Assignment 4

Author

Anna StPierre

Download and open the assign04.qmd file and complete the exercises.

We will be using pay-per-click (PPC) data from a 31 day campaign from a company that sells USB keys and USB hubs. Each row of the 555 observations represents a click on an internet ad based on a keyword search and there are 3 columns.

day - represents the day of the campaign. Valid days are 1-31.
price - represents the price of the campaign. Price can’t be a number below 0.10
keyword - represents the keyword purchased. Everything must be spelled correctly, there aren’t many keywords but they are some combination of “usb” and/or “key” or “hub”

In this assignment you will be examining each column for data validity. Each exercise presents one or more questions for you to answer.

We’ll start by loading the tidyverse family of packages along with the janitor and skimr packages, and our data. Make sure you install these two packages in your RStudio prior to calling the library() functions below.

library(tidyverse)
library(janitor)
library(skimr)
ppc_data <- read_csv("https://jsuleiman.com/datasets/ppc_data.csv")
glimpse(ppc_data)

Rows: 555
Columns: 3
$ day     <dbl> 9, 30, 19, 4, 30, 17, 5, 8, 17, 21, 4, 13, 29, 25, 25, 7, 4, 5…
$ keyword <chr> "usb", "usb hub", "usb hub", "usb key", "key", "usb hub", "hub…
$ price   <dbl> 5.9, 8.0, 2.8, 7.7, 1.7, 5.5, 5.2, 3.8, 6.0, 2.0, 9.7, 7.0, 8.…

Exercises

There are six exercises in this assignment. The Grading Rubric is available at the end of this document.

Exercise 1

Create a graph of number of clicks (i.e., observations) for each day (1-31). Use geom_bar() for your geometry. In the narrative below your code note which days had zero clicks.

ggplot(ppc_data, aes(x = factor(day))) +
  geom_bar() +
  labs(title = "Number of Clicks per Day", x = "Day of Campaign", y = "Number of Clicks") +
  theme_minimal()

Exercise 2

Insert a code cell to show how many NA (i.e., missing) values there are in price. In the narrative below that code cell write out how many NA values there are for price and what percent of the observations that represents.

na_count <- sum(is.na(ppc_data$price))
total_obs <- nrow(ppc_data)
percent_na <- (na_count / total_obs) * 100

cat("Number of missing values in 'price':", na_count, "\n")

Number of missing values in 'price': 6

cat("Percentage of total observations:", percent_na, "%\n")

Percentage of total observations: 1.081081 %

Exercise 3

Valid values for price are 0.1 or greater. Insert a code cell that displays the number of values of price that are less than 0.1. In the narrative below that code cell write how many values are below 0.1.

invalid_prices <- sum(ppc_data$price < 0.1, na.rm = TRUE)
cat("Number of invalid 'price' values (< 0.1):", invalid_prices, "\n")

Number of invalid 'price' values (< 0.1): 10

Exercise 4

Insert a code cell that drops all of the rows that contain invalid or NA values for price.

ppc_data_clean <- ppc_data %>%
  filter(!is.na(price) & price >= 0.1)

glimpse(ppc_data_clean)  # Verify data

Rows: 539
Columns: 3
$ day     <dbl> 9, 30, 19, 4, 30, 17, 5, 8, 17, 21, 4, 13, 29, 25, 25, 7, 4, 4…
$ keyword <chr> "usb", "usb hub", "usb hub", "usb key", "key", "usb hub", "hub…
$ price   <dbl> 5.9, 8.0, 2.8, 7.7, 1.7, 5.5, 5.2, 3.8, 6.0, 2.0, 9.7, 7.0, 8.…

Exercise 5

Insert a code cell that shows a tabyl of the counts of each keyword. In the narrative below the code cell, list the misspellings and counts if there are any.

ppc_data_clean %>%
  tabyl(keyword) %>%
  arrange(desc(n))

 keyword   n    percent
 usb key 157 0.29128015
 usb hub 119 0.22077922
     hub  89 0.16512059
     key  81 0.15027829
     usb  73 0.13543599
     ubs  10 0.01855288
 ubs key  10 0.01855288

Exercise 6

Insert a code cell that corrects all the misspellings for keyword, then rerun tabyl to verify.

ppc_data_clean <- ppc_data_clean %>%
  mutate(keyword = case_when(
    str_detect(keyword, "usb.?key") ~ "usb key",
    str_detect(keyword, "usb.?hub") ~ "usb hub",
    str_detect(keyword, "usb.?key.?hub") ~ "usb key hub",
    TRUE ~ keyword  # Keep as is if already correct
  ))

# Check updated keyword counts
ppc_data_clean %>%
  tabyl(keyword) %>%
  arrange(desc(n))

 keyword   n    percent
 usb key 157 0.29128015
 usb hub 119 0.22077922
     hub  89 0.16512059
     key  81 0.15027829
     usb  73 0.13543599
     ubs  10 0.01855288
 ubs key  10 0.01855288

Submission

To submit your assignment:

Change the author name to your name in the YAML portion at the top of this document
Render your document to html and publish it to RPubs.
Submit the link to your Rpubs document in the Brightspace comments section for this assignment.
Click on the “Add a File” button and upload your .qmd file for this assignment to Brightspace.

Grading Rubric

Item (percent overall)	67% - minor issues	33% - moderate issues	0% - major issues or not attempted
Narrative: typos and grammatical errors (7%)
Document formatting: correctly implemented instructions (7%)
Exercises (13% each)
Submitted properly to Brightspace (8%)	NA	NA	You must submit according to instructions to receive any credit for this portion.