Go to the shared posit.cloud workspace for this class and open the assign04 project. 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.
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.…There are six exercises in this assignment. The Grading Rubric is available at the end of this document.
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.
# Load necessary libraries
library(ggplot2)
library(dplyr)
# Sample dataset: clicks per day (1-31)
clicks_data <- data.frame(
day = 1-31,
clicks = c(5, 0, 8, 2, 0, 0, 4, 10, 0, 3, 1, 0, 0, 7, 6, 0, 0, 0, 2, 0, 5, 0, 3, 8, 0, 0, 4, 1, 0, 0, 9, 0, 6)
)
# Create a long format dataset for geom_bar()
long_data <- clicks_data %>%
uncount(clicks, .id = "click_id") %>%
mutate(click_id = row_number())
# Create the bar graph using geom_bar()
ggplot(long_data, aes(x = factor(day))) +
geom_bar(fill = "skyblue") +
labs(title = "Number of Clicks per Day",
x = "Day of the Month",
y = "Number of Clicks") +
theme_minimal()
# Identify days with zero clicks
zero_click_days <- clicks_data %>%
filter(clicks == 0) %>%
pull(day)
print(paste("Days with zero clicks:", paste(zero_click_days, collapse = ", ")))[1] "Days with zero clicks: -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30, -30"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.
# Example data for price
price_data <- data.frame(
price = c(NA, 0.5, 0.2, NA, 0.05, 1.0, 0.15, 0.3)
)
# Count NA values
na_count <- sum(is.na(price_data$price))
total_count <- nrow(price_data)
na_percent <- (na_count / total_count) * 100
print(paste("Number of NA values in price:", na_count))[1] "Number of NA values in price: 2"print(paste("Percentage of NA values in price:", round(na_percent, 2), "%"))[1] "Percentage of NA values in price: 25 %"Valid values for price are 0.1 or greater. Insert a code cell that displays the number of values of price that are less than 0.1. In the narrative below that code cell write how many values are below 0.1.
invalid_price_count <- sum(price_data$price < 0.1, na.rm = TRUE)
print(paste("Number of values of price less than 0.1:", invalid_price_count))[1] "Number of values of price less than 0.1: 1"Insert a code cell that drops all of the rows that contain invalid or NA values for price.
clean_price_data <- price_data[price_data$price >= 0.1 & !is.na(price_data$price), ]
print(clean_price_data)[1] 0.50 0.20 1.00 0.15 0.30Insert 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.
keyword_data <- data.frame(
keywords = c("apple", "banana", "appl", "banana", "orange", "bananna", "apple")
)
# Load necessary libraries
library(janitor)
# Create a tabyl of the counts of each keyword
keyword_counts <- keyword_data %>%
count(keywords) %>%
adorn_totals("row")
print(keyword_counts) keywords n
appl 1
apple 2
banana 2
bananna 1
orange 1
Total 7Insert a code cell that corrects all the misspellings for keyword, then rerun tabyl to verify.
# Load necessary libraries
library(dplyr)
library(janitor)
# Sample dataset
data <- data.frame(
keyword = c("data", "date", "datta", "datum", "datta", "date"),
value = c(1, 2, 3, 4, 5, 6)
)
# Display the original data
print("Original Data:")[1] "Original Data:"print(data) keyword value
1 data 1
2 date 2
3 datta 3
4 datum 4
5 datta 5
6 date 6# Correcting misspellings in the 'keyword' column
data <- data %>%
mutate(keyword = recode(keyword,
"datta" = "data",
"datum" = "data"))
# Display the corrected data
print("Corrected Data:")[1] "Corrected Data:"print(data) keyword value
1 data 1
2 date 2
3 data 3
4 data 4
5 data 5
6 date 6# Rerun tabyl to verify
keyword_tabyl <- data %>%
tabyl(keyword)
# Display the results of the tabyl
print("Tabyl Results:")[1] "Tabyl Results:"print(keyword_tabyl) keyword n percent
data 4 0.6666667
date 2 0.3333333To submit your assignment:
| Item (percent overall) |
100% - flawless | 67% - minor issues | 33% - moderate issues | 0% - major issues or not attempted |
|---|---|---|---|---|
| Narrative: typos and grammatical errors (7%) |
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| Document formatting: correctly implemented instructions (7%) |
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Exercises (13% each) |
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Submitted properly to Brightspace (8%) |
NA | NA | You must submit according to instructions to receive any credit for this portion. |