Example 1

mydata <- data.frame("ID" = c(1, 2, 3, 4),
                     "Age" = c(22, 23, 24, 25),
                     "Height" = c(180, 186, 175, 170),
                     "Gender" = c(0, 0, 1, 1))

print(mydata)

##   ID Age Height Gender
## 1  1  22    180      0
## 2  2  23    186      0
## 3  3  24    175      1
## 4  4  25    170      1

mydata[ 4 , 3 ] <- 169

print(mydata)

##   ID Age Height Gender
## 1  1  22    180      0
## 2  2  23    186      0
## 3  3  24    175      1
## 4  4  25    169      1

mydata$Weight <- c(85, 70, 72, 92)

Calculate the new vartiable body mass index

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

summary(mydata$bmi)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   20.23   22.69   24.87   25.55   27.73   32.21

#install.packages("pastecs")

library(pastecs)

round(stat.desc(mydata [  , -c(1, 4)]), 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          22.00 169.00  70.00  20.23
## max          25.00 186.00  92.00  32.21
## range         3.00  17.00  22.00  11.98
## sum          94.00 710.00 319.00 102.19
## median       23.50 177.50  78.50  24.87
## mean         23.50 177.50  79.75  25.55
## SE.mean       0.65   3.62   5.27   2.54
## CI.mean.0.95  2.05  11.51  16.76   8.08
## var           1.67  52.33 110.92  25.76
## std.dev       1.29   7.23  10.53   5.08
## coef.var      0.05   0.04   0.13   0.20

sd(mydata$Weight)

## [1] 10.5317

sapply(mydata[ , -c(1,4)], FUN =sd)

##       Age    Height    Weight       bmi 
##  1.290994  7.234178 10.531698  5.075201

mydata$Gender <- factor(mydata$Gender,
                        levels = c(0, 1),
                        labels = c( "M", "F"))

summary(mydata)

##        ID            Age            Height      Gender     Weight     
##  Min.   :1.00   Min.   :22.00   Min.   :169.0   M:2    Min.   :70.00  
##  1st Qu.:1.75   1st Qu.:22.75   1st Qu.:173.5   F:2    1st Qu.:71.50  
##  Median :2.50   Median :23.50   Median :177.5          Median :78.50  
##  Mean   :2.50   Mean   :23.50   Mean   :177.5          Mean   :79.75  
##  3rd Qu.:3.25   3rd Qu.:24.25   3rd Qu.:181.5          3rd Qu.:86.75  
##  Max.   :4.00   Max.   :25.00   Max.   :186.0          Max.   :92.00  
##       bmi       
##  Min.   :20.23  
##  1st Qu.:22.69  
##  Median :24.87  
##  Mean   :25.55  
##  3rd Qu.:27.73  
##  Max.   :32.21

Average height just for females

mean(mydata$Height[mydata$Gender == "F"])

## [1] 172

z # ustaviš funkcijo, torej je program ne bo zagnal

#install.packages("psych")
library(psych)
describeBy(mydata$Height, mydata$Gender)

## 
##  Descriptive statistics by group 
## group: M
##    vars n mean   sd median trimmed  mad min max range skew kurtosis se
## X1    1 2  183 4.24    183     183 4.45 180 186     6    0    -2.75  3
## ------------------------------------------------------------ 
## group: F
##    vars n mean   sd median trimmed  mad min max range skew kurtosis se
## X1    1 2  172 4.24    172     172 4.45 169 175     6    0    -2.75  3