mydata <- data.frame("ID" = c(1, 2, 3),
"Age" = c(30, 40, 20),
"Gender" = c("F", "M", "M"))
print(mydata)
## ID Age Gender
## 1 1 30 F
## 2 2 40 M
## 3 3 20 M
mydata [1,2] <- 28
mydata2 <- mydata [ ,-3]
mydata3 <- mydata[c(1,2), ]
print (mydata3)
## ID Age Gender
## 1 1 28 F
## 2 2 40 M
mydata$Height <- c(178, 170, 190)
print (mydata)
## ID Age Gender Height
## 1 1 28 F 178
## 2 2 40 M 170
## 3 3 20 M 190
mydata$Height1 <- mydata$Height + 2
print (mydata)
## ID Age Gender Height Height1
## 1 1 28 F 178 180
## 2 2 40 M 170 172
## 3 3 20 M 190 192
summary(mydata[,-3])
## ID Age Height Height1
## Min. :1.0 Min. :20.00 Min. :170.0 Min. :172.0
## 1st Qu.:1.5 1st Qu.:24.00 1st Qu.:174.0 1st Qu.:176.0
## Median :2.0 Median :28.00 Median :178.0 Median :180.0
## Mean :2.0 Mean :29.33 Mean :179.3 Mean :181.3
## 3rd Qu.:2.5 3rd Qu.:34.00 3rd Qu.:184.0 3rd Qu.:186.0
## Max. :3.0 Max. :40.00 Max. :190.0 Max. :192.0
mean(mydata$Age)
## [1] 29.33333
sd(mydata$Height1)
## [1] 10.06645
#install.packages("psych")
library(psych)
describe(mydata)
## vars n mean sd median trimmed mad min max range skew kurtosis
## ID 1 3 2.00 1.00 2 2.00 1.48 1 3 2 0.00 -2.33
## Age 2 3 29.33 10.07 28 29.33 11.86 20 40 20 0.13 -2.33
## Gender* 3 3 1.67 0.58 2 1.67 0.00 1 2 1 -0.38 -2.33
## Height 4 3 179.33 10.07 178 179.33 11.86 170 190 20 0.13 -2.33
## Height1 5 3 181.33 10.07 180 181.33 11.86 172 192 20 0.13 -2.33
## se
## ID 0.58
## Age 5.81
## Gender* 0.33
## Height 5.81
## Height1 5.81
#install.packages("pastecs")
library(pastecs)
round(stat.desc(mydata[,c(-1,-3)]),2)
## Age Height Height1
## nbr.val 3.00 3.00 3.00
## nbr.null 0.00 0.00 0.00
## nbr.na 0.00 0.00 0.00
## min 20.00 170.00 172.00
## max 40.00 190.00 192.00
## range 20.00 20.00 20.00
## sum 88.00 538.00 544.00
## median 28.00 178.00 180.00
## mean 29.33 179.33 181.33
## SE.mean 5.81 5.81 5.81
## CI.mean.0.95 25.01 25.01 25.01
## var 101.33 101.33 101.33
## std.dev 10.07 10.07 10.07
## coef.var 0.34 0.06 0.06