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 data set
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))

#Modify facet-ex-2

Create a plot with the mpg data set
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

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 2013
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: 2013
set x and y to NULL so they won’t be labeled
assign the output to p1
display p1

p1  <- spend_time %>% filter(year == "2013")  %>% 
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: 2013", x = NULL, y = NULL)

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)

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)

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

- 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)

Use patchwork to display p4 and p5 on top of p6

( p4 | p5 ) / p6

ggsave(filename = "preview.png", 
       path = here::here("_posts", "2022-03-14-exploratory-analysis-ii"))