Data Visualization

Code for quiz 9.

1. Load the R package we’ll use

library(tidyverse)
library(echarts4r)
library(ggforce)
library(tidyquant)
library(hrbrthemes)
theme_set(theme_ipsum()) ###set all of plot themes

2. Quiz Questions

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

• Replace all of the ??? instances. They’re answers to the quiz

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

• After you check all your code chunks run then you can knit it. It won’t knit until all ??? are replaced

• The quiz assumes you have watched the videos, downloaded (to your examples folder) and worked through the exercises in “exercises_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 timeline argument 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

• group_by year

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

• use e_timeline_opts to set autoPlay to TRUE

• use e_barto represent the variable avg_hours with a bar chart

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

• 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 dat on each activity') %>% 
  e_legend(show = FALSE)

---

Question: echarts-2

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

Start with spend_time

• 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 paste each year to 12 and 31 (separated by -) 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 characters to date objects.

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

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

• use e_line to add a line to the variable avg_hours

• add a tooltip with e_tooltip

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

• 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 exmaple in the video

Retrieve stock price for SEE QUIZ, ticker: FB, using tq_get - from 2019-08-01 to 2020-07-28 - assign output to df

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

Create a plot with the 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 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

  • 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 Facebook

  • 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 == "2020-01-08",
    description = "CDC issued first alert"), fill = "yellow") +
  geom_mark_ellipse(aes(
    filter = date == "2020-07-28", 
    description = "CDC calls to reopen American schools"), color ="red", ) +
  labs(title = "Facebook",
       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 on top.

ggsave(filename = "preview.png",
       path = here::here("_posts", "2021-04-20-data-visualization"))