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Nesting and summarizing data is a very common task for data analysis. I thought it would be nice to view parallel ways of nesting and summarizing with both
tidyr and dplyrd3.js ...v4 for funTo avoid context switching, I'll take advantage of the R package V8. If you are an R user, then these d3.js gists might be helpful d3 nest examples and Mister Nester.
| library(DiagrammeR) | |
| library(magrittr) | |
| library(visNetwork) | |
| moments <- | |
| 'https://gist.githubusercontent.com/rich-iannone/9c4e50cd5d811b841349/raw/65bc97ddac2f9e52a4252fcf2060007fe1afeb7d/moment.csv' %>% | |
| read_csv() | |
| # Generate the court | |
| court <- |
| # 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 |
| # Set a seed | |
| set.seed(500) | |
| library(MASS) | |
| data <- Boston | |
| # Check that no data is missing | |
| apply(data,2,function(x) sum(is.na(x))) | |
| # Train-test random splitting for linear model |
Although very handy and useful, I think most new R developers do not even know that tables exist. They are a little tricky to use, but they offer some nice functionality. These tables become even more useful when we can visualize them as networks. This little bit of code is a rough first attempt at converting tables to network nodes and edges.
| ### Title: Back to basics: High quality plots using base R graphics | |
| ### An interactive tutorial for the Davis R Users Group meeting on April 24, 2015 | |
| ### | |
| ### Date created: 20150418 | |
| ### Last updated: 20150423 | |
| ### | |
| ### Author: Michael Koontz | |
| ### Email: mikoontz@gmail.com | |
| ### Twitter: @michaeljkoontz | |
| ### |
| #devtools::install_github("rstudio/leaflet", ref="feature/color-legend") | |
| library(leaflet) | |
| library(RColorBrewer) | |
| set.seed(100) | |
| pdf <- data.frame(Latitude = runif(100, -90,90), Longitude = runif(100, -180,180)) | |
| #make a property with colors | |
| pdf$Study <- rep(1:10,10) | |
| #need to create a pal using colorbin |
| # Get the latest version of 'DiagrammeR' | |
| install_github("rich-iannone/DiagrammeR") | |
| # Create a 'nodes' data frame | |
| nodes <- sample(1:100, 100, replace = FALSE) | |
| style <- rep("filled", 100) | |
| fillcolor <- c(rep("turquoise", 20), rep("pink", 20), | |
| rep("ochre", 20), rep("gray40", 20), |
| # Reading in 'dogs.csv' | |
| # Created by Alex Bresler, available at: | |
| # https://github.com/abresler/abresler.github.io/blob/master/blog/2015/february/exploring_agility_show/data/dogs.csv | |
| dogs <- read.csv("dogs.csv", header = TRUE, stringsAsFactors = FALSE) | |
| # Setting a numeric id | |
| node_id <- 1:nrow(dogs) | |
| # Getting unique values for the 'breed' column | |
| unique_node_2 <- unique(dogs$breed) |
| ## Analyze R packages for popularity, using pagerank algorithm | |
| # Inspired by Antonio Piccolboni, http://piccolboni.info/2012/05/essential-r-packages.html | |
| library(miniCRAN) | |
| library(igraph) | |
| library(magrittr) | |
| # Download matrix of available packages at specific date ------------------ |