podatki <- data.frame("ID"=c(1,2,3,4,5),
                      "visina"=c(170,172,168,180,184),
                      "starost"=c(20,22,22,23,24),
                      "spol"=c("z","z","m","m","m"))
mean(podatki$visina)
## [1] 174.8
summary(podatki[ , c(-1,-4) ])
##      visina         starost    
##  Min.   :168.0   Min.   :20.0  
##  1st Qu.:170.0   1st Qu.:22.0  
##  Median :172.0   Median :22.0  
##  Mean   :174.8   Mean   :22.2  
##  3rd Qu.:180.0   3rd Qu.:23.0  
##  Max.   :184.0   Max.   :24.0
podatki$teza <- c(65,60,70,72,80)

Izračunaj BMI

podatki$BMI <- (podatki$teza / (podatki$visina/100)^2)
#install.packages("pastecs")
library(pastecs)
round(stat.desc(podatki), 3)
##                  ID  visina starost spol    teza     BMI
## nbr.val       5.000   5.000   5.000   NA   5.000   5.000
## nbr.null      0.000   0.000   0.000   NA   0.000   0.000
## nbr.na        0.000   0.000   0.000   NA   0.000   0.000
## min           1.000 168.000  20.000   NA  60.000  20.281
## max           5.000 184.000  24.000   NA  80.000  24.802
## range         4.000  16.000   4.000   NA  20.000   4.520
## sum          15.000 874.000 111.000   NA 347.000 113.426
## median        3.000 172.000  22.000   NA  70.000  22.491
## mean          3.000 174.800  22.200   NA  69.400  22.685
## SE.mean       0.707   3.072   0.663   NA   3.370   0.755
## CI.mean.0.95  1.963   8.531   1.842   NA   9.358   2.096
## var           2.500  47.200   2.200   NA  56.800   2.850
## std.dev       1.581   6.870   1.483   NA   7.537   1.688
## coef.var      0.527   0.039   0.067   NA   0.109   0.074
#install.packages("psych")
library(psych)
describe(podatki$visina)
##    vars n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 5 174.8 6.87    172   174.8 5.93 168 184    16 0.29    -2.05 3.07