Function of the Week:

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Breaks_Pretty

In this document, I will introduce the Breaks_Pretty function and show what it’s for.

#load tidyverse up
library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.1     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

#example dataset
library(palmerpenguins)

#breaks pretty is in "scales" package
library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

What is it for?

The breaks_pretty function is used to create easy, incremental break formatting - n= will specify the amount of breaks. This function is used more commonly for dates/times. Extended_breaks() is more commonly used for numeric scales. In the example below, I used numeric variables anyway.

#create plot & designate how many "breaks"

ggplot(penguins,
       aes(x=bill_length_mm,
           y=flipper_length_mm))+
  geom_point() +
 
  scale_x_continuous(name= "Bill Length in mm",
                     breaks = breaks_pretty(4))                     

## Warning: Removed 2 rows containing missing values (geom_point).

  scale_y_continuous(name= "Flipper Length in mm",
                     breaks = breaks_pretty(8))

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

#R doesn't always use the amount of "breaks" that you assign - apparently because it wants to best fit the data. For example - it won't allow for n=50 for y-axis.
ggplot(penguins,
       aes(x=bill_length_mm,
           y=flipper_length_mm))+
  geom_point() +
 
  scale_x_continuous(name= "Bill Length in mm",
                     breaks = breaks_pretty(2))                     

## Warning: Removed 2 rows containing missing values (geom_point).

  scale_y_continuous(name= "Flipper Length in mm",
                     breaks = breaks_pretty(50))  

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

Here is an example using dates!

one_year <- as.POSIXct(c("2022-01-01", "2023-01-01"))
demo_datetime(one_year)

## scale_x_datetime()

demo_datetime(one_year, breaks = breaks_pretty(12))

## scale_x_datetime(breaks = breaks_pretty(12))

demo_datetime(one_year, breaks = breaks_pretty(6))

## scale_x_datetime(breaks = breaks_pretty(6))

demo_datetime(one_year, breaks = breaks_pretty(100))

## scale_x_datetime(breaks = breaks_pretty(100))

Is it helpful?

Discuss whether you think this function is useful for you and your work. Is it the best thing since sliced bread, or is it not really relevant to your work?

I find breaks_pretty much more useful in the dates example! Which makes sense considering it’s not generally used for numerical variables (penguin example).