From 9f0889a11bcf3d38fece8eff0557886d7a98203e Mon Sep 17 00:00:00 2001
From: ChrisPDelaney <60331530+ChrisPDelaney@users.noreply.github.com>
Date: Wed, 8 Dec 2021 12:16:41 -0500
Subject: [PATCH] Add files via upload

---
 Final_Report_Code/Hypothesis_Testing.R | 99 ++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 Final_Report_Code/Hypothesis_Testing.R

diff --git a/Final_Report_Code/Hypothesis_Testing.R b/Final_Report_Code/Hypothesis_Testing.R
new file mode 100644
index 0000000..c10cc2b
--- /dev/null
+++ b/Final_Report_Code/Hypothesis_Testing.R
@@ -0,0 +1,99 @@
+library(tidyverse)
+
+#Uploading data, cleaning county census data population estimates & mask data
+#-------------------------------------------------------------------------------------
+countyDeaths <- read.csv('/Users/christopherdelaney/Desktop/GU-ANLY511-FinalProject-main/live/us-counties.csv')#deaths by county dataset
+countyPopulations <- read.csv('/Users/christopherdelaney/Desktop/PopulationEstimatesCSV.csv')#populations by county dataset
+masks <- read.csv('/Users/christopherdelaney/Desktop/GU-ANLY511-FinalProject-main/mask-use/mask-use-by-county.csv')#mask usage dataset
+names <- countyPopulations[1, ]
+names[1] <- "fips" #Changing name for merging
+names[1,8] <- "Population"
+countyPopulations <- countyPopulations[-c(1:2), ]
+names(countyPopulations) <- names
+countyPopulations$fips <- as.numeric(countyPopulations$fips) #converting fips codes to numeric for converting
+maskNames <- names(masks)
+maskNames[1] <- "fips" #Changing name for merging
+names(masks) <- maskNames
+
+#Merging based on fips code, selecting key variables, cleaning resulting dataset
+#-------------------------------------------------------------------------------------
+df <- merge(countyPopulations, countyDeaths, by = "fips" ) %>% 
+  select ('fips', 'state', 'Area name', 'Population', 'confirmed_cases', 'confirmed_deaths') 
+df$Population <- gsub("\\,", "", df$Population) #removing commas from population data
+df$Population <- as.numeric(df$Population)
+df$confirmed_deaths <- as.numeric(df$confirmed_deaths)
+df$confirmed_cases <- as.numeric(df$confirmed_cases)
+df <- df %>% mutate(case_rate = confirmed_cases/Population, death_rate = confirmed_deaths/Population) #calcualting case rate
+newdf <- merge(df, masks, by = "fips")
+noMaskCounties <- newdf %>% arrange(desc(NEVER, RARELY)) %>% drop_na() %>% slice(1:30) %>% mutate(label = "Low Mask Usage") #Selecting 30 lowest mask using counties with sufficient data
+maskCounties <- newdf %>% arrange(desc(ALWAYS, FREQUENTLY)) %>% drop_na() %>% slice(1:30) %>% mutate(label = "High Mask Usage")#Selecting 30 highest mask using counties with sufficient data
+finalDF <- rbind(noMaskCounties, maskCounties)
+View(finalDF)
+
+#Creating visualizations
+#------------------------------------------------------------------------------------
+finalDF %>% ggplot(aes(x = label, y = death_rate, fill = label)) + geom_boxplot() +
+  labs(title = "Death Rates by Counties with Differing Mask Usage", x = "Mask Usage", y = "Death Rate") +
+  scale_y_continuous(labels = scales::percent)
+finalDF %>% ggplot(aes(x = label, y = case_rate, fill = label)) + geom_boxplot() +
+  labs(title = "Case Rates by Counties with Differing Mask Usage", x = "Mask Usage", y = "Case Rate") +
+  scale_y_continuous(labels = scales::percent)
+
+
+#T-Test
+#-------------------------------------------------------------------------------------
+freqMaskDR <- subset(finalDF, select=death_rate, subset=label=="High Mask Usage", drop=T)
+rarelyMaskDR <- subset(finalDF, select=death_rate, subset=label=="Low Mask Usage", drop=T)
+t.test(rarelyMaskDR, freqMaskDR, alt="greater")
+
+freqMaskCR <- subset(finalDF, select=case_rate, subset=label=="High Mask Usage", drop=T)
+rarelyMaskCR <- subset(finalDF, select=case_rate, subset=label=="Low Mask Usage", drop=T)
+t.test(rarelyMaskCR, freqMaskCR, alt="greater")
+
+
+#CHI-SQR Test
+#-------------------------------------------------------------------------------------
+#DEATH RATE:
+chisqDF <- finalDF %>% select(label, death_rate)
+
+diffMean = function(chisqDF) { 
+  agg = aggregate(death_rate ~ label, data = chisqDF, FUN = mean) 
+  return(agg$death_rate[1] - agg$death_rate[2]) #xbar_c - xbar_t 
+}
+myPerm <- function(){
+  dfCopy = chisqDF
+  dfCopy$death_rate <- dfCopy$death_rate[sample(60,60,replace=F)]
+  diffMean(dfCopy)
+}
+stat = diffMean(chisqDF)
+test = replicate(1000, myPerm())
+mean(test < stat) #p-value
+
+#Frequency distribution visualization
+hist(test, main = "Null Distribution, Difference of Means", prob = T, col = "cadetblue")
+abline(v = stat,col = 2, lwd = 2)
+
+#----
+
+#CASE RATE:
+chisqDF <- finalDF %>% select(label, case_rate)
+
+diffMean = function(chisqDF) { 
+  agg = aggregate(case_rate ~ label, data = chisqDF, FUN = mean) 
+  return(agg$case_rate[1] - agg$case_rate[2]) #xbar_c - xbar_t 
+}
+myPerm <- function(){
+  dfCopy = chisqDF
+  dfCopy$case_rate <- dfCopy$case_rate[sample(60,60,replace=F)]
+  diffMean(dfCopy)
+}
+stat = diffMean(chisqDF)
+test = replicate(1000, myPerm())
+mean(test < stat) #p-value
+
+#Frequency distribution visualization
+range <- c(-0.04, 0.03)
+hist(test, main = "Null Distribution, Difference of Means", prob = T, col = "cadetblue", xlim = range)
+abline(v = stat,col = 2, lwd = 2)
+
+