Today we’re training how to handle missing values in a data set. Before starting the exercises, please first read section 2.5 of An Introduction to R.

Solutions are available here.

Exercise 1
If X <- c (22,3,7,NA,NA,67) what will be the output for the R statement length(X)

Exercise 2
If X = c(NA,3,14,NA,33,17,NA,41) write some R code that will remove all occurrences of NA in X.
a. X[!is.na(X)]
b. X[is.na(X)]
c. X[X==NA]= 0

Exercise 3
If Y = c(1,3,12,NA,33,7,NA,21) what R statement will replace all occurrences of NA with 11?
a. Y[Y==NA]= 11
b. Y[is.na(Y)]= 11
c. Y[Y==11] = NA

Exercise 4
If X = c(34,33,65,37,89,NA,43,NA,11,NA,23,NA) then what will count the number of occurrences of NA in X?
a. sum(X==NA)
b. sum(X == NA, is.na(X))
c. sum(is.na(X))

Exercise 5
Consider the following vector W <- c (11, 3, 5, NA, 6)
Write some R code that will return TRUE for value of W missing in the vector.

Exercise 6
Load ‘Orange’ dataset from R using the command data(Orange) . Replace all values of age=118 to NA.

Exercise 7
Consider the following vector A <- c (33, 21, 12, NA, 7, 8) .
Write some R code that will calculate the mean of A without the missing value.

Exercise 8
Let:
c1 <- c(1,2,3,NA) ;
c2 <- c(2,4,6,89) ;
c3 <- c(45,NA,66,101) .
If X <- rbind (c1,c2,c3, deparse.level=1) , write a code that will display all rows with missing values.

Exercise 9
Consider the following data obtained from df <- data.frame (Name = c(NA, “Joseph”, “Martin”, NA, “Andrea”), Sales = c(15, 18, 21, 56, 60), Price = c(34, 52, 21, 44, 20), stringsAsFactors = FALSE)
Write some R code that will return a data frame which removes all rows with NA values in Name column

Exercise 10
Consider the following data obtained from df <- data.frame(Name = c(NA, “Joseph”, “Martin”, NA, “Andrea”), Sales = c(15, 18, 21, NA, 60), Price = c(34, 52, 33, 44, NA), stringsAsFactors = FALSE)
Write some R code that will remove all rows with NA values and give the following output
Name Sales Price 2 Joseph 18 52 3 Martin 21 33