1 Where Do People Drink The Most Beer, Wine And Spirits?

Back in 2014, fivethiryeight.com published an article on alchohol consumption in different countries. The data drinks is available as part of the fivethirtyeight package. Make sure you have installed the fivethirtyeight package before proceeding.

library(fivethirtyeight)
data(drinks)



# or download directly
# alcohol_direct <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/alcohol-consumption/drinks.csv")

What are the variable types? Any missing values we should worry about?

# YOUR CODE GOES HERE
skim(drinks)

Data summary
Name	drinks
Number of rows	193
Number of columns	5
_______________________
Column type frequency:
character	1
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
country	0	1	3	28	0	193	0

Variable type: numeric

skim_variable	complete_rate	mean	sd	p25	p50	p75	p100	hist
beer_servings	1	106.16	101.14	20.0	76.0	188.0	376.0	▇▃▂▂▁
spirit_servings	1	80.99	88.28	4.0	56.0	128.0	438.0	▇▃▂▁▁
wine_servings	1	49.45	79.70	1.0	8.0	59.0	370.0	▇▁▁▁▁
total_litres_of_pure_alcohol	1	4.72	3.77	1.3	4.2	7.2	14.4	▇▃▅▃▁

Make a plot that shows the top 25 beer consuming countries

# YOUR CODE GOES HERE
drinks %>% 
  arrange(desc(beer_servings)) %>% 
  head(25) %>% 
  ggplot(aes(y= reorder(country,beer_servings),
             x=beer_servings,
                  fill= country))+
  geom_col()+
  theme(legend.position = "none")

Make a plot that shows the top 25 wine consuming countries

drinks %>% 
  arrange(desc(wine_servings)) %>% 
  head(25) %>% 
  ggplot(aes(y= reorder(country,wine_servings),
             x=wine_servings,
                  fill= country))+
  geom_col()+
  theme(legend.position = "none")

Finally, make a plot that shows the top 25 spirit consuming countries

drinks %>% 
  arrange(desc(spirit_servings)) %>% 
  head(25) %>% 
  ggplot(aes(y= reorder(country,spirit_servings),
             x=spirit_servings,
                  fill= country))+
  geom_col()+
  theme(legend.position = "none")

What can you infer from these plots? Don’t just explain what’s in the graph, but speculate or tell a short story (1-2 paragraphs max).

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

2 Analysis of movies- IMDB dataset

We will look at a subset sample of movies, taken from the Kaggle IMDB 5000 movie dataset

movies <- read_csv(here::here("data", "movies.csv"))
glimpse(movies)

Besides the obvious variables of title, genre, director, year, and duration, the rest of the variables are as follows:

gross : The gross earnings in the US box office, not adjusted for inflation
budget: The movie’s budget
cast_facebook_likes: the number of facebook likes cast memebrs received
votes: the number of people who voted for (or rated) the movie in IMDB
reviews: the number of reviews for that movie
rating: IMDB average rating

2.1 Use your data import, inspection, and cleaning skills to answer the following:

Are there any missing values (NAs)? Are all entries distinct or are there duplicate entries?

#skim(movies)

Produce a table with the count of movies by genre, ranked in descending order
Produce a table with the average gross earning and budget (gross and budget) by genre. Calculate a variable return_on_budget which shows how many $ did a movie make at the box office for each $ of its budget. Ranked genres by this return_on_budget in descending order

#movies %>% 
 #group_by(genre) %>% 
  #summarise(
  #average_gross = mean(gross),
   #average_budget = mean(budget)
  #)

Produce a table that shows the top 15 directors who have created the highest gross revenue in the box office. Don’t just show the total gross amount, but also the mean, median, and standard deviation per director.
Finally, ratings. Produce a table that describes how ratings are distributed by genre. We don’t want just the mean, but also, min, max, median, SD and some kind of a histogram or density graph that visually shows how ratings are distributed.

2.2 Use `ggplot` to answer the following

Examine the relationship between gross and cast_facebook_likes. Produce a scatterplot and write one sentence discussing whether the number of facebook likes that the cast has received is likely to be a good predictor of how much money a movie will make at the box office. What variable are you going to map to the Y- and X- axes?
Examine the relationship between gross and budget. Produce a scatterplot and write one sentence discussing whether budget is likely to be a good predictor of how much money a movie will make at the box office.
Examine the relationship between gross and rating. Produce a scatterplot, faceted by genre and discuss whether IMDB ratings are likely to be a good predictor of how much money a movie will make at the box office. Is there anything strange in this dataset?

3 Returns of financial stocks

You may find useful the material on finance data sources.

We will use the tidyquant package to download historical data of stock prices, calculate returns, and examine the distribution of returns.

We must first identify which stocks we want to download data for, and for this we must know their ticker symbol; Apple is known as AAPL, Microsoft as MSFT, McDonald’s as MCD, etc. The file nyse.csv contains 508 stocks listed on the NYSE, their ticker symbol, name, the IPO (Initial Public Offering) year, and the sector and industry the company is in.

nyse <- read_csv(here::here("data","nyse.csv"))

Based on this dataset, create a table and a bar plot that shows the number of companies per sector, in descending order

# YOUR CODE GOES HERE

Next, let’s choose the Dow Jones Industrial Aveareg (DJIA) stocks and their ticker symbols and download some data. Besides the thirty stocks that make up the DJIA, we will also add SPY which is an SP500 ETF (Exchange Traded Fund).

djia_url <- "https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average"

#get tables that exist on URL
tables <- djia_url %>% 
  read_html() %>% 
  html_nodes(css="table")


# parse HTML tables into a dataframe called djia. 
# Use purr::map() to create a list of all tables in URL
djia <- map(tables, . %>% 
               html_table(fill=TRUE)%>% 
               clean_names())


# constituents
table1 <- djia[[2]] %>% # the second table on the page contains the ticker symbols
  mutate(date_added = ymd(date_added),
         
         # if a stock is listed on NYSE, its symbol is, e.g., NYSE: MMM
         # We will get prices from yahoo finance which requires just the ticker
         
         # if symbol contains "NYSE*", the * being a wildcard
         # then we jsut drop the first 6 characters in that string
         ticker = ifelse(str_detect(symbol, "NYSE*"),
                          str_sub(symbol,7,11),
                          symbol)
         )

# we need a vector of strings with just the 30 tickers + SPY
tickers <- table1 %>% 
  select(ticker) %>% 
  pull() %>% # pull() gets them as a sting of characters
  c("SPY") # and lets us add SPY, the SP500 ETF

# Notice the cache=TRUE argument in the chunk options. Because getting data is time consuming, # cache=TRUE means that once it downloads data, the chunk will not run again next time you knit your Rmd

myStocks <- tickers %>% 
  tq_get(get  = "stock.prices",
         from = "2000-01-01",
         to   = "2020-08-31") %>%
  group_by(symbol) 

glimpse(myStocks) # examine the structure of the resulting data frame

## Rows: 153,121
## Columns: 8
## Groups: symbol [31]
## $ symbol   <chr> "MMM", "MMM", "MMM", "MMM", "MMM", "MMM", "MMM", "MMM", "MMM"~
## $ date     <date> 2000-01-03, 2000-01-04, 2000-01-05, 2000-01-06, 2000-01-07, ~
## $ open     <dbl> 48.0, 46.4, 45.6, 47.2, 50.6, 50.2, 50.4, 51.0, 50.7, 50.4, 4~
## $ high     <dbl> 48.2, 47.4, 48.1, 51.2, 51.9, 51.8, 51.2, 51.8, 50.9, 50.5, 4~
## $ low      <dbl> 47.0, 45.3, 45.6, 47.2, 50.0, 50.0, 50.2, 50.4, 50.2, 49.5, 4~
## $ close    <dbl> 47.2, 45.3, 46.6, 50.4, 51.4, 51.1, 50.2, 50.4, 50.4, 49.7, 4~
## $ volume   <dbl> 2173400, 2713800, 3699400, 5975800, 4101200, 3863800, 2357600~
## $ adjusted <dbl> 27.6, 26.5, 27.3, 29.5, 30.0, 29.9, 29.4, 29.5, 29.5, 29.0, 2~

Financial performance analysis depend on returns; If I buy a stock today for 100 and I sell it tomorrow for 101.75, my one-day return, assuming no transaction costs, is 1.75%. So given the adjusted closing prices, our first step is to calculate daily and monthly returns.

#calculate daily returns
myStocks_returns_daily <- myStocks %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "daily", 
               type       = "log",
               col_rename = "daily_returns",
               cols = c(nested.col))  

#calculate monthly  returns
myStocks_returns_monthly <- myStocks %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               type       = "arithmetic",
               col_rename = "monthly_returns",
               cols = c(nested.col)) 

#calculate yearly returns
myStocks_returns_annual <- myStocks %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "yearly", 
               type       = "arithmetic",
               col_rename = "yearly_returns",
               cols = c(nested.col))

Create a dataframe and assign it to a new object, where you summarise monthly returns since 2017-01-01 for each of the stocks and SPY; min, max, median, mean, SD.

# YOUR CODE GOES HERE

Plot a density plot, using geom_density(), for each of the stocks

# YOUR CODE GOES HERE

What can you infer from this plot? Which stock is the riskiest? The least risky?

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

Finally, produce a plot that shows the expected monthly return (mean) of a stock on the Y axis and the risk (standard deviation) in the X-axis. Please use ggrepel::geom_text_repel() to label each stock with its ticker symbol

# YOUR CODE GOES HERE

What can you infer from this plot? Are there any stocks which, while being riskier, do not have a higher expected return?

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

4 On your own: IBM HR Analytics

For this task, you will analyse a data set on Human Resoruce Analytics. The IBM HR Analytics Employee Attrition & Performance data set is a fictional data set created by IBM data scientists. Among other things, the data set includes employees’ income, their distance from work, their position in the company, their level of education, etc. A full description can be found on the website.

First let us load the data

hr_dataset <- read_csv(here::here("data", "datasets_1067_1925_WA_Fn-UseC_-HR-Employee-Attrition.csv"))
glimpse(hr_dataset)

## Rows: 1,470
## Columns: 35
## $ Age                      <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2~
## $ Attrition                <chr> "Yes", "No", "Yes", "No", "No", "No", "No", "~
## $ BusinessTravel           <chr> "Travel_Rarely", "Travel_Frequently", "Travel~
## $ DailyRate                <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 1358,~
## $ Department               <chr> "Sales", "Research & Development", "Research ~
## $ DistanceFromHome         <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, ~
## $ Education                <dbl> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, ~
## $ EducationField           <chr> "Life Sciences", "Life Sciences", "Other", "L~
## $ EmployeeCount            <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
## $ EmployeeNumber           <dbl> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16,~
## $ EnvironmentSatisfaction  <dbl> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, ~
## $ Gender                   <chr> "Female", "Male", "Male", "Female", "Male", "~
## $ HourlyRate               <dbl> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 4~
## $ JobInvolvement           <dbl> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, ~
## $ JobLevel                 <dbl> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, ~
## $ JobRole                  <chr> "Sales Executive", "Research Scientist", "Lab~
## $ JobSatisfaction          <dbl> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, ~
## $ MaritalStatus            <chr> "Single", "Married", "Single", "Married", "Ma~
## $ MonthlyIncome            <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 269~
## $ MonthlyRate              <dbl> 19479, 24907, 2396, 23159, 16632, 11864, 9964~
## $ NumCompaniesWorked       <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, ~
## $ Over18                   <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", ~
## $ OverTime                 <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",~
## $ PercentSalaryHike        <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1~
## $ PerformanceRating        <dbl> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, ~
## $ RelationshipSatisfaction <dbl> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, ~
## $ StandardHours            <dbl> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8~
## $ StockOptionLevel         <dbl> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, ~
## $ TotalWorkingYears        <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3~
## $ TrainingTimesLastYear    <dbl> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, ~
## $ WorkLifeBalance          <dbl> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, ~
## $ YearsAtCompany           <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4,~
## $ YearsInCurrentRole       <dbl> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, ~
## $ YearsSinceLastPromotion  <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, ~
## $ YearsWithCurrManager     <dbl> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, ~

I am going to clean the data set, as variable names are in capital letters, some variables are not really necessary, and some variables, e.g., education are given as a number rather than a more useful description

hr_cleaned <- hr_dataset %>% 
  clean_names() %>% 
  mutate(
    education = case_when(
      education == 1 ~ "Below College",
      education == 2 ~ "College",
      education == 3 ~ "Bachelor",
      education == 4 ~ "Master",
      education == 5 ~ "Doctor"
    ),
    environment_satisfaction = case_when(
      environment_satisfaction == 1 ~ "Low",
      environment_satisfaction == 2 ~ "Medium",
      environment_satisfaction == 3 ~ "High",
      environment_satisfaction == 4 ~ "Very High"
    ),
    job_satisfaction = case_when(
      job_satisfaction == 1 ~ "Low",
      job_satisfaction == 2 ~ "Medium",
      job_satisfaction == 3 ~ "High",
      job_satisfaction == 4 ~ "Very High"
    ),
    performance_rating = case_when(
      performance_rating == 1 ~ "Low",
      performance_rating == 2 ~ "Good",
      performance_rating == 3 ~ "Excellent",
      performance_rating == 4 ~ "Outstanding"
    ),
    work_life_balance = case_when(
      work_life_balance == 1 ~ "Bad",
      work_life_balance == 2 ~ "Good",
      work_life_balance == 3 ~ "Better",
      work_life_balance == 4 ~ "Best"
    )
  ) %>% 
  select(age, attrition, daily_rate, department,
         distance_from_home, education,
         gender, job_role,environment_satisfaction,
         job_satisfaction, marital_status,
         monthly_income, num_companies_worked, percent_salary_hike,
         performance_rating, total_working_years,
         work_life_balance, years_at_company,
         years_since_last_promotion)

Produce a one-page summary describing this dataset. Here is a non-exhaustive list of questions:

How often do people leave the company (attrition)
How are age, years_at_company, monthly_income and years_since_last_promotion distributed? can you roughly guess which of these variables is closer to Normal just by looking at summary statistics?
How are job_satisfaction and work_life_balance distributed? Don’t just report counts, but express categories as % of total
Is there any relationship between monthly income and education? Monthly income and gender?
Plot a boxplot of income vs job role. Make sure the highest-paid job roles appear first
Calculate and plot a bar chart of the mean (or median?) income by education level.
Plot the distribution of income by education level. Use a facet_wrap and a theme from ggthemes
Plot income vs age, faceted by job_role

5 Challenge 1: Replicating a chart

The purpose of this exercise is to make a publication-ready plot using your dplyr and ggplot2 skills. Open the journal article “Riddell_Annals_Hom-Sui-Disparities.pdf”. Read the abstract and have a look at Figure 3. The data you need is “CDC_Males.csv”.

Don’t worry about replicating it exactly, try and see how far you can get. You’re encouraged to work together if you want to and exchange tips/tricks you figured out.

You may find these helpful:

6 Challenge 2: 2016 California Contributors plots

As discussed in class, I would like you to reproduce the plot that shows the top ten cities in highest amounts raised in political contributions in California during the 2016 US Presidential election.

To get this plot, you must join two dataframes; the one you have with all contributions, and data that can translate zipcodes to cities. You can find a file with all US zipcodes, e.g., here http://www.uszipcodelist.com/download.html.

The easiest way would be to create two plots and then place one next to each other. For this, you will need the patchwork package. https://cran.r-project.org/web/packages/patchwork/index.html

While this is ok, what if one asked you to create the same plot for the top 10 candidates and not just the top two? The most challenging part is how to reorder within categories, and for this you will find Julia Silge’s post on REORDERING AND FACETTING FOR GGPLOT2 useful.

# Make sure you use vroom() as it is significantly faster than read.csv()
CA_contributors_2016 <- vroom::vroom(here::here("data","CA_contributors_2016.csv"))

7 Deliverables

There is a lot of explanatory text, comments, etc. You do not need these, so delete them and produce a stand-alone document that you could share with someone. Knit the edited and completed R Markdown file as an HTML document (use the “Knit” button at the top of the script editor window) and upload it to Canvas.

8 Details

Who did you collaborate with: TYPE NAMES HERE
Approximately how much time did you spend on this problem set: ANSWER HERE
What, if anything, gave you the most trouble: ANSWER HERE

Please seek out help when you need it, and remember the 15-minute rule. You know enough R (and have enough examples of code from class and your readings) to be able to do this. If you get stuck, ask for help from others, post a question on Slack– and remember that I am here to help too!

As a true test to yourself, do you understand the code you submitted and are you able to explain it to someone else?

9 Rubric

Check minus (1/5): Displays minimal effort. Doesn’t complete all components. Code is poorly written and not documented. Uses the same type of plot for each graph, or doesn’t use plots appropriate for the variables being analyzed.

Check (3/5): Solid effort. Hits all the elements. No clear mistakes. Easy to follow (both the code and the output).

Check plus (5/5): Finished all components of the assignment correctly and addressed both challenges. Code is well-documented (both self-documented and with additional comments as necessary). Used tidyverse, instead of base R. Graphs and tables are properly labelled. Analysis is clear and easy to follow, either because graphs are labeled clearly or you’ve written additional text to describe how you interpret the output.

Session 2: Homework 1

Yameogo Philippe

2021-03-22

1 Where Do People Drink The Most Beer, Wine And Spirits?

2 Analysis of movies- IMDB dataset

2.1 Use your data import, inspection, and cleaning skills to answer the following:

2.2 Use `ggplot` to answer the following

3 Returns of financial stocks

4 On your own: IBM HR Analytics

5 Challenge 1: Replicating a chart

6 Challenge 2: 2016 California Contributors plots

7 Deliverables

8 Details

9 Rubric

Session 2: Homework 1

Yameogo Philippe

2021-03-22

1 Where Do People Drink The Most Beer, Wine And Spirits?

2 Analysis of movies- IMDB dataset

2.1 Use your data import, inspection, and cleaning skills to answer the following:

2.2 Use ggplot to answer the following

3 Returns of financial stocks

4 On your own: IBM HR Analytics

5 Challenge 1: Replicating a chart

6 Challenge 2: 2016 California Contributors plots

7 Deliverables

8 Details

9 Rubric

2.2 Use `ggplot` to answer the following