Last articles - STHDA : Tips & Tricks

Clear User Interface and Free Memory in R/RStudio

Sat, 26 Aug 2017 17:03:00 +0200

Personally, to optimize the memory and work on a clean/fresh R/RStudio interface, I manage to start all my R scripts with this code:

Code R :

# Clear plots
if(!is.null(dev.list())) dev.off()
# Clear console
cat("\014") 
# Clean workspace
rm(list=ls())
# Set working directory
setwd("~/Downloads/MyScripts")

Let me explain the code line by line:

Code R :

if(!is.null(dev.list())) dev.off()

This is equivalent to click on the button clear all plots in the plots panel.

Code R :

cat("\014")

This is identical to Ctrl+L or to click on the clear console within the Edit menu.

Code R :

rm(list=ls())

This is equivalent to click on the button clear objects from the workspace in the environment panel.

These code lines allow to clear the panels and so free memory.

Code R :

setwd("~/Downloads/MyScripts")

Here you set up your working directory, in my case (Linux Ubuntu user), I choose to use the directory Myscripts inside my Downloads directory. You could use your own path to the directory you want to use there.

I hope this code would be helpful and you would choose to use it in your scripts.

If anyone knows a code to clear the history, please let me know, i would integrate it to this article.

R: Find the Length of Every Elements in a List

Wed, 16 Aug 2017 13:10:00 +0200

In R programming language, to find the length of every elements in a list, the function lengths() can be used.

A simplified format is as follow:

Code R :

 
lengths(x)

This function loops over x and returns a compatible vector containing the length of each element in x.

For example:

Code R :

 
my.list <- list(name = c("A", "B", "C"), age = c(20, 30, 40, 50))
lengths(my.list)

Output:

name  age 
   3    4

What is Variable Loadings in PCA?

Mon, 14 Aug 2017 16:31:00 +0200

Loadings are interpreted as the coefficients of the linear combination of the initial variables from which the principal components are constructed.

From a numerical point of view, the loadings are equal to the coordinates of the variables divided by the square root of the eigenvalue associated with the component.

The loadings are the default outputs of the functions princomp() and prcomp().

Source: Husson et al., Exploratory Multivariate Analysis by Examples Using R, 2011.

dplyr: How to Add Cumulative Sums by Groups Into a Data Frame?

Tue, 08 Aug 2017 16:50:00 +0200

To add into a data frame, the cumulative sum of a variable by groups, the syntax is as follow using the dplyr package and the iris demo data set:

Code R :

 
library(dplyr)
iris %>%
  group_by(Species) %>%
  mutate(cum_sep_len = cumsum(Sepal.Length))

Output:

# A tibble: 150 x 6
# Groups:   Species [3]
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species cum_sep_len
                                         
 1          5.1         3.5          1.4         0.2  setosa         5.1
 2          4.9         3.0          1.4         0.2  setosa        10.0
 3          4.7         3.2          1.3         0.2  setosa        14.7
 4          4.6         3.1          1.5         0.2  setosa        19.3
 5          5.0         3.6          1.4         0.2  setosa        24.3
 6          5.4         3.9          1.7         0.4  setosa        29.7
 7          4.6         3.4          1.4         0.3  setosa        34.3
 8          5.0         3.4          1.5         0.2  setosa        39.3
 9          4.4         2.9          1.4         0.2  setosa        43.7
10          4.9         3.1          1.5         0.1  setosa        48.6
# ... with 140 more rows

How to Export Multiple ggplots Using a for Loop

Sat, 05 Aug 2017 21:16:00 +0200

To save multiple ggplots using for loop, you need to call the function print() explicitly to plot a ggplot to a device such as PDF, PNG, JPG file.

For example:

Code R :

 
library(ggplot2)
p <- ggplot(iris, aes(x = Species, y = Sepal.Length))
colors <- c("black", "red", "green")
 
for(color in colors){
 
   final.plot <- p + geom_boxplot(color = color)
 
  pdf(paste0(color, ".pdf"))
  print(final.plot)
  dev.off()
}

Open Multiple Independent Rstudio Sessions in Mac OS X

Thu, 27 Jul 2017 11:42:00 +0200

To open multiple independent R sessions in Mac OSX, type the following bash command in the UNIX Terminal:

Code BASH :

 
open -n -a "Rstudio"