Data Visualization

Code for quiz 9

1. Lad the R package we will use.

library(tidyverse)
library(echarts4r)
library(ggforce) #install before using for the first time
library(hrbrthemes)
library(pacman)
library(tidyquant)
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 teh instances of ‘???’. These are answers on your moodle quiz.

• Run all the individual 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: 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.

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

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

• THEN remove the lgend 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 Americans 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 use mutate to convert year from a character object ot 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 from each activity)

• THEN intitiate 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 maint 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 activit to color

• ADD points with geom_point

• 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 the most time on leisure/sport"))

#Question: tidyquant

Modify the tidyquant example in the video

Retrieve stock price for Microsoft, ticker: MSFT, using tq_get

• from 2019-08-01 to 2020-07-28

• assign output to df

df <- tq_get("MSFT", get = "stock.prices", 
             from = "201-9-08-01", 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 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”

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

msft %>%
  ggplot(aes(x = date, y = close)) +
  geom_line() +
  geom_mark_ellipse(aes(
    filter = date == "2019-08-01",
    description = "The pandemic had not yet started"
    ), fill = "yellow") +
  geom_mark_ellipse(aes(
    filter = date == "2020-07-01",
    description = "The House Appropriations Committee approved a measure requiring masks on public transportation"
  ), 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", "2021-04-15-data-visualization"))