38 Data: Window Functions

Purpose: Window functions are another family of dplyr verbs that are related to aggregates like mean and sd. These functions are useful for building up more complicated filters, enabling aesthetic tricks in plots, and some advanced data wrangling we’ll do next exercise.

Reading: Window Functions, Types of window functions, Ranking functions, and Lead and lag

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.5.0 
## ✔ readr   2.1.3      ✔ forcats 0.5.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(ggrepel)

38.1 Lead and Lag

The lead and lag functions simply provide a “shifted” copy of a vector.

## NOTE: No need to edit this; just an example
v <- c(1, 2, 3, 4, 5)
lead(v)

## [1]  2  3  4  5 NA

lag(v)

## [1] NA  1  2  3  4

These are particularly useful for computing things like differences:

## NOTE: No need to edit this; just an example
x <- seq(-1, +1, length.out = 6)
f <- x ^ 2

## Forward finite difference
df_dx <- (lead(f) - f) / (lead(x) - x)
df_dx

## [1] -1.600000e+00 -8.000000e-01  2.255141e-16  8.000000e-01  1.600000e+00
## [6]            NA

Make sure to order your data or use the order_by argument when using lead or lag! GGplot automatically reorders your data when making a line plot, but lead and lag will use the order of the data you provide.

38.1.1 q1 Use a window function modify the following visual to color each segment differently based on whether the period of time was increasing or decreasing.

economics %>%
  arrange(date) %>%
  mutate(
    delta = lead(unemploy, order_by = date) - unemploy,
    Positive = delta > 0
  ) %>%

  ggplot(aes(date, unemploy, color = Positive)) +
  geom_segment(aes(
    xend = lead(date, order_by = date),
    yend = lead(unemploy, order_by = date)
  ))

## Warning: Removed 1 rows containing missing values (`geom_segment()`).

38.2 Ranks

The rank functions allow you to assign (integer) ranks to smallest (or largest) values of a vector.

## NOTE: No need to edit this; just an example
v <- c(1, 1, 2, 3, 5)
row_number(v)

## [1] 1 2 3 4 5

min_rank(v)

## [1] 1 1 3 4 5

dense_rank(v)

## [1] 1 1 2 3 4

You can use the desc() function (or a negative sign) to reverse the ranking order.

## NOTE: No need to edit this; just an example
v <- c(1, 1, 2, 3, 5)
row_number(desc(v))

## [1] 4 5 3 2 1

min_rank(desc(v))

## [1] 4 4 3 2 1

dense_rank(-v)

## [1] 4 4 3 2 1

I find it difficult to remember how the rank functions behave, so I created the following visual to help remind myself how they function..

## NOTE: No need to edit this; just an example
set.seed(101)
tribble(
  ~x, ~y,
  0,  0,
  1,  0,
  1,  1,
  0,  2,
  2,  2,
  0,  3,
  2,  3,
  3,  3
) %>%
  mutate(
    rk_row = row_number(y),
    rk_min = min_rank(y),
    rk_dense = dense_rank(y)
  ) %>%
  pivot_longer(
    names_to = "fcn",
    names_prefix = "rk_",
    values_to = "rk",
    cols = c(-x, -y)
  ) %>%

  ggplot(aes(x, y)) +
  geom_point(size = 4) +
  geom_point(
    data = . %>% filter(rk <= 3),
    size = 3,
    color = "orange"
  ) +
  geom_label(aes(label = rk), nudge_x = 0.2, nudge_y = 0.2) +

  facet_wrap(~fcn) +
  theme_minimal() +
  theme(panel.border = element_rect(color = "black", fill = NA, size = 1)) +
  labs(
    x = "",
    y = "Minimum Three Ranks"
  )

## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.

38.2.1 q2 Use a rank function to filter the largest 3 hwy values and all vehicles that have those values.

q2 <-
  mpg %>%
  filter(dense_rank(desc(hwy)) <= 3)
q2

## # A tibble: 4 × 11
##   manufacturer model      displ  year   cyl trans  drv     cty   hwy fl    class
##   <chr>        <chr>      <dbl> <int> <int> <chr>  <chr> <int> <int> <chr> <chr>
## 1 toyota       corolla      1.8  2008     4 manua… f        28    37 r     comp…
## 2 volkswagen   jetta        1.9  1999     4 manua… f        33    44 d     comp…
## 3 volkswagen   new beetle   1.9  1999     4 manua… f        35    44 d     subc…
## 4 volkswagen   new beetle   1.9  1999     4 auto(… f        29    41 d     subc…

Use the following test to check your work.

## NOTE: No need to change this
assertthat::assert_that(dim(q2)[1] == 4)

## [1] TRUE

print("Nice!")

## [1] "Nice!"