MonkmanMH

## make it reproducible

set.seed(8675309)


## two normally distributed variables

X1 <- rnorm(100, mean = 0, sd = 1) 
X2 <- rnorm(100, mean = 0, sd = 1) 

MonkmanMH

#
# for details see
# http://bayesball.blogspot.ca/2013/06/annotating-select-points-on-x-y-plot.html
#
# load the ggplot2 and grid packages
library(ggplot2)
library(grid)
# read data (note csv files are renamed)
tbl1 = read.csv("FanGraphs_Leaderboard_h.csv")
tbl2 = read.csv("FanGraphs_Leaderboard_d.csv")

MonkmanMH

### ---
#
# from @expersso

set.seed(894)  # number of regular season NHL goals Wayne Gretzky scored 
 
x <- replicate(10000, sum(sample(0:1, 20, TRUE, c(0.945, 0.055))))

table(ifelse(x == 0, "Team A win", ifelse(x == 1, "Draw", "Team B win"))) / 100

MonkmanMH

---
title: "Transform Data"
subtitle: "hands-on examples, with answers"
output: html_notebook
---

<!-- This file by Charlotte Wickham (with some modifications by Martin Monkman) is licensed under a Creative Commons Attribution 4.0 International License, adapted from the orignal work at https://github.com/rstudio/master-the-tidyverse by RStudio and https://github.com/cwickham/data-science-in-tidyverse-solutions. -->

```{r setup}
library(tidyverse)

MonkmanMH

---
title: "geom_col vs geom_bar"
author: "Martin Monkman"
date: "2020/04/19"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

MonkmanMH

# dplyr::case_when to find and clean FSA
#
# Notes:
# * FSA = "Forward Sortation Area" in Canadian postal parlance
# * the regex finds British Columbia FSAs (starting with "V")

FSA_list <- df %>% 
  mutate(FSA_clean = case_when(
    str_detect(FSA, "V\\d.$") == TRUE ~ FSA,
    TRUE ~ NA_character_

MonkmanMH

Random numbers in R

The creation of random numbers, or the random selection of elements in a set (or population), is an important part of statistics and data science. From simulating coin tosses to selecting potential respondents for a survey, we have a heavy reliance on random number generation.

R offers us a variety of solutions for random number generation; here's a quick overview of some of the options.

runif, rbinom, rnorm

One simple solution is to use the runif function, which generates a stated number of values between two end points (but not the end points themselves!) The function uses the continuous uniform distribution, meaning that every value between the two end points has an equal probability of being sampled.

MonkmanMH

# short script to install the {annotater} package
# reference: https://github.com/luisDVA/annotater

# Step 1: install the {remotes} package
install.packages("remotes")

# Step 2: install {annotater} from the GitHub source
remotes::install_github("luisDVA/annotater")

MonkmanMH

---
title: "rename github repo"
author: "Martin Monkman"
date: "2020/10/05"
output: html_document
---

From
https://www.r-bloggers.com/2020/07/5-steps-to-change-github-default-branch-from-master-to-main/

MonkmanMH

rename from "master" to "main"

git branch -m master main

push to github

git push -u origin main

and delete the original

git push origin --delete master

But...you can do this as an Rmd with the git commands in a bash chunk