rich-iannone

---
output: html_document
---

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

rich-iannone

library(gt)
library(tidyverse)

color_sequence <- seq_along(grDevices::colors())

dplyr::tibble(
  x11c = grDevices::colors(),
  wcag = color_sequence,
  apca = color_sequence
) %>%

rich-iannone

# Install the latest version of DiagrammeR from GitHub
devtools::install_github("rich-iannone/DiagrammeR")

# Ensure that the package is loaded
library("DiagrammeR")

# So, here's a script that essentially takes an empty graph series, and
# creates a new graph on each new day it is triggered. It will create
# random nodes each time it's triggered and add those nodes to the graph
# belonging to the current day. Throughout the script, '_SELF_' refers

rich-iannone

# Installation
#install.packages("devtools")
devtools::install_github("rich-iannone/DiagrammeR")

library(DiagrammeR)

# Create a graph of border coordinates for USA, Canada, and Mexico
country_graph <- country_graph(iso_a2 = c("US", "CA", "MX"))

# Read in a CSV with US city data

rich-iannone

# Installation
#install.packages("devtools")
devtools::install_github("rich-iannone/DiagrammeR")

library(DiagrammeR)

# Create a graph of border coordinates
country_graph <- country_graph()

# Read in a CSV with location and population data for cities all over the world

rich-iannone

library(gt)
library(tidyverse)

countrypops |>
  dplyr::filter(year == 2021) |>
  dplyr::select(country_code_2, population) |>
  dplyr::mutate(population_class = cut(
    population,
    breaks = scales::breaks_log(n = 20)(population)
  )) |>

rich-iannone

# Create a simple data frame for testing
df <- data.frame(POSIXtime = seq(as.POSIXct('2013-08-02 12:00'),
                             as.POSIXct('2013-08-06 05:00'), len = 45),
                 x = seq(45))

# The Subset Examples
#
# All data on 2013-08-06
sub.1 <- subset(df, format(POSIXtime,'%d')=='06')

rich-iannone

library(gt)
library(tidyverse)

# Table reading
news_orgs <-
  readr::read_csv(
    file = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-04-05/news_orgs.csv',
    col_types = cols(
      publication_name = col_character(),
      parent_publication = col_character(),

rich-iannone

library(tidyverse)
library(paletteer)
library(gt)

pizzaplace %>%
  mutate(type = case_when(
    type == "chicken" ~ "chicken (pizzas with chicken as a major ingredient)",
    type == "classic" ~ "classic (classical pizzas)",
    type == "supreme" ~ "supreme (pizzas that try a little harder)",
    type == "veggie" ~ "veggie (pizzas without any meats whatsoever)",

rich-iannone

library(DiagrammeR)
library(DiagrammeRsvg)
library(magrittr)

# The CSV file is located inside a zip file at:
# https://www.kaggle.com/kaggle/sf-salaries/downloads/
# sf-salaries-release-2015-12-21-03-21-32.zip
salaries <- read.csv("Salaries.csv",
                     stringsAsFactors = FALSE)