8:Data Visualization

Code for Quiz 9

1. Load the R package we will use.

library(tidyverse)
library(echarts4r)
library(ggforce) #install  before using for the first time
library(hrbrthemes)
theme_set(theme_ipsum()) # set all of the plot themes

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 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

T- he 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: 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

• 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: 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 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

• 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 - 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 SEE QUIZ, ticker: SEE QUIZ, using tq_get

  • from 2019-08-01 to 2020-07-28

  • assign output to df

library(tidyquant)
library(tidyverse)


df  <- tq_get("MSFT", 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 Microsoft

  • 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 = "The CDC announced it's first public alert about the coronavirus"),fill = "yellow") +
  geom_mark_ellipse(aes(
   filter = date == "2020-07-01",
    description = "WHO describes the pandemic as 'accelerating'"
  ), color = "red", ) +
  labs(
    title = "Microsoft",
    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", "2021-04-13-data-visualization"))