core_commons.R 8.41 KB
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########################################################################################################################
## set a default cran r mirror  and customize environment

#cat(".Rprofile: Setting Goettingen repository\n")
r = getOption("repos") # hard code the UK repo for CRAN

r["CRAN"] = "http://ftp5.gwdg.de/pub/misc/cran/"
options(repos = r)
rm(r)


## user browser for help
options(help_type="html")

## plot more characters per line
options(width=150)

# for sqldf to avoid the use of tckl
options(gsubfn.engine = "R")

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########################################################################################################################
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## automatic package installation
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require.auto <-  function(x){
    x <- as.character(substitute(x))

    if(isTRUE(x %in% .packages(all.available=TRUE))) {
        eval(parse(text=paste("require(", x, ",  quietly=T)", sep="")))
    } else {
    #        update.packages(ask=F) # update dependencies, if any.
        eval(parse(text=paste("install.packages('", x, "')", sep="")))
    }

    if(isTRUE(x %in% .packages(all.available=TRUE))) {
        eval(parse(text=paste("require(", x, ",  quietly=T)", sep="")))
    } else {
        source("http://bioconductor.org/biocLite.R")
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#        biocLite(character(), ask=FALSE) # update dependencies, if any.
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        eval(parse(text=paste("biocLite('", x, "')", sep="")))
        eval(parse(text=paste("require(", x, ",  quietly=T)", sep="")))
    }
}

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########################################################################################################################
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## load core packages
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require.auto(plyr)
require.auto(stringr)
require.auto(reshape2)
#require.auto(reshape2, quietly=T, warn.conflicts=F)

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## load on purpose after plyr
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require.auto(dplyr)
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require.auto(magrittr)
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require.auto(tidyr)
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## needed for caching
require.auto(digest)

## moved into datatable_commons because replaced almost everywhere with dplyr
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#require.auto(data.table)




########################################################################################################################
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#### Convenience aliases
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echo <- function(...) cat(paste(...), fill=T)
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ac <- function(...) as.character(...)

# string concatenation without space gaps (could/should use paste0 instead)
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## Deprecated: use paste0 instead
#concat <- function(...) paste(..., sep="")
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unlen <- function(x) length(unique(x))

pp <- function(dat) page(dat, method = "print")

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as.df <- function(dt) as.data.frame(dt)

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########################################################################################################################
#### data.frame manipulation


shuffle <- function(df) df[sample(nrow(df)),]

first <- function(x, n=1) head(x,n)



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#vec2df <- function(namedVec) namedVec %>% {data.frame(name=names(.), value=., row.names=NULL)}
vec2df <- function(namedVec) data.frame(name=names(namedVec), value=namedVec, row.names=NULL)

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rownames2column <- function(df, colname){
    df <- as.df(df)
    df$tttt <- rownames(df);
    rownames(df) <- NULL;
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    plyr::rename(df, c(tttt=colname))
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}


column2rownames<- function(df, colname){
#browser()
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    ## force into df to avoid dplyr problems
    df <- as.df(df)

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    rownames(df) <- ac(df[,colname])
    df[colname] <- NULL
    return(df)
}

## pushing some columns to the right end of a data.frame
push_right <- function(df, pushColNames){
    df[,c(setdiff(names(df),pushColNames), pushColNames)]
}


push_left <- function(df, pushColNames){
    df[,c(pushColNames, setdiff(names(df),pushColNames))]
}


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#http://astrostatistics.psu.edu/datasets/R/html/base/html/formals.html
set_names <- function(df, ...){
#browser()
    newnames <- as.character(unlist(list(...)))
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    ## automatically convert matrices to data.frames (otherwise the names set would fail
    if(is.matrix(df)) df %<>% as.data.frame()

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    names(df) <- newnames;
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    return(df)
}
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#iris %>% set_names(c("setosa", "hallo")) %>% head
#iris %>% set_names("setosa", "hallo") %>% head
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head_html <- function(df, n=5) head(df, n) %>% knitr::kable(format="html") %>% print()

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print_head <- function(df, desc=NULL){
    print(head(df))
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    print(nrow(df))
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    return(df)
}


fac2char <- function(mydata, convert=names(mydata)[sapply(mydata, is.factor)]){
    if(length(convert)==0)
        return(mydata)

    inputColOrder <- names(mydata)

    convertData <- subset(mydata, select= names(mydata)%in%convert)
    convertData <- as.data.frame(lapply(convertData, as.character), stringsAsFactors = FALSE)

    keepData <-  subset(mydata, select=!(names(mydata)%in%convert))
    newdata <- cbind(convertData, keepData)
    newdata <- newdata[,inputColOrder]

    return(newdata)
}

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## replace R within pipe change just use ... %>% do(replaceNA(0)) %>% ...
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replaceNA <- function(x, withValue) { x[is.na(x)] <- withValue; x }
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## workaround for biomart
## Deprecated: load dplyr after biomart to avoid this problem
#dselect <- function(...) dplyr::select(...)

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########################################################################################################################
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#### Result Caching for long running tasks
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## related: http://cran.r-project.org/web/packages/R.cache/R.cache.pdf

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cache_it <- function(expr, filePrefix="cache"){
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    cacheFile <- paste0(filePrefix, "_", substr(digest::digest(deparse(expr)), 1,6)) %>% paste0(".", ., ".RData")
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    if(file.exists(cacheFile)){
        local(get(load(cacheFile)))
    } else {
        result <- eval(expr)
        save(result, file=cacheFile)
        result
    }
}

## Examples
#mydata <- quote(iris %>% filter(Species=="setosa")) %>% cache_it("tt")
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#mydata <- quote(iris %>% filter(Species=="setosa")) %>% cache_it()
#mydata <- quote( { print("evaluate expr"); iris %>% filter(Species=="setosa") } ) %>% cache_it()
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########################################################################################################################
#### File System

is.directory <- function(dirname) !is.na(file.info(dirname)$isdir)


mcdir <- function(dirname){
    if(!file.exists(dirname)){
        dir.create(dirname)
    }

    setwd(dirname)
}

locload <- function(fileName) local(get(load(fileName)))


rmerge <- function(LDF, by, ...){
    DF <- LDF[[1]]
    for (i in 2:length(LDF)) {
        DF <- merge(DF, LDF[[i]], by=by)
    }
    DF
}



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## Deprecated: use trim_ext instead
trimEnd <- function(fileNames, ...) trim_ext(fileNames, ...)
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trim_ext <-function(fileNames, exts=c()){
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    for(fileExt in exts){
        fileNames <- str_replace(fileNames, paste(fileExt, "$",sep=""), "")
    }

    fileNames
}

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write.delim <- function(df, file, header=TRUE,...){
    write.table(df, file, row.names=FALSE, col.names=header, sep="\t", ...)
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}

rmSomeElements <- function(vec, toDel) vec[!(vec %in% toDel)]

rmLastElement <- function(vec) vec[-length(vec)]


########################################################################################################################
## Memory management

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# improved list of objects
lsos <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    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)/1000000
    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")
    if (!missing(order.by))
        out <- out[order(out[[order.by]], decreasing=decreasing), ]
    if (head)
        out <- head(out, n)
#        out
    out <- transform(out, var_name=rownames(out))
    rownames(out) <- NULL
    arrange(out, Size)
}

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# shorthand that just shows top 1 results
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lsosh <- function(..., n=10) {
    lsos(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}

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########################################################################################################################
### Statistics


## outlier handling
trim_outliers <- function(values, range=quantile(values, c(0.05, 0.95)))  pmax(range[1], pmin(range[2], values))

## use trim_outliers instead
#limit_range <- function(values, range)  pmax(range[1], pmin(range[2], values))

se<-function(x)	sd(x, na.rm=TRUE) / sqrt(sum(!is.na(x)))