Week_5_lab_blunt_connor

Loading all of the libraries

library(completejourney)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(naniar)
library(lubridate)

Merging data

transactions <- get_transactions() %>%
  inner_join(products) %>%
  inner_join(demographics)

First graph

transactions %>%
  filter(str_detect(product_category, regex("fruit", ignore_case = TRUE))) %>%
  group_by(product_category, age) %>%
  summarize(total_sales = sum(sales_value)) %>%
  ggplot(aes(x = product_category, y = total_sales)) +
  geom_col(fill = "orange") +
  facet_wrap(~age, nrow = 2, ncol = 3) +
  coord_flip() +
  ggtitle("Type and Amount of Fruit Purchased by Age Group") +
  scale_y_continuous(name = "Sales", labels = scales::dollar) +
  scale_x_discrete(name = "Fruits Product Category")

`summarise()` has grouped output by 'product_category'. You can override using the `.groups` argument.

Second graph

transactions %>%
  filter(str_detect(product_category, regex("fruit", ignore_case = TRUE))) %>%
  group_by(age, Month = month(transaction_timestamp, label = TRUE)) %>%
  summarize(total_sales = sum(sales_value)) %>%
  ggplot(aes(x = Month, y = total_sales)) +
  geom_col(fill = "purple") +
  facet_wrap(~age, nrow = 3, ncol = 2) +
  ggtitle("Fruit Purchases by Age Group Throughout the Year") +
  scale_y_continuous(name = "Sales", labels = scales::dollar) +
  scale_x_discrete(name = "Month")

`summarise()` has grouped output by 'age'. You can override using the `.groups` argument.

Third graph

transactions %>%
  filter(str_detect(product_category, regex("fruit", ignore_case = TRUE))) %>%
  group_by(product_category, age, household_comp) %>%
  summarize(total_sales = sum(sales_value)) %>%
  ggplot(aes(x = household_comp, y = total_sales)) +
  geom_col(fill = "green") +
  coord_flip() +
  facet_wrap(~age, nrow = 2, ncol = 3) +
  ggtitle("Fruit Purchases by Age Group and Household Composition") +
  scale_y_continuous(name = "Sales", labels = scales::dollar) +
  scale_x_discrete(name = "Household Composition")

`summarise()` has grouped output by 'product_category', 'age'. You can override using the `.groups` argument.