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library(tidyverse) | ||
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#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 | ||
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#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) | ||
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#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) | ||
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#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") | ||
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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") | ||
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#CHI-SQR Test | ||
#------------------------------------------------------------------------------------- | ||
#DEATH RATE: | ||
chisqDF <- finalDF %>% select(label, death_rate) | ||
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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 | ||
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#Frequency distribution visualization | ||
hist(test, main = "Null Distribution, Difference of Means", prob = T, col = "cadetblue") | ||
abline(v = stat,col = 2, lwd = 2) | ||
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#---- | ||
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#CASE RATE: | ||
chisqDF <- finalDF %>% select(label, case_rate) | ||
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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 | ||
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#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) | ||
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