Day 1 Homework Exercises

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• The exercises below should be uploaded to the R_nanocourse_assignment#1 Dropbox folder by 5pm on Wednesday, April 29th.
• Add your solutions to the exercises in the downloaded .R file and upload the saved file, renamed with your initials/name to Dropbox.
• Specific questions regarding the homework that you would like to have reviewed in class can be asked here.
• Full attendance and submission of all assignments are required for course completion.

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R syntax and data structures

1. Try changing the value of the variable x to 5. What happens to number?

2. Now try changing the value of variable y to contain the value 10. What do you need to do, to update the variable number?

3. Try to create a vector of numeric and character values by combining the two vectors that we just created (glengths and species). Assign this combined vector to a new variable called combined. Hint: you will need to use the combine c() function to do this. Print the combined vector in the console, what looks different compared to the original vectors?

4. Let’s say that in our experimental analyses, we are working with three different sets of cells: normal, cells knocked out for geneA (a very exciting gene), and cells overexpressing geneA. We have three replicates for each celltype.

   1. Create a vector named samplegroup with nine elements: 3 control (“CTL”) values, 3 knock-out (“KO”) values, and 3 over-expressing (“OE”) values.

   2. Turn samplegroup into a factor data structure.

5. Create a data frame called favorite_books with the following vectors as columns:

     titles <- c("Catch-22", "Pride and Prejudice", "Nineteen Eighty Four")
     pages <- c(453, 432, 328)
   

6. Create a list called list2 containing species, glengths, and number.

Functions and arguments

1. Let’s use base R function to calculate mean value of the glengths vector. You might need to search online to find what function can perform this task.

2. Create a new vector test <- c(1, NA, 2, 3, NA, 4). Use the same base R function from exercise 1 (with addition of proper argument), and calculate mean value of the test vector. The output should be 2.5.

   NOTE: In R, missing values are represented by the symbol NA (not available). It’s a way to make sure that users know they have missing data, and make a conscious decision on how to deal with it. There are ways to ignore NA during statistical calculations, or to remove NA from the vector. More information related to missing data can be found here.

3. Another commonly used base function is sort(). Use this function to sort the glengths vector in descending order.

4. Write a function called multiply_it, which takes two inputs: a numeric value x, and a numeric value y. The function will return the product of these two numeric values, which is x * y. For example, multiply_it(x=4, y=6) will return output 24.

Reading in and inspecting data

1. Download this tab-delimited .txt file and save it in your project’s “data” folder.

   1. Read it in to R using read.table() and store it as the variable proj_summary. As you use read.table(), keep in mind that:
      • all the columns in the input text file have column names
      • you want the first column of the text file to be used as row names (hint: look up the row.names = argument
   2. Display the contents of proj_summary in your console

2. Use the class() function on the glengths and metadata objects, how does the output differ between the two?

3. Use the summary() function on the proj_summary dataframe
   • What is the median “rRNA_rate”?
   • How many samples got the “low” level of treatment?

4. How long is the samplegroup factor?

5. What are the dimensions of the proj_summary dataframe?

6. When you use the rownames() function on metadata, what is the data structure of the output?

7. [Optional] How many elements in (how long is) the output of colnames(proj_summary)? Don’t count, but use another function to determine this.