Load the R package we will use.

library(tidyverse)
library(echarts4r)
library(ggforce)
library(tidyquant)
theme_set(theme_minimal())

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 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 the ??? are replaced
The quiz assumes that you have watched the videos, downloaded (to your examples folder) and worked through the exercises in exercises_slides-73-108.Rmd. Knitted file is here.

Question 1: 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")

Start with spend_time

THEN group_by year
THEN create an e_chart that assigns activity to the x-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 2: echarts-2

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

Start with spend_time

THEN use mutate to convert year from a 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
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.
THEN group_by the variable 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 3: 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"))

ggsave(filename = "preview.png",
       path = here::here("_posts", "2022-04-05-data-visualization"))

Question 4: 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 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 Amazon
- set x to NULL
- set y to “Close 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 == "2020-01-03",
    description = "An unknown respiratory illness was going around"
  ), fill = "yellow") +
  geom_mark_ellipse(aes(
   filter = date == "2020-04-27",
    description = "Ohio mandates mask and capacity orders"
  ), color = "red", ) +
  labs(
    title = "Amazon",
    x = NULL,
    y = "Close price per share",
    caption = "Source: https://en.wikipedia.org/wiki/Timeline_of_the_COVID-19_pandemic_in_the_United_States")

Data Visualization

Question 1: e_charts-1

Question 2: echarts-2

Question 3: Modify slide 82

Question 4: Modify the tidyquant example in the video