EvaMaeRey

compute_panel_ols_ind <- function(data, scales) {
  
  model <- lm(y ~ x*indicator,
              data = data)
  
  data.frame(x = data$x,
             y = model$fitted.values,
             indicator = data$indicator)
  
}

EvaMaeRey

compute_panel_ols_ind <- function(data, scales) {
  
  model <- lm(y ~ x*indicator,
              data = data)
  
  data.frame(x = data$x,
             y = model$fitted.values,
             indicator = data$indicator)
  
}

EvaMaeRey

compute_panel_ols_ind <- function(data, scales) {

  data$indicator = factor(indicator)
  
  model <- lm(y ~ x + indicator,
              data = data)

  data.frame(x = data$x,
             y = model$fitted.values,
             indicator = data$indicator)

EvaMaeRey

tabyl_flatten <- function(tabyl3d){
  
  my_tibble <- tibble()
  
  for (i in 1:length(tabyl3d)){
    
    meta_name <- names(tabyl3d)[i]
  
    my_tibble %>% 
    bind_rows(tabyl3d[[i]] %>% 

EvaMaeRey

library(tidyverse) 
library(gapminder) 

gapminder %>% # data from package 
  filter(year == 2002) -> # filtering 
gapminder_2002 # saving subset object 

gapminder_2002 %>% 
  filter(continent == "Europe") -> 
gapminder_2002_europe 

EvaMaeRey

# RStudio overrides `history()`, `loadhistory()` and `savehistory()`, 
# so they work somewhat differently than on the console version of R.
# This saves the current history to a temporary file then reads the last
# line of it (or more depending on the `n` argument)
#
# It does not handle multi-line commands, which would be more tricky to handle...
previous_commands <- function(n = 1) {
  tf <- tempfile()
  on.exit(unlink(tf))
  savehistory(tf)

EvaMaeRey

# 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:

EvaMaeRey

library(rvest)
library(tidyverse)
library(stringr)
library(lubridate)
library(ggrepel)

pres_terms <- 
  read_html("https://www.presidentsusa.net/presvplist.html") %>%
  html_nodes("table") %>%
  pluck(1) %>%

EvaMaeRey

#webscraping livrustkammaren to create a age-tenure graph of Swedish regents
#inspired by https://gist.github.com/acoppock

library(rvest)
library(tidyverse)
library(stringr)
library(lubridate)
library(ggrepel)
library(stringr)

EvaMaeRey

---
title: "ggplot tutorial"
subtitle: "with Emi Tanaka's kunoichi + ninjutsu theme"
author: "<br><br>Gina Reynolds"
date: "<br>2018/09/16"
output:
  xaringan::moon_reader:
    chakra: libs/remark-latest.min.js
    lib_dir: libs
    css: ["kunoichi", "ninjutsu"]