R Notes

Installation

Install from source on Rocky

Following the instruction in RStudio Documentation: Install R from Source

export R_VERSION=X.X.X
curl -O https://cran.rstudio.com/src/base/R-4/R-${R_VERSION}.tar.gz
tar -xzvf R-${R_VERSION}.tar.gz
cd R-${R_VERSION}

Build with

./configure \
    --prefix=/opt/R/${R_VERSION} \
    --enable-memory-profiling \
    --enable-R-shlib \
    --with-blas \
    --with-lapack

make
sudo make install

then manage the version with module.

2022-08-24 10:32:20

On T460P, change the prefix as follows

./configure \
    --prefix=/home/weiya/Programs/R/${R_VERSION} \
    --enable-memory-profiling \
    --enable-R-shlib \
    --with-blas \
    --with-lapack

installation of module

Follow the official instruction

curl -LJO https://github.com/cea-hpc/modules/releases/download/v5.1.1/modules-5.1.1.tar.gz
tar xvf modules-5.1.1.tar.gz
cd modules-5.1.1/
sudo apt-get install tcl-dev
make
# sudo make install
make install

Different from other software, module load command cannot be found, we need to configure as follows,

$ sudo ln -s $PWD/init/profile.sh /etc/profile.d/modules.sh
$ sudo ln -s $PWD/init/profile.csh /etc/profile.d/modules.csh

So the above make install indeed does not need sudo. Strangely, it still cannot find module, (require reboot?), so simply put source /etc/profile.d/modules.sh into ~/.bashrc

Install 4.1.0 from source

it throws when ./configure,

configure: error: libcurl >= 7.28.0 library and headers are required with support for https

try to install libcurl4-openssl-dev, suggested in Installing R on Linux: configure: error: libcurl >= 7.28.0 library and headers are required with support for https.

Be careful when installing the package, and to avoid the uninstallation in the next section.

!!! “2022-08-24 10:35:01” Run sudo apt-get install libcurl4-openssl-dev, and monitor the message, no packages are needed to be removed.

Install Latest R3.6

Accidentally, I uninstall my R3.6 when installing libcurl4, the apt-log shows that

Start-Date: 2021-05-01  10:40:15
Commandline: apt install libcurl4
Requested-By: weiya (1000)
Install: libcurl4:amd64 (7.58.0-2ubuntu3.13)
Remove: r-cran-foreign:amd64 (0.8.75-1xenial0), r-cran-mgcv:amd64 (1.8-31-1cran1xenial0), libcurl3:amd64 (7.58.0-2ubuntu2), r-base-html:amd64 (3.6.3-1xenial), r-cran-class:amd64 (7.3-16-1xenial0), r-cran-boot:amd64 (1.3-23-2xenial0), r-cran-cluster:amd64 (2.1.0-2xenial0), r-cran-rpart:amd64 (4.1-15-2xenial0), r-cran-kernsmooth:amd64 (2.23-16-1+1xenial0), r-cran-matrix:amd64 (1.2-18-1xenial0), r-cran-spatial:amd64 (7.3-11-2xenial0), r-base-core:amd64 (3.6.3-1xenial), r-cran-codetools:amd64 (0.2-16-1xenial0), r-base:amd64 (3.6.3-1xenial), r-recommended:amd64 (3.6.3-1xenial), r-cran-nlme:amd64 (3.1.144-1xenial0), r-cran-lattice:amd64 (0.20-41-1cran1xenial0), r-cran-nnet:amd64 (7.3-13-1xenial0), r-cran-mass:amd64 (7.3-51.5-1xenial0), r-cran-survival:amd64 (3.1-11-1cran1xenial0)
End-Date: 2021-05-01  10:40:29

So I need to reinstall it. Following the instruction on CRAN

1. add deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ to /etc/apt/sources.list, actually I found a xenial version, which means that I had followed the procedure on Ubuntu 16.04. Currently I am on Ubuntu 18.04, so I use a bionic version.
2. run sudo apt-get update, but it throws an error,

W: GPG error: https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ InRelease: The following signatures were invalid: EXPKEYSIG 51716619E084DAB9 Michael Rutter marutter@gmail.com E: The repository ‘https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ InRelease’ is not signed. N: Updating from such a repository can’t be done securely, and is therefore disabled by default. N: See apt-secure(8) manpage for repository creation and user configuration details.

The error has been reported in Linux#GPG error, and the solution is

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 51716619E084DAB9

3. run sudo apt-get install r-base.

Multiple Versions

prefix + module (optionally)

specify --prefix when ./configure, e.g. Installing multiple versions of R on Linux – RStudio Support

Conda

Using R language with Anaconda

• 单独安装 rstudio conda install -c r rstudio (waste memory!!)
• 共用 Rstudio，但是切换环境时找不到 shared library，而原生 R session 完全没问题。目前解决方案，命令行调用 rstudio 之前添加环境变量

conda activate R4.1.0
export LD_LIBRARY_PATH=/media/weiya/PSSD/Programs/anaconda3/envs/R4.1.0/lib:$LD_LIBRARY_PATH
rstudio

详见 Issue #32 use rstudio in env R4.1.0

sys.nframe()

Here is an equivalent way for pythonic if __name__ == "__main__"

# runs only when script is run by itself
if (sys.nframe() == 0){
# ... do main stuff
}

refer to

Example

My Repos

Common Tips

• 删除当前工作区所有变量: rm(list = ls(all = TRUE))
• RStudio shows all shortcuts: Alt-Shift-K.

sort(), rank(), order()

• sort(x): sort x increasingly (by default)
• rank(x): the ranks of each element
• order(x): the permutation for sort(x)

> x = c(97, 93, 85, 74, 32, 100, 99, 67)
> sort(x)
[1]  32  67  74  85  93  97  99 100
> order(x)
[1] 5 8 4 3 2 1 7 6
> rank(x)
[1] 6 5 4 3 1 8 7 2

and they satisfy (assuming no ties and in increasing order)

$$\begin{align*} \mathrm{x[order(x)]} & = \mathrm{sort(x)}\\ \mathrm{rank(x)} &= \mathrm{order(order(x))}\\ \mathrm{sort(x)[rank(x)]} &= \mathrm{x} \end{align*}$$

> x[order(x)]
[1]  32  67  74  85  93  97  99 100
> order(order(x))
[1] 6 5 4 3 1 8 7 2

In particular, if x = 1:n, then x = order(x) = sort(x), and hence

$$\mathrm{x[x] = x}$$

Python uses np.argsort to return the index of the sorted array, such as

>>> a = np.array([97, 93, 85, 74, 32, 100, 99, 67])
>>> sorted(a)
[32, 67, 74, 85, 93, 97, 99, 100]
>>> idx = np.argsort(a)
[32, 67, 74, 85, 93, 97, 99, 100]
>>> a[idx]
array([ 32,  67,  74,  85,  93,  97,  99, 100])

but keep in mind that the index starts from 0 instead of 1. Here is another way in Python | Returning index of a sorted list (TODO).

In julia, we use sort and sortperm.

Run from Command Line

• run R -q -e "1+1" in the command line
• run Rscript test.R in the command line
• put #!/usr/bin/env Rscript on the first line of test.R and allow it executable via chmod +x test.R

To pass arguments, we can use

args = commandArgs(trailingOnly=TRUE)

to extract the arguments.

Test with the following scripts

test1.Rtest2.R

args = commandArgs(trailingOnly=TRUE)
print(length(args))
cat(args)

args = commandArgs(trailingOnly=FALSE)
print(length(args))
cat(args)

the results are

$ Rscript test1.R xxx yyy
[1] 2
xxx yyy
$ Rscript test2.R xxx yyy
[1] 7
/usr/lib/R/bin/exec/R --slave --no-restore --file=test2.R --args xxx yyy

which shows that trailingOnly=TRUE is necessary and it only takes the arguments after --args.

Refer to Passing arguments to an R script from command lines | R-bloggers

Round numbers

I want to approximate a numerical value with 3 significant digits, but the trailing zeros are always be dropped, then I tried several methods.

First of all, define

p <- function(x) { cat(deparse(substitute(x)), "=", x, "\n") }

for convenient and clear comparisons, which takes the advantage of R’s Lazy Evaluation.

x = 0.04898246 # want 0.0490
p(signif(x, digits=3))
p(prettyNum(x))
p(prettyNum(x, digits=3))
p(formatC(x, drop0trailing = F))
p(formatC(x, drop0trailing = T))
p(format(signif(x, digits=3), nsmall = 3))
p(format(signif(x, digits=3), nsmall = 4))
p(format(signif(x, digits=3), nsmall = 3 + floor(-log10(x))))
# signif(x, digits = 3) = 0.049
# prettyNum(x) = 0.04898246
# prettyNum(x, digits = 3) = 0.049
# formatC(x, drop0trailing = F) = 0.04898
# formatC(x, drop0trailing = T) = 0.04898
# format(signif(x, digits = 3), nsmall = 3) = 0.049
# format(signif(x, digits = 3), nsmall = 4) = 0.0490
# format(signif(x, digits = 3), nsmall = 3 + floor(-log10(x))) = 0.0490

where floor(-log10(x)) returns the number of zeros between the decimal symbol . and the first nonzero value (assume x < 1).

With scientific = TRUE option, nsmall would fail,

p(format(signif(x, digits=3), scientific = T, nsmall = 3 + floor(-log10(x))))
# format(signif(x, digits = 3), scientific = T, nsmall = 3 + floor(-log10(x))) = 4.9e-02

as the manual ?format said,

the minimum number of digits to the right of the decimal point in formatting real/complex numbers in non-scientific formats. Allowed values are 0 <= nsmall <= 20.

For a larger value, such as 37289.75, the scientific form might be necessary,

y = 37289.75
p(format(signif(y, digits=3), nsmall= max(0, 3 + floor(-log10(y))) ) )
p(format(signif(y, digits=3), scientific = T))
# format(signif(y, digits = 3), nsmall = max(0, 3 + floor(-log10(y)))) = 37300
# format(signif(y, digits = 3), scientific = T) = 3.73e+04

Finally, I found that sprintf("%.2e", x) would be helpful, and actually the approximation has been consider, instead of just truncating.

p(sprintf("%.2e, %.1e", x, y))
#sprintf("%.2e, %.1e", x, y) = 4.90e-02, 3.7e+04

序列减去常数

for (i in c(1:n-1))
  print(i)
##0
##1
##2
for (i in c(1:(n-1)))
  print(i)
##1
##2

Rstudio 清空历史图象

Error in plot.new() : figure margins too large in R

linux 下更新 R

参考https://mirrors.tuna.tsinghua.edu.cn/CRAN/ 中的README.md文件

若已经通过源码安装，则可以通过找到源码文件夹，使用sudo make uninstall进行卸载。

然后通过配置source.list，进行安装。

window 安装包

切换到R的安装路径下，在etc文件夹中编辑文件Rprofile.site文件

## set a CRAN mirror
    local({r <- getOption("repos")
        r["CRAN"] <- "http://mirrors.ustc.edu.cn/CRAN/"
          options(repos=r)})

Interpreting Residual and Null Deviance in GLM R

Refer to https://stats.stackexchange.com/questions/108995/interpreting-residual-and-null-deviance-in-glm-r

缺少libRblas.so和libRlapack.so的解决办法

虽然缺少libRblas.so和libRlapack.so，但却有libblas.so和liblapack.so，而它们应该是一样的，只是文件名不同而已，为此添加链接即可。

cd /usr/lib
ln -s libblas.so libRblas.so
ln -s /usr/lib/R/module/lapack.so libRlapack.so

参考： 1. https://bugs.launchpad.net/ubuntu/+source/rkward/+bug/264436 2. http://promberger.info/linux/2009/03/20/r-lme4-matrix-not-finding-librlapackso/

RSQLite

参考博文https://statr.me/2011/10/large-regression/

代码见sqlite_ex.R

Rcpp

• Rcpp-quickref

Info

Another repo for learning Rcpp: RcppNotes

See also: - Reproducing R rep with the times argument in C++ and Rcpp

cannot found -lRcpp

手动设置

cd /usr/local/lib
##cd /usr/lib
ln -s /home/weiya/R/x86_64-pc-linux-gnu-library/library/Rcpp/libs/Rcpp.so libRcpp.so

function ‘dataptr’ not provided by package ‘Rcpp’

原因是因为没有在

dyn.load()

前面添加

library(Rcpp)
## 或require(Rcpp)

R check package about description

check locale

par cheatsheet

r-graphical-parameters-cheatsheet

Mathematical Annotation in R plot

plot(..., main = expression(paste("...", mu[1])))

参考 1. Mathematical Annotation in R

function ‘dataptr’ not provided by package ‘Rcpp’

参考function ‘dataptr’ not provided by package ‘Rcpp’

remove outliers from the boxplot

How to remove outliers from a dataset

rmarkdown转化中文字符为PDF的设置

---
title: "test"
author: "weiya"
output:
    pdf_document:
        latex_engine: xelatex
        includes:
            in_header: header.tex
---

rmarkown compiler does not show captions for two consecutive figures

add at least two spacing newline.

Some figure captions from RMarkdown not showing

在grid排列图

Arranging plots in a grid

x11 font cannot be loaded

参考X11 font -adobe-helvetica-%s-%s---%d-------*, face 2 at size 11 could not be loaded

semi-transparency is not supported on this device

semi-transparency is not supported on this device

MC, MCMC, Gibbs采样 原理

MC, MCMC, Gibbs采样 原理&实现（in R）

贝叶斯集锦（3）：从MC、MC到MCMC

随机采样方法整理与讲解（MCMC、Gibbs Sampling等）

简单易学的机器学习算法——马尔可夫链蒙特卡罗方法MCMC

DP: Collapsed Gibbs Sampling

Metropolis Hasting算法

Running R in batch mode on Linux

Running R in batch mode on Linux

“Kernel density estimation” is a convolution of what?

“Kernel density estimation” is a convolution of what?

unable to start rstudio in centos getting error “unable to connect to service”

unable to start rstudio in centos getting error “unable to connect to service”

发布R包

Releasing a package

Presentations with Slidy

Presentations with Slidy

Estimation of the expected prediction error

Estimation of the expected prediction error

协方差矩阵的几何解释

参考协方差矩阵的几何解释

ROCR包中prediction函数

prediction定义如下

prediction(predictions, labels, label.ordering = NULL)

在绘制ROC曲线时，必要时需要指定label.ordering中negative和positive，否则结果会完全相反。举个例子

## generate some data with a non-linar class boundary
set.seed(123)
x = matrix(rnorm(200*2), ncol = 2)
x[1:100, ] = x[1:100, ] + 2
x[101:150, ] = x[101:150, ] - 2
y = c(rep(1, 150), rep(2, 50))
dat = data.frame(x = x, y = as.factor(y))
plot(x, col = y)

## randomly split into training and testing groups
train = sample(200, 100)

## training data using radial kernel
svmfit = svm(y~., data = dat[train, ], kernel = "radial", cost = 1)
plot(svmfit, dat[train, ])

## cross-validation
set.seed(123)
tune.out = tune(svm, y~., data = dat[train, ], kernel = "radial",
                ranges = list(cost = c(0.1, 1, 10, 100, 1000),
                              gamma = c(0.5, 1, 2, 3, 4)))
summary(tune.out)

## prediction
table(true = dat[-train, "y"], pred = predict(tune.out$best.model, newdata = dat[-train, ]))

## ROC curves
library(ROCR)
rocplot = function ( pred , truth , ...) {
  predob = prediction ( pred, truth , label.ordering = c("2", "1"))
  perf = performance ( predob , "tpr" , "fpr")
  plot ( perf,...)
}
svmfit.opt = svm(y~., data = dat[train, ], kernel = "radial",
                 gamma = 3, cost = 10, decision.values = T)
fitted = attributes(predict(svmfit.opt, dat[train, ], decision.values = T))$decision.values

rocplot ( fitted , dat [ train ,"y"] , main ="Training Data")

对于上述代码，如果不指定label.ordering = c("2", "1")，则得到的ROC曲线如下图

原因是因为fitted与y大小关系相反，即前者大时后者小，而前者小时后者大。

神奇的[

来自R语言中以矩阵引用多维数组的元素

比如

A = array(sample(0:255, 100*100*3, replace = T), dim = c(100,100,3))
B = array(sample(1:100, 2*5), dim = c(2,5))
apply(A, 3, `[`, t(B))

proxy 代理

参考

1. Proxy setting for R
2. How to use Tor socks5 in R getURL

lm() 中有无 I() 的差异

注意

lm(Y ~ X + X^2)

和

lm(Y ~ X + I(X^2))

是不一样的。若要表示多项式回归，则应该用 I(X^2)，而前者等价于 lm(Y ~ X)。详见 ?formula, 其中举了一个例子，(a+b+c)^2 - a:b 等价于 a + b + c + a:c + b:c，注意二阶项只存在于交叉项中。

多项式作图

参考Plot polynomial regression curve in R

custom print

class(obj) = "example"
print.example <- function(x)
{

}

refer to Example Needed: Change the default print method of an object

write lines to file

fileConn<-file("output.txt")
writeLines(c("Hello","World"), fileConn)
close(fileConn)

refer to Write lines of text to a file in R

combine base and ggplot graphics in R figure

refer to Combine base and ggplot graphics in R figure window

specify CRAN mirror in install.package

r <- getOption("repos")
r["CRAN"] <- "https://cran.r-project.org"
# r["CRAN"] <- "r["CRAN"] <- "https://mirrors.ustc.edu.cn/CRAN/"" ## for mainland China
options(repos=r)

we also can wrap it with local({...}) and save in ~/.Rprofile.

Refer to How to select a CRAN mirror in R

For temporary use, use repos argument in install.packages, such as

install.packages('RMySQL', repos='http://cran.us.r-project.org')

refer to How to select a CRAN mirror in R

R 符号运算

参考 R 语言做符号计算。

NormDensity <- expression(1 / sqrt(2 * pi) * exp(-x^2 / 2))
D(NormDensity, "x")
DD <- function(expr, name, order = 1) {
 if (order < 1)
     stop("'order' must be >= 1")
 if (order == 1)
     D(expr, name) else DD(D(expr, name), name, order - 1)
 }
DD(NormDensity, "x", 3)

DFun <- deriv(NormDensity, "x", function.arg = TRUE)
DFun(1)

Mistake with colon operator

vec <- c()
for (i in 1:length(vec)) print(vec[i])

would print two NULL because 1:length(vec) would be c(1,0). A method to avoid this

for (i in seq_along(vec)) print(vec[i])

refer to Two common mistakes with the colon operator in R

error in install gRbase

environment: Ubuntu 16.04 (gcc 5.4.0)

g++: error: unrecognized comma line option ‘-fno-plt’

the reason should be that the current gcc is too old.

In conda env R:

1. install latest gcc v7.3.0, but it still does not work
2. Sys.getenv() indeed switch to the latest gcc
3. remove ~/.R/Makevars, which would force the gcc to be the gcc declared in that file.
4. then it works well.

refer to

R Packages Fail to Compile with gcc

Note that some packages cannot be installed via CRAN, and you can check bioconductor.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("graph")

protection stack overflow

use

R --max-ppsize 500000

or for rstudio

rstudio --max-ppsize 500000

refer to How to solve ‘protection stack overflow’ issue in R Studio

not found libhdf5.a

check if compiling under anaconda environment, if so, exit and retry.

install.packages returns failed to create lock directory

in bash

R CMD INSTALL --no-lock <pkg>

or in R session

install.packages("Rcpp", dependencies=TRUE, INSTALL_opts = c('--no-lock'))

refer to R install.packages returns “failed to create lock directory”

nonzero in dgCMatrix

refer to R package Matrix: get number of non-zero entries per rows / columns of a sparse matrix

plot.new() : figure margins too large

Rstudio 中对于太大的图片有可能报错，比如当我试图以 par(mfrow=c(4,1)) 画四个 matplot，于是报错。这时候，可以直接在 R session 里面绘制。

S3 method

初体验，ESL-CN/code/boosting/s3ex.R

j = list(name = "Joe", salary = 5500, union = T)
class(j) = "employee"

print.employee <- function(wrkr){
  cat(wrkr$name, "\n")
  cat("salary", wrkr$salary, "\n")
  cat("union member", wrkr$union, "\n")
}
summary.employee <- function(wrkr){
  cat(wrkr$name, "\n")
  cat("salary", wrkr$salary, "\n")
  cat("union member", wrkr$union, "\n")
}

以及一个相关的问题 How to override default S3 function in R?

Parallel Computing

related packages

• parallel: makeCluster and stopCluster
• doParallel: registerDoParallel
• foreach: %dopar%

example

adapt from my project

cl <- makeCluster(ncl)
registerDoParallel(cl)


res = foreach(j=1:Nnset, .combine = 'c', .export = c('calc_lda_BIC'), .packages = 'nnet') %dopar%
{
  jj = not_set[j];
  new_set   = sort(c(jj, cur_set));
  new_score = calc_lda_BIC(xx, yy, new_set, D, K, debug, gam=gam);
  new_score
}
stopCluster(cl)

references

• Using the foreach package

using R in JupyterLab

install.packages('IRkernel')
#IRkernel::installspec()
IRkernel::installspec(name="3.6.0", displayname = "R 3.6.0")

refer to IRkernel

and try to use jupyterlab, which seems much powerful than jupyter.

But note that -X option is needed before creating a tmux session for running jupyter, otherwise the kernel keeps dead in the browser and check the monitor message from jupyter, which throws,

Error in .External2(C_X11, paste0("png::", filename), g$width, g$height,  :
  unable to start device PNG
Calls: <Anonymous> ... evaluate -> dev.new -> do.call -> <Anonymous> -> ok_device
In addition: Warning message:
In ok_device(filename, ...) : unable to open connection to X11 display ''
Execution halted

although I am just trying to calculate 1+1, nothing related to X11.

The error would appear again if the -X ssh session has been stopped. Then I found such issue has been reported in https://github.com/IRkernel/IRkernel/issues/388, and there might be solution by https://github.com/IRkernel/IRkernel/issues/388#issuecomment-237080809, but the working R version is built from source by myself, and Cairo is not supported. When I am trying to install the package, it throws that

configure: error: Cannot find cairo.h! Please install cairo (http://www.cairographics.org/) and/or set CAIRO_CFLAGS/LIBS correspondingly.
ERROR: configuration failed for package ‘Cairo’

Even I retried after specifying

export CAIRO_LIBS=/home/project09/miniconda3/envs/r3.6.3/lib
export CAIRO_CFLAGS=/home/project09/miniconda3/envs/r3.6.3/include

by linking it to other R version with Cairo support, similar as in https://stat.ethz.ch/pipermail/r-help/2014-April/374096.html, but it does not work.

Then I change to a different R version with cairo support, and it can work without -X option. And the setting in .Rprofile can also been removed (or actually the default is exactly the same).

Try to install the shortcuts as recommended, but it throws,

$ jupyter labextension install @techrah/text-shortcuts
An error occured.
ValueError: Please install Node.js and npm before continuing installation. You may be able to install Node.js from your package manager, from conda, or directly from the Node.js website (https://nodejs.org).
See the log file for details:  /tmp/jupyterlab-debug-7mmu8jy1.log

then

conda install -c conda-forge nodejs

as suggested in Extensions

However the default version is outdated.

libgcc-7.2.0         | 304 KB    | ############################################################################################################################################# | 100%
nodejs-6.13.1        | 11.9 MB   | ############################################################################################################################################# | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(base) [project09@stapc390 ~]$ jupyter labextension install @techrah/text-shortcuts
An error occured.
ValueError: Please install nodejs >=10.0.0 before continuing. nodejs may be installed using conda or directly from the nodejs website.
See the log file for details:  /tmp/jupyterlab-debug-lhf6bjwm.log

The solution is quite easy, just use the default conda channel,

conda install nodejs

which will install nodejs-10.13.0.

reload data

> mtcars$transmission = factor(mtcars$am, levels = c(0, 1), labels = c("Automatic", "Manual"))
> str(mtcars)
'data.frame':   32 obs. of  12 variables:
...
 $ am          : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear        : num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb        : num  4 4 1 1 2 1 4 2 2 4 ...
 $ transmission: Factor w/ 2 levels "Automatic","Manual": 2 2 2 1 1 1 1 1 1 1 ...
> data("mtcars")
> str(mtcars)
'data.frame':   32 obs. of  11 variables:
...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

scale or not

下面两个结果是一样的。

m = matrix(rnorm(4*6, 2), ncol = 4)
cov(m)
sm = scale(m, scale = FALSE) # only center, no scale.
t(sm) %*% sm/ (6-1)

Check Memory Usage

memuse = function () {
    strtoi(system(paste("ps -p", Sys.getpid(), "-o rss="), intern = T)) / 1024
}

Stats Functions

• stats::ecdf: empirical cumulative distribution function

Update Library Path

Info

Post: 2022-08-30 12:00:39 +0800

The disk is nearly occupied, and I found that

$ du -sh *
2.6G    R

which stores the installed packages for R3.6. I want to move it to ~/Programs/R, which is on a portable SSD, so I need to update .libPaths().

The personal path is defined in /usr/lib/R/etc/Renviron

R_LIBS_USER=${R_LIBS_USER-'~/R/x86_64-pc-linux-gnu-library/3.6'}

Replace it with

R_LIBS_USER=${R_LIBS_USER-'~/Programs/R/x86_64-pc-linux-gnu-library/3.6'}

Tip

Start from R4.0.0, the path string can be constructed via r(...) without escaping /.

> r"(~/R/x86_64-pc-linux-gnu-library/3.6)"
[1] "~/R/x86_64-pc-linux-gnu-library/3.6"

See

String

• repeat string: strrep('str', 2), paste(rep('str', 2), collapse='')