Exploratory Analysis II

Data visualization, part 2. Code for Quiz 8.

1. Load the R package we will use.

library(tidyverse)
library(patchwork)  

2. Quiz questions

• Replace all the ???s. 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-50-61.Rmd
• Pick one of your plots to save as your preview plot. Use the ggsave command at the end of the chunk of the plot that you want to preview.

Question: Modify Slide 51

• Create a plot with the mpg dataset
• add points with geom_point
• assign the variable displ to the x-axis
• assign the variable hwy to the y-axis
• add facet_wrap to split the data into panels based on the manufacturer

ggplot(data = mpg) + 
   geom_point(aes(x = displ, y = hwy)) +
   facet_wrap(facets = vars(manufacturer))

Question: Modify facet-ex-2

• Create a plot with the mpg dataset

• add bars with with geom_bar

  • assign the variable manufacturer to the y-axis

• add facet_grid to split the data into panels based on the class

  • let scales vary across columns
  • let space taken up by panels vary by columns

ggplot(mpg) + 
  geom_bar(aes(y = manufacturer)) + 
  facet_grid(vars(class), scales = "free_y", space = "free_y")

Question: spend_time

• To help you complete this question use:

• the patchwork slides and the vignette: https://patchwork.data-imaginist.com/articles/patchwork.html Download the file spend_time.csv from moodle into directory for this post. Or read it in directly:

  • read_csv(“https://estanny.com/static/week8/spend_time.csv”)

• 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("spend_time.csv")

Start with spend_time

• extract observations for 2011
• THEN create a plot with that data
• ADD a barchart with with geom_col
• assign activity to the x-axis
• assign avg_hours to the y-axis
• assign activity to fill
• ADD scale_y_continuous with breaks every hour from 0 to 6 hours
• ADD labs to
• set subtitle to Avg hours per day: 2011
• set x and y to NULL so they won’t be labeled
• assign the output to p1
• display p1

p1  <- spend_time %>% filter(year == "2011")  %>% 
ggplot() + 
  geom_col(aes(x = activity, y = avg_hours, fill = activity)) +
  scale_y_continuous(breaks = seq(0, 6, by = 1)) +
  labs(subtitle = "Avg hours per day: 2011", x = NULL, y = NULL)

p1

Start with spend_time

• THEN create a plot with it
• ADD a barchart with with geom_col
• assign year to the x-axis
• assign avg_hours to the y-axis
• assign activity to fill
• ADD labs to
• set subtitle to “Avg hours per day: 2010-2019”
• set x and y to NULL so they won’t be labeled
• assign the output to p2
• display p2

p2  <- spend_time  %>% 
ggplot() + 
  geom_col(aes(x = year, y = avg_hours, fill = activity)) +
  labs(subtitle  = "Avg hours per day: 2010-2019", x = NULL, y = NULL) 

p2

Use patchwork to display p1 on top of p2

• assign the output to p_all
• display p_all

p_all  <-  p1 / p2 

p_all

Start with p_all

• AND set legend.position to ‘none’ to get rid of the legend
• assign the output to p_all_no_legend
• display p_all_no_legend

p_all_no_legend  <- p_all & theme(legend.position = 'none')
p_all_no_legend

Start with p_all_no_legend

• see how annotate the composition here: https://patchwork.data-imaginist.com/reference/plot_annotation.html
• ADD plot_annotation set
• title to “How much time Americans spent on selected activities”
• caption to “Source: American Time of Use Survey, https://data.bls.gov/cgi-bin/surveymost?tu”

p_all_no_legend  +
 plot_annotation(title = "How much Time Americans spent on Selected Activities", 
                  caption = "Source: American Time of Use Survey, https://data.bls.gov/cgi-bin/surveymost?tu")

Question: Patchwork 2

use spend_time from last question patchwork slides

Start with spend_time

• extract observations for food prep
• THEN create a plot with that data
• ADD points with geom_point
• assign year to the x-axis
• assign avg_hours to the y-axis
• ADD line with geom_smooth
• assign year to the x-axis
• assign avg_hours to the y-axis
• ADD breaks on for every year on x axis with with scale_x_continuous
• ADD labs to
• set subtitle to Avg hours per day: food prep
• set x and y to NULL so x and y axes won’t be labeled
• assign the output to p4
• display p4

p4  <- 
spend_time %>% filter(activity == "food prep")  %>% 
ggplot() + 
  geom_point(aes(x = year, y = avg_hours)) +
  geom_smooth(aes(x = year, y = avg_hours)) +
  scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
  labs(subtitle = "Avg hours per day: food prep", x = NULL, y = NULL) 

p4

Start with p4

• ADD coord_cartesian to change range on y axis to 0 to 6
• assign the output to p5
• display p5

p5 <-  p4 + coord_cartesian(ylim = c(0, 6))
p5

Start with spend_time

• create a plot with that data
• ADD points with geom_point
• assign year to the x-axis
• assign avg_hours to the y-axis
• assign activity to color
• assign activity to group
• ADD line with geom_smooth
• assign year to the x-axis
• assign avg_hours to the y-axis
• assign activity to color
• assign activity to group
• ADD breaks on for every year on x axis with with scale_x_continuous
• ADD coord_cartesian to change range on y axis to 0 to 6
• ADD labs to
• set x and y to NULL so they won’t be labeled
• assign the output to p6
• display p6

p6   <- 
 spend_time  %>% 
ggplot() + 
  geom_point(aes(x = year, y = avg_hours, color = activity, group = activity)) +
  geom_smooth(aes(x = year, y = avg_hours, color = activity, group = activity)) +
  scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
  coord_cartesian(ylim = c(0, 6)) + 
  labs(x = NULL, y = NULL) 

p6

Use patchwork to display p4 and p5 on top of p6

( p4 | p5 ) / p6

ggsave(filename = "preview.png", path = here::here("_posts", "2021-04-06-exploratory-analysis-ii"))