FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
The Leaflet JS mapping library has lots of plugins available. The Leaflet package for R provides direct support for some, but far from all, of these plugins, by providing R functions for invoking the plugins.
If you as an R user find yourself wanting to use a Leaflet plugin that isn't directly supported in the R package, you can use the technique shown here to load the plugin yourself and invoke it using JS code.
The dplyr package in R makes data wrangling significantly easier.
The beauty of dplyr is that, by design, the options available are limited.
Specifically, a set of key verbs form the core of the package.
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).
dplyr is organised around six key verbs:
| library(markovchain) | |
| text <- readLines('text.txt') | |
| text <- text[nchar(text) > 0] | |
| text <- gsub('.', ' .', text, fixed = TRUE) | |
| text <- gsub(',', ' ,', text, fixed = TRUE) | |
| text <- gsub('!', ' !', text, fixed = TRUE) | |
| text <- gsub('(', '( ', text, fixed = TRUE) | |
| text <- gsub(')', ' )', text, fixed = TRUE) |
The core of this tutorial gist lies in bestPracticesWalkThrough.R. Running assumes you have the following packages at versions equal (or above) those specified
library('devtools') # 1.9.1
library('testthat') # 0.11.0
library('stringr') # 1.0.0
library('git2r') # 0.12.1
| name | gender | race | |
|---|---|---|---|
| barjraj | m | indian | |
| ramdin verma | m | indian | |
| sharat chandran | m | indian | |
| birender mandal | m | indian | |
| amit | m | indian | |
| kushal | m | indian | |
| kasid | m | indian | |
| shiv prakash | m | indian | |
| vikram singh | m | indian |
| # Set a seed | |
| set.seed(500) | |
| library(MASS) | |
| data <- Boston | |
| # Check that no data is missing | |
| apply(data,2,function(x) sum(is.na(x))) | |
| # Train-test random splitting for linear model |
| # Set a seed | |
| set.seed(500) | |
| library(MASS) | |
| data <- Boston | |
| # Check that no data is missing | |
| apply(data,2,function(x) sum(is.na(x))) | |
| # Train-test random splitting for linear model |
| # install the new/experimental plotly R package | |
| # devtools::install_github("ropensci/plotly@carson-dsl") | |
| # ---------------------------------------------------------------------- | |
| # https://plot.ly/r/3d-line-plots/ | |
| # ---------------------------------------------------------------------- | |
| library(plotly) | |
| # initiate a 100 x 3 matrix filled with zeros |
Rich Hickey • 3 years ago
Sorry, I have to disagree with the entire premise here.
A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.
Mastery comes from a combination of at least several of the following: