Reading data
library(ggplot2); library(gridExtra); library(ggthemes); library(table1)
##
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
##
## units, units<-
# Updated data published by Bloomberg
t = "/Users/tuannguyen/Dropbox/Temp Files/Coronavirus/Coronavirus data II.csv"
cvr = read.csv(t)
cvr$Status = 1
table1(~Age + Gender + Duration, data=cvr)
|
Overall
(n=24) |
| Age |
|
| Mean (SD) |
71.9 (13.3) |
| Median [Min, Max] |
71.5 [36.0, 89.0] |
| Gender |
|
| F |
8 (33.3%) |
| M |
16 (66.7%) |
| Duration |
|
| Mean (SD) |
12.5 (10.6) |
| Median [Min, Max] |
10.0 [4.00, 56.0] |
Visualizing data
# Distribution of age at death
p1 = ggplot(data=cvr, aes(x=Age)) + geom_dotplot(col="white", fill="blue") + labs(x="Age", y="Relative frequency") + theme_economist()
p2 = ggplot(data=cvr, aes(x=Duration)) + geom_dotplot(col="white", fill="blue") + labs(x="Time from diagnosis to death (days)", y="Relative frequency") + theme_economist()
grid.arrange(p1, p2, ncol=2)
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
Age and duration
ggplot(data=cvr, aes(x=Age, y=Duration), col="blue") + geom_point() + geom_smooth(se=F, method="lm", formula=y~x+I(x^2)) + theme_economist()
Survival analysis
library(survival); library(survminer)
## Loading required package: ggpubr
## Loading required package: magrittr
s = survfit(Surv(Duration, Status) ~ Gender, data = cvr)
ggsurvplot(s, xlab = "Days", ylab = "Overall survival probability")
