Plot of dated data (covid)

library(scales); library(ggplot2); library(readxl)

## Warning: package 'scales' was built under R version 4.0.2

## Warning: package 'ggplot2' was built under R version 4.0.2

# https://covidlive.com.au/report/daily-positive-test-rate/nsw

df = read_excel("~/Dropbox/Bao chi/Khoa hoc & Y te/Du bao dich NSW/Number tests and postives.xlsx")

df$date = as.Date(df$Date)

df 

## # A tibble: 86 x 5
##    Date                  Pos  Tests    Rate date      
##    <dttm>              <dbl>  <dbl>   <dbl> <date>    
##  1 2021-08-25 00:00:00   919 149252 0.00616 2021-08-25
##  2 2021-08-24 00:00:00   755 138472 0.00545 2021-08-24
##  3 2021-08-23 00:00:00   818 311083 0.00263 2021-08-23
##  4 2021-08-22 00:00:00   830 206193 0.00403 2021-08-22
##  5 2021-08-21 00:00:00   832 124610 0.00668 2021-08-21
##  6 2021-08-20 00:00:00   646 127590 0.00506 2021-08-20
##  7 2021-08-19 00:00:00   684 119310 0.00573 2021-08-19
##  8 2021-08-18 00:00:00   634 102749 0.00617 2021-08-18
##  9 2021-08-17 00:00:00   454 151767 0.00299 2021-08-17
## 10 2021-08-16 00:00:00   480 156495 0.00307 2021-08-16
## # … with 76 more rows

Data visualization

ggplot(data=df, aes(x=date, y=Pos)) + geom_point(col="blue") + labs(x="Date", y="Number of cases") + scale_x_date(date_breaks = "1 week") + theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplot(data=df, aes(x=Tests, y=Pos)) + geom_point() + labs(x="Number of tests", y="Number of cases") + scale_x_continuous(labels = comma) + scale_y_continuous(labels = comma)