**R-exercises**, and kindly contributed to R-bloggers)

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

**leave a comment**for the author, please follow the link and comment on their blog:

**R-exercises**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...