rcquan

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

rcquan

Packages

• lxml - Pythonic binding for the C libraries libxml2 and libxslt.
• boto - Python interface to Amazon Web Services
• Django - Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
• Fabric - Library and command-line tool for streamlining the use of SSH for application deployment or systems administration task.
• PyMongo - Tools for working with MongoDB, and is the recommended way to work with MongoDB from Python.
• Celery - Task queue to distribute work across threads or machines.
• pytz - pytz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.4 or higher.

Guides

rcquan

library(png)
library(abind)
library(impute)

SplitImage <- function(rgb.array) {
    # decompose image into RGB elements
    rgb.list <- list()
    for (i in 1:dim(rgb.array)[3]) {
        rgb.list[[i]] <- rgb.array[, , i]
    }

rcquan

library(png)
library(abind)

ProjectImage <- function(prcomp.obj, pc.num) {
    # project image onto n principal components
    scores <- prcomp.obj$x
    loadings <- prcomp.obj$rotation
    img.proj <- scores[, c(1:pc.num)] %*% t(loadings[, c(1:pc.num)])
    return(img.proj)
}

rcquan

library(BiSeq)

readBedgraph <- function(inputDirectory, nSamples = 8) {

	## get list of file names
	fileNames <- list.files(path = inputDirectory, pattern = ".bedGraph")
	## parse sample name from file name
    	sampleNames <- sapply(fileNames,function(fileName) {
				gsub("_r123_R1_val_1.fq_bismark_bt2_pe.bedGraph", "", fileName)
				}, USE.NAMES = FALSE)

rcquan

library(RCurl)
library(XML)

escapeAmpersands <- function(url) {

	doc <- getURL(url)
	## escape ampersands in XML
	validDoc <- gsub("&", "&amp;", doc)
	validDoc
}

rcquan

# Check if file exists in working directory; if not, downloads
fileName = "medMis.csv"
if (!file.exists(fileName)){
        url <- "https://health.data.ny.gov/api/views/ebmi-8ctw/rows.csv?accessType=DOWNLOAD"
        fileName <- "medMis.csv"
        download.file(url, fileName, method = "curl")
}
medMis <- read.csv(fileName)

rcquan

# Convert variable from factor into "Date" class
medMis$Effective.Date <- as.character(medMis$Effective.Date)
medMis$Effective.Date <- as.POSIXct(medMis$Effective.Date, format = "%m/%d/%Y")

# Order dataset by Effective.Date from 1990-2014
medMis <- (medMis[order(medMis$Effective.Date),])

# Create time-series data 
medMis_month <- as.data.frame(table(format(medMis$Effective.Date, "%Y-%m-01")), stringsAsFactors = FALSE)
medMis_plot <- as.data.frame(table(format(medMis$Effective.Date, "%Y-%m")), stringsAsFactors = FALSE)

rcquan

# Time-series plot
library(ggplot2)
library(scales)

# By month
m = ggplot(data = medMis_month, aes(x = Time, y = Freq)) + geom_line()
m = m + scale_x_datetime(labels = date_format("%Y"), breaks = date_breaks("2 years"))
m = m + ylab("No. of Physicians Disciplined") + 
        ggtitle("Physicians Disciplined by NYS DOH from 1990-2013")
m

rcquan

library(rCharts)
library(zoo)

# Data transformations
names(medMis_plot) <- c("Month", "MedicalMisconductCount")
medMis_plot$Month <- as.yearmon(medMis_plot$Month)
medMis_plot <- transform(medMis_plot, Month = as.character(Month))

# Morris Line Plot
m1 <- mPlot(x = "Month", y = colnames(medMis_plot)[2], data = medMis_plot, type = "Line")