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)) + 

jthomasmock

library(gt)
library(gtExtras)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':

gadenbuie

gist_to_carbon <- function(
  gist_id,
  file = "code.png",
  bg = "#4A90E2",
  bg_alpha = 1,
  theme = "shades-of-purple",
  font = "Fira+Code",
  lang = "auto",
  drop_shadow = TRUE,
  width = 680,

mrcaseb

library(magrittr)

df <- tibble::tibble(
  var_a = c("a", "b", "c", "d"),
  var_b = c(1.234, 12.34, 123.4, 1234)
)

num_to_html <- function(num, separator = ".") {
  stringr::str_split_fixed(num, stringr::fixed(separator), n = 2) %>%
    as.data.frame() %>%

nntrn

List of NFL API Endpoints

This page has been updated a lot in the past 3 years. Older revisions you might like more than this one:

• June 2021 - list of endpoints for other sports/leagues (i.e. basketball, baseball, lacrosse, rugby)
• August 2021 - get historical fantasy league data
• September 2021 - list of endpoints in plain text
• May 2023 - collapsed endpoint response examples

Additional Resources

gadenbuie

library(tweetrmd)
library(htmltools)
library(reactable)

x <- tibble::tibble(
  name = c("Tom", "Julia"),
  handle = c("@thomas_mock", "@b0rk"),
  tweet = c(
    "https://twitter.com/thomas_mock/status/1294670115590545408",
    "https://twitter.com/b0rk/status/1295029728127135746"

johnburnmurdoch

read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv") %>%
  gather(date, cases, 5:ncol(.)) %>%
  mutate(date = as.Date(date, "%m/%d/%y")) %>%
  group_by(country = `Country/Region`, date) %>%
  summarise(cases = sum(cases)) %>%
  filter(country != "Others" & country != "Mainland China") %>%
  bind_rows(
    tibble(country = "Republic of Korea", date = as.Date("2020-03-11"), cases = 7755)
  ) %>%
  group_by(country) %>%

guga31bb

Make box scores with EPA and other stuff

Code below. To get the game you want, need to choose season, type (regular, postseason, etc), home team, away team, and the rest will go on itself.

Code from @benbbaldwin

library(nflscrapR)
library(tidyverse)
library(na.tools)

johnburnmurdoch

# Load the packages we’re going to be using:
# Alongside the usual stuff like tidyverse and magrittr, we’ll be using rvest for some web-scraping, jsonline to parse some JSON, and extrafont to load some nice custom fonts
needs(tidyverse, magrittr, rvest, jsonlite, extrafont)

# Before we go on, two things to note:

# First, on web scraping:
# You should always check the terms of the site you are extracting data from, to make sure scraping (often referred to as `crawling`) is not prohibited. One way to do this is to visit the website’s `robots.txt` page, and ensure that a) there is nothing explicitly stating that crawlers are not permitted, and b) ideally, the site simply states that all user agents are permitted (indicated by a line saying `User-Agect: *`). Both of those are the case for our use-case today (see https://www.ultimatetennisstatistics.com/robots.txt).

# And second, about those custom fonts:

adamconroy

body {
    background-color: #1d1d1d;
    color: #e2e2e2;
    font-family: -apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";
    line-height: 1.5;
}

a {
    color: #509eeb;
}