Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. 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 ` 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 `
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 `.
Write some R code that will calculate the mean of A without the missing value.

Exercise 8
Let:
` c1 `;
` c2 `;
` c3 `.
If ` X `, write a code that will display all rows with missing values.

Exercise 9
Consider the following data obtained from ` df `
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 `
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 ```