Data Visualization

Code for Quiz 9.

  1. Load the R package we will use.
library(tidyverse)
library(echarts4r)
library(ggforce) #install  before using for the first time
library(tidyquant)  #install  before using for the first time
library(hrbrthemes)
theme_set(theme_ipsum()) # set all of the plot themes 
  1. Quiz questions

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

e_charts-1

Start with spend_time

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

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

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 Microsoft, ticker: MSFT, using tq_get

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

Create a plot with the df data

ggplot(df, aes(x = date, y = close)) +
  geom_line() +
  geom_mark_ellipse(aes(
    filter = date == "2019-12-31",
    description = "The COVID virus is being exlpored"
  ), fill= "yellow") +
  geom_mark_ellipse(aes(
   filter  = date == "2020-06-30",
    description = "At this point there was a total of 2.59 million confirmed cases."
  ), 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", "2022-04-05-data-visualization"))