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# Display data frame as a nicely formatted and interactive GT table | |
display <- function(df){ | |
df |> | |
gt::gt() |> | |
gt::opt_interactive(use_search = TRUE, | |
use_compact_mode = TRUE, | |
use_text_wrapping = TRUE) |> | |
gt::opt_stylize(style = 2) |> | |
gt::cols_width(everything() ~ px(250)) | |
} |
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library(ggplot2) | |
library(patchwork) # For composing plots | |
# See https://patchwork.data-imaginist.com/ | |
library(ggtext) # For formatting text using markdown syntax | |
# See https://wilkelab.org/ggtext/ | |
# Make a ggplot histogram from a vector of data | |
gg_hist <- function(x){ | |
data.frame(x) |> |
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library(MASS) | |
library(plotrix) | |
library(tidyverse) | |
library(gganimate) | |
#################################### | |
## Example 1 (Data by Group with means and estimated means) | |
#################################### | |
## Number of groups, observations per group |
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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) |
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# 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} \\") |
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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 |
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library(tidyverse) | |
set.seed(1) | |
df <- tibble(x=rnorm(2000), | |
y=rnorm(2000)) %>% | |
mutate(flag = x+y > 1.5) | |
df %>% | |
ggplot() + |
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library(tidymodels) | |
library(tidyverse) | |
library(discrim) | |
library(gganimate) | |
# grid size | |
n <- 100 | |
parabolic_grid <- | |
expand.grid(X1 = seq(-5, 5, length = n), |
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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) |
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library(tidyverse) | |
library(glue) | |
library(gganimate) | |
grid <- seq(-10,5,length.out=1e4) | |
y <- (grid + 2)^2 + 3 | |
plot_data <- tibble(grid, y) |
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