chelseaparlett

library(ggplot2)
library(pwr)

#funcs and params-----------------------------
pwrf <- function(ef,pow){
  normal <- pwr.t.test(d = ef, type = "two.sample",
             alternative = "two.sided",
             power = pow)$n
  half <- pwr.t.test(d = (ef/2), type = "two.sample",
             alternative = "two.sided",

chelseaparlett

getRandSamples <- function(seed = NA){
  if (!is.na(seed)){
    set.seed(seed) #incase you need repeatable data generation.
    #but remember to set a different seed for each person if you want them to get different items at each time point.
  }
  
  True_New <- sample(1:20,20,replace = F) #items that are true + new
  True_Rep <- sample(21:40,20,replace = F) #items that are true + repeated
  False_New <- sample(41:60,20,replace = F) #items that are false + new
  False_Rep <-sample(61:80,20,replace = F) # items that are false + repeated

chelseaparlett

library(pwr)

ratioSS <- function(n){

  #calculate SESOI based on 33% power
  SESOI <- pwr.t.test(n = n, sig.level = 0.05,
                      power = 0.33, type = "one.sample",
                      alternative = "two.sided")$d
  
  #calculate exact n needed to get 80% power on inferiority test

chelseaparlett

library(ranger)

y <- rnorm(1000)
x1 <- rnorm(1000)
x2 <- rnorm(1000)

df <- data.frame(y,x1,x2)
rf <- ranger(y~ x1 + x2, data = df, importance = "permutation")
rf$variable.importance

chelseaparlett

#1-----------------------
a = 19
b = 20
c = 18

(a+b+c)/3

#2-----------------------
color = input("What is your favorite color? ")
sentence = "Your favorite color is " + color

chelseaparlett

import numpy as np

def randomPrior():
    '''randomly chooses a type of distribution then randomly selects
    parameters based on limitations of said distribution'''

    possPriors = {"Cauchy": {"loc": [-100,100], "scale": [0,100]},
     "Normal": {"mean": [-100,100],
      "sd": [0,100]},
    "InverseGamma": {"a": [0,100], "b":[0,100]},

chelseaparlett

library(tidyverse)

# 1
d <- read.csv(url("https://raw.githubusercontent.com/cmparlettpelleriti/CPSC392ParlettPelleriti/master/Data/Beyonce_data.csv"))

ggplot(d,aes(x = factor(mode), y = danceability)) + geom_boxplot(aes(fill = factor(mode))) + 
  scale_fill_manual(values = c("#f83703", "#000000")) +
  labs(title = "Dancibility of Beyonce Songs by Major and Minor Key", x = "mode") +
  theme(legend.position = "none", panel.background = element_rect(fill = "#78a741", colour = "#83ba7d"),
        panel.grid.major = element_line(colour = "burlywood3"),

chelseaparlett

library(tidyverse)

calculateWeights <- function(dfData, row){
  wi <- dnorm(dfData$x, row[,"means"], row[,"var"])
  return(wi)
}
normalize.choose <- function(weights, dfDist){
  wis <- sapply(1:ncol(weights),
                    function(x) (weights[,x]*dfDist[,"ak"])/sum(weights[,x]*dfDist[,"ak"]))
  chosen <- sapply(1:ncol(weights), function(x) which.max(wis[,x]))

chelseaparlett

a <- read.csv(url("https://raw.githubusercontent.com/cmparlettpelleriti/CPSC392ParlettPelleriti/master/Data/PopDivas_data.csv"))
b <- read.csv(url("https://raw.githubusercontent.com/cmparlettpelleriti/CPSC392ParlettPelleriti/master/Data/cereal.csv"))
c <- read.csv(url("https://raw.githubusercontent.com/cmparlettpelleriti/CPSC392ParlettPelleriti/master/Data/BreastCancer.csv"))

#red yellow blue purple green pink black white rainbow
library(ggplot2)
red <- ggplot(a, aes(x = artist_name, y = energy, fill = artist_name)) + 
  geom_violin() + scale_fill_manual(values = c("red", "red1", "red2", "red3", "red4", "firebrick4")) +
  geom_boxplot() + ggtitle("Pop Divas Song Energy")
red

chelseaparlett

library(ggplot2)
library(palmerpenguins)
library(MASS)

#penguin--------------------------------------------------
penguins <- na.omit(penguins)

ggplot(penguins, aes(x = bill_length_mm, bill_depth_mm)) +
  geom_point() +
  facet_wrap(sex~species) +