Q1. Using R as a calculator
| Exercise 1: Calculate square root of 729 |
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sqrt (729)
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Q2. Creating a numeric variable
| Exercise 2: Create a new variable 'b' with value 1947.0 |
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b <- 1947.0
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Q3. How to convert a numeric variable to character
| Exercise 3: Convert 'b' from previous exercise to character |
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b <- as.character(b)
print (b)
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Q4. Setting up working directory
| Exercise 4: Setup your working directory to a new 'work' folder in your desktop |
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setwd ("path/to/my/desktop/work")
getwd()
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Q5. Create an environment within the global environment
| Exercise 5.1: Create a new environment called 'myEnv' |
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myEnv <- new.env()
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| Exercise 5.2: Create a variable inside it |
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assign('b', 3, envir = myEnv)
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| Exercise 5.3: Access the variable inside the new environment |
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get('b', envir=myEnv)
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| Exercise 5.4: Delete the variable inside that environment |
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rm(b, envir=myEnv)
# Note that the ‘envir’ argument was common in ‘assign’, ‘get’ and ‘rm’ functions. Also note that the assign and get functions take in the variable name as a string while the rm function took the variable object itself.
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Q6. Create a numeric vector
| Exercise 6.a: Create a vector numbers from 1 to 6 and find out its class |
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one_to_six <- c(1, 2, 3, 4, 5, 6)
class(one_to_six)
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| Exercise 6.b: Create a vector containing following mixed elements {1, 'a', 2, 'b'} and find out its class |
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one_a <- c(1, "a", 2, "b")
class(one_a) # character
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Q7. Initialise Character vector
| Exercise 7.a: Initialise a character vector of length 26 |
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charHundred <- character(26)
charHundred
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| Exercise 7.b: Assign the character 'a' to the first element in above vector. |
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charHundred[1] <- "a"
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Q8. Vector operations
| Exercise 8.1: Create a vector with some of your friend's names |
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myFriends <- c("alan", "bala", "amir", "tsong", "chan")
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| Exercise 8.2: Get the length of above vector |
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length(myFriends)
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| Exercise 8.3: Get the first two friends from above vector |
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myFriends[1:2]
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| Exercise 8.4: Get the 2nd and 3rd friends |
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myFriends[c(2,3)]
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| Exercise 8.5: Sort your friends by names using 2 methods |
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sort(myFriends)
myFriends[order(myFriends)]
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| Exercise 8.6: Reverse direction of the above sort |
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sort(myFriends, decreasing=TRUE)
myFriends[rev(order(myFriends))]
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Q9. Create customised vector sequences
| Exercise 9: Create with rep and seq: 'a','a',1,2,3,4,5,7,9,11 |
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out <- c(rep('a', 2), seq(1, 5), seq(7, 11, by=2)) # because of the presence of 'a' character, the numbers are converted to characters as well.
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Q10. Remove missing values
| Exercise 10: Remove missing value from c(1, 2, NA, 4) |
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myVec <- c(1, 2, NA, 4)
myVec[!is.na(myVec)]
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Q11. Random sampling
| Exercise 11: Pick 50 random numbers between 1 to 100, with replacement |
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mySample <-sample(1:100, 50, replace=T)
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Q12. Checking the class
| Exercise 12: Check the class of mySample |
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class (mySample)
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Q13. Find the class of ‘iris’ dataframe, find the class of all the columns of ‘iris’, get the summary of ‘iris’, get the top 6 rows, view it in a spreadsheet format, get row names, get column names, get number of rows and get number of columns.
| Exercise 13: Apply the above functions and inspect results on 'iris' (a base R dataframe) |
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class (iris) # get class
sapply (iris, class) # get class of all columns
str (iris) # structure
summary (iris) # summary of airquality
head (iris) # view the first 6 obs
fix (iris) # view spreadsheet like grid
rownames (iris) # row names
colnames (iris) # columns names
nrow (iris) # number of rows
ncol (iris) # number of columns |
Q14. Subsetting a dataframe
| Exercise 14.1: Get the last 2 rows in last 2 columns from iris dataset |
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numRows <- nrow(iris)
numCols <- ncol(iris)
iris[(numRows-1):numRows, (numCols-1):numCols]
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| Exercise 14.2: Get rows with Sepal.Width > 3.5 using which() from iris |
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iris[iris$Sepal.Width > 3, ]
iris[which(iris$Sepal.Width > 3), ]
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| Exercise 14.3: Get the rows with 'versicolor' species using subset() from iris |
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subset(iris, Species == "versicolor")
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Q15. With the dataframes created from code below, perform the various merge operations.
set.seed(100)
Df1 <- iris[sample(1:nrow(iris), 10), c(1,2,3,5)]
Df2 <- iris[sample(1:nrow(iris), 10), c(1,2,4,5) ]
# induce NAs
Df1 <- Df1[sample(1:nrow(Df1), 3), 4]
Df2 <- Df2[sample(1:nrow(Df1), 3), 4]
| Exercise 15.1: Find out how to do inner join, outer join, left join and right join. Merge using the by variable as 'Species' |
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# Not Run
merge(Df1, Df2, by="Species", all=FALSE) # inner join
merge(Df1, Df2, by="Species", all=TRUE) # outer join
merge(Df1, Df2, by="Species", all.x=TRUE) # left join
merge(Df1, Df2, by="Species", all.y=TRUE) # right join
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| Exercise 15.2: How would you specify the 'by' variables if the two data frames to be merged have different 'by' variables. |
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# Not Run
merge(Df1, Df2, by ="var1") # both DFs have a common variable name'var1'
merge(Df1, Df2, by.x="var1", by.y="var2") # both DFs have different names for by variables
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Q16. Create customised character vector using the paste function
| Exercise 16: Create this character vector using paste: 'var1', 'var2', 'var3', 'pred1', 'pred2', 'pred3' |
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paste0 (c(rep("var", 3), rep("pred", 3)), 1:3)
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Q17. Creating tables
| Exercise 17: Tabulate the Sepal.Width for each Species from iris |
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table(iris$Species, iris$Sepal.Width)
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Problem Logic for Q18, 19 and 20: Create a character vector with length of number-of-rows-of-iris-dataset, such that, each element gets a character value - “greater than 5″ if the corresponding ‘Sepal.Length’ > 5, else it should get “lesser than 5″.
Q18. Create a logic for the above problem using a For-loop
| Exercise 18: Make the logic for above problem statement using a 'for-loop' and a 'if-else' statement |
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output <- character(nrow(iris))
for(i in c(1:nrow(iris))){
if (iris$Sepal.Length[i] > 5){
output[i] <- "greater than 5"
} else {
output[i] <- "lesser than 5"
}
}
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Q19.
ifelse() function
| Exercise 19: Make the logic for above problem statement using a ifelse() function |
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output <- ifelse(iris$Sepal.Length > 5, "greater than 5", "lesser than 5") # works like the 'if' function in MS Excel, except that the condition is checked for every element of iris$Sepal.Length
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Q20. The apply() function
| Exercise 20: Create a logic for the same problem statement using apply() function |
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# define the function for apply() statement
myFunc <- function(x){
if(x['Sepal.Length'] > 5){
"greater than 5"
} else {
"lesser than 5"
}
}
output <- apply(iris, 1, FUN=myFunc)
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Editor’s Note: The ‘Learn R By Intensive Practice’ video course has real coding challenges at the end of each video, plus, dedicated videos for practice exercises. This course would be great you are looking get a good handle of base R programming by solving a lot of practice challenges.
