han-tun

Basic nflscrapR tutorial

I get a lot of questions about how to get nflscrapR up and running. This guide is intended to help new users build interesting tables or charts from the ground up, taking the raw nflscrapR data.

Quick word if you're new to programming: all of this is happening in R. Obviously, you need to install R on your computer to do any of this. Make sure you save what you're doing in a script (in R, File --> New script) so you can save your work and run multiple lines of code at once. To run code from a script, highlight what you want, right click, and select Run line. As you go through your R journey, you might get stuck and have to google a bunch of things, but that's totally okay and normal. That's how I wrote this thing!

A huge thank you to Josh Hornsby (@Josh_ADHD) and Zach Feldman (@ZachFeldman3) for sharing code snippets for me to work with, and of course to the nflscrapR team ([Maksim Horowitz](https://twit

han-tun

pip install streamlit
pip install spacy
python -m spacy download en_core_web_sm
python -m spacy download en_core_web_md
python -m spacy download de_core_news_sm

han-tun

library(tidyverse)
library(gganimate)

logistic_map <- function(r, steps = 500, init = .5) {
  x <- vector('numeric', steps)
  x[1] <- init
  for (n in 2:steps) x[n] <- r * x[n-1] * (1 - x[n-1])
  return(unique(round(tail(x, -100), 4)))
}

han-tun

library(tidyverse)

# The actual dataset has ~3000 columns and 100ish rows per column
# Each column refers to a country and a state within that country
# There is also an "average" for the country column
# What is the best approach to grabbing ONLY the average column

bad_df <- tibble::tribble(
  ~category,    ~usa_1,  ~usa_2,            ~usa_3, ~canada_1,  ~canada_2,         ~canada_13,
         NA, "Alabama", "Texas", "Country Average", "Ontario", "Manitoba", "Country Average",

han-tun

walk_source <- function(
  file = NULL, 
  delay = 1.5, 
  clear_each_cmd = TRUE,
  style = styler::tidyverse_style
) {
  options("walk_source_cancel" = FALSE)
  if (is.null(file)) {
    txt <- rstudioapi::getSourceEditorContext()$contents
  } else {

han-tun

/*SAS PROC LIFETEST Survival Plot Template

Modifed by: F. Khurshed
Date: July 27, 2011

Description: I modified the basic GTL plot to 
1) Changed the thickness of the lines plotted
2) Changed the X axis markers to 12 month intervals
3) Changed the Y axis markers to 0.25 level increments
4) Changed the label of the legend to be a macro variable called legend_label. This needs to be specified prior to output

han-tun

/*SAS PROC LIFETEST Survival Plot Template

Modifed by: F. Khurshed
Date: July 27, 2011

Description: I modified the basic GTL plot to 
1) Changed the thickness of the lines plotted
2) Changed the X axis markers to 12 month intervals
3) Changed the Y axis markers to 0.25 level increments
4) Changed the label of the legend to be a macro variable called legend_label. This needs to be specified prior to output

han-tun

library(cartography)
library(sf)
mtq <- st_read(system.file("gpkg/mtq.gpkg", 
                           package="cartography"))

bks <- getBreaks(v = mtq$MED, nclass = 5, 
                 method = "quantile")
cols <- carto.pal(pal1 = "turquoise.pal", n1 = 5)
choroLayer(x = mtq, var = "MED", 
           breaks = bks, col = cols, 

han-tun

# Code for quick exploration of
# https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-05-26
# Video at https://youtu.be/kHFmtKCI_F4

library(tidyverse)

cocktails <- readr::read_csv("boston_cocktails.csv")


# Are name and row_id equivalent? -----------------------------------------

han-tun

---
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r message=FALSE, warning=FALSE}
library(tidyverse)