Exploratory Analysis 2

Data visualization, part 2. Code for Quiz 8.

Load the R packages 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 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/drug_cos.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 2015 -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: 2015 -set x and y to NULL so they won’t be labeled -assign the output to p1 -display p1

p1  <- spend_time %>% filter(year == "2015")  %>% 
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: 2015", 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 leisure/sports -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: leisure/sports -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 == "leisure/sports")  %>% 
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: leisure/sports", 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", "2021-04-05-exploratory-analysis-2"))