These provides three families of functions to tell you if values are always increasing, decreasing, or unvarying, with the functions, increasing(), decreasing(), or unvarying(). Under the hood it uses diff to find differences, so if you like you can pass extra arguments to diff.

## Usage

increasing(x, ...)

decreasing(x, ...)

unvarying(x, ...)

monotonic(x, ...)

## Arguments

x

numeric or integer

...

extra arguments to pass to diff

## Value

logical TRUE or FALSE

## Examples

vec_inc <- c(1:10)
vec_dec<- c(10:1)
vec_ran <- c(sample(1:10))
vec_flat <- rep.int(1,10)

increasing(vec_inc)
#> [1] TRUE
increasing(vec_dec)
#> [1] FALSE
increasing(vec_ran)
#> [1] FALSE
increasing(vec_flat)
#> [1] FALSE

decreasing(vec_inc)
#> [1] FALSE
decreasing(vec_dec)
#> [1] TRUE
decreasing(vec_ran)
#> [1] FALSE
decreasing(vec_flat)
#> [1] FALSE

unvarying(vec_inc)
#> [1] FALSE
unvarying(vec_dec)
#> [1] FALSE
unvarying(vec_ran)
#> [1] FALSE
unvarying(vec_flat)
#> [1] TRUE

library(ggplot2)
library(gghighlight)
library(dplyr)

heights_mono <- heights %>%
features(height_cm, feat_monotonic) %>%
left_join(heights, by = "country")

ggplot(heights_mono,
aes(x = year,
y = height_cm,
group = country)) +
geom_line() +
gghighlight(increase)
#> Warning: Tried to calculate with group_by(), but the calculation failed.
#> Falling back to ungrouped filter operation...
#> label_key: country
#> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps

ggplot(heights_mono,
aes(x = year,
y = height_cm,
group = country)) +
geom_line() +
gghighlight(decrease)
#> Warning: Tried to calculate with group_by(), but the calculation failed.
#> Falling back to ungrouped filter operation...
#> label_key: country

heights_mono %>%
filter(monotonic) %>%
ggplot(aes(x = year,
y = height_cm,
group = country)) +
geom_line()

heights_mono %>%
filter(increase) %>%
ggplot(aes(x = year,
y = height_cm,
group = country)) +
geom_line()