added solution for 2nd tutorial

R/BIA_T02_RIntro2.Rmd

@@ -54,34 +54,41 @@ summary(Shower_clean) # all rows having NA values in one or many columns are now
 
 ```{r Simple Statistics}
 # Task 5
-
+mean(Shower$ShowerTime) #mean without parameters does not work because we have some NA values in this vector
+mean(Shower_clean$ShowerTime) #this data frame is cleaned and mean should work
+mean(Shower$ShowerTime, na.rm = T)
 
 # Task 6
-
+var(Shower_clean$ShowerTime)  #variance (implements sample variance)
 
 # Task 7
-
+median(Shower_clean$ShowerTime)
 
 # Task 8
-
+sd(Shower_clean$ShowerTime)       #standard deviation (implements sample formula)
 
 # Task 9
 #compare variance and square of standard deviation
-
+(sd(Shower_clean$ShowerTime)^2) == var(Shower_clean$ShowerTime)
 #the comparison of numbers should be made considering a precision:
-
+all.equal((sd(Shower_clean$ShowerTime)^2), var(Shower_clean$ShowerTime))
 
 # Task 10
+max(Shower_clean$ShowerTime)
+min(Shower_clean$ShowerTime)
 
 # Task 11
+quantile(Shower_clean$ShowerTime)
 ```
 
 
 ```{r Write and filter data}
 # Task 12
-
+write.csv2(x = Shower[Shower$Hh_ID == 8899,], file="../output/problematic_shower_data.csv")
 
 # Task 13
-
+write.csv2(x = Shower[Shower$Hh_ID != 8899,], file="../output/cleaned_shower_data.csv")
 ```
 
+After cleaning data we have stored the data to the folder "output".
+