## 2.10 Exercises

### 2.10.1 Computations in R

1. Sum 2 and 3 using the + operator. [Difficulty: Beginner]

2. Take the square root of 36, use sqrt(). [Difficulty: Beginner]

3. Take the log10 of 1000, use function log10(). [Difficulty: Beginner]

4. Take the log2 of 32, use function log2(). [Difficulty: Beginner]

5. Assign the sum of 2,3 and 4 to variable x. [Difficulty: Beginner]

6. Find the absolute value of the expression 5 - 145 using the abs() function. [Difficulty: Beginner]

7. Calculate the square root of 625, divide it by 5, and assign it to variable x.Ex: y= log10(1000)/5, the previous statement takes log10 of 1000, divides it by 5, and assigns the value to variable y. [Difficulty: Beginner]

8. Multiply the value you get from previous exercise by 10000, assign it to variable x Ex: y=y*5, multiplies y by 5 and assigns the value to y. KEY CONCEPT: results of computations or arbitrary values can be stored in variables we can re-use those variables later on and over-write them with new values. [Difficulty: Beginner]

### 2.10.2 Data structures in R

1. Make a vector of 1,2,3,5 and 10 using c(), and assign it to the vec variable. Ex: vec1=c(1,3,4) makes a vector out of 1,3,4. [Difficulty: Beginner]

2. Check the length of your vector with length(). Ex: length(vec1) should return 3. [Difficulty: Beginner]

3. Make a vector of all numbers between 2 and 15. Ex: vec=1:6 makes a vector of numbers between 1 and 6, and assigns it to the vec variable. [Difficulty: Beginner]

4. Make a vector of 4s repeated 10 times using the rep() function. Ex: rep(x=2,times=5) makes a vector of 2s repeated 5 times. [Difficulty: Beginner]

5. Make a logical vector with TRUE, FALSE values of length 4, use c(). Ex: c(TRUE,FALSE). [Difficulty: Beginner]

6. Make a character vector of the gene names PAX6,ZIC2,OCT4 and SOX2. Ex: avec=c("a","b","c") makes a character vector of a,b and c. [Difficulty: Beginner]

7. Subset the vector using [] notation, and get the 5th and 6th elements. Ex: vec1 gets the first element. vec1[c(1,3)] gets the 1st and 3rd elements. [Difficulty: Beginner]

8. You can also subset any vector using a logical vector in []. Run the following:

myvec=1:5
# the length of the logical vector
# should be equal to length(myvec)
myvec[c(TRUE,TRUE,FALSE,FALSE,FALSE)]
myvec[c(TRUE,FALSE,FALSE,FALSE,TRUE)]

[Difficulty: Beginner]

1. ==,>,<, >=, <= operators create logical vectors. See the results of the following operations:
myvec > 3
myvec == 4
myvec <= 2
myvec != 4

[Difficulty: Beginner]

1. Use the > operator in myvec[ ] to get elements larger than 2 in myvec which is described above. [Difficulty: Beginner]

2. Make a 5x3 matrix (5 rows, 3 columns) using matrix(). Ex: matrix(1:6,nrow=3,ncol=2) makes a 3x2 matrix using numbers between 1 and 6. [Difficulty: Beginner]

3. What happens when you use byrow = TRUE in your matrix() as an additional argument? Ex: mat=matrix(1:6,nrow=3,ncol=2,byrow = TRUE). [Difficulty: Beginner]

4. Extract the first 3 columns and first 3 rows of your matrix using [] notation. [Difficulty: Beginner]

5. Extract the last two rows of the matrix you created earlier. Ex: mat[2:3,] or mat[c(2,3),] extracts the 2nd and 3rd rows. [Difficulty: Beginner]

6. Extract the first two columns and run class() on the result. [Difficulty: Beginner]

7. Extract the first column and run class() on the result, compare with the above exercise. [Difficulty: Beginner]

8. Make a data frame with 3 columns and 5 rows. Make sure first column is a sequence of numbers 1:5, and second column is a character vector. Ex: df=data.frame(col1=1:3,col2=c("a","b","c"),col3=3:1) # 3x3 data frame. Remember you need to make a 3x5 data frame. [Difficulty: Beginner]

9. Extract the first two columns and first two rows. HINT: Use the same notation as matrices. [Difficulty: Beginner]

10. Extract the last two rows of the data frame you made. HINT: Same notation as matrices. [Difficulty: Beginner]

11. Extract the last two columns using the column names of the data frame you made. [Difficulty: Beginner]

12. Extract the second column using the column names. You can use [] or $ as in lists; use both in two different answers. [Difficulty: Beginner] 13. Extract rows where the 1st column is larger than 3. HINT: You can get a logical vector using the > operator , and logical vectors can be used in [] when subsetting. [Difficulty: Beginner] 14. Extract rows where the 1st column is larger than or equal to 3. [Difficulty: Beginner] 15. Convert a data frame to the matrix. HINT: Use as.matrix(). Observe what happens to numeric values in the data frame. [Difficulty: Beginner] 16. Make a list using the list() function. Your list should have 4 elements; the one below has 2. Ex: mylist= list(a=c(1,2,3),b=c("apple,"orange")) [Difficulty: Beginner] 17. Select the 1st element of the list you made using $ notation. Ex: mylist$a selects first element named “a”. [Difficulty: Beginner] 18. Select the 4th element of the list you made earlier using $ notation. [Difficulty: Beginner]

19. Select the 1st element of your list using [ ] notation. Ex: mylist selects the first element named “a”, and you get a list with one element. mylist["a"] selects the first element named “a”, and you get a list with one element. [Difficulty: Beginner]

20. Select the 4th element of your list using [ ] notation. [Difficulty: Beginner]

21. Make a factor using factor(), with 5 elements. Ex: fa=factor(c("a","a","b")). [Difficulty: Beginner]

22. Convert a character vector to a factor using as.factor(). First, make a character vector using c() then use as.factor(). [Difficulty: Intermediate]

23. Convert the factor you made above to a character using as.character(). [Difficulty: Beginner]

### 2.10.3 Reading in and writing data out in R

1. Read CpG island (CpGi) data from the compGenomRData package CpGi.table.hg18.txt. This is a tab-separated file. Store it in a variable called cpgi. Use
cpgFilePath=system.file("extdata",
"CpGi.table.hg18.txt",
package="compGenomRData")

to get the file path within the installed compGenomRData package. [Difficulty: Beginner]

1. Use head() on CpGi to see the first few rows. [Difficulty: Beginner]

2. Why doesn’t the following work? See sep argument at help(read.table). [Difficulty: Beginner]

cpgtFilePath=system.file("extdata",
"CpGi.table.hg18.txt",
package="compGenomRData")
cpgtFilePath
head(cpgiSepComma)
1. What happens when you set stringsAsFactors=FALSE in read.table()? [Difficulty: Beginner]
cpgiHF=read.table("intro2R_data/data/CpGi.table.hg18.txt",
stringsAsFactors=FALSE)
1. Read only the first 10 rows of the CpGi table. [Difficulty: Beginner/Intermediate]

2. Use cpgFilePath=system.file("extdata","CpGi.table.hg18.txt", package="compGenomRData") to get the file path, then use read.table() with argument header=FALSE. Use head() to see the results. [Difficulty: Beginner]

3. Write CpG islands to a text file called “my.cpgi.file.txt”. Write the file to your home folder; you can use file="~/my.cpgi.file.txt" in linux. ~/ denotes home folder.[Difficulty: Beginner]

4. Same as above but this time make sure to use the quote=FALSE,sep="\t" and row.names=FALSE arguments. Save the file to “my.cpgi.file2.txt” and compare it with “my.cpgi.file.txt”. [Difficulty: Beginner]

5. Write out the first 10 rows of the cpgi data frame. HINT: Use subsetting for data frames we learned before. [Difficulty: Beginner]

6. Write the first 3 columns of the cpgi data frame. [Difficulty: Beginner]

7. Write CpG islands only on chr1. HINT: Use subsetting with [], feed a logical vector using == operator.[Difficulty: Beginner/Intermediate]

8. Read two other data sets “rn4.refseq.bed” and “rn4.refseq2name.txt” with header=FALSE, and assign them to df1 and df2 respectively. They are again included in the compGenomRData package, and you can use the system.file() function to get the file paths. [Difficulty: Beginner]

9. Use head() to see what is inside the data frames above. [Difficulty: Beginner]

10. Merge data sets using merge() and assign the results to a variable named ‘new.df’, and use head() to see the results. [Difficulty: Intermediate]

### 2.10.4 Plotting in R

Please run the following code snippet for the rest of the exercises.

set.seed(1001)
x1=1:100+rnorm(100,mean=0,sd=15)
y1=1:100
1. Make a scatter plot using the x1 and y1 vectors generated above. [Difficulty: Beginner]

2. Use the main argument to give a title to plot() as in plot(x,y,main="title"). [Difficulty: Beginner]

3. Use the xlab argument to set a label for the x-axis. Use ylab argument to set a label for the y-axis. [Difficulty: Beginner]

4. Once you have the plot, run the following expression in R console. mtext(side=3,text="hi there") does. HINT: mtext stands for margin text. [Difficulty: Beginner]

5. See what mtext(side=2,text="hi there") does. Check your plot after execution. [Difficulty: Beginner]

6. Use mtext() and paste() to put a margin text on the plot. You can use paste() as ‘text’ argument in mtext(). HINT: mtext(side=3,text=paste(...)). See how paste() is used for below. [Difficulty: Beginner/Intermediate]

paste("Text","here")
##  "Text here"
myText=paste("Text","here")
myText
##  "Text here"
1. cor() calculates the correlation between two vectors. Pearson correlation is a measure of the linear correlation (dependence) between two variables X and Y. Try using the cor() function on the x1 and y1 variables. [Difficulty: Intermediate]

2. Try to use mtext(),cor() and paste() to display the correlation coefficient on your scatter plot. [Difficulty: Intermediate]

3. Change the colors of your plot using the col argument. Ex: plot(x,y,col="red"). [Difficulty: Beginner]

4. Use pch=19 as an argument in your plot() command. [Difficulty: Beginner]

5. Use pch=18 as an argument to your plot() command. [Difficulty: Beginner]

6. Make a histogram of x1 with the hist() function. A histogram is a graphical representation of the data distribution. [Difficulty: Beginner]

7. You can change colors with ‘col’, add labels with ‘xlab’, ‘ylab’, and add a ‘title’ with ‘main’ arguments. Try all these in a histogram. [Difficulty: Beginner]

8. Make a boxplot of y1 with boxplot().[Difficulty: Beginner]

9. Make boxplots of x1 and y1 vectors in the same plot.[Difficulty: Beginner]

10. In boxplot, use the horizontal = TRUE argument. [Difficulty: Beginner]

11. Make multiple plots with par(mfrow=c(2,1))

• run par(mfrow=c(2,1))
• make a boxplot
• make a histogram [Difficulty: Beginner/Intermediate]
12. Do the same as above but this time with par(mfrow=c(1,2)). [Difficulty: Beginner/Intermediate]

13. Save your plot using the “Export” button in Rstudio. [Difficulty: Beginner]

14. You can make a scatter plot showing the density of points rather than points themselves. If you use points it looks like this:

x2=1:1000+rnorm(1000,mean=0,sd=200)
y2=1:1000
plot(x2,y2,pch=19,col="blue") If you use the smoothScatter() function, you get the densities.

smoothScatter(x2,y2,
colramp=colorRampPalette(c("white","blue",
"green","yellow","red"))) Now, plot with the colramp=heat.colors argument and then use a custom color scale using the following argument.

colramp = colorRampPalette(c("white","blue", "green","yellow","red")))

[Difficulty: Beginner/Intermediate]

### 2.10.5 Functions and control structures (for, if/else, etc.)

Read CpG island data as shown below for the rest of the exercises.

cpgtFilePath=system.file("extdata",
"CpGi.table.hg18.txt",
package="compGenomRData")
head(cpgi)
##   chrom chromStart chromEnd     name length cpgNum gcNum perCpg perGc obsExp
## 1  chr1      18598    19673 CpG: 116   1075    116   787   21.6  73.2   0.83
## 2  chr1     124987   125426  CpG: 30    439     30   295   13.7  67.2   0.64
## 3  chr1     317653   318092  CpG: 29    439     29   295   13.2  67.2   0.62
## 4  chr1     427014   428027  CpG: 84   1013     84   734   16.6  72.5   0.64
## 5  chr1     439136   440407  CpG: 99   1271     99   777   15.6  61.1   0.84
## 6  chr1     523082   523977  CpG: 94    895     94   570   21.0  63.7   1.04
1. Check values in the perGc column using a histogram. The ‘perGc’ column in the data stands for GC percent => percentage of C+G nucleotides. [Difficulty: Beginner]

2. Make a boxplot for the ‘perGc’ column. [Difficulty: Beginner]

3. Use if/else structure to decide if the given GC percent is high, low or medium. If it is low, high, or medium: low < 60, high>75, medium is between 60 and 75; use greater or less than operators, < or >. Fill in the values in the code below, where it is written ‘YOU_FILL_IN’. [Difficulty: Intermediate]

GCper=65

# check if GC value is lower than 60,
# assign "low" to result
if('YOU_FILL_IN'){
result="low"
cat("low")
}
else if('YOU_FILL_IN'){  # check if GC value is higher than 75,
#assign "high" to result
result="high"
cat("high")
}else{ # if those two conditions fail then it must be "medium"
result="medium"
}

result
1. Write a function that takes a value of GC percent and decides if it is low, high, or medium: low < 60, high>75, medium is between 60 and 75. Fill in the values in the code below, where it is written ‘YOU_FILL_IN’. [Difficulty: Intermediate/Advanced]
GCclass<-function(my.gc){

YOU_FILL_IN

return(result)
}
GCclass(10) # should return "low"
GCclass(90) # should return "high"
GCclass(65) # should return "medium"
1. Use a for loop to get GC percentage classes for gcValues below. Use the function you wrote above.[Difficulty: Intermediate/Advanced]
gcValues=c(10,50,70,65,90)
for( i in YOU_FILL_IN){
YOU_FILL_IN
}
1. Use lapply to get GC percentage classes for gcValues. [Difficulty: Intermediate/Advanced]
vec=c(1,2,4,5)
power2=function(x){ return(x^2)  }
lapply(vec,power2)
1. Use sapply to get values to get GC percentage classes for gcValues. [Difficulty: Intermediate]

2. Is there a way to decide on the GC percentage class of a given vector of GCpercentages without using if/else structure and loops ? if so, how can you do it? HINT: Subsetting using < and > operators. [Difficulty: Intermediate]