Testni podatki

podatki <- data.frame("ID" = c(1, 2, 3, 4, 5),
                      "visina" = c(170, 175, 177, 180, 182),
                      "starost" = c(20, 22, 22, 23, 24),
                      "spol" = c("Z", "Z", "M", "M", "M"))

mean(podatki$visina)

## [1] 176.8

summary(podatki[ , c(-1, -4) ])

##      visina         starost    
##  Min.   :170.0   Min.   :20.0  
##  1st Qu.:175.0   1st Qu.:22.0  
##  Median :177.0   Median :22.0  
##  Mean   :176.8   Mean   :22.2  
##  3rd Qu.:180.0   3rd Qu.:23.0  
##  Max.   :182.0   Max.   :24.0

podatki$teza <- c(65, 60, 70, 72, 80)

podatki$BMI <- round(podatki$teza/(podatki$visina/100)^2, 2)

library(pastecs)
round(stat.desc(podatki[-1, -4]), 3)

##                  ID  visina starost    teza    BMI
## nbr.val       4.000   4.000   4.000   4.000  4.000
## nbr.null      0.000   0.000   0.000   0.000  0.000
## nbr.na        0.000   0.000   0.000   0.000  0.000
## min           2.000 175.000  22.000  60.000 19.590
## max           5.000 182.000  24.000  80.000 24.150
## range         3.000   7.000   2.000  20.000  4.560
## sum          14.000 714.000  91.000 282.000 88.300
## median        3.500 178.500  22.500  71.000 22.280
## mean          3.500 178.500  22.750  70.500 22.075
## SE.mean       0.645   1.555   0.479   4.113  0.939
## CI.mean.0.95  2.054   4.947   1.523  13.089  2.987
## var           1.667   9.667   0.917  67.667  3.524
## std.dev       1.291   3.109   0.957   8.226  1.877
## coef.var      0.369   0.017   0.042   0.117  0.085

library(psych)
describe(podatki$visina)

##    vars n  mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 5 176.8 4.66    177   176.8 4.45 170 182    12 -0.29    -1.73 2.08