我总是发现其他人的创业简介文件对这门语言既有用又有指导意义。此外,虽然我对Bash和Vim进行了一些定制,但对R没有任何定制。

例如,我一直想要的一件事是在窗口终端中输入和输出文本的颜色不同,甚至可能是语法高亮显示。


当前回答

setwd("C://path//to//my//prefered//working//directory")
library("ggplot2")
library("RMySQL")
library("foreign")
answer <- readline("What database would you like to connect to? ")
con <- dbConnect(MySQL(),user="root",password="mypass", dbname=answer)

我用mysql数据库做了很多工作,所以马上连接是天赐良机。我只希望有一种方法可以列出可用的数据库,这样我就不必记住所有不同的名称。

其他回答

我在个人资料中设置了格子颜色主题。以下是我使用的另外两个调整方法:

# Display working directory in the titlebar
# Note: This causes demo(graphics) to fail
utils::setWindowTitle(base::getwd())
utils::assignInNamespace("setwd",function(dir)   {.Internal(setwd(dir));setWindowTitle(base::getwd())},"base")

# Don't print more than 1000 lines
options(max.print=2000)
sink(file = 'R.log', split=T)

options(scipen=5)

.ls.objects <- function (pos = 1, pattern, order.by = "Size", decreasing=TRUE, head =     TRUE, n = 10) {
  # based on postings by Petr Pikal and David Hinds to the r-help list in 2004
  # modified by: Dirk Eddelbuettel (http://stackoverflow.com/questions/1358003/tricks-to-    manage-the-available-memory-in-an-r-session) 
  # I then gave it a few tweaks (show size as megabytes and use defaults that I like)
  # a data frame of the objects and their associated storage needs.
  napply <- function(names, fn) sapply(names, function(x)
          fn(get(x, pos = pos)))
  names <- ls(pos = pos, pattern = pattern)
  obj.class <- napply(names, function(x) as.character(class(x))[1])
  obj.mode <- napply(names, mode)
  obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
  obj.size <- napply(names, object.size) / 10^6 # megabytes
  obj.dim <- t(napply(names, function(x)
            as.numeric(dim(x))[1:2]))
  vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
  obj.dim[vec, 1] <- napply(names, length)[vec]
  out <- data.frame(obj.type, obj.size, obj.dim)
  names(out) <- c("Type", "Size", "Rows", "Columns")
  out <- out[order(out[[order.by]], decreasing=decreasing), ]
  if (head)
    out <- head(out, n)
  out
}

我的不太花哨:

# So the mac gui can find latex
Sys.setenv("PATH" = paste(Sys.getenv("PATH"),"/usr/texbin",sep=":"))

#Use last(x) instead of x[length(x)], works on matrices too
last <- function(x) { tail(x, n = 1) }

#For tikzDevice caching 
options( tikzMetricsDictionary='/Users/cameron/.tikzMetricsDictionary' )

这是我的。我总是使用主要的cran存储库,并且有代码可以使它很容易地获得开发包中的代码。

.First <- function() {
    library(graphics)
    options("repos" = c(CRAN = "http://cran.r-project.org/"))
    options("device" = "quartz")
}

packages <- list(
  "describedisplay" = "~/ggobi/describedisplay",
  "linval" = "~/ggobi/linval", 

  "ggplot2" =  "~/documents/ggplot/ggplot",
  "qtpaint" =  "~/documents/cranvas/qtpaint", 
  "tourr" =    "~/documents/tour/tourr", 
  "tourrgui" = "~/documents/tour/tourr-gui", 
  "prodplot" = "~/documents/categorical-grammar"
)

l <- function(pkg) {
  pkg <- tolower(deparse(substitute(pkg)))
  if (is.null(packages[[pkg]])) {
    path <- file.path("~/documents", pkg, pkg)
  } else {
    path <- packages[pkg]
  }

  source(file.path(path, "load.r"))  
}

test <- function(path) {
  path <- deparse(substitute(path))
  source(file.path("~/documents", path, path, "test.r"))  
}

我有一个环境变量R_USER_WORKSPACE,它指向包的顶部目录。在. rprofile中,我定义了一个函数devlib,它设置了工作目录(以便data()工作),并在R子目录中获取所有.R文件。它与上面Hadley的l()函数非常相似。

devlib <- function(pkg) {
  setwd(file.path(Sys.getenv("R_USER_WORKSPACE", "."), deparse(substitute(pkg)), "dev"))
  sapply(list.files("R", pattern=".r$", ignore.case=TRUE, full.names=TRUE), source)
  invisible(NULL)
}

.First <- function() {
  setwd(Sys.getenv("R_USER_WORKSPACE", "."))
  options("repos" = c(CRAN = "http://mirrors.softliste.de/cran/", CRANextra="http://www.stats.ox.ac.uk/pub/RWin"))
}

.Last <- function() update.packages(ask="graphics")