Data Visualization

Code for quiz 9

1. Load the R packages we will use

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

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Question: e_charts-1

• Create a bar chart that shows the average hours Americans spend on five activities by year.

• Use timeline argument to create an animation that will animate through the years.

• Read it into spend_time

spend_time <- read_csv("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.

• 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: echarts-2

• Create a line chart for 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 function paste

  • paste will paste each year to 12 and 31 (separated by -) THEN

• Use mutate to convert year from character object to a date object using ymd from the lubridate package.

• ymd function 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 yearon the x-axis

• Then use e_line to add a line to the variable avg_hours

• Then add tooltip with e_tooltip

• Then use e_title to set the main title ‘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: Modify Slide 82

• Create a plot with 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: “Americans spend the most time on leisure/sports”

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/sports"))

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Question: tidyquant

• Retrieve stock price for Google, ticker: GOOG using tg_get

  • From 2019-08-01 to 2020-07-28

  • Assign the output to df

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

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• Create a plot with 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 a date that had 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 title to Google

  • Set x to NULL

  • Set y to “Closing price per share”

  • Set caption: “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 == "2020-04-14",
    description = "Trump announced decision to halt funding to WHO"), 
    fill = "yellow") +
  geom_mark_ellipse(aes(
    filter = date == "2020-07-17",
    description = "U.S. records highest single day infections"), 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" )

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ggsave(filename = "preview.png", path = here::here("_posts", "2022-04-01-data-visualization"))