Notes on R
Functions et al
February 3, 2022
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 3.3.6.9000 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
DT Table with Download Buttons with Search
my_DT = function(df)
{
return(DT::datatable(
df,
extensions = "Buttons",
options = list(
paging = TRUE,
scrollX = TRUE,
searching = TRUE,
ordering = TRUE,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf'),
pageLength = 5,
lengthMenu = c(3, 5, 10)
)
))
}
Show in Excel
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
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
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
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
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
The code below replaces all commas with nothing.
x = "300,000"
x = gsub(",", "", x)
print(x)
## [1] "300000"
GGPlot2 Themes
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.
# creating theme
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 fonts. This theme has now been implemented in my garlic package. It can be set via theme_set(garlic::gg_serif().
Theme Clean
There are many other alternatives available — beyond the default options.
This website has a wonderful compilation of a few of them. I really like theme_clean() from ggthemes package.
theme_set(ggthemes::theme_clean())
Tech Themes
ggtech has
themes related to tech companies. Here are they in the order of my preference.
theme_set(ggtech::theme_airbnb_fancy())
theme_set(ggtech::theme_tech(theme="etsy"))
theme_set(ggtech::theme_tech(theme="google"))
theme_set(ggtech::theme_tech(theme="facebook"))
theme_set(ggtech::theme_tech(theme="twitter"))
Better Quality Images in R Markdown
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.
# set output device to svg
# this can fail sometimes -- I haven't investigated when
knitr::opts_chunk$set(dev = 'svg')
Update (March 5, 2022): I finally wrote a package with some of these functions. You can learn more about it here.1
-
I do not imagine this package to be useful to many people but I use these functions very frequently. Particularly my
ggplot2theme. ↩︎