Exploratory analysis II

Video and question practice of Exploratory analysis / data visualization II for Quiz 8

library(tidyverse)
library(patchwork)

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 ))

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 3: 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 2018

• THEN create a plot with that data

• ADD a barchart with with geom_col

  • assign activity to the x-axi s
  • 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: 2018
  • set x and y to NULL so they won’t be labeled assign the output to p1 display p1

p1  <- spend_time %>% filter(year == "2018")  %>% 
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: 2018", 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", "2022-03-28-exploratory-analysis-ii"))