{DT} is a package to render html tables. It is an interface to the datatables javascript library. It should not be confused with the {data.table} package, which is a package useful for data wrangling.
A similar package of the same purpose is {kableExtra}. I found that {kableExtra} is more suitable for making static tables, whereas {DT} is more suitable for making interactive tables.
DT Adding captions DT::datatable(iris[1:10,],caption = htmltools::tags$caption( style = 'caption-side: top; text-align: center; color:black; font-size:200% ;','Table1: Iris Dataset Table') )
This contains notes for Rmarkdown and markdown. All notes for markdown are generally applicable for Rmarkdown.
markdown Footnote Rmarkdown Markdown extras Adding toggle <details><summary>toggle title</summary> toggle content </details> Image quality knitr::opts_chunk$set(dpi=300)
Case snakecase::to_any_case() help to fix the case of words - useful for converting between presentation data and processing data.
Regex The regex in R is not 100% perl flavored. For example, escape character is \\ instead of \.
I love the [{rev}](https://github.com/kevinushey/rex package). The most salient point that this package solves is on the interpretation of regex.
When not to use regex Not all strings are interpreted as a regex. Sometimes, one needs to opt-in via a parameter in the function.
Font Font size Font format (e.g. subscripts) Combine plots Helpful packages: 3D rendering It will be useful to consult Rmarkdown notes because I often use Rmarkdown to render ggplot.
Font Font size Global adjustment (e.g. the default font size is small when rendering with Rmarkdown with ) theme’s basic size
See individual adjustment here and also in this RStudio2021 conference talk
Font format (e.
NA are necessary markers for missing data. However, Working with them can be tricky because of their special properties. Care should also be taken when reading in and presenting the data.
Properties of NA Types There are different types of NA that are denoted by the NA_*. This shhould be noted when working with NA data in a data.frame. Operations like case_when require all output data to be of the same type.
Why use functional programming Avoid intermediate objects In any loop, the standard practice is to create a new list before the loop, do some processing for each element of the list in the loop and then add the processing result as an element to the new list (following the same index). It makes programming more fun Thinking about that index i is simply not as fun as working with the whole list.
Intro This is a documentation of how I split a string type column by its length, and combine them together in a directory format (which was a necessary step for me to check whether each directory existed in my analysis).
library(tidyverse) data <- tibble(string = c("123456", "987654")) print(data) ## # A tibble: 2 x 1 ## string ## <chr> ## 1 123456 ## 2 987654 Step 1 strsplit splits the string into a list of strings, and in tibble it will show up as a column of list type.
I have just published my first document with bookdown and I am feeling euphoric at the moment. Somehow this gives me a very different satisfaction as compared to creating just a google document. Yes, they serve almost the same function, but the presentation of a bookdown document is much better. It also provides more functionalities and flexibilities than a google doc.
Here are the minimal steps needed to customize a document from Yihui’s guide.
I have created this package to create a blank Rmd file by saving a nearly blank Rmd template to the menu when you create Rmd files.
I think it at least follows the first half of the UNIX orthodox: programs that do one thing and do it well.
😂
Reflection: the main purpose for me to create this package, other than making life easier, is to practise how to publish a R package onto github and to install it from there.
I had this quesition when preparing my manuscript and a quick search brings me to this stackoverflow question by Johanna. I find the answer by Henrick to be highly effective, but can be further elaborated so that readers can be clearer about the functions of each line. Thus, I will base my post largely on Henrick’s answer but at the same time add my explanation to the rationale behind the lines.