By way of demonstration, my environment will create a window for music, and a second window, split in two panes, for podcasts. I use cmus for music, and the command line version of vlc for podcasts.

Description

First, check if the session exists already

tmux has-session -t Audiophile

If not, then execute something; otherwise exit and (probably) attach the session.

if [ $? != 0 ]
then
 <execute something>
fi

The first command creates a new session called ‘Audiophile’, and immediately names the only existing window as ‘Music’.

 tmux new-session -s Audiophile -n Music -d

In the same session/window (since there is only one), call cmus, and divide the screen. The size of the split is defined to 70 lines. Once divided, return the selection to the main screen.

 tmux send-keys -t Audiophile 'cmus' C-m
 tmux split-window -v -p 70 -t Audiophile
 tmux select-layout -t Audiophile main-vertical

Without moving the selection of pane, go to a specific folder in the smaller one.

 tmux send-keys -t Audiophile:1.2 'cd <some-folder>; clear' C-m

Now create a new window named ‘Podcasts’.

 tmux new-window -n Podcasts -t Audiophile

Likewise the first window, create a second pane, go the podcast folder, and display its content. In the main pane, call podui, which is an alias for the command line version of vlc.

podui=‘/Applications/VLC.app/Contents/MacOS/VLC –intf ncurses .’

tmux send-keys -t Audiophile:2 'cd <pod-folder>; clear; ls -lh' C-m
tmux split-window -v -t Audiophile:2
tmux select-layout -t Audiophile:2 main-horizontal
tmux send-keys -t Audiophile:2.2 'podui' C-m

Once the second window is made, return to the first window and attach to the session. This will exit the script.

tmux select-window -t Audiophile:1
tmux attach -t Audiophile

As aforementioned, the else section of the condition will immediately attach to the session.

tmux attach -t Audiophile

Script

tmux has-session -t Audiophile
if [ $? != 0 ]
then
 tmux new-session -s Audiophile -n Music -d
 tmux send-keys -t Audiophile 'cmus' C-m
 tmux split-window -v -p 70 -t Audiophile
 tmux select-layout -t Audiophile main-vertical
 tmux send-keys -t Audiophile:1.2 'cd <some-folder>; clear' C-m
 tmux new-window -n Podcasts -t Audiophile
 tmux send-keys -t Audiophile:2 'cd <pod-folder>; clear; ls -lh' C-m
 tmux split-window -v -t Audiophile:2
 tmux select-layout -t Audiophile:2 main-horizontal
 tmux send-keys -t Audiophile:2.2 'podui' C-m
 tmux select-window -t Audiophile:1
 tmux attach -t Audiophile
fi
tmux attach -t Audiophile

The script might be called from an alias in a new terminal session (outside tmux). For example:

$: tmxenv

where,

 tmxenv='sh <folder-with-script>/tmux_env_maker'    

+ setup

The template uses the class article and a pretty basic library. A double-side document is defined in the geometry library, but can be edited easily. The biblatex library is the important one here (see the library documentation). Here the parameters to be displayed are selected, as well as some pre-formatting commands. For instance, the style may be defined as numeric, reading, alphabetic or authoryear; likewise, the sorting method may be defined as anyt, ynt, none or ydnt.

\documentclass[a4paper,9pt,twoside]{article}

\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{csquotes}
\usepackage{hyperref}
\usepackage{multicol}
\usepackage[top=1.6cm,
            bottom=2cm,
            outer=0.9cm,
            inner=1.8cm,
            headsep=14pt]{geometry}
\usepackage[backend=bibtex,
            style=reading,
            entryhead=full,
            entrykey=true,
            annotation=true,
            abstract = true,
            library=true,
            file=false,
            doi=false,
            eprint=false,
            url=false,
            sorting=ynt]{biblatex}

In addition, some metadata is defined in the header to be then later displayed in the document (i.e. title, author, date, …). The bibliography file: ./library.bib is also referenced in the header.

\title{myBibliography: \texttt{Biblatex} \& \texttt{Mendeley} - \LaTeX}
\author{author name}
\date{any date}

\bibliography{library}

+ mendeley (…hints)

Mendeley (Elsevier) is a free reference management application and an academic social network tool… Mendeley helps to manage references and generate bibliographies.

One of the main features of Mendeley is its multi-platform capability. A second feature shoudl be its ability to customise the stored references. In this case, the value assigned to tags or personal keywords can be employed to filter bibliographies. These values can add another layer of categorisation, either in the .bib file or firectly in the .tex file.

In case tags are used, these need to be converted in the generated mendeley-tag value into keywords or other fields that can be recognised by biblatex. Otherwise, biblatex will include keywords values into the mendeley-tags. The following defintion can be used to manage these values during the latex compilation; it converts ‘mendeley-tags’ to ‘keywords’.

\DeclareSourcemap{
  \maps[datatype=bibtex]{
    \map[overwrite=false]{
       \step[fieldsource=mendeley-tags, fieldtarget=keywords, append]
       \step[fieldset=keywords, fieldvalue=freud]
    }
  }
}

+ template

Finally, the tex file can be structured as usual, with sections and subsections. The result: a filtered bibliography with annotations.

\begin{document}

\maketitle

\medskip
\tableofcontents
\nocite{*}
\medskip


\section{Themes / Keywords}

\begin{multicols}{3}

\subsection{Cognition}
\printbibliography[keyword={Cognition},heading=none]

\subsection{Literature}
\printbibliography[keyword={Literature},heading=none]

\subsection{Methods}
\printbibliography[keyword={Methods},heading=none]

\subsection{Latex}
\printbibliography[keyword={Latex},heading=none]

\end{multicols}


\clearpage

\section{Type of Work}

\subsection{Articles}
\printbibliography[type=article, title={Articles only}]

\subsection{Theses}
\printbibliography[type=thesis, title={Thesis only}]

\end{document}

I hope this has been useful.

Source file

The source file has been included as a template in Overleaf

  find . -type d | sed -e "s/[^-][^\/]*\//  |/g" -e "s/|\([^ ]\)/|-\1/" | tee output_.txt       

As noted, three parts are combined:

1. find : This command searches the directory tree rooted at each given file name by evaluating the given expression from left to right, according to the rules of precedence, until the outcome is known (the left hand side is false for and operations, true for or), at which point find moves on to the next file name.

2. sed : (s)tream (ed)itor. A stream editor is used to perform basic text transformations on an input stream (a file or input from a pipeline). While in some ways similar to an editor which permits scripted edits (such as ed), sed works by making only one pass over the input(s), and is consequently more efficient. But it is sed’s ability to filter text in a pipeline which particularly distinguishes it from other types of editors. See ‘sed –help’ for usage information.

3. tee : The command reads standard input, then writes its content to standard output. It simultaneously copies the result into the specified file(s) or variables.

Example:

  cd myDirectory/                                                                                  
  find . -type d | sed -e "s/[^-][^\/]*\//  |/g" -e "s/|\([^ ]\)/|-\1/" | tee output_.txt       

|-B.B. King
|  |-Kansas City 1972
|-Bob Forrest
|  |-Modern folk and blues
|-Eric Clapton
|  |-MTV Unplugged
|  |-Old Sock
|  |-On the crossroad
|  |-Wynton Marsalis & Eric Clapton Play The Blues
|-Eric Gales Band
|  |-The Psychedelic Underground
|-Eric Johnson
|  |-Acoustic Bootleg
|  |-Venus Isle
|-J.J. Cale & Eric Clapton
|  |-The Road to Escondido
|-James Brown
|  |-At Studio 54
|-Jeff Beck
|  |-Emotion And Commotion
|-Jimi Hendrix
|  |-Are You Experienced
|  |-Axis Bold as Love
|  |-Band of Gypsys
|  |-BBC Sessions
|  |-Electric Ladyland
|  |-Experience Hendrix- The Best of Jimi Hendrix
|  |-The Early Years
|-Jimi Hendrix & Stephen Stills
|  |-At Stills Basement
|-John Mayall
|  |-Blues From Laurel Canyon
|  |-Life In The Jungle
|-Johnny Cash
|  |-American Recordings
|  |-Unchained
|-Muddy Waters
|  |-They Call Me Muddy Waters
|-Robert Johnson
|  |-Genius Of The Blues
|-Steve Ray Vaughan
|  |-albert king & steve ray vaughan
|  |-Live at Pier
|  |-texas flood
|-Stevie Ray Vaughan & Jeff Beck
|  |-The Fire Meets The Fury
|-The Yardbirds
|  |-Blue Eyed Blues

Including files?

  find . -print | sed -e 's;[^/]*/;|____;g;s;____|; |;g' | tee output_.txt       

The following tutorial explains how to generate an interactive map employing the R-library: leaflet.

library(leaflet)
library(geojsonio)      # required to read json file
library(leaflet.extras) # simplifies addition of features

The following example displays the Köppen-Geiger climate classification map¹, which categorises world regions based on observed temperature and precipitation conditions. In the study¹, a set of underlying data regarding both observed and projected climates shifts is made freely available². This example only utilises the observed conditions for four different periods: 1900-1925, 1926-1950, 1951-1975, 1976-2000. Each of these files with observed conditions contains lines of grid box centre coordinates (latitude, longitude), with their corresponding climate class.

To simplify the processing of information, the example employs a set of processed topo-json files ³, as well as a json file outlining the countries of the world ⁴.

Hands-on

First, a table of colours is loaded, whose values are those defined in the aforementioned study ¹.

tblColors <- read.table("myData/koeppen/koeppen_lgd.txt", header = TRUE, stringsAsFactors = F)

Now, these datasets contain the class for each polygon/square, but do not contain the preferred colour. This can be achieved in two ways: 1) by comparing the class with the table of colours when the leaflet object is called, or 2) by assigning the colour to the class in the dataset. The later is expectedly faster, and the process is developed in the function fnAddFeatures.

fnAddFeatures <- function(dtaJson, dtaPal=tblColors){

  dtaJson$style = list(
    weight = 1,
    color = "#555555",
    opacity = 1,
    fillOpacity = 0.8
  )

  geometries <- sapply(dtaJson$features, function(feat) {
    feat$properties$gridcode}
  )

  dtaJson$features <- lapply(dtaJson$features, function(feat){
    feat$properties$style <- list(
      fillColor = dtaPal$col[dtaPal$class %in% feat$properties$gridcode]
    )
    feat

  })

  return(dtaJson)
}

The function fnAddFeatures adds a general style attribute. It then extracts the gridcode class, and stores it in the object geometries. Finally, a new feature called style compares the gridcode with the table of colours for each element of the list—therefore the use of lapply.

Now the four datasets are loaded, and the style feature is added.

Clim1900 <- fnAddFeatures(geojson_read(paste(path.Project,"myData/ClimateMap/climate-map-master/1901-1925.topo.json",sep="/")))
Clim1926 <- fnAddFeatures(geojson_read(paste(path.Project,"myData/ClimateMap/climate-map-master/1926-1950.topo.json",sep="/")))
Clim1951 <- fnAddFeatures(geojson_read(paste(path.Project,"myData/ClimateMap/climate-map-master/1951-1975.topo.json",sep="/")))
Clim1976 <- fnAddFeatures(geojson_read(paste(path.Project,"myData/ClimateMap/climate-map-master/1976-2000.topo.json",sep="/")))

The map

A random location is defined for the setview, and the extent to which the map can be zoomed-out is defined.

setView(lng = -105, lat = 23, zoom = 3) %>%

setMaxBounds(lng1 = -178.5241, lat1 = -72.05115,
             lng2 = 178.52414, lat2 = 65.05115 ) %>%

Two base maps are defined here: light and dark, which can be selected in the control panel.

addProviderTiles("CartoDB.DarkMatterNoLabels",
                 options = providerTileOptions(minZoom = 2, maxZoom = 7),
                 group = "Dark") %>%
addProviderTiles("OpenStreetMap.BlackAndWhite",
                 options = providerTileOptions(minZoom = 2, maxZoom = 7, noWrap = TRUE),
                 group = "Light") %>%
addLayersControl(baseGroups = c("Dark", "Light"),
                 options = layersControlOptions(collapsed = FALSE))

Each dataset is aded to a addGeoJSON object, and a group is assigned to be included in the control menu/legend.

addGeoJSON(Clim1900, weight = 0.4, group = "1900") %>% hideGroup("1900") %>%
addGeoJSON(Clim1926, weight = 0.4, group = "1926") %>% hideGroup("1926") %>%
addGeoJSON(Clim1951, weight = 0.4, group = "1951") %>% hideGroup("1951") %>%
addGeoJSON(Clim1976, weight = 0.4, group = "1976") %>% hideGroup("1976") %>%
addGeoJSONv2(World, color = "white", weight = 0.6, popupProperty = "popup", labelProperty = "ADMIN") %>%

addLegend(position = "bottomleft", colors = tblColors$col,
          labels = tblColors$class, opacity = 0.75,
          labFormat = labelFormat(
            prefix = "(", suffix = ")%", between = ", "),
          title = "koeppen-geiger") %>%

The following action wraps up the definition of the leaflet object.

m <- leaflet() %>%
  setView(lng = -105, lat = 23, zoom = 3) %>%
  addProviderTiles("CartoDB.DarkMatterNoLabels",
                   options = providerTileOptions(minZoom = 2, maxZoom = 7),
                   group = "Dark") %>%
  addProviderTiles("OpenStreetMap.BlackAndWhite",
                   options = providerTileOptions(minZoom = 2, maxZoom = 7, noWrap = TRUE),
                   group = "Light") %>%
  setMaxBounds( lng1 = -178.5241, lat1 = -72.05115,
                lng2 = 178.52414, lat2 = 65.05115 ) %>%
  addLegend(position = "bottomleft", colors = tblColors$col,
            labels = tblColors$class, opacity = 0.75,
            labFormat = labelFormat(
              prefix = "(", suffix = ")%", between = ", "),
            title = "koeppen-geiger") %>%
  addGeoJSON(Clim1900, weight = 0.4, group = "1900") %>% hideGroup("1900") %>%
  addGeoJSON(Clim1926, weight = 0.4, group = "1926") %>% hideGroup("1926") %>%
  addGeoJSON(Clim1951, weight = 0.4, group = "1951") %>% hideGroup("1951") %>%
  addGeoJSON(Clim1976, weight = 0.4, group = "1976") %>% hideGroup("1976") %>%
  addGeoJSONv2(World, color = "white", weight = 0.6, popupProperty = "popup", labelProperty = "ADMIN") %>%
  addLayersControl(overlayGroups = c("1900", "1926", "1951", "1976"),
                   baseGroups = c("Dark", "Light"),
                   options = layersControlOptions(collapsed = FALSE))

m

One last thing, how about a different projection?

crs.molvidde <- leafletCRS(
  crsClass="L.Proj.CRS", code='ESRI:53009',
  proj4def= '+proj=moll +lon_0=0 +x_0=0 +y_0=0 +a=6371000 +b=6371000 +units=m +no_defs',
  resolutions = c(65536, 32768, 16384, 8192, 4096, 2048))

leaflet(options = leafletOptions(maxZoom = 2,
  crs= crs.molvidde, attributionControl = FALSE)) %>%                           

I hope this has been useful.

[- Download table of colours]

.

Maps ©OpenStreetMap contributors unless otherwise noted.

man column

DESCRIPTION
     The column utility formats its input into multiple columns. Rows are filled before columns. Input is taken from file operands, or, by default, from the standard input. Empty lines are ignored.

Say there is a table separated by commas as following:

object,categoryA,categoryB,categoryC,categoryD,categoryE,categoryF
nameA,3553,1200,100,0,150,240
nameB,1000,500,736,0,0,0
nameC,4882,500,0,400,0,0
nameD,500,350,0,0,0,1000
nameE,250,0,500,0,175,0
nameF,60,250,0,0,786,560
nameG,0,0,1000,0,3000,0
nameH,491,200,0,120,277,0

In vim, this can be then converted using the column utility:

:%!column -t -s ','

And the table changes to:

object  categoryA  categoryB  categoryC  categoryD  categoryE  categoryF
nameA   3553       1200       100        0          150        240
nameB   1000       500        736        0          0          0
nameC   4882       500        0          400        0          0
nameD   500        350        0          0          0          1000
nameE   250        0          500        0          175        0
nameF   60         250        0          0          786        560
nameG   0          0          1000       0          3000       0
nameH   491        200        0          120        277        0

It fetches lyrics of current song playing in CMus or directly typed in the terminal.

A few comments about the figure above:

• tmux session with two panes and status bar

• the status bar displays the current song with the same script used by cmus-lyrics

• there is an alias in the corresponding BASH profile to call cmus-lyrics

See details in cmus-lyrics at github

Particularly, here is an example of how to edit bibliography-related fields within the *.bib file using vim.

Matches are made with regular expressions (aka regex). Hence, it is convenient to check for consistency along the fields in the document (i.e. What kind of format is used? What kind of separators?).

Please don”t forget to use :help to find out more about matches and patterns in vim (:help regex, :h search, etc).

Preliminaries

A *.bib file contains the following structure.

    @article{keyentry,
       author    = {author},
       abstract  = {some text},
       annote    = {some text},
       url       = {someurlinwww},
       title     = {some text},
       journal   = {some text},
       year      = {some year},
    }

Alternative structures may suppress brackets or use quotation marks instead. For example,

    @article{keyentry,
       author    = "author",
       abstract  = "some text",
       annote    = "some text",
       url       = "someurlinwww",
       title     = "some text",
       journal   = "some text",
       year      = some year,
    }

The search + replace methods implemented in vim follow the structure: :range s[ubstitute]/pattern/string/cgiI where c confirms each substitution; g replaces all occurrences in the line (without g - only first); i ignores case for the pattern; and I doesn”t ignore case for the pattern.

.

Example

Here is the bibliography file used for the example - Sample.bib

In normal mode, / is used as a preliminary search to check for the pattern recognition. For instance, the following command searches for fields containing url.

    / url\s*=

Here \s means “any space” and * means “any number of occurrences”.

To display all occurrences in the file, the following command can be used:

    :g/ url\s*= {.*\}

where .*\ means “any number of characters excluding the new line”. Therefore, if the bib entry is expected to be a single line, this expression is sufficient; however for an entry with multiple lines, such as the abstract section, the command may require an additional method. The following command includes \{-} which stands for “null or more matches of the preceding atom”. As a result, the search is made iterative and everything contained between the brackets is thus found.

    :g/ url\s*= {\_.\{-}},

The final search + replace command results as following:

    :%s/ url\s*= {\_.\{-}},/url = {},/g

Using a similar search + replace command is then used to replace other fields; however, if more than one field must be changed, then a set of vim instructions may be called from the terminal, using vim -c. The following command (i) replaces information contained in 4 fields, (ii) saves the edited document in a new one, and (iii) exits without saving in the original file.

  vim -c "%s/ url\s*= {\_.\{-}}/url = {}/g" \
      -c "%s/ annote\s*= {\_.\{-}}/ annote = {}/g" \
      -c "%s/abstract\s*= {\_.\{-}}/ abstract = {}/g" \
      -c "%s/ file\s*= {\_.\{-}}/ file = {}/g" \
      -c "wq temp.bib" \
      -c "q\!" \
      sample.bib

.

Of course, as is common practice in vim, such a command might be replaced by a more sophisticated regex method which reduces the number of instructions and employs different patterns. By the way, a maximum of 10 instructions is supported in vim -c mode.

To end, cleaning a *.bib file in/with vim can be performed in a single command. Pretty cool!

I hope this has been useful.

For example, the following file contains some information in a typical fortran structure:

!- An example with objects and variables

Object A,  !- Some comment or additional label for objects
 Name A1,  !- Name of object
      10,  !- First variable in object
       9,  !- Second variable in object
       8,  !- Third variable in object
       7;  !- Fourth variable in object

Object A,  !- Some comment or additional label for objects
 Name A2,  !- Name of object
      20,  !- First variable in object
      19,  !- Second variable in object
      18,  !- Third variable in object
      17;  !- Fourth variable in object

Object A,  !- Some comment or additional label for objects
 Name A3,  !- Name of object
      10,  !- First variable in object
      19,  !- Second variable in object
       8,  !- Third variable in object
      17;  !- Fourth variable in object

Object B,  !- Some comment or additional label for objects
 Name B1,  !- Name of object
      20,  !- First variable in object
       9,  !- Second variable in object
       8,  !- Third variable in object
       7;  !- Fourth variable in object

Object C,  !- Some comment or additional label for objects
 Name C1,  !- Name of object
      10,  !- First variable in object
       9,  !- Second variable in object
      18,  !- 3rd variable in object
       7;  !- Fourth variable in object

Object C,  !- Some comment or additional label for objects
 Name C2,  !- Name of object
      30,  !- First variable in object
       9,  !- Second variable in object
       8,  !- Third variable in object
       7;  !- Fourth variable in object

And we want to replace the first variable in objects A and B, with 55.

To this end, we need to: 1. Define the logic 2. Translate the logic to a regex command 3. Manage the repetition of this logic.

1. Define the logic

One way of doing this would be to search fo the common pattern: “First variable in object”, and then check if it is contained in either “Object A” or “Object B”.

Or the other way round, to search for “Object A” or “Object B” and delimit the pattern to the presence of “First variable in object”.

Let’s use the second alternative. First we need to find “Object A” or “Object B”, followed by any text. Second we need to stop the search until the pattern “First variable in object” appears. Because the later pattern appears multiple times, we delimit the second search to a range of lines so it doesn’t match a pattern in the following object.

2. Translate the logic to regex command

A line with “Object A” or “Object B”, followed by any text:

/Object [BC].*\n

[BC] : B or C character
\n   : a newline character (line ending)

Stop until the pattern “First variable” appears, in a range of 6 lines.

.*\(\_s.*\)\{0,6\}\_sFirst variable

.*      : any character except new line, multiple times
\_s     : a whitespace (space or tab) or newline character
\{0,6\} : a range of 6 lines

The full regular expression:

/Object [BC].*\n .*\(\_s.*\)\{0,6\}\_sFirst variable

3. Manage the repetition of this logic.

We record a macro (in t) to make the substitution.

qt
/Object [BC].*\n .*\(\_s.*\)\{0,6\}\_sFirst variable
2jwcw55<esc>j
q

2j    : move two lines down
w     : move one word
cw    : change word
55    : new value
<esc> : return to command mode
j     : move one line down

And recursively run the macro in the file

:%normal @t

Let’s now replace the third variable in all objects with 99.

qt
/Object.*\n .*\(\_s.*\)\{0,3\}\_svariable
2jwcw55<esc>j
q
:%normal @t

I hope this has been useful.

In particular, the Focused Principal Component Analysis (fPCA) conveys the structure of a correlation matrix into a low-dimensional diagram but, unlike PCA, it makes it possible to represent accurately the correlations of a given variable with the other variables (and even to test graphically the hypothesis that one of these correlations is equal to zero).

In sum, the fPCA provides a focused outcome, so that the distances between the predictors and the outcome can be interpreted as a representation of their correlations. The relative positions of the predictors can give an idea of their correlations and can be interpreted as in a classic PCA.

Example:

The iris dataset is used to exemplify the use of the fPCA method. The dataset contains four measurements for 150 flowers representing three species of iris (i.e. setosa, versicolor and virginica).… see more

setosa	versicolor	virginica

The first step which is usually taken when analysing data is to quickly find a linear correlation. However, when the data contains several factors, the correlation may be unclear and the next step consists on finding clusters between the data to complement the correlation.

The following figure shows that there are clearly clusters between versicolor and virginica (green and blue), and a linear correlation between Petal.Length, Petal.Width.

> summary(mod)

  Call:
  lm(formula = iris$Species ~ iris$Sepal.Length + iris$Sepal.Width +
      iris$Petal.Length + iris$Petal.Width)

  Residuals:
       Min       1Q   Median       3Q      Max
  -0.59215 -0.15368  0.01268  0.11089  0.55077

  Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
  (Intercept)        1.18650    0.20484   5.792 4.15e-08 ***
  iris$Sepal.Length -0.11191    0.05765  -1.941   0.0542 .  
  iris$Sepal.Width  -0.04008    0.05969  -0.671   0.5030    
  iris$Petal.Length  0.22865    0.05685   4.022 9.26e-05 ***
  iris$Petal.Width   0.60925    0.09446   6.450 1.56e-09 ***
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

  Residual standard error: 0.2191 on 145 degrees of freedom
  Multiple R-squared:  0.9304,  Adjusted R-squared:  0.9285
  F-statistic: 484.5 on 4 and 145 DF,  p-value: < 2.2e-16

As seen, the linear correlation suggests these four variables as predictors with R-squared > 90%. This values indicates how well a regression model predicts responses for the reference observation. But it needs to be used with caution at all times. It might be the case—and this a very common error case in descriptive statistics—that the correlation found can be considered as a well fitting method to generate an equation. But in reality, the method is unable to explain the true relationship between the variables.

If the purpose of the preliminary analysis is to provide an overview of the dataset, then the fPCA method is a good candidate due to its ability to provide both correlations and clusters in a fast way.

In brief, the variables inside the dashed line are significantly associated with the focused outcome (Species). Green dots correspond to positive associations, and yellow dots to negative associations. Dots in opposite quadrants represent negative correlation between them. And finally, the red line represents a significant correlation with the outcome; this is also given by the r values in the axis.

As seen in the figure, the correlation between Petal.Length and Petal.Width is maintained in this method; the lm model supports the further explanation of the correlation. It can also be seen that Sepal.Width is the less accurate predictor as found in both lm and fPCA methods; however, in the former the variable is neglectable (for the model) but in the later is considered because of its relation with the other variables. This is a relevant attribute of the fPCA method.