Poskus

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mydata <- data.frame("ID" = c(1, 2, 3, 4),
                    "Age" = c(20, 22, 18, 24),
                    "Gender" = c("M", "F", "M", "M"))

print (mydata)

##   ID Age Gender
## 1  1  20      M
## 2  2  22      F
## 3  3  18      M
## 4  4  24      M

mean(mydata$Age)

## [1] 21

sd(mydata$Age)

## [1] 2.581989

The average age of students is 21.

mydata$Height <- c(180, 170, 176, 177)

mydata$Weight <- c(76, 60, 72, 73)

mydata$BMI <- mydata$Weight / (mydata$Height/100)^2

Creating new dataframe, which includes only Age and Height.

mydata2 <- mydata[ ,c(2,4) ]

From mydata2, remove the third row

mydata3 <- mydata2 [-3, ]

summary(mydata)

##        ID            Age          Gender              Height     
##  Min.   :1.00   Min.   :18.0   Length:4           Min.   :170.0  
##  1st Qu.:1.75   1st Qu.:19.5   Class :character   1st Qu.:174.5  
##  Median :2.50   Median :21.0   Mode  :character   Median :176.5  
##  Mean   :2.50   Mean   :21.0                      Mean   :175.8  
##  3rd Qu.:3.25   3rd Qu.:22.5                      3rd Qu.:177.8  
##  Max.   :4.00   Max.   :24.0                      Max.   :180.0  
##      Weight           BMI       
##  Min.   :60.00   Min.   :20.76  
##  1st Qu.:69.00   1st Qu.:22.62  
##  Median :72.50   Median :23.27  
##  Mean   :70.25   Mean   :22.69  
##  3rd Qu.:73.75   3rd Qu.:23.34  
##  Max.   :76.00   Max.   :23.46

Let’s exclude ID and Gender when calculating the descriptive statistics

summary(mydata[, c (-1, -3)])

##       Age           Height          Weight           BMI       
##  Min.   :18.0   Min.   :170.0   Min.   :60.00   Min.   :20.76  
##  1st Qu.:19.5   1st Qu.:174.5   1st Qu.:69.00   1st Qu.:22.62  
##  Median :21.0   Median :176.5   Median :72.50   Median :23.27  
##  Mean   :21.0   Mean   :175.8   Mean   :70.25   Mean   :22.69  
##  3rd Qu.:22.5   3rd Qu.:177.8   3rd Qu.:73.75   3rd Qu.:23.34  
##  Max.   :24.0   Max.   :180.0   Max.   :76.00   Max.   :23.46

The youngest person in my sample was 18.

#install.packages("pastecs")

library(pastecs)
round(stat.desc(mydata[ ,c(-1, -3) ]), 2)

##                Age Height Weight   BMI
## nbr.val       4.00   4.00   4.00  4.00
## nbr.null      0.00   0.00   0.00  0.00
## nbr.na        0.00   0.00   0.00  0.00
## min          18.00 170.00  60.00 20.76
## max          24.00 180.00  76.00 23.46
## range         6.00  10.00  16.00  2.70
## sum          84.00 703.00 281.00 90.76
## median       21.00 176.50  72.50 23.27
## mean         21.00 175.75  70.25 22.69
## SE.mean       1.29   2.10   3.52  0.64
## CI.mean.0.95  4.11   6.67  11.20  2.05
## var           6.67  17.58  49.58  1.66
## std.dev       2.58   4.19   7.04  1.29
## coef.var      0.12   0.02   0.10  0.06

Only including rows where Gender = Male; <- means assign, == means equal

mydata_M <- mydata[mydata$Gender == "M", ]

Let’s filter only those that are above 20 years old.

mydata_M1 <- mydata[mydata$Gender == "M" & mydata$Age <= 20 ]