Data Visualization

Code for Quiz 9.

  1. Load the packages we will use. 
    library(tidyverse)
library(echarts4r)
library(ggforce)
library(tidyquant)
library(hrbrthemes)
  1. 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: 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 Mircosoft, ticker: MSFT, using tq_get from 2019-08-01 to 2020-07-28 -assign output to df 
msft  <- 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” 
msft %>% 
ggplot(aes(x = date, y = close)) +
  geom_line() +
  geom_mark_ellipse(aes(
    filter = date == "2020-03-23",
    description = "REPLACE WITH A DESCRIPTION"
  ), fill = "yellow") +
  geom_mark_ellipse(aes(
   filter  = date == "2020-03-23",
    description = "CDC issued its first public alert about coronavirus"
  ), color = "red", ) +
  labs(
    title = "SEE Quiz",
    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", "2021-04-19-data-visualization"))