chelseaparlett

library(tidyverse)
library(MASS)
library(patchwork)
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# generate data with given cor matrix
a <- 0.9
s1 <- matrix(c(1,a,
               a,1), ncol = 2)

chelseaparlett

library(brickr)

make_bricks <- function(brick_width = 5,
                        brick_length = 5,
                        n_layers = 3,
                        colors = c("Bright red",
                                   "Bright green",
                                   "Bright blue"),
                        render = FALSE){
  x <- rep(sort(rep(1:brick_width, brick_length)), n_layers)

chelseaparlett

#libs
library(tidyverse)

#---CHELSEA---
# load in data
california <- read.csv("./Data/namesbystate/STATE.CA.TXT", header = FALSE)
colnames(california) <- c("State", "Gender", "Year", "Name", "Count")

# filter data
california_sub <- california %>% filter(Name == "Chelsea", Gender == "F")

chelseaparlett

library(tidyverse)
library(ggtext)
library(grid)
library(crayon)

title <- "The <b><span style='color:#FF0000'>Sigmoid Function</span></b> and its <b><span style='color:#0000FF'>Derivative</span></b>"

df <- data.frame(x = -5:5)

ggplot(df, aes(x)) + 

chelseaparlett

library(tidyverse)
library(MASS)

# plot e^x
x <- -5:5
dat <- data.frame(x,y=exp(x))

f <- function(x) exp(x)
p <- ggplot(dat, aes(x,y)) +
  stat_function(fun=f, colour="red") +

chelseaparlett

from shiny import App, render, ui, reactive

from pathlib import Path

# Import modules for plot rendering
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

chelseaparlett

library(tidyverse)
# create named list
l <- list(x = data.frame(a = rnorm(10), b = rnorm(10)),
                     y = data.frame(a = rnorm(10), b = rnorm(10)),
                                z = data.frame(a = rnorm(10), b = rnorm(10)))
          

for (i in names(l)){
  if (i %in% c("x", "y")){ # if the name of the list item is in a specified sublist
    l[[i]] <- l[[i]] %>% mutate(c = a + 1) # then do the mutate

chelseaparlett

library(readr)
library(tidyverse)

# read in data
urlfile <- "https://raw.githubusercontent.com/Z-Unlocked/Unlocked_Challenge_1/main/temperature_change_data_11-29-2021.csv"

mydata <-read_csv(url(urlfile))

# filter
countries <- c("China", "Germany", "Japan", "United States of America")

chelseaparlett

#libs--------------
library(grid)
library(tidyverse)
library(ggdark)
#funs--------------

irt <- function(a,b,c){
  theta <- seq(-10,10, length = 500)
  exponent <- exp(a*(theta-b))
  phat <- c + (1-c)*(exponent/(1 + exponent))

chelseaparlett

terms <- c("error", "random", "model",
           "mixed", "linear", "effects",
           "variance")
probs <- c(0.1,0.1, 0.55, 0.05, 0.05, 0.1, 0.05)

new_jargon <- sample(terms,
                     size = sample(c(2,3),1),
                     prob = probs) 
new_jargon <- paste(new_jargon, collapse = " ")