Class Activity 2

Problem 1

Create and Save Your Name Trend Plot

In this problem, you will use R to explore the popularity of your name over time using the babynames package. You will then save this plot into a folder. Follow the steps below:

  1. Replace "Dee" with your own name in the code below.
  2. Run the code chunk to generate and save the plot in the img/ folder.
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library(ggplot2)
library(dplyr)
library(babynames)
library(stringr)

# Replace 'Dee' with your name
your_name <- "Dee"
your_name_data <- babynames %>% filter(name == your_name)

ggplot(data=your_name_data, aes(x=year, y=prop)) +
  geom_point(size = 3, alpha = 0.6) +
  geom_line(aes(colour = sex), size = 1) +
  scale_color_brewer(palette = "Set1") +
  labs(x = 'Year', y = str_c('Proportion of Babies Named ', your_name),
       title = str_c('Trends in Names: ', your_name))

Load and Display Your Saved Plot

After saving your plot in the img/ folder, the next task is to load and display the saved image. This demonstrates how to reuse images in your R Markdown documents.

  1. Ensure your plot was saved in the previous task.
  2. Use the code below to load and display your saved plot image.
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knitr::include_graphics("img/your-plot-filename.png")

Problem 2

In this problem, we’ll explore some basic data assignments and manipulations in R. Understanding these fundamental concepts will help you work effectively with data in R. Let’s dive into some practical exercises.

a. Creating a Simple Vector

Vectors are one of the most basic data types in R. They hold elements of the same type. Let’s create a vector containing all integers from 4 to 10. Call it a1.

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Answer:

a1 <- 4:10
a1
[1]  4  5  6  7  8  9 10

b. Creating a Vector of Even Integers

Now, let’s create a vector that only contains even integers from 4 to 10. Call it a2.

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Answer:

a2 <- seq(4, 10, by=2)
a2
[1]  4  6  8 10

c. Adding Two Vectors

What do you think happens when we add two vectors of the same length in R? Let’s find out by adding a1 and a2.

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Answer:

a1_plus_a2 <- a1 + a2
a1_plus_a2
[1]  8 11 14 17 12 15 18

d. Summing Up Vector Elements

The sum() function calculates the total sum of all the elements in a vector. Let’s see how it works with our vector a1.

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Answer:

sum(a1)
[1] 49

e. Finding the Length of a Vector

To find out how many elements a vector has, we can use the length() function. Let’s apply it to a1.

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Answer:

length(a1)
[1] 7

f. Calculating the Average

  1. Use the sum and length commands to calculate the average of the values in a1.
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Answer:

average_a1 <- sum(a1) / length(a1)
average_a1
[1] 7

g. Conditional Operations with ifelse()

The ifelse() function is useful for performing conditional operations on vectors. It takes a condition, a result for TRUE values, and a result for FALSE values.

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Answer:

# Example: Replace even numbers with 1, and odd numbers with 0
ifelse(a1 %% 2 == 0, 1, 0)
[1] 1 0 1 0 1 0 1

h. Combining Strings with paste()

The paste() function concatenates strings together. Let’s combine some text with the elements of a vector.

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Answer:

paste("Value is", a1)
[1] "Value is 4"  "Value is 5"  "Value is 6"  "Value is 7"  "Value is 8" 
[6] "Value is 9"  "Value is 10"

i. Creating a Matrix

A matrix in R is a two-dimensional array that holds data of a single basic type. Let’s create a simple matrix.

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Answer:

my_matrix <- matrix(1:9, nrow=3, ncol=3)
my_matrix
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

j. Making a DataFrame using cbind or rbind

Data frames are used to store tabular data in R. They can be created using the cbind() (column-bind) or rbind() (row-bind) functions. Here’s how:

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Answer:

df_cbind <- data.frame(cbind(a1, a2))
df_cbind
  a1 a2
1  4  4
2  5  6
3  6  8
4  7 10
5  8  4
6  9  6
7 10  8
df_rbind <- data.frame(rbind(a1, a1)) 
df_rbind
     X1 X2 X3 X4 X5 X6 X7
a1    4  5  6  7  8  9 10
a1.1  4  5  6  7  8  9 10

k. Creating a List

Lists in R can hold elements of different types. They are incredibly versatile. Let’s create a simple list.

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Answer:

my_list <- list(numbers=a1, evenNumbers=a2, average=average_a1)
my_list
$numbers
[1]  4  5  6  7  8  9 10

$evenNumbers
[1]  4  6  8 10

$average
[1] 7

l. (Bonus) What would the following evaluate to? Could you think of a reason.

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x <- c(7, 5, 3, 9)
y <- c(FALSE, factor(c("cellar", "door")), 2)
#x - y

Answer: