turgeonmaxime

#' Set Working Directory
#'
#' \code{setwd(dir)} is used to set the working directory to \code{dir}.
#' 
#' This version of \code{setwd} is different from the one in \code{base} in that it allows for paths containing Unix-like
#' environment variables. For example, if HOME is set (globally) to your home directory, then \code{base::setwd($HOME)} 
#' will not work. However, the version below will work.
#' 
#' @param dir	A character string: tilde expansion will be done.

turgeonmaxime

library(clusterProfiler)
library(org.Hs.eg.bd) 
library(KEGGREST)
library(dplyr)
library(magrittr)

# List of genes of interest
x <- c("GPX3",  "GLRX",   "LBP",   "CRYAB", "DEFB1", "HCLS1",   "SOD2",   "HSPA2",
       "ORM1",  "IGFBP1", "PTHLH", "GPC3",  "IGFBP3","TOB1",    "MITF",   "NDRG1",
       "NR1H4", "FGFR3",  "PVR",   "IL6",   "PTPRM", "ERBB2",   "NID2",   "LAMB1",

turgeonmaxime

library(tidyverse)

# 1. Theoretical answer
# It's the probability that both teams score no goal, 
# plus the probability they both score one goal, 
# plus ... plus the probability they both score five goals
#
# If we assume the scores are binomial random variables
# and that the scores of each team are independent,
# this is simply a sum of products of binomial probabilities.

turgeonmaxime

# This is a short script that shows how to compute the KL divergence using the kernel density estimator
# The key point is to use linear interpolation to evaluate the density at the same points

# 1. Generate a sample of standard normal variates
x <- rnorm(100)

# 2. Compute kernel density estimator
dens_obs <- density(x)

# 3. Use linear interpolation to evaluate over a grid

turgeonmaxime

library(tidyverse)

# Create dataset with fantasy scores
data_ottawa <- tibble::tribble(
  ~harris, ~Sinopoli, ~Spencer, ~Ellingson,
  19.8, 13.4, 20.2, 18.4,
  3.4, 8, 4.1, 21.7,
  25.7, 31.8, 5.9, 9.9,
  -0.3, 7.7, 6.6, 5.6,
  18.5, 34.1, 7.8, 7.7,

turgeonmaxime

library(tidyverse)
library(lubridate)
library(weathercan)

# Lethbridge and Saskatoon
data <- weather_dl(station_ids = c(2265, 50091), 
                   start = "2012-01-01", end = "2018-08-15")

# Plot full data
data %>%

turgeonmaxime

# Loop through list of departments
list_depts <- c("Adult Medicine",
                "Heart Health",
                "Maternal Services")

for (dept in list_depts) {
  # Add underscores
  dept_f <- stringr::str_replace_all(dept, " ", "_")
  # Change the name of output
  output <- "report_template.Rmd" %>% 

turgeonmaxime

library(rvest)
library(tidyverse)
library(lubridate)

url <- "https://www.hockey-reference.com/teams/TBL/2019_games.html"

webpage <- read_html(url)

results <- webpage %>% 
  html_table(fill = TRUE) %>% 

turgeonmaxime

library(tidyverse)

# What I want
target <- mtcars %>% 
  filter(cyl %in% c(4, 8),
         gear %in% c(3, 4))
         
# Solution
dynamic_filter <- function(df, variables, conditions){ 
  filter_conditions <- purrr::map2(variables, conditions, 

turgeonmaxime

set.seed(12345)

mu <- 0.5
sigma <- 1
n <- 25
B <- 1000

results <- replicate(B, {
  data <- rnorm(n, mu, sigma)