoint=mid, low="blue", mid="gray55", high="red", space="Lab") +
labs(x= "Government Intervention, days before 1st death", y = "Personal Coronavirus Concerns", color = "Welfare State\nStrength") +
theme(panel.background = element_rect(fill = "white", colour = "grey50"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_text(size = 9))
```
## Regressions
### Table 2. Regression Models, Personal Concerns
```{r T2reg, warning = F, message=F}
m1 <- lm(concern_self ~ days_since_peak + conf_delta, data = finaldf_C)
m2 <- lm(concern_self ~ days_since_peak + conf_delta + gdp, data = finaldf_C)
m3 <- lm(concern_self ~ days_since_peak + conf_delta + socpolicy, data = finaldf_C)
m4 <- lm(concern_self ~ days_since_peak + conf_delta + intervention*gdp, data = finaldf_C)
m5 <- lm(concern_self ~ days_since_peak + conf_delta + intervention*socpolicy, data = finaldf_C)
tab_model(m1, m2, m3, m4, m5, p.style = "stars", p.threshold = c(0.10, 0.05, 0.01), show.ci = F, rm.terms = c("(Intercept)"), dv.labels = c("M1", "M2","M3","M4", "M5"), pred.labels = c("Days Since Curve Inflection", "New Cases", "GDP", "Welfare State Strength", "Government Intervention", "Intervention*GDP", "Intervention*Welfare State"), file = here::here("data/Tbl2.html"))
```
#### Model X. reviewer requested model with intervention alone
```{r reviwermodel}
m3x <- lm(concern_self ~ days_since_peak + conf_delta + intervention, data = finaldf_C)
tab_model(m3x)
```
Standardized effect for M3
```{r M3Z, warning = F, message=F}
tab_model(m3, p.style = "stars", p.threshold = c(0.10, 0.05, 0.01), show.ci = F, rm.terms = c("(Intercept)"), show.std = T, dv.labels = c("M3"), pred.labels = c("Case Increase, past week", "Days Since Curve Inflection", "Welfare State, scale", "Government Intervention", "Intervention*Welfare State"))
```
### Table 3. Regression Models, Societal Concerns
```{r T3reg, warning = F, message=F}
m11 <- lm(concern_society ~ days_since_peak + conf_delta, data = finaldf_C)
m12 <- lm(concern_society ~ days_since_peak + conf_delta + gdp + socpolicy, data = finaldf_C)
m13 <- lm(concern_society ~ days_since_peak + conf_delta + socpolicy, data = finaldf_C)
m14 <- lm(concern_society ~ days_since_peak + conf_delta + intervention*gdp, data = finaldf_C)
m15 <- lm(concern_society ~ days_since_peak + conf_delta + intervention*socpolicy, data = finaldf_C)
tab_model(m11, m12, m13, m14, m15, p.style = "stars", p.threshold = c(0.10, 0.05, 0.01), show.ci = F, rm.terms = c("(Intercept)"), dv.labels = c("M1", "M2","M3","M4", "M5"), pred.labels = c("Days Since Curve Inflection", "New Cases", "GDP", "Welfare State Strength", "Government Intervention", "Intervention*GDP", "Intervention*Welfare State"))
```
### Fig 4. Margins Plot for Table 2 and 3
```{r Fig4marg, warning = F, message = F}
m5plot <- plot_model(m5, type = "pred", terms = c("socpolicy", "intervention [10,-30]"))
agg_png(file = here::here("results/Fig4.png"), width = 1000, height = 700, res = 144)
m5plot + labs(title = "", x = "Welfare State Strength", y = "Coronavirus Concerns, Personal") +
scale_color_manual(name="Government\nIntervention",
labels=c("WEAK \n30-days late","STRONG \n5-days advance"),
values=c("red","blue")) +
theme(panel.background = element_blank(),
axis.line = element_line(color = "black"),
legend.spacing.x = unit(0.5, 'cm'),
legend.text = element_text(margin = margin(t = 5), size = 12),
legend.title = element_text(size = 14),
axis.title = element_text(size = 14))
dev.off()
knitr::include_graphics(here::here("results/Fig4.png"))
```
### Fig 5. Margins Plot for Table 3
```{r Fig5marg, warning = F}
m15plot <- plot_model(m15, type = "pred", terms = c("socpolicy", "intervention [10,-30]"))
m15plot + labs(title = "", x = "Welfare State Strength", y = "Coronavirus Concerns, Societal") +
scale_color_manual(name="Government\nIntervention",
labels=c("WEAK \n30-days late","STRONG \n5-days advance"),
values=c("red","blue")) +
theme(panel.background = element_blank(),
axis.line = element_line(color = "black"),
legend.spacing.x = unit(0.5, 'cm'),
legend.text = element_text(margin = margin(t = 5), size = 12),
legend.title = element_text(size = 14),
axis.title = element_text(size = 14))
```
### Table 2a. Table 2 With Outlier Countries.
```{r T1areg, warning = F, message=F}
m1a <- lm(concern_self ~ days_since_peak + conf_delta, data = finaldf_Ca)
m2a <- lm(concern_self ~ days_since_peak + conf_delta + gdp, data = finaldf_Ca)
m3a <- lm(concern_self ~ days_since_peak + conf_delta + socpolicy, data = finaldf_Ca)
m4a <- lm(concern_self ~ days_since_peak + conf_delta + intervention*gdp, data = finaldf_C)
m5a <- lm(concern_self ~ days_since_peak + conf_delta + intervention*socpolicy, data = finaldf_Ca)
tab_model(m1a, m2a, m3a, m4a, m5a, p.style = "stars", p.threshold = c(0.10, 0.05, 0.01), show.ci = F, rm.terms = c("(Intercept)"), dv.labels = c("M1", "M2","M3","M4", "M5"), pred.labels = c("Days Since Curve Inflection", "New Cases", "GDP", "Welfare State Strength", "Government Intervention", "Intervention*GDP", "Intervention*Welfare State"), file = here::here("results/Tbl2a.html"))
```
### Table 3a. Table 3 With Outlier Countries.
```{r T2areg, warning = F, message=F}
m11a <- lm(concern_society ~ days_since_peak + conf_delta, data = finaldf_Ca)
m12a <- lm(concern_society ~ days_since_peak + conf_delta + gdp + socpolicy, data = finaldf_Ca)
m13a <- lm(concern_society ~ days_since_peak + conf_delta + socpolicy, data = finaldf_Ca)
m14a <- lm(concern_society ~ days_since_peak + conf_delta + intervention*gdp, data = finaldf_Ca)
m15a <- lm(concern_society ~ days_since_peak + conf_delta + intervention*socpolicy, data = finaldf_Ca)
tab_model(m11a, m12a, m13a, m14a, m15a, p.style = "stars", p.threshold = c(0.10, 0.05, 0.01), show.ci = F, rm.terms = c("(Intercept)"), dv.labels = c("M1", "M2","M3","M4", "M5"), pred.labels = c("Days Since Curve Inflection", "New Cases", "GDP", "Welfare State Strength", "Government Intervention", "Intervention*GDP", "Intervention*Welfare State"))
```