#Importing the data

data()
data(package = .packages(all.available = TRUE))
mydata <- force(USArrests) #Reading the dataset, saved in R
summary(mydata)
##      Murder          Assault         UrbanPop          Rape      
##  Min.   : 0.800   Min.   : 45.0   Min.   :32.00   Min.   : 7.30  
##  1st Qu.: 4.075   1st Qu.:109.0   1st Qu.:54.50   1st Qu.:15.07  
##  Median : 7.250   Median :159.0   Median :66.00   Median :20.10  
##  Mean   : 7.788   Mean   :170.8   Mean   :65.54   Mean   :21.23  
##  3rd Qu.:11.250   3rd Qu.:249.0   3rd Qu.:77.75   3rd Qu.:26.18  
##  Max.   :17.400   Max.   :337.0   Max.   :91.00   Max.   :46.00
library(psych)
mydata2 <- force(sat.act)

Calculate the descriptive statistics for variables ACT, SATV, SATQ, separate by gender

mydata2$gender <- factor(mydata2$gender,
                         levels = c(1, 2),
                         labels = c("M", "F"))

describeBy(mydata2[ , c("ACT", "SATV", "SATQ")],
           group = mydata2$gender)
## 
##  Descriptive statistics by group 
## group: M
##      vars   n   mean     sd median trimmed    mad min max range  skew kurtosis
## ACT     1 247  28.79   5.06     30   29.23   4.45   3  36    33 -1.06     1.89
## SATV    2 247 615.11 114.16    630  622.07 118.61 200 800   600 -0.63     0.13
## SATQ    3 245 635.87 116.02    660  645.53  94.89 300 800   500 -0.72    -0.12
##        se
## ACT  0.32
## SATV 7.26
## SATQ 7.41
## ------------------------------------------------------------ 
## group: F
##      vars   n   mean     sd median trimmed    mad min max range  skew kurtosis
## ACT     1 453  28.42   4.69     29   28.63   4.45  15  36    21 -0.39    -0.42
## SATV    2 453 610.66 112.31    620  617.91 103.78 200 800   600 -0.65     0.42
## SATQ    3 442 596.00 113.07    600  602.21 133.43 200 800   600 -0.58     0.13
##        se
## ACT  0.22
## SATV 5.28
## SATQ 5.38
mydata2F <- mydata2 [mydata2$gender == "F", ]
mean(mydata2F$SATQ, na.rm = TRUE)
## [1] 595.9955