Notes on R

Update (March 5, 2022): I finally wrote a package with all these functions. You can learn more about it here.¹

On one fine day when I have enough time, they’ll all be wrapped into a package hosted on my Github. Until then, this page in their home.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5          ✓ purrr   0.3.4     
## ✓ tibble  3.1.6          ✓ dplyr   1.0.8.9000
## ✓ tidyr   1.2.0          ✓ stringr 1.4.0     
## ✓ readr   2.0.2          ✓ forcats 0.5.1

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

Show in Excel < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

show_in_excel = function(.data)
{
   temp = paste0(tempfile(), ".csv")
   write.csv(.data, temp)
   fs::file_show(path = temp)
}

This can be used with pipes too.

iris %>% 
  show_in_excel()

Convert Missing Values to Zero < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

This function converts missing values in a vector to zero.

n2z = function(x)
{
   x = ifelse(is.na(x), 0, x)
   return(x)
}

Finding Index of Missing Elements < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

This function returns index of elements which are missing. Very useful in finding what observations are missing.

which.na = function(x)
{
   return(which(is.na(x)))
}

Removing Rows Based on Missing Values in a Column < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

Sometimes, I do not want to na.omit() because it will treat all features equally. I want to check values only for one column.

na.rm.feature = function(x, colname)
{
   nas = which(is.na(x[,colname]))
   x = x[-nas, ]
   return(x)
}

Find row where a condition is satisfied < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

This function can find observations that satisfy a condition. Typically, they are useful in finding specific elements. It is kind of a wrapper around dplyr’s filter().

which.this = function(df, x)
{
  df %>% 
    filter(eval(parse(text = x)))
}

Example

which.this(iris, "Sepal.Length > 6.5") %>% 
  head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1          7.0         3.2          4.7         1.4 versicolor
## 2          6.9         3.1          4.9         1.5 versicolor
## 3          6.6         2.9          4.6         1.3 versicolor
## 4          6.7         3.1          4.4         1.4 versicolor
## 5          6.6         3.0          4.4         1.4 versicolor
## 6          6.8         2.8          4.8         1.4 versicolor

Remove commas, dollars, or any other such characters < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

The code below replaces all commas with nothing.

x = "300,000"
x = gsub(",", "", x)
print(x)

## [1] "300000"

GGPlot2 Theme < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

See official guide for more details. Also see Benjamin’s blog.

The default plot looks like this.

iris %>% 
  ggplot(aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) +
  geom_point() +
  labs(title = "Without my theme")

Once I run and set my theme, its way prettier.

theme_h = function(base_size = 14) {
  theme_bw(base_size = base_size) %+replace%
    theme(
      # Specify plot title
      plot.title = element_text(
        size = rel(1),
        face = "bold",
        family = "serif",
        margin = margin(0, 0, 5, 0),
        hjust = 0
      ),
      # Specifying grid and border
      panel.grid.minor = element_blank(),
      panel.border = element_blank(),
      # Specidy axis details
      axis.title = element_text(
        size = rel(0.85),
        face = "bold",
        family = "serif"
      ),
      axis.text = element_text(size = rel(0.70), family = "serif"),
      axis.line = element_line(
        color = "black",
        arrow = arrow(length = unit(0.3, "lines"),
                      type = "closed")
      ),
      # Specify legend details
      legend.title = element_text(
        size = rel(0.85),
        face = "bold",
        family = "serif"
      ),
      legend.text = element_text(
        size = rel(0.70),
        face = "bold",
        family = "serif"
      ),
      legend.key = element_rect(fill = "transparent", colour = NA),
      legend.key.size = unit(1.5, "lines"),
      legend.background = element_rect(fill = "transparent", colour = NA),
      # Remove default background
      strip.background = element_rect(fill = "#17252D", color = "#17252D"),
      strip.text = element_text(
        size = rel(0.85),
        face = "bold",
        family = "serif",
        color = "white",
        margin = margin(5, 0, 5, 0)
      )
    )
}

theme_set(theme_h())

iris %>% 
  ggplot(aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) +
  geom_point() +
  labs(title = "With my theme")

I like the arrowed axes and serif s.

Better Quality Images in R Markdown < svg class="anchor-symbol" aria-hidden="true" height="26" width="26" viewBox="0 0 22 22" xmlns="http://www.w3.org/2000/svg"> < path d="M0 0h24v24H0z" fill="currentColor"> < path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z">

Using .svg as the image output format gives much better graphics quality than the default option. To use that, include the following code in R Markdown. Source.

knitr::opts_chunk$set(dev = 'svg') # set output device to svg

1. I do not imagine this package to be useful to many people but I use these functions very frequently. Particularly my ggplot2 theme. ↩︎