My Blog: Exploratory analysis II

Load the R package we will use.

library(tidyverse)
library(patchwork)

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 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("https://estanny.com/static/week8/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", "2021-04-01-exploratory-analysis-II"))