516 solutions

Mission516Solutions.Rmd

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+---
+title: "Guided Project: Creating An Efficient Data Analysis Workflow, Part 2"
+output: html_document
+---
+
+```{r}
+library(tidyverse)
+library(lubridate)
+
+sales = read_csv("sales2019.csv")
+```
+
+# Data Exploration
+
+```{r}
+# How big is the dataset?
+dim(sales)
+```
+
+```{r}
+# What are the column names?
+colnames(sales)
+```
+
+The `date` column shows the date that the order of books was made. This will help us distinguish between orders that were made before and after the new program was implemented. `quantity` describes how many books were made, and `user_submitted_review` looks like it's a hand typed review of the books themselves. `customer_type` indicates whether or not the customer was an individual or a business. It seems that the company has started selling in bulk to other business too.
+
+```{r}
+# What are the types of all the columns?
+for (col in colnames(sales)) {
+  paste0(col, " : ", typeof(sales[[col]])) %>% print
+}
+```
+
+
+```{r}
+# Is there missing data anywhere?
+for (col in colnames(sales)) {
+  paste0(col, 
+         ", number of missing data rows: ", 
+         is.na(sales[[col]]) %>% sum) %>% print
+}
+```
+
+The `user_submitted_review` column has some missing data in it. We'll have to handle this later in the data cleaning, but at least we know about it ahead of time. The `total_purchased` column also has missing data, which we'll handle with imputation.
+
+# Handling Missing Data
+
+```{r}
+# Remove the rows with no user_submitted_review
+complete_sales = sales %>% 
+  filter(
+    !is.na(user_submitted_review)
+  )
+
+# Calculate the mean of the total_purchased column, without the missing values
+purchase_mean = complete_sales %>% 
+  filter(!is.na(total_purchased)) %>% 
+  pull(total_purchased) %>% 
+  mean
+
+# Assign this mean to all of the rows where total_purchased was NA
+complete_sales = complete_sales %>% 
+  mutate(
+    imputed_purchases = if_else(is.na(total_purchased), 
+                                purchase_mean,
+                                total_purchased)
+  )
+```
+
+# Processing Review Data
+
+```{r}
+complete_sales %>% pull(user_submitted_review) %>% unique
+```
+
+The reviews range from outright hate ("Hated it") to positive ("Awesome!"). We'll create a function that uses a `case_when()` function to produce the output. `case_when()` functions can be incredibly bulky in cases where there's many options, but housing it in a function to `map` can make our code cleaner.
+
+```{r}
+is_positive = function(review) {
+  review_positive = case_when(
+  str_detect(review, "Awesome") ~ TRUE,
+  str_detect(review, "OK") ~ TRUE,
+  str_detect(review, "Never") ~ TRUE,
+  str_detect(review, "a lot") ~ TRUE,
+  TRUE ~ FALSE # The review did not contain any of the above phrases
+  )
+}
+
+complete_sales = complete_sales %>% 
+  mutate(
+    is_positive = unlist(map(user_submitted_review, is_positive))
+  )
+```
+
+# Comparing Book Sales Between Pre- and Post-Program Sales
+
+```{r}
+complete_sales = complete_sales %>% 
+  mutate(
+    date_status = if_else(mdy(date) < ymd("2019/07/01"), "Pre", "Post")
+  )
+
+complete_sales %>% 
+  group_by(date_status) %>% 
+  summarize(
+    books_purchased = sum(imputed_purchases)
+  )
+```
+
+It doesn't seem that the program has increased sales. Maybe there were certain books that increased in sales?
+
+```{r}
+complete_sales %>% 
+  group_by(date_status, title) %>% 
+  summarize(
+    books_purchased = sum(imputed_purchases)
+  ) %>% 
+  arrange(title, date_status)
+```
+
+It turns out that certain books actually got more popular after the program started! R For Dummies and Secrets of R For Advanced Students got more popular.
+
+# Comparing Book Sales Within Customer Type
+
+```{r}
+complete_sales %>% 
+  group_by(date_status, customer_type) %>% 
+  summarize(
+    books_purchased = sum(imputed_purchases)
+  ) %>% 
+  arrange(customer_type, date_status)
+```
+
+Baserd on the table, it looks like businesses started purchasing more books after the program! There was actually a drop in individual sales.
+
+# Comparing Review Sentiment Between Pre- and Post-Program Sales
+
+```{r}
+complete_sales %>% 
+  group_by(date_status) %>% 
+  summarize(
+    num_positive_reviews = sum(is_positive)
+  )
+```
+
+There's slightly more reviews before the program, but this difference seems negigible.
+