In this assignment, you are going to explore data from the convenience store retail giant NANSE. NANSE owns over seven hundred small-form convenience stores throughout Canada. This dataset only includes sales from 2015 for food and beverage. It also excludes alcohol and tobacco. Each row represents the weekly average for one store for all of 2015. Thus, each row in the data represents the average weekly results for that one store for the whole year. You are going to use EDA to investigate different features of these data.
Instructions
ETL Tasks:
1. Import the `store_3HE.csv` as dataframe df. Learn about the data from the data description file.
2. (0.5 points) Familiarize yourself with the features of the data by using the `str()`, `summary()`, `head()`, `tail()`, `slice_sample()`, `unique()`, and `n_distinct()` functions. Give a brief summary of what you learn (just two or three sentences).
Note - A few of these functions are from the package “dplyr”. Thus, load “dplyr” or the meta-package “tidyverse” into your RStudio notebook. The package “tidyverse” is used in each R notebook we will work with, so, you will see it often.
3. (0.5 points) Convert the values in the `revenue` column to a numeric data type.
4. (0.5 points) Delete rows that contain missing values in any of the columns.
Exploratory Data Analysis Tasks:
5. (1 point) We are interested in understanding what factors influence the revenue for an individual store. From dataframe df, create two dataframes df_low and df_high such that df_low contains data on all the stores with revenue less than the average revenue for the complete data and df_high contains data on all the stores with revenue equal or greater than the average revenue for the complete data. Calculate the average size of the stores in the two dataframes (df_lows and df_high) and comment on the relationship between revenue and size of the stores by discussing the two means (just one or two sentences).
6. (1.5 points) Report and comment on the correlation between revenue and size. First, calculate the correlation between revenue and size in the main dataframe. Next, using the `ggplot()` function, plot the relationship between the revenue and size using an appropriate chart. Finally, discuss in one or two sentences.
7. (1.5 points) Create boxplots of revenue for each region and comment on the distribution of revenue within each region (in one or two sentences).
8. (1.5 points) Calculate the correlation between the revenue and promo_units and comment on whether the value aligns with your expectation. Based on this value of correlation, can you conclude that selling units on promotion helps in increasing revenue? Please explain your reason for the conclusion in a few sentences.
9. (1.5 points) Create a bar chart that shows total gross_profit for each province. Comment on a few findings from the chart in one or two sentences.
10. (1.5 points) Create a correlation matrix for showing correlations among the average store sales for all product categories. Which two product categories sell together the least and which sell together the most?
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