TysonStanley

# Simulating Correlated Data 
# (this is a variant of something I found online but I can't find out who posted it...)
n     <- 2000                  # length
rho   <- 0.6                   # desired correlation = cos(angle)
theta <- acos(rho)             # corresponding angle
pred    <- rnorm(n, 1, 1)      # predictor vector
x2    <- rnorm(n, 2, 0.5)      # new random data for response vector
X     <- cbind(pred, x2)       # matrix of pred and x2
Xctr  <- scale(X, center=TRUE, scale=FALSE)   # centered columns (mean 0)
Id   <- diag(n)                               # an identity matrix

TysonStanley

# Steph Curry Shot Selection in 2015
# Data from: NBAsavant.com (awesome data source)

packs <- c("png","grid","ggplot2")
lapply(packs, require, character.only=TRUE)

steph <- read.csv("data.csv") # from NBAsavant.com
court <- readPNG("court.png") # diagram of NBA court
g <- rasterGrob(court, interpolate=TRUE)

TysonStanley

## Code for Example Plot for @evergreendata
## Tyson S. Barrett

## Data
x_num = c(4.2, 4.5, 4, 4.5, 3.9, 3.6)
year  = c(2011:2016)
x_per = c(.52, .58, .6, .63, .64, .67)
df = data.frame(x_num, year, x_per)

## Libraries and Plots

TysonStanley

# Animated dplyr joins with gganimate
# * Garrick Aden-Buie
# * garrickadenbuie.com
# * MIT License: https://opensource.org/licenses/MIT

# Note: I used Fira Sans and Fira Mono fonts.
#       Use search and replace to use a different font if Fira is not available.

library(tidyverse)
library(gganimate)

TysonStanley

## Beta Regression Power Analysis
## Simulations to assess statistical power given alpha, n, effect size
## Tyson S. Barrett

library(tidyverse)
library(betareg)

set.seed(84322)

## Population Model

TysonStanley

library(shiny)
library(datasets)
Logged = FALSE;
PASSWORD <- data.frame(Brukernavn = "withr", Passord = "25d55ad283aa400af464c76d713c07ad")
# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
  source("www/Login.R",  local = TRUE)
  
  observe({
    if (USER$Logged == TRUE) {

TysonStanley

library(bench)      # assess speed and memory
library(data.table) # data.table for all of its stuff
library(dplyr)      # compare it to data.table
library(profmem)    # assess the process of R functions

set.seed(84322)

# Example Data
d <- data.table(
  grp = sample(c(1,2), size = 1e6, replace = TRUE) %>% factor,

TysonStanley

# Tidy Tuesday (on Friday)
# 25 October 2019
# Tyson S. Barrett

# Packages ----
library(tidyverse)     # CRAN
library(dtplyr)        # CRAN
library(data.table)    # CRAN
library(tidyfast)      # remotes::install_github("tysonstanley/tidyfast")

TysonStanley

``` r
library(tibble)

df = tibble(
  var1 = rnorm(5),
  var2 = rnorm(5)
)

first = function(data, x){
  x = substitute(x)