Homework to present at the next session: Ensure you have completed the second homework, described at the end of itsleeds.github.io/tds/p2 by Thursday 13th February.
# load packageslibrary(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Hello this is some text.
casualty_type =c("cat", "dog", "person")casualty_age =seq(from =20, to =60, by =20)crashes =data.frame(casualty_type, casualty_age)plot(crashes$casualty_age)
Subsetting.
crashes$casualty_type
[1] "cat" "dog" "person"
crashes[[1]]
[1] "cat" "dog" "person"
crashes[2,1]
[1] "dog"
crashes |>select(casualty_type)
casualty_type
1 cat
2 dog
3 person
crashes |>filter(casualty_age >35)
casualty_type casualty_age
1 dog 40
2 person 60
crashes |>filter(casualty_age-20>35)
casualty_type casualty_age
1 person 60
crashes |>ggplot() +geom_bar(aes(x = casualty_age, fill = casualty_type))
ac = stats19::get_stats19(year =2020, type ="collision")
# aggregate this by day to show # how crash numbers varied over the yearac_by_year = ac |>group_by(date) |>summarise(n_crashes =n() )ac_by_year |>mutate(`N. crashes per year`= n_crashes,`Week average`= zoo::rollmean(n_crashes, 7, na.pad =TRUE),Date = date, ) |>ggplot(aes(x = Date, y =`N. crashes per year`)) +geom_point(alpha =0.1) +ylim(c(0, NA)) +# geom_smooth() +# weekly rolling averagegeom_line(aes(Date, `Week average`), colour ="red") +theme_minimal()
Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_line()`).
# Updated plot with title and legend...ac_by_year |>mutate(`N. crashes per year`= n_crashes,`Week average`= zoo::rollmean(n_crashes, 7, na.pad =TRUE),Date = date, ) |>ggplot(aes(x = Date, y =`N. crashes per year`)) +geom_point(alpha =0.1) +ylim(c(0, NA)) +# geom_smooth() +# weekly rolling averagegeom_line(aes(Date, `Week average`, colour ="Week average")) +theme_minimal() +labs(colour ="Legend" ) +scale_colour_manual(values =c("Week average"="red")) +ggtitle("Collions/day, 2020 to 2021") +theme(legend.position ="bottom" )
Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_line()`).
