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
read_csv("https://estanny.com/static/week8/spend_time.csv")
# A tibble: 50 x 3
activity year avg_hours
<chr> <dbl> <dbl>
1 leisure/sports 2019 5.19
2 leisure/sports 2018 5.27
3 leisure/sports 2017 5.24
4 leisure/sports 2016 5.13
5 leisure/sports 2015 5.21
6 leisure/sports 2014 5.3
7 leisure/sports 2013 5.26
8 leisure/sports 2012 5.37
9 leisure/sports 2011 5.21
10 leisure/sports 2010 5.18
# ... with 40 more rowsggplot(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
read_csv("https://estanny.com/static/week8/spend_time.csv")
# A tibble: 50 x 3
activity year avg_hours
<chr> <dbl> <dbl>
1 leisure/sports 2019 5.19
2 leisure/sports 2018 5.27
3 leisure/sports 2017 5.24
4 leisure/sports 2016 5.13
5 leisure/sports 2015 5.21
6 leisure/sports 2014 5.3
7 leisure/sports 2013 5.26
8 leisure/sports 2012 5.37
9 leisure/sports 2011 5.21
10 leisure/sports 2010 5.18
# ... with 40 more rowsspend_time <- read_csv("https://estanny.com/static/week8/spend_time.csv")
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)
p1
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
p_all_no_legend <- p_all & theme(legend.position = 'none')
p_all_no_legend
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/sur")
#question : patchwork 2
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:", x = NULL, y = NULL)
p4
p5 <- p4 + coord_cartesian(ylim = c(0, 6))
p5
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
( p4 | p5) / p6
