| # Load the raw training data and replace missing values with NA | |
| training.data.raw <- read.csv('train.csv',header=T,na.strings=c("")) | |
| # Output the number of missing values for each column | |
| sapply(training.data.raw,function(x) sum(is.na(x))) | |
| # Quick check for how many different values for each feature | |
| sapply(training.data.raw, function(x) length(unique(x))) | |
| # A visual way to check for missing data |
| library(ggplot2) # devtools::install_github("hadley/ggplot2") or subtitles won't work | |
| library(tidyr) | |
| library(dplyr) | |
| library(readr) | |
| library(scales) | |
| URL <- "https://static01.nyt.com/newsgraphics/2016/04/21/undervote/ad8bd3e44231c1091e75621b9f27fe31d116999f/data.tsv" | |
| fil <- "nytimes_vote.tsv" | |
| if (!file.exists(fil)) download.file(URL, fil) |
| library(dplyr) | |
| library(tidyr) | |
| library(ggplot2) | |
| library(animation) | |
| #Data from https://crudata.uea.ac.uk/cru/data/temperature/ | |
| #As well as data read in script | |
| source("read_cru_hemi.R") | |
| temp_dat <- read_cru_hemi("./HadCRUT4-gl.dat") | |
| #remove cover |
A few ways to visualize 1D distributions.
Swarm and pile plots show all the data points but can't be used when there are a lot of data points. Histograms and boxplots work for any number of data points since they are visualizing summary statistics. Histograms provide more information than a boxplot. Boxplots fit in small spaces, making them nice for comparing many distributions side-by-side.
Other chart types for 1D distributions: violin plot,
This map shows the number of commuters to Dutch municipalities in 2012. Click on a municipality to see were the commuters working there are living.
| # | |
| # This Shiny web application demonstrates the use of custom image files | |
| # in place of icons for value boxes in Shiny Dashboard by overriding two | |
| # functions: | |
| # | |
| # 'icon' from the shiny package and 'valueBox' from the shinydashboard package. | |
| # | |
| # Each function adds minimal, specific additional handling of image files. | |
| # Note: A custom css file must also be included so that value boxes can | |
| # display the icons. For that reason, do not expect images in place of icons to |
| # Most of R, from an applied point of view anyway, is the process of creating objects and feeding them into functions to make amazing, | |
| # new objects. | |
| # amazing_new_object <- f(object) | |
| x <- c(3, 4, 5) | |
| y <- mean(x) | |
| # This is true in the big picture sense as well | |
| # information |
This is certainly not the intended purpose of mapedit, but I thought it would be fun to use some accumulated R knowledge to use mapedit and leaflet to edit an igraph layout. To run the code, make sure to run the edit_map and Shiny parts separately.
library(igraph)
library(leaflet)
library(mapedit)
karate <- graph.famous("Zachary")
igrf_layout <- layout.auto(karate)
| library(grImport2) | |
| library(grConvert) | |
| library(egg) | |
| convertPicture("noun_3663.svg", "balloon.svg") | |
| balloon <- readPicture("balloon.svg") | |
| d <- data.frame(x=1:9, y=rnorm(9), | |
| data = I(Map(function(c, s) list(c=c,s=s),c=blues9,s=1:9)), |
| # This function will walk you through the creation of a remote repository on github | |
| # without wasting your precious time going to the website :) | |
| # how: | |
| # - source it in your (bash|zsh)rc | |
| # - change the alias at the bottom of the script to your needs | |
| # - call it, follow instructions... profit | |
| function createRemoteGitRepository() { | |
| local RED='\033[0;31m' | |
| local NC='\033[0m' # No Color | |
| local GITHUB_USER='' |