Data Visualization

Code for quiz 9.

1. Load the R package we will use.

library(tidyverse)
library(echarts4r)
library(ggforce)
library(tidyquant)
library(hrbrthemes)
theme_set(theme_ipsum())

2. Quiz questions

• Replace all the instances of SEE QUIZ. These are inputs from your moodle quiz.

• Replace all the instances of ???. These are answers on your moodle quiz.

• Run all the individual code chunks to make sure the answers in the correspond with your quiz answers

• After you check all your code chunks run then you can knit it. I t won’t knit unit the ??? are replace

• The quiz assumes that you have watched the videos, downloaded (to your examples folder) and worked through the exercises in exercise_slides-73-108.Rmd.

Question: e-charts-1

Create a bar chart that shows the average hours Americans spend on five activities by year. Use the timelineargument to create an animation that will animate through the years.

• spend-time contains 10 years of data on how many hours Americans spend each day on 5 activities

• read it into spend-time

spend_time <- read_csv("https://estanny.com/static/week8/spend_time.csv")

e_charts-1

Start with spend-time

• THEN group_by year

• THEN create an e_chart that assigns activity to the axis and will show activity by year (the variable that you grouped the data on)

• THEN use e_timeline_opts to set autoPlay to TRUE

• THEN use e_bar to represent the variable avg_hours with a bar chart

• THEN use e_title to set the main title to ‘Average hours Americans spend per day on each activity’

• THEN remove the legend with e_legend

spend_time %>% 
  group_by(year) %>% 
  e_charts(x = activity, timeline = TRUE) %>% 
  e_timeline_opts(autoPlay = TRUE) %>% 
  e_bar(serie = avg_hours) %>% 
  e_title(text = 'Average hours Americans spend per day on each activity') %>% 
  e_legend(show =FALSE )

---

Question: e_charts-2

Create a line chart for the activities that American spend time on.

Start with spend_time

• THEN use mutate to convert year from an number to a string (year-month-day) using mutate

  • first convert year to a string “201X-12-31” using the function paste

    • paste will past each year to 12 and 31 (separated by -) THEN

• THEN use mutate to convert year from a character object to a date object using the ymd function from the lubridate package (part of the tidyverse, but not automatically loaded). ymd converts dates stored as charaters to date objects.

• THEN group_by the variables activity (to get a line for each activity)

• THEN initiate an e_charts object with year on the x-axis

• THEN use e_line to add a line to the variable avg_hours

• THEN add a tooltip with e_tooltip

• THEN use e_title to set the main title to ‘Average hours Americans spend per day on each activity’

• THEN use e_legend(top = 40) to move the legend down (from the top)

spend_time %>% 
  mutate(year = paste(year, "12", "31", sep = "-")) %>% 
  mutate(year = lubridate::ymd(year)) %>% 
  group_by(activity) %>% 
  e_charts(x = year) %>% 
  e_line(serie = avg_hours) %>% 
  e_tooltip() %>% 
  e_title(text = 'Average hours Americans spend per day on each activity') %>%
  e_legend(top = 40)

---

Question- Modify slide 82

• Create a plot with the spend_time data

  • assign year to the x-axis

  • assign avg_hours to the y-axis

  • assign activity to color

• ADD points with geom_point

• ADD geom_mark_ellipse

  • filter on activity ==“leisure/sports”

  • description is “Americans spend the most time on leisure/sport”

ggplot(spend_time, aes(x = year, y = avg_hours, color = activity)) +
  geom_point() +
  geom_mark_ellipse(aes(filter = activity == "leisure/sports",
                        description = "Americans spend on average more time each day on leisure/sports than the other activities"))

---

Question: tidyquant

Modify the tidyquant example in the video

Retrieve stock price for Amazon, ticker: AMZN, using tq_get

• from 2019-08-01 to 2020-07-28

• assign output to df

df <- tq_get("AMZN", get = "stock.prices",
             from = "2019-08-01", to = "2020-07-28")

---

Create a plot with df data

• assign date to the x-axis

• assign close to the y-axis

• ADD a line with with geom_line

• ADD geom_mark_ellipse

  • filter on a date to mark. Pick a date after looking at the line plot. Include the date in you Rmd code chunk.

  • include a description of something that happened on that date from the pandemic timeline. Include the description in you Rmd code chunk.

  • fill the ellipse yellow

• ADD geom_mark_ellipse

  • filter on the date that had the minimum close price. Include the date in your Rmd code chunk.

  • include a description of something that happened on that date from the pandemic timeline. Include the description in your Rmd code chunk

  • color the ellipse red

• ADD labs

  • set the title to Amazon

  • set x to NULL

  • set y to “closing price per share”

  • set caption to “Source:https://en.wikipedia.org/wiki/Timeline_of_the_COVID-19_pandemic_in_the_United_States”

ggplot(df, aes(x = date, y = close)) +
  geom_line() +
  geom_mark_ellipse(aes(
    filter = date == "2019-08-01",
    description = "AMZN stock price increased 23.03% in 2019, as compared to 2018."
  ), fill = "yellow") +
  geom_mark_ellipse(aes(
   filter  = date == "2020-04-28",
    description = "No stock market value has benefited from pandemic as Amazon."
  ), color = "red", ) +
  labs(
    title = "Amazon",
    x = NULL,
    y = "Closing price per share",
    caption = "Source: https://en.wikipedia.org/wiki/Timeline_of_the_COVID-19_pandemic_in_the_United_States"
  )

Save the previous plot to preview.png and add to the yaml chunk at the top

ggsave(filename = "preview.png",
       path = here::here("_posts", "2021-04-19-data-visualization"))