Lab 5, October 10, Solutions
Exam review exercises (solutions)
dayvariable is a factor (categorical) variable containing the day of the week when the cyclist crash occurred.
monthcontains the month name.
Consider the following graphs:
geomis used in both graphs?
(b) What variables are mapped to the
(c) Write the commands to create both graphs.
ggplot(cr)+geom_bar(aes(x=month, fill=day), position='fill') ggplot(cr) + geom_bar(aes(x=month, fill=day))
(d) How would a
ggplotexpert describe the difference between the two graphs (using R and
The position adjustment is
fillin the left graph and
stack(the default) in the right-side graph.
(e) How would a regular person describe the difference between the two graphs (what do they communicate about cyclist-involved crashes)?
position='fill', proportions are displayed instead of raw counts. It is difficult to compare the distribution of crashes over days of the week when displaying the raw counts.
Write a command which creates a new data frame, called
cr_wayne, which only contains crashes that occurred in Wayne county in 2013, 2014, and 2015 (the latest year in the data frame
cr_wayne <- filter(cr, County=='Wayne', year >= 2013)
Then consider this command:
cr_wayne %>% group_by(month_num, year) %>% summarize(ncr = n()) %>% ggplot(aes(x=month_num, y=ncr)) + geom_line()
What aesthetic should be altered to fix the plot? Write the correct
ggplotcommand so that the number of crashes in each month is plotted seperately for each year.
We need to map the
cr_wayne %>% group_by(month_num, year) %>% summarize(ncr = n()) %>% ggplot(aes(x=month_num, y=ncr, group=year)) + geom_line()
Consider the following command.
filter(cr, County=="Washtenaw") %>% # line 1 group_by(year, month) %>% # 2 summarise(ncr = n()) %>% # 3 group_by(year) %>% # 4 mutate(rank_ncr = min_rank(desc(ncr))) %>% filter(rank_ncr <= 2) %>% # 5 select(-rank_ncr) %>% # 6 arrange(desc(year)) # 7
(a) Describe, in words, what is acocmplished by lines 1–3. Write down two (possible) rows of the data frame that results from running only lines 1–3 (with the final
Comptutes the number of crashes in each year-month combination for Washtenaw county:
## # A tibble: 137 x 3 ## # Groups: year [?] ## year month ncr ## <int> <fctr> <int> ## 1 2004 March 2 ## 2 2004 April 5 ## 3 2004 May 12
(b) To describe lines 4 and 5, complete these sentences:
For each year, rank the months (in that year) by the number of crashes that occured. Then keep the two months with the highest number of crashes.
(c) Now describe what the entire command accomplishes. Write down possible values for the first three rows of the result.
For each year in Washtenaw county, find the two months with the highest number of cyclist-involved crashes. Sort from latest year to earliest year.
(d) How many rows does the resulting data frame contain? Assume that there were cyclist-car crashes in all years and months in Washtenaw county. There are 12 years represented in this data set.
Recall the data frame
cr_year, which contains the number of cyclist-car crashes in each year and County:
## # A tibble: 6 x 3 ## # Groups: County  ## County year ncrash ## <fctr> <int> <int> ## 1 Allegan 2004 18 ## 2 Alpena 2004 12 ## 3 Antrim 2004 2 ## 4 Baraga 2004 1 ## 5 Barry 2004 9 ## 6 Bay 2004 31
Suppose I create a list of the counties that surround Washtenaw county, like this:
county_list <- c('Wayne','Washtenaw', "Livingston","Jackson","Ingham", "Oakland","Lenawee","Monroe")
Fill in the following code to create the graph below. The blue line and points plot the number of crashes for Washtenaw county.
ggplot(filter(cr_year, County %in% county_list), aes(x=year,y=ncrash,group=County)) + geom_line(aes(color=County=="Washtenaw"), show.legend = FALSE) + geom_point(aes(color=County=="Washtenaw"),show.legend = FALSE)
Extra exercise (using your computer)
Continuing with the crash data, recreate the following plot:
x aesthetic to the
hour_num variable. You will need to compute the
y variable using
mutate. This displays the proportion of crashes, in each day of the week, that occur during each hour of the day (all years and counties are pooled).
cr %>% filter(!is.na(hour_num)) %>% group_by(day, hour_num) %>% summarise(ncr_hour = n()) %>% group_by(day) %>% mutate(cr_prop_hour = ncr_hour / sum(ncr_hour)) %>% ggplot(aes(x=hour_num,y=cr_prop_hour)) + geom_line(aes(group=day)) + facet_wrap(~day) + xlab("Hour of day")+ ylab("Proportion of crashes")+ ggtitle("Within-day timing of cyclist-car crashes")