maxdrohde

library(tidyverse)
library(gganimate)

# Grid for plotting
x <- seq(0,10, length.out = 1e3)

# Sample Sizes
n <- 1:20

# Create the densities at each value of the grid

maxdrohde

library(tidyverse)
library(gganimate)

sample_size <- seq(5, 30, by=5)

create_data <- function(sample_size){
  tibble(ss=sample_size, n=1:1e6, x=map(n, ~runif(sample_size))) %>%
  mutate(mean = map_dbl(x, mean),
         median = map_dbl(x, median),
         range = map_dbl(x, ~(max(.x) - min(.x))/2)) %>%

maxdrohde

library(tidyverse)
library(gganimate)
set.seed(8)

grid <- seq(-5,12, by=0.01)

x <- seq(-3,9, by=1)
y <- x^2 + rnorm(n=length(x), sd=30)
data <-tibble(x=x, y=y)

maxdrohde

library(tidyverse)
library(glue)
library(gganimate)


grid <- seq(-10,5,length.out=1e4)
y <- (grid + 2)^2 + 3

plot_data <- tibble(grid, y)

maxdrohde

library(tidyverse)
library(gganimate)
library(magick)

# Number of Bern trials to run
n_trials <- 100

# Grid for plotting posterior
x <- seq(0,1, length.out = 500)

maxdrohde

library(tidymodels)
library(tidyverse)
library(discrim)
library(gganimate)

# grid size
n <- 100

parabolic_grid <-
  expand.grid(X1 = seq(-5, 5, length = n),

maxdrohde

library(tidyverse)

set.seed(1)

df <- tibble(x=rnorm(2000),
             y=rnorm(2000)) %>%
  mutate(flag = x+y > 1.5)

df %>%
  ggplot() +

maxdrohde

library(tidyverse)
library(gganimate)

set.seed(777)

points <- 2* 1e3

x <- runif(points, min = -1, max=1)
y <- runif(points, min=-1, max=1)
inside <- x^2 + y^2 < 1

maxdrohde

# Using manim community edition

from manim import *

config.frame_width = 22

text1 = MathTex(r"\begin{bmatrix} 3 & 9 & 5 \\ 9 & 4 & 2 \\ 7 & 3 & 6 \end{bmatrix} \begin{bmatrix} 5 & 7 & 2 & 4 \\ 3 & 6 & 9 & 8 \\ 2 & 7 & 1 & 4 \end{bmatrix}")

text2 = MathTex(r"\begin{bmatrix} 3 & 9 & 5 \\ 9 & 4 & 2 \\ 7 & 3 & 6 \end{bmatrix} \begin{bmatrix} 5 & 7 & 2 & 4 \\ 3 & 6 & 9 & 8 \\ 2 & 7 & 1 & 4 \end{bmatrix}", r"&=", r"\begin{bmatrix} 3 \\ 9 \\ 7 \end{bmatrix} \begin{bmatrix} 5 & 7 & 2 & 4 \end{bmatrix} + \begin{bmatrix} 9 \\ 4 \\ 3 \end{bmatrix} \begin{bmatrix} 3 & 6 & 9 & 8 \end{bmatrix} + \begin{bmatrix} 5 \\ 2 \\ 6 \end{bmatrix} \begin{bmatrix} 2 & 7 & 1 & 4 \end{bmatrix} \\")

maxdrohde

gen_dataset <- function(){
  b0 <- 3
  b1 <- 7
  x <- 1:10
  linear_predictor <- b0 + b1*x
  y <- rnorm(n = length(linear_predictor), mean=linear_predictor, sd=8)
  df <- tibble(x,y)
  
  # Fit OLS model
  ols_model <- lm(y ~ x, data=df)