datalorax

library(tidyverse)

by_species <- iris %>%
  group_by(Species) %>% 
  nest()

specific_models <- tibble(
  Species = unique(iris$Species),
  model = list(function(d) lm(Sepal.Length ~ Petal.Width + Petal.Length, d),
               function(d) lm(Sepal.Length ~ Petal.Width, d),

DavisVaughan

library(dplyr)
library(slider)
library(lubridate)
library(tsibbledata)

# Google, Apple, Facebook, Amazon stock
gafa_stock <- as_tibble(gafa_stock)
gafa_stock <- select(gafa_stock, Symbol, Date, Close, Volume)

head(gafa_stock, 2)

kdev33

SQL Cheatsheet

Table of Contents

1. DDL (Data Definition Language)
2. DML (Data Manipulation Language)
3. DCL (Data Control Language)
4. TCL (Transaction Control Language)

Resources

jebyrnes

library(ggplot2)

#A glm from the glm helpfile
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())

#The old way...

thomasp85

library(magick)
library(reshape2)
library(dplyr)
library(tidygraph)
library(particles)
library(animation)

plot_fun <- function(sim) {
  df <- as_tibble(sim)
  plot(df$x, df$y, col = df$color, pch = '.', axes = FALSE, xlim = c(-100, 317), ylim = c(-268, 100), xlab = NA, ylab = NA)

jbburant

## GOAL:
## re-create a figure similar to Fig. 2 in Wilson et al. (2018), 
## Nature 554: 183-188. Available from:
## https://www.nature.com/articles/nature25479#s1
##
## combines a boxplot (or violin) with the raw data, by splitting each
## category location in two (box on left, raw data on right)


# initial set-up ----------------------------------------------------------

santisbon

Enter this in the search box along with your search terms:

Get all gists from the user santisbon.
user:santisbon

Find all gists with a .yml extension.
extension:yml

Find all gists with HTML files.
language:html

cderv

#' From a stackoverflow question : https://stackoverflow.com/questions/46128164/extract-data-from-a-nested-list-with-loops#46128164
nested_list_ex <- structure(list(scheduleList = structure(list(
  schedule = structure(list(
    score = structure(list(
      class = structure(list(name = list("011c"), people = list("2"), teacher = structure(list(name = list("A")), .Names = "name", id = "D29")), .Names = c("name", "people", "teacher"), id = "011c", status = "-2"), 
      class = structure(list(name = list("013"), people = list("0"), teacher = structure(list(name = list("B")), .Names = "name", id = "D14")), .Names = c("name", "people", "teacher"), id = "602d", status = "-4"), 
      class = structure(list(name = list("603"), people = list("6"), teacher = structure(list(name = list("C")), .Names = "name", id = "D31")), .Names = c("name", "people", "teacher"), id = "603", status = "-4")), 
      .Names = c("class", "class", "class"), id = "1"), 
    score = structure(list(
      class = structure(list(name = list

jflanaga

#----------------------------------------------------------------------------------------
# File: 
# Author: Joseph Flanagan, adopted from https://stackoverflow.com/questions/10002021/split-dataframe-into-multiple-output-files-in-r
# email:   [email protected]
# Purpose: Split a dataframe by group, then save each as separate .csv file
#----------------------------------------------------------------------------------------

# new tidyverse solution with `group_walk`
library(dplyr)
library(readr)

Mikuana

# Shamlessly stolen from:
# https://www.r-bloggers.com/how-to-remove-all-user-installed-packages-in-r/

# create a list of all installed packages
 ip <- as.data.frame(installed.packages())
 head(ip)
# if you use MRO, make sure that no packages in this library will be removed
 ip <- subset(ip, !grepl("MRO", ip$LibPath))
# we don't want to remove base or recommended packages either\
 ip <- ip[!(ip[,"Priority"] %in% c("base", "recommended")),]