HarryZhu's Blog

Build A Blog in the R Way: Jekyll + Rmarkdown + RStudio

Sat, 30 Jan 2016 00:00:00 +0000

Thanks for Yihui Xie and his GitHub repo, I find a good way to write blog in Rmarkdwon, which could support dynamic documentation, and display code and result simutaniously, and the command is pretty simple in RStudio.

servr::jekyll()

Now, this blog is hosted by GitHub, which provide a free jekyll server for bloger.

My main editor switch from Atom to RStudio, which give me a more powerful computation and interactive environment.

Reading Notebook with Advanced R by Hadley Wickham

Wed, 27 Jan 2016 00:00:00 +0000

the difference between closure and builtin

example:

f <- function(){}
typeof(f)

## [1] "closure"

# > "closure"
typeof(sum)

## [1] "builtin"

# > "builtin"

mode() is just a shadow of typeof()

To see if an object is a pure base type, i.e., it doesn’t also have S3, S4, or RC behaviour, check that is.object(x) returns FALSE.

Check Static Class by otype() and ftype()

In S3, methods belong to functions, called generic functions, or generics for short. S3 methods do not belong to objects or classes. This is different from most other programming languages, but is a legitimate OO style.

x = seq(10)
y = rep(1,10)
t = data.frame(x,y)
otype(x)

## Error in eval(expr, envir, enclos): could not find function "otype"

# > "base"
otype(t)

## Error in eval(expr, envir, enclos): could not find function "otype"

# > "S3"

ftype(sum)

## Error in eval(expr, envir, enclos): could not find function "ftype"

# > [1] "primitive" "generic"

ftype(mean)

## Error in eval(expr, envir, enclos): could not find function "ftype"

# > [1] "s3"      "generic"

methods("mean")

## [1] mean.Date     mean.default  mean.difftime mean.POSIXct 
## [5] mean.POSIXlt 
## see '?methods' for accessing help and source code

# > [1] mean.Date     mean.default  mean.difftime mean.POSIXct  mean.POSIXlt

foo <- structure(list(), class = "foo")

# 1. Pipe to first-argument of function
rnorm(100) %>>%
  plot

## Error in eval(expr, envir, enclos): could not find function "%>>%"

# 
# 2. Pipe to . in an expression
# 
rnorm(100) %>>%
  plot(col="red", main=length(.))

## Error in eval(expr, envir, enclos): could not find function "%>>%"

# pass left side result into right side point.

a[1, , drop = FALSE]

Improve Your R Engine

Sun, 10 Jan 2016 00:00:00 +0000

Today, I am introducing a power tool to improve your R engine performance without changing any R code in lots of features, like parallel, pipeline operation, compatible with Rstudio. Revolution R Open executes a typical R benchmark 2.5 times faster than the standard R distribution and some functions, such as linear regression, run up to 20 times faster.

Here is the download link for Revolution R Open.

# Revolution R Open 3.2.2
# Default CRAN mirror snapshot taken on 2015-08-27
# The enhanced R distribution from Revolution Analytics
# Visit mran.revolutionanalytics.com/open for information
# about additional features.
# 
# Multithreaded BLAS/LAPACK libraries detected. Using 4 cores for math algorithms.

Making an Import Wheel in R

Tue, 05 Jan 2016 00:00:00 +0000

When we switch from a *nix development environment to a Windows one, or reverse the process, we have to be confront with the dependancies problem.

In that, despite of the previous wheel packrat existing, which is built by Rstudio, a robust tool for virtualizing the environment, I am glad to make another wheel, an import function, which inspired from Python.

This import function will load the dependancies, and install these uninstalled one. These will improve the environment problem.

import <- function(packages){
  for (i in 1:length(packages)){
    package = packages[i];
    char_package = package;
    # char_package <- as.character(substitute(char_package))
    loaded <- paste("package", char_package, sep = ":") %in% search() #

    # is it existing the package
    if(loaded){
      library(char_package,character.only = TRUE)
      cat(paste0("load  ",char_package," package"),"\n")
    }
    else{
      cat(paste0("can not find ",char_package,", we need to install it"),"\n");
      install.packages(package);
      library(char_package,character.only = TRUE)
    }
  }
}
# import dependencies

dependencies = c("RMySQL","ggplot2","httr")
import(dependencies)

## can not find RMySQL, we need to install it

## Warning: unable to access index for repository https://
## mran.revolutionanalytics.com/snapshot/2015-08-27/src/contrib

## Warning: package 'RMySQL' is not available (for R version 3.2.2)

## Loading required package: DBI
## Loading required package: methods

## can not find ggplot2, we need to install it 
## 
## The downloaded binary packages are in
## 	/var/folders/f2/9jwh0h8s4y70r1jl3s7cq_5c0000gn/T//Rtmpqbv9uE/downloaded_packages
## can not find httr, we need to install it 
## 
## The downloaded binary packages are in
## 	/var/folders/f2/9jwh0h8s4y70r1jl3s7cq_5c0000gn/T//Rtmpqbv9uE/downloaded_packages

Using R and Google Sheets

Sun, 03 Jan 2016 00:00:00 +0000

Using R and Google Sheets

Today, I meet a big problem about my ERP system built in Google Sheets, and I don’t want to shape a too complicated one such as WordPress or Django.

In that, inspired by gspread, a python package, I am trying googlesheets package, a counterpart of another R package.

Firstly, download the git repository and install.

library(devtools)
devtools::install_github("hadley/readr")

## Downloading GitHub repo hadley/readr@master

## Error in curl::curl_fetch_memory(url, handle = handle): Timeout was reached

devtools::install_github("jennybc/googlesheets")

## Downloading GitHub repo jennybc/googlesheets@master
## Installing googlesheets
## Skipping 5 packages ahead of CRAN: dplyr, jsonlite, mime, Rcpp, tidyr
## '/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file  \
##   --no-environ --no-save --no-restore CMD INSTALL  \
##   '/private/var/folders/f2/9jwh0h8s4y70r1jl3s7cq_5c0000gn/T/RtmpN3HfZO/devtoolsc3c37718540a/jennybc-googlesheets-067e4a3'  \
##   --library='/Library/Frameworks/R.framework/Versions/3.2/Resources/library'  \
##   --install-tests

If you’re network blocked, you could download manually.

$git clone $git@github.com:jennybc/googlesheets.git
$R CMD INSTALL googlesheets

Now, we could manipulate googlesheets with R.

library(googlesheets)
gs_ls()                           # authenticated in a popup website

## Error: oauth_listener() needs an interactive environment.

ss <- gs_title("My_December_Table") # read a SpreadSheet from your accout

## Error: oauth_listener() needs an interactive environment.

gs_read(bb,ws=3,range="A1:D20")    # read a sheet, ws means WorkSheet number

## Error in inherits(ss, "googlesheet"): object 'bb' not found

bb <- gs_edit_cells(ss, input = head(iris), trim = TRUE) # edit the table

## Error in inherits(ss, "googlesheet"): object 'ss' not found

gs_read(ss)

## Error in inherits(ss, "googlesheet"): object 'ss' not found

the more method you can see the official manual and shiny exampl!

Reference:Jennifer in UsingR Conference 2015

Related Shiny Examples