Data Visualization

Data visualization- Code for Quiz 9.

1. Load the R package we will use.

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

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.

• 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.

spend_time  <- read_csv("https://estanny.com/static/week8/spend_time.csv")

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.

• 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: e_charts-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"))

ggsave(filename = "preview.png", 
       path = here::here("_posts", "2022-04-02-data-visualization"))