In andrewGhazi/ghzutils: A small set of simple utility functions to make life easier in R, RStudio, and the tidyverse

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
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Contents

Right now this package contains only a handful of functions: ws_size, pull1, get/add_local_density and theme_pres.

ws_size

It's easy to see the memory usage of a single object with object.size but doing that in a sorted, pretty way for everything in the workspace is a bit more involved. This function has taken care of that.

> a = 1:10
> tmp = mtcars
> ws_size()
# A tibble: 2 x 3
    obj obj_size n_bytes
  <chr>    <chr>   <int>
1   tmp   6.6 Kb    6736
2     a 88 bytes      88

pull1

dplyr::pull is a convenient way to pull out the values of a column from a data frame, but if you've got a long list column and just want to check one to make sure everything's working, printing the whole list can clutter up the console. pull1 just pulls out one.

> tmp = data_frame(mu = 5:10, samples = purrr::map(mu, ~rnorm(10, mean = .x, sd = 1)))
> tmp
# A tibble: 6 x 2
     mu    samples
  <int>     <list>
1     5 <dbl [10]>
2     6 <dbl [10]>
3     7 <dbl [10]>
4     8 <dbl [10]>
5     9 <dbl [10]>
6    10 <dbl [10]>

> tmp %>% pull1(samples)
 [1] 4.651063 3.986187 5.421502 4.554197 4.880644 4.511554 5.572671 4.948658 6.132115 5.545000

theme_pres

This is simply a modified version of ggplot2::theme_light with larger text and dark facet labels. This makes it easier to prepare easy-to-read plots for presentations.

diamonds %>% sample_n(1000) %>% ggplot(aes(carat, price)) + geom_point() + theme_pres() + facet_wrap('cut')

get/add_local_density

This is handy for dealing with severe overplotting in situations where you still want to plot individual points rather than a density estimate. You simply give add_local_density the two variables used in your plot, then you aesthetically map the resulting local_density column to color.

library(ggplot2)
library(magrittr)
overplotted = data.frame(x = rt(1e4, df = 5),
                         y = rt(1e4, df = 5))

overplotted %>% 
  ggplot(aes(x,y)) +
  geom_point(alpha = .1) + 
  theme_light() + 
  labs(title = expression('Intractable overplotting, even with'~alpha~' = 0.1'))

knitr::include_graphics('pictures/overplotted.png')

library(ghzutils)

overplotted %>% 
  add_local_density(x, y) %>% 
  ggplot(aes(x,y)) +
  geom_point(aes(color = local_density)) + # <-- this is the important bit
  theme_light() + 
  scale_color_viridis_c() + 
  labs(title = 'Coloring by local density naturally transitions between\nscattered points to a density estimate')

knitr::include_graphics('pictures/color_density.png')

This is a trivial example but sometimes there's simultaneously important structure hidden in the overplotted region AND important meaning associated with individual points in the sparse outer regions. This sort of plot shows both.

I'm working on a color_density_scatter function that goes straight from the data to the plot.

Installation

Install with:

devtools::install_github('andrewGhazi/ghzutils')

andrewGhazi/ghzutils documentation built on Nov. 30, 2020, 5:23 a.m.