37 lines
2.6 KiB
Go
37 lines
2.6 KiB
Go
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package main
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var rCode string = `
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library(tidyverse)
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library(ggplot2)
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library(gridExtra)
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library(lubridate)
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gc()
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options(scipen=100000)
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d <- read_csv('~/prog/covid/nyopendata/rows.csv')
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d <- pivot_longer(d, c('New Positives', 'Total Number of Tests Performed'))
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d <- transmute(d, date=mdy(d$'Test Date'), county=County, name=name, value=value)
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queens <- subset(d, county=='Queens')
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qrate <- pivot_wider(queens, names_from='name') %>% transmute(date=date,county=county,name='Positive Rate', value=ifelse(`+"`"+`New Positives`+"`"+`==0,0,`+"`"+`New Positives`+"`"+`/`+"`"+`Total Number of Tests Performed`+"`"+`))
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totals <- group_by(d, date, county='New York', name) %>% summarize(value=sum(value)) %>% ungroup
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trate <- pivot_wider(totals, names_from='name') %>% transmute(date=date,county=county,name='Positive Rate', value=ifelse(`+"`"+`New Positives`+"`"+`==0,0,`+"`"+`New Positives`+"`"+`/`+"`"+`Total Number of Tests Performed`+"`"+`))
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d2 <- rbind(queens, totals)
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p1 <- ggplot(queens, aes(x=date, y=value, color=name))+geom_line()+scale_y_log10(n.breaks=6)+labs(y='',color='')+ggtitle('Queens')+theme(plot.title=element_text(hjust = 0.5))+theme(legend.position='bottom')
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#p2 <- ggplot(d, aes(x=date, y=value, color=name))+geom_line(data=queens)+stat_smooth(geom='line', linetype='dotted', data=queens, method='gam', se=FALSE)+geom_line(alpha=1/3, data=totals)+stat_smooth(geom='line', linetype='dotted', data=totals, method='gam', se=FALSE)+scale_y_log10(n.breaks=6)+labs(y='')+labs(color='')+ggtitle('New York')+theme(plot.title=element_text(hjust = 0.5))+theme(legend.position='bottom')
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p2 <- ggplot(totals, aes(x=date, y=value, color=name))+geom_line()+stat_smooth(geom='line', linetype='dotted', method='gam', se=FALSE)+scale_y_log10(n.breaks=6)+labs(y='')+labs(color='')+ggtitle('New York')+theme(plot.title=element_text(hjust = 0.5))+theme(legend.position='bottom')
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pr1 <- ggplot(qrate, aes(x=date, y=value)) + geom_line()+geom_smooth(method='gam',formula=y~s(x, bs="cs"),se=FALSE)+scale_y_continuous(labels = scales::percent,)+labs(y='Positive Rate')+coord_cartesian(ylim=c(0,0.04))+ggtitle('Queens')+theme(plot.title=element_text(hjust = 0.5))
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pr2 <- ggplot(trate, aes(x=date, y=value)) + geom_line()+geom_smooth(method='gam',formula=y~s(x, bs="cs"),se=FALSE)+scale_y_continuous(labels = scales::percent)+labs(y='Positive Rate')+coord_cartesian(ylim=c(0,0.04))+ggtitle('New York')+theme(plot.title=element_text(hjust = 0.5))
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p <- grid.arrange(p1,p2,pr1,pr2,ncol=2)
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zip <- read_csv('~/prog/covid/nyopendata/timeseries.csv')
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zip <- pivot_longer(zip, c('COVID_CASE_COUNT','COVID_DEATH_COUNT','TOTAL_COVID_TESTS'))
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zip$date <- mdy(zip$date)
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`
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