1 Instructions

2 Problems

2.1 Vectors:

2.1.1 Code with Vectors:
2.1.2 Questions about Vectors:

2.2 Matrices:

2.2.1 Code with Matrices:
2.2.2 Questions about Matrices:

2.3 Lists:

2.3.1 Code with Lists:
2.3.2 Questions About Lists:

2.4 Data Frames:

2.4.1 Code with Data Frames:
2.4.2 Questions About Data Frames:

1 Instructions

For this assignment you will practice creating, examining, and combining different data structures in R. This assignment is different from others in that it takes a worksheet format with built-in error checking. Each time you complete an answer, if you knit the document it should check your answer (but don’t remove “NULL” on an answer before you want it checked or you’ll get an error!) For this reason, you should re-knit every time you answer a question, so that if something goes wrong, you know the last edit caused the issue!

You will need to do the following:

Fill in code as needed in the designated locations in code chunks.
- You will not need to create new code chunks or edit chunk options.
- Instructions in chunks will be shown as comments (after #); remove and replace the comments with your code:
- Where you are assigning things to objects, replace the “NULL” values with your code. See code example 1 for an example!
- You may need to add elements, such as for subsetting, to objects on the left side of operators. See code example 2 for an example!
- Some answers may require multiple lines of code, so add as necessary.
- Do not modify chunks labeled with “# DO NOT EDIT”; these are used to test your code.
Fill in text as block quotes (use >) in designated locations to answer questions.
- Be brief and specific. If you reference a single value or short (<10 element) vector, use in-line R code. If the answer can be clearly and accurately answered without referencing any values, you can do so. When I bold a word in a question, take it to mean I want you to reference a value or values.
- Do not directly reference matrices, lists, or data frames in text (don’t display them). They can be big!
- You should not need to include any code chunks in this section, but you can if you need.

Example for Code Questions:

# Ex 1) Create a vector of the numbers 1, 5, 3, 2, 4.
example <- NULL
# You would write this:
example <- c(1,5,3,2,4)
# Ex 2) Add the number 10 to the sixth position of example2
example2 <- example
example2 <- NULL
# You would write:
example2[6] <- 10

Example for Text Questions: 1) “How do vectors differ from lists?”

2 Problems

2.1 Vectors:

2.1.1 Code with Vectors:

# 1) Use "seq()" to create a vector of numbers from 0 to 45 in increments of 5
vec_num <- NULL
# 2) Use ":" to create an integer vector of the numbers 11 through 20.
vec_int <- NULL
# 3) "LETTERS" contains the 26 capital letters in order. Use "LETTERS" and "[ ]" to create a vector of the last 10 capital letters.
vec_cha <- NULL
# 4) "letters" contains the 26 lowercase letters in order. Use "factor", "letters", and "[ ]" to create a factor variable using the first ten lower case letters.
vec_fac <- NULL
# 5) Use "c()" to combine "vec_cha" and "vec_fac" into "vec_let". Do not convert it to a factor!
vec_let <- NULL
# 6) Use "c()" and "[ ]" to combine the first 4 elements of "vec_num" with the last
# 4 elements of "vec_int" to create "vec_ni".
vec_ni <- NULL
# 7) Use "rev()" to reverse the order of "vec_fac".
fac_vec <- NULL

How’d you do?

No code entered for vec_num yet.
No code entered for vec_int yet.
No code entered for vec_cha yet.
No code entered for vec_fac yet.
No code entered for vec_let yet.
No code entered for vec_ni yet.
No code entered for fac_vec yet.

2.1.2 Questions about Vectors:

If you used c() to combine vec_int with vec_fac, what class of vector would you get? Why?

Consider this code:

new_vec <- c(TRUE, FALSE, TRUE, TRUE)

What class of vector is this?
In words, what happens to its values when you try to convert it to numeric? To character? To numeric and then character?
What happens if you convert it first to character and second to numeric?

2.2 Matrices:

2.2.1 Code with Matrices:

# 1) Use matrix() to create a matrix with 10 rows and four columns filled with NA
mat_empty <- NULL

# 2) Assign "vec_num" to the first column of "mat_1" below.
mat_1 <- mat_empty # DO NOT EDIT THIS LINE; add code below it.
mat_1 <- NULL

# 3) Assign "vec_int" to the second column of "mat_2" below
mat_2 <- mat_1 # DO NOT EDIT THIS LINE; add code below it.
mat_2 <- NULL

# 4) Assign "vec_cha" and "vec_fac" to the third and fourth columns of "mat_3" using one assignment operator.
mat_3 <- mat_2 # DO NOT EDIT THIS LINE; add code below it.
mat_3 <- NULL

# 5) Select the fourth row from "mat_3" and assign it to the object "row_4" as a vector.
row_4 <- NULL

# 6) Assign the element in the 6th row and 2nd column of "mat_3" to "val_6_2" as a numeric value (using as.numeric).
val_6_2 <- NULL

# 7) Use "cbind()" to combine "vec_num", "vec_int", "vec_cha", and "vec_fac" into "mat_4".
mat_4 <- NULL

# 8) Next, first transpose mat_4, then select only the first four columns and assign to mat_t
mat_t <- NULL

# 9)  Then use rbind() to add the rows from mat_3 to mat_t (mat_t first, mat_3 second) and assign this combination to mat_big.
mat_big <- NULL

How’d you do?

No code entered for mat_empty yet.
No code entered for mat_1 yet.
No code entered for mat_2 yet.
No code entered for mat_3 yet.
No code entered for row_4 yet.
No code entered for val_6_2 yet.
No code entered for mat_4 yet.
No code entered for mat_t yet.
No code entered for mat_big yet.

2.2.2 Questions about Matrices:

Note the column names on mat_4. What do you get when you try to get names() from mat_4? What about colnames()? What about rownames()? Can you guess why you get all these results?

Consider the code below:

mat_letters <- matrix(letters, ncol=2)

This matrix goes from "a" to "m" in the first column and "n" to "z" in the second. What would be an easy way to make the matrix go in alphabetical order left to right, top to bottom?

Consider the code below:

math_mat <- matrix(1:5, nrow=5, ncol=5)
math_vec <- 1:5

Without showing your code (use the console), look at math_mat and math_vec. When you add math_mat + math_vec, what happens?
Without showing your code (use the console), what is the difference between the results from math_mat %*% math_vec and from math_mat * math_vec. Can you tell what is happening?

2.3 Lists:

2.3.1 Code with Lists:

# 1) Use "list()" to create a list that contains "vec_num" and "row_4", and assign the names
#   "vec_num" and "row_4" to these two elements of "list_1".
list_1 <- NULL

# 2) Using "$", extract "row_4" from "list_1" and assign it to the object "row_4_2".
row_4_2 <- NULL

# 3) Create another list that contains "val_6_2" and "mat_big".
list_2 <- NULL

# 4) Combine list_1 and list_2 together using "c()" and assign them to "list_3"
list_3 <- NULL

# 5) Use "unlist()" to turn "list_3" into a vector and assign it to "vector_3"
vector_3 <- NULL

# 6) Use "as.list()" to convert "vector_3" into a list and assign it to "list_big"
list_big <- NULL

# 7) Now copy "list_3" as "list_4" (one line of code). Then use "[[ ]]" to assign "list_3" as the last (fifth) element of "list_4";
# that is, you should have a list object with five elements named "list_4" that contains the same four
# elements as "list_3" plus a fifth element that is -all- four elements of "list_3" as one object.
list_4 <- NULL

# 8) Select the third element (that is, the sub-element) of the the fifth element of "list_4" and assign it 
# to element_5_3 using "[[ ]]".
element_5_3 <- NULL

# 9) Lastly, repeat the previous assignment of the third element of the fifth element, but
# extract the element as a list rather than scalar using "[ ]" and assign it to "list_5_3".
list_5_3 <- NULL

How’d you do?

No code entered for list_1 yet.
No code entered for row_4_2 yet.
No code entered for list_2 yet.
No code entered for list_3 yet.
No code entered for vector_3 yet.
No code entered for list_big yet.
No code entered for list_4 yet.
No code entered for element_5_3 yet.
No code entered for list_5_3 yet.

2.3.2 Questions About Lists:

Many functions in R produce lists as output because they produce objects with different types of data and of different lengths. For instance, consider the linear regression saved to lm.output below. Don’t worry if you are not familiar with regression, we’re just concerned with what the function produces!

lm.output <- lm(mpg ~ wt, data=mtcars)
lm.output

## 
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
## 
## Coefficients:
## (Intercept)           wt  
##      37.285       -5.344

How many elements does this call to lm() produce? Hint: use the function length().

What are the dimensions of the model element?

What are the values of the coefficients for the intercept and wt? Remember to call on them from lm.output object for your answer!

2.4 Data Frames:

2.4.1 Code with Data Frames:

# 1) Use "data.frame()" to combine "vec_num" (first column) and "vec_int" (second column) into "df_1".
df_1 <-  NULL

# 2) Use "$" to extract "vec_num" from "df_1", reverse it with "rev()", and assign it as the vector "vec_num_2".
vec_num_2 <- NULL

# 3) Use "$" to add "vec_num_2" to "df_2" as a new column with the name "number_vector".
df_2 <- df_1 # DO NOT EDIT THIS LINE; modify code below it.
df_2 <- NULL

# 4) Combine "df_2" with itself using "rbind()" to create "df_3"
df_3 <- NULL

How’d you do?

No code entered for df_1 yet.
No code entered for vec_num_2 yet.
No code entered for df_2 yet.
No code entered for df_3 yet.

2.4.2 Questions About Data Frames:

Use names(), colnames(), and rownames() on df_1. How does this compare to the behavior of these functions on lists and matrices?

Similarly, how do the results of length() and dim() differ between data frames, lists, matrices, and vectors?

CSSS 508: Homework 4

YOUR NAME

DATE

1 Instructions

2 Problems

2.1 Vectors:

2.1.1 Code with Vectors:

2.1.2 Questions about Vectors:

2.2 Matrices:

2.2.1 Code with Matrices:

2.2.2 Questions about Matrices:

2.3 Lists:

2.3.1 Code with Lists:

2.3.2 Questions About Lists:

2.4 Data Frames:

2.4.1 Code with Data Frames:

2.4.2 Questions About Data Frames: