Data Visualization

Code for Quiz 7

Introduction

1. Load the R packages we will use

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

2. QUiz Questions

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

• Replace all 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.

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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")

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.

• Start with spent_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)

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Question 2: echarts-2

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 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)

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Question 3 - modify slide 82

• Create a plot with the spend_time data
  • assign year to the x-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 aerage more time each day on leisure/sports than other activities"))

---

Question 4 - Modify the tidyquant example in the video

Retrieve stock price for Google, ticker: GOOG using tq_get

• from 2019-08-01 to 2020-07-28
• Assign outputs to df

df <- tq_get("GOOG", 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 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 Google
  • 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 == "2020-01-02",
    description = "WHO activates its incident management system"
  ), fill = "yellow",) +
  geom_mark_ellipse(aes(
    filter = date == "2020-02-21",
    description = "Novel coronavirus gets official name from WHO: Covid-19"
  ), color ="red", ) +
  labs(
    title = "Google",
    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 yaml chunk at the top

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