| library("rvest") | |
| library("XML") | |
| links_list <- html("http://www.burnaby.ca/City-Services/Building/Permits-Issued.html") %>% | |
| html_nodes("#ctl15_nestedList a") | |
| ## Where do links lead? This is encoded in the attribute "href". First get all the attributes | |
| links_attr <- sapply(links_list, xmlAttrs) | |
| ## We have the attributes! each link just became a single named character |
| library(rvest) | |
| library(magrittr) | |
| library(tidyr) | |
| library(dplyr) | |
| library(stringr) | |
| character_data <- function(chname){ | |
| paste0("http://en.memory-alpha.org/wiki/", chname) %>% | |
| html %>% | |
| html_nodes(".wiki-sidebar") %>% |
| library(dplyr) | |
| library(lubridate) | |
| ## when will the two friends be in the same town? | |
| visits <- data.frame(am_in_town = c( | |
| ymd(20140501) %--% ymd(20140520), | |
| ymd(20140615) %--% ymd(20140620), | |
| ymd(20140701) %--% ymd(20140710), | |
| ymd(20141101) %--% ymd(20141112) |
| values <- c(2,5,3,6,7, | |
| 9,5,4,9,9, | |
| 1,5,4,8,1, | |
| 3,1,5,6,2, | |
| 2,9,4,7,4) | |
| my.mat <- matrix(values, nrow = 5, byrow = TRUE) | |
| library(dplyr) | |
| library(tidyr) |
| ## Empty list -- just use the empty environment for this. | |
| nil <- function() { | |
| emptyenv() | |
| } | |
| ## Test if a list is the empty list: | |
| is_empty <- function(lis) { | |
| identical(lis, nil()) | |
| } |
| ## via base #### | |
| "%gcd%" <- function(u, v) {ifelse(u %% v != 0, v %gcd% (u%%v), v)} | |
| n <- 20L | |
| js <- 0:n | |
| Jp1 <- js + 1L | |
| Ns <- matrix(NA,nrow = n + 1, ncol = n + 1) | |
| Ds <- Ns | |
| Ns[1,] <- 1L |
On Rosetta Code, the list of Tasks uncompleted in R contains the following challenge: print the Bernoulli numbers from B0 to B60. After seeing some solutions, including one in in Python , I wanted to try in R. There are examples of code on that website, and a nice description of the algorithm here.
library(MASS)
Another version of this gist with figures included is on Rpubs
Recently I was trying to put confidence intervals on a regression line, and I got some excellent advice from @davidjayharris on Twitter, who suggested the below method in an excellent
| set.seed(1) | |
| # Create fake data | |
| x = runif(100, 0, 5) | |
| y = .25 * x^3 - x^2 + rnorm(length(x)) | |
| data = data.frame(x = x, y = y) | |
| # Identify 500 points to include in the plots | |
| x.sequence = seq(0, 5, length = 500) |