Environment, Packages, Functions, Import/Export

Install packages… and more

Most of the data analysis tasks you will perform won’t come from base R: you most often load additional packages for specialized functions

install.packages("effsize")

install.packages( c("effsize","psych","ggplot2") )

For development or personal use, you may occasionally install packages from outside CRAN, such as from GitHub:

devtools::install_github("FilippoGambarota/filor")
devtools::install_github("EnricoToffalini/toffee")

After installing, you need to load the packages using function library:

library(effsize)
library(ggplot2)

Install packages… and more

call functions

After loading a package, its functions are directly callable throughout the R session:

library(psych)

fisherz(rho=0.5) # use a function from the "psych" package

[1] 0.5493061

you may directly call any function from any installed package, even without loading it, using “::”; this is especially useful when there is a risk of functions with conflicting names, or if you just don’t want to load an entire package for using a single function:

psych::fisherz(rho=0.5)

[1] 0.5493061

Some R packages that you will or may need in the future (1/3)

(You may actually use these “base” packages very often without even realizing that they are packages)

Some R packages that you will or may need in the future (2/3)

Some R packages that you will or may need in the future (3/3)

Reproducibility tip: sessionInfo()

When sharing your full R project, also share info on your R version, OS, and loaded package versions for reproducibility

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United Kingdom.utf8 
[2] LC_CTYPE=English_United Kingdom.utf8   
[3] LC_MONETARY=English_United Kingdom.utf8
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.utf8    

time zone: Europe/Rome
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.5.2  semTools_0.5-6 lme4_1.1-36    Matrix_1.6-2   lavaan_0.6-19 
[6] psych_2.5.6   

loaded via a namespace (and not attached):
 [1] generics_0.1.3     sandwich_3.1-1     lattice_0.22-6     digest_0.6.37     
 [5] magrittr_2.0.3     evaluate_1.0.3     grid_4.5.1         estimability_1.5.1
 [9] RColorBrewer_1.1-3 mvtnorm_1.3-3      fastmap_1.2.0      jsonlite_1.9.1    
[13] survival_3.8-3     multcomp_1.4-28    scales_1.4.0       TH.data_1.1-3     
[17] pbivnorm_0.6.0     codetools_0.2-20   mnormt_2.1.1       reformulas_0.4.0  
[21] Rdpack_2.6.2       cli_3.6.4          rlang_1.1.5        rbibutils_2.3     
[25] splines_4.5.1      withr_3.0.2        yaml_2.3.10        tools_4.5.1       
[29] parallel_4.5.1     nloptr_2.1.1       coda_0.19-4.1      dplyr_1.1.4       
[33] minqa_1.2.8        boot_1.3-31        vctrs_0.6.5        R6_2.6.1          
[37] stats4_4.5.1       zoo_1.8-13         lifecycle_1.0.4    emmeans_1.10.7    
[41] MASS_7.3-60        pkgconfig_2.0.3    pillar_1.10.2      gtable_0.3.6      
[45] glue_1.8.0         Rcpp_1.0.14        tidyselect_1.2.1   xfun_0.51         
[49] tibble_3.3.0       rstudioapi_0.17.1  knitr_1.49         farver_2.1.2      
[53] xtable_1.8-4       htmltools_0.5.8.1  nlme_3.1-167       rmarkdown_2.29    
[57] compiler_4.5.1     quadprog_1.5-8    

Functions and arguments

Functions typically take some input parameters, known as arguments, process that, and yield some output/result(s)

Functions and arguments

arguments

• values or variables you pass to a function as input, or to control its behavior

for example, seq() generates a sequence of numbers; “from” and “to” are arguments: it will provide the integers between these two extremes:

seq(from = 3, to = 7)

[1] 3 4 5 6 7

length.out controls how many equally spaced numbers must be generated:

seq(from = 3, to = 7, length.out = 4)

[1] 3.000000 4.333333 5.666667 7.000000

alternatively, by defines the step size between numbers:

seq(from = 3, to = 7, by = 0.6)

[1] 3.0 3.6 4.2 4.8 5.4 6.0 6.6

Functions and arguments

arguments

• values or variables you pass to a function as input, or to control its behavior

rnorm() will generate “n” random numbers from a normal distribution with “mean” as the average and “sd” as the standard deviation:

rnorm(n = 5, mean = 100, sd = 15)

[1] 100.06901  99.94485 118.77112 111.39752 107.70383

Positional matching - arguments names may be omitted if placed in the correct order

rnorm(5, 100, 15)

[1] 103.25357  67.64805 105.93901  81.90522 103.79463

Functions and arguments

Default arguments - a function might still work even if some arguments are omitted, if it can use its own default values (in this case “mean=0, sd=1”)

rnorm(n = 5)

[1]  1.25903182 -0.32462645 -0.08280682 -0.29398609  0.33012823

Errors - however, omitting mandatory arguments will result in an Error

rnorm(mean = 100, sd = 15)

Error in rnorm(mean = 100, sd = 15): argument "n" is missing, with no default

Warnings - Some inputs may cause the function to produce Warnings and bad output, but do not stop code execution

rnorm(n = 5, mean = 100, sd = -15)

Warning in rnorm(n = 5, mean = 100, sd = -15): NAs produced

[1] NaN NaN NaN NaN NaN

Functions and arguments

HELP! see the documentation of a function

There are two ways to access documentation: using “?” and using help()

?rnorm # this will work
help(rnorm) # this does the same

Set up Working Directory

getwd()

[1] "C:/Users/enric/Desktop/Basics R DataScience/Slides"

Set up Working Directory

As a general rule:

• When you open R or the RStudio app, the default WD may be the documents folder (in Windows) or the home directory (e.g., /home/username; in Linux or macOS);

• This default may be reset at any time from inside RStudio on Tools > Global Options... > General;

• If you open a file (e.g., a .R script) using RStudio, the WD is set at that file location (unless the RStudio app was already open before);

• However, you can set a new WD at any time from within the R code, using the setwd() function, for example:

setwd( "C:/Users/enric/" )

Set up Working Directory

⚠️ Windows users, be careful!

When you copy-and-paste a folder address, Windows will probably take “\” as path separator, but “\” is the escape character in R, so you incur an error:

setwd( "C:\Users\enric\" )

Error: '\U' used without hex digits in character string (<input>:1:12)

How to fix

setwd( "C:\\Users\\enric\\" )

or

setwd( "C:/Users/enric/" )

Absolute vs Relative paths

• ABSOLUTE paths: indicate the full path from the root, e.g., "C:/Users/enric/" (in Linux it might be "/home/enric/"; in macOS "/Users/enric/")

• RELATIVE paths: indicate the path starting from (relative to) the current directory. Most often, you prefer this for import/export, so the same script works on any device; e.g., "figures/" or ../figures/:

  • png(filename = "figures/Fig1.png") saves a file Fig1.png into figures, which a subfolder inside the current WD;
  • png(filename = "../figures/Fig1.png") saves a file Fig1.png into figures, which is a folder outside, one level up ("../") the current WD

RStudio Projects

RStudio Projects

Opening the .Rproj file ensures that RStudio automatically sets the project folder as the working directory, so all files are managed relative to here:

Import/export

Import/export

Workspace

All your R code (script) is generally stored in text files with a .R extension. But where do you save your results and objects?! Maybe you just don’t…

However, you can export the entire workspace using save.image()

# let's populate the workspace first
N = 100
x = rnorm(N,0,1)
y = 2 + 0.6*x + rnorm(N,0,0.8)
df = data.frame(x, y)
fit = lm(y ~ x, data=df)

# now let's save it
save.image("myWS.RData") 

Specifying "myWS.RData" is not mandatory but recommended, otherwise your file will simply be named ".RData". (By the way… where will it be saved?)

Import/export

Workspace

Alternatively, you can save just one or a few workspace objects using save()

# let's populate the workspace first
N = 100
x = rnorm(N,0,1)
y = 2 + 0.6*x + rnorm(N,0,0.8)
df = data.frame(x, y)
fit = lm(y ~ x, data=df)

# now let's save only two objects
save(df, fit, file="myWS.RData") 

This will save only objects df and fit into a newly created file named myWS.RData. This is useful when you have an overcrowded workspace and want to save only a few objects containing the final results

Import/export

Workspace

Once you open a new R session, you may load the previously stored workspace using the load() function, specifying load("workspace_name.RData"), like this:

# empty the workspace to make sure there's actually nothing!
rm(list=ls())

# now load the previously saved workspace
load("myWS.RData") 

# make sure that the objects have been loaded
ls()

Import/export

Data

Import/export

Data

Here’s an example of using read.csv() for importing data:

# IMPORT csv data from a "data" subfolder, and store it in an object named "df"
df = read.csv("data/Performance.csv")

# OR if you want to be explicit on settings:
df = read.csv("data/Performance.csv", header=TRUE, sep=",", dec=".")

head(df) # have a look at the first few rows

  id  name anx acc     time
1  1 nydga  20  15 2.077932
2  2 bwknr  14   9 2.436858
3  3 sauuj  18  12 2.549814
4  4 vnjgi  27  15 4.386718
5  5 oueiy  21  11 5.248933
6  6 neebj  12  13 3.463094

Import/export

Data

If you absolutely want to import your data directly from a MS Excel document (.xlsx), you may use function read_excel() from the package readxl:

library(readxl)
df = data.frame( read_excel("data/Performance.xlsx") )
# data.frame() forces it to be a dataframe, otherwise it's a tibble
head(df)

  id  name anx acc     time
1  1 nydga  20  15 2.077932
2  2 bwknr  14   9 2.436858
3  3 sauuj  18  12 2.549814
4  4 vnjgi  27  15 4.386718
5  5 oueiy  21  11 5.248933
6  6 neebj  12  13 3.463094

Import/export

Data

A good trick if you don’t want to specify any relative or absolute path, and want to manually select data each time, is using the file.choose() function:

df = read.csv( file.choose() )

Import/export

Data

Other “tricks” for importing data involve using the functions in the RStudio menu, particularly:

• File > Import Dataset > From text (base)…

• File > Import Dataset > From Excel

• File > Import Dataset > From SPSS…

However … using these functions is not best practice, because they are specific to the RStudio IDE. It’s better to use code for reproducibility

Import/export

Data

You have processed data with R, now… how to export it?

When collaborating with someone also using R, you may choose to exchange data directly by exporting the object or the entire workspace as a .RData file, using the save() or save.image() function respectively.

However, if you need to export a dataframe in a more universally readable tabular format, such as .csv, you may use write.csv():

# specify the dataframe to export (here named "df")
# along with the desired file name, and other arguments
write.csv(df, file="myExportedData.csv")

# if you don't want row numbering / row names
write.csv(df, file="myExportedData.csv", row.names=F)

Import/export

Figures

R has a collection of functions for exporting figures in different formats: pdf(), png(), jpeg(), bmp(), tiff(), svg().

Here is an example using png() :

# set up a graphic output file named "MyFigure.png" with some settings
png("MyFigure.png", height=1500, width=2000, units="px", res=300)

# code for creating a simple boxplot
boxplot(iris$Sepal.Width) 

# close the graphic output file and actually export the plot
dev.off()