This gist contains some useful stuff for profiles.
Currently:
- bash
- R
- Visual Studio code
library(dplyr) | |
library(ggplot2) | |
probs <- c(home = 0.35, draw = 0.4, away = 0.25) | |
points <- c(home = 0, draw = 1, away = 3) | |
n_sim <- 1e4 | |
n_games <- 11 | |
samples <- sample(points, size = n_sim * n_games, prob = probs, replace = TRUE) |
library(dplyr) | |
library(ggplot2) | |
length_army <- 60 | |
army_front <- function(x) (length_army * x) + length_army | |
army_back <- function(x) (length_army * x) | |
velocity_king <- length_army + length_army * sqrt(2) |
data { | |
int<lower=0> N; // N games | |
int<lower=0> P; // P teams | |
// Each team is referred to by an integer that acts as an index for the ratings vector. | |
int team1[N]; // Indicator arrays for team 1 | |
int team2[N]; // Indicator arrays for team 1 | |
int results[N]; // Results. 1 if home win, 2 if draw, 3 if away win. | |
real<lower=0> nu_sigma; |
""" | |
A python script to test strategies for the for the riddle described on TED-ed's | |
youtube channel: www.youtube.com/watch?v=dh4nEuhZBgg | |
Your interstellar police squad has tracked a group of criminals to a cluster of | |
seven planets. Now you must apprehend them before their reinforcements arrive. | |
Of course, the fugitives won’t just stay put – they’ll try to dodge you by | |
moving from planet to planet. Can you devise a sequence for searching the | |
planets that’s guaranteed to catch them in ten warps or less? | |
Edwin F. Meyer shows how. |
library(tidyverse) | |
library(gganimate) | |
library(ggforce) | |
library(tweenr) | |
# Making up data | |
n_balls <- 20 | |
d <- tibble(x = rnorm(n_balls), | |
y = rnorm(n_balls), | |
time = sample(100, n_balls), |
library(tidyverse) | |
library(tidytext) | |
# Load data ---- | |
# Slightly different to https://juliasilge.com/blog/word-vectors-take-two/ | |
# just because I have this data locally | |
austen_text <- janeaustenr::northangerabbey %>% | |
as_tibble() %>% |
log <- function(f, ...) { | |
function_name <- as.character(substitute(f)) | |
dots <- list(...) | |
args <- paste0(dots, collapse = ",") | |
function_result <- f(...) | |
print(stringr::str_glue("{function_name}({args}) -> {function_result}")) | |
function_result |
library(tidyverse) | |
stirling_formula <- function(n) sqrt(2*pi*n)*(n / exp(1))^n | |
x <- 1:10 | |
breaks_log10 <- 1:10 %>% | |
map_dbl(~ 10^.x) %>% | |
map(~ .x*(1:10)) %>% | |
unlist() |
This gist contains some useful stuff for profiles.
Currently:
library(tidyverse) | |
shot_counts <- read_csv(here::here("data/shot_counts.csv")) | |
# Interpolate points | |
shot_counts_tween <- shot_counts %>% | |
filter(season < 2018) %>% | |
group_by(season) %>% | |
arrange(desc(count)) %>% | |
mutate(rank = row_number()) %>% |