AdrianAntico

library(RemixAutoML)
library(data.table)

###########################################
# Prepare data for AutoTS()----
###########################################

# Load Walmart Data from Dropbox----
data <- data.table::fread("https://www.dropbox.com/s/2str3ek4f4cheqi/walmart_train.csv?dl=1")

talegari

R libraries for recommender systems

A list of R libraries for Recommender systems. Most of the libraries are good for quick prototyping.

Maintainer: Srikanth KS(talegari) Email: gmail me at sri dot teach (do write to me about packages ommited)

Package	Dev page	Description
recommenderlab	github	Provides a research infrastructure to test and develop recommender algorithms including UBCF, IBCF, FunkSVD and association rule-based algorithms
rrecsys	github	Implementations of several popular recommendation systems like Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR and weighted ALS for rapid prototyping

jilmun

library(tidyverse)
library(ggrepel)
library(scales)
results <- read_csv("results.csv")

# add GDP rank, winning candidate, chart labels
results <- results %>% 
  arrange(desc(GDP)) %>%
  mutate(GDPRank = 1:51) %>%
  mutate(Win = ifelse(ClintonNum>TrumpNum, "Clinton", "Trump")) %>%

jilmun

# Martingale Betting System

# initialize parameters ---------------------------------------------------
init_start <- 12800
init_goal <- init_start + 1000
init_bet <- 100    # initial bet value
prob_win <- 0.47   # probability of win
n <- 500000        # simulation count

# run simulation ----------------------------------------------------------

tomhopper

library(ggplot2)
#' create a data frame with test data.
my.df <- data.frame(index = 1:10, value = rnorm(10))

#' create the ggplot object
my.ggp <- ggplot(data = my.df, aes(x = index, y = value)) + geom_point() + geom_line()

#' get the x- and y-axis ranges actually used in the graph

# This worked in early versions of ggplot2 (probably <2.2)