Assignment 4

Author

Brady Guay

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.

clicks_per_day <- ppc_data %>%
  count(day) %>%
  ggplot(aes(x = factor(day), y = n)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Clicks per Day",
       x = "Day",
       y = "Number of Clicks")

clicks_per_day

Narrative: The only two days that had zero clicks were days 1 and 27. ### 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.

ppc_data %>%
  count(is.na(price))%>%
  mutate(percent = n/sum(n)*100)

# A tibble: 2 × 3
  `is.na(price)`     n percent
  <lgl>          <int>   <dbl>
1 FALSE            549   98.9 
2 TRUE               6    1.08

Narrative: Out of 555 observations there are 6 missing values for price which represents 1.08% of the observations.

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.

ppc_data %>%
  count(price < 0.1)

# A tibble: 3 × 2
  `price < 0.1`     n
  <lgl>         <int>
1 FALSE           539
2 TRUE             10
3 NA                6

Narrative: There are 10 values of price that are less than 0.1. However, there are 6 missing values for price which are also invalid. So there are 16 invalid values for price, 10 being less than 0.1 and 6 being missing values.

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)

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

 keyword   n    percent
 usb key 163 0.29369369
 usb hub 121 0.21801802
     hub  90 0.16216216
     key  85 0.15315315
     usb  75 0.13513514
     ubs  11 0.01981982
 ubs key  10 0.01801802

There are two misspellings for keyword in the dataset. The first is “ubs” meant to be “usb” and there is 11 counts of this mistake. Along with this “ubs key” is another misspelling meant to be “usb key” and there are 10 counts of this mistake.

Exercise 6

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

ppc_data_clean <- ppc_data %>%
  mutate(keyword = replace(keyword, keyword == "ubs", "usb"),
         keyword = replace(keyword, keyword == "ubs key", "usb key"))

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

 keyword   n   percent
 usb key 173 0.3117117
 usb hub 121 0.2180180
     hub  90 0.1621622
     usb  86 0.1549550
     key  85 0.1531532

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.