data_cleaning.Rmd

---
title: "datacleaning"
author: "Zhe Zheng"
date: "9/13/2021"
output: html_document
---

```{r}
library(dplyr)
library(ggplot2)
```


```{r}
onset <- data.frame(state=onset.2020$state,previous=onset.whole$mean,now=onset.2020$onset)
onset_FL <- data.frame(state=onset.2020$state[-which(onset.2020$state=="FL")],previous=onset.whole$mean[-which(onset.whole$state=="FL")]-onset.whole$mean[which(onset.whole$state=="FL")],now=onset.2020$onset[-which(onset.2020$state=="FL")]-onset.2020$onset[which(onset.2020$state=="FL")])


y <- onset_FL$now
saveRDS(y,"D:/re-emergent RSV timing/sample_requiredata/y.rds")
```

```{r}
Index<- read.csv("D:/re-emergent RSV timing/stringency index/StrIndexApr05.csv")
Index <- Index[Index$state%in%onset_FL$state,]
pd_hz <- readRDS("D:/re-emergent RSV timing/sample_requiredata/pd_hz.rds")
pd_hz <- pd_hz[pd_hz$state%in%onset_FL$state,]

x_1 <- rep(1,nrow(onset_FL))
x_2 <- as.numeric(scale(pd_hz$`Population Density 2020 Census`))
x_3 <- as.numeric(scale(pd_hz$familysize))
x_4 <- as.numeric(scale(Index$Index))
x_5 <- onset_FL$previous
x <- matrix(data=c(x_1,x_2,x_3,x_4,x_5),nrow = 47,ncol = 5)

saveRDS(x,"D:/re-emergent RSV timing/sample_requiredata/x.rds")
```