library(knitr) opts_chunk$set(eval = TRUE, message=FALSE, warnings=FALSE, results="hold")
Momocs aims to provide a complete and convenient toolkit for morphometrics. It is intended for scientists interested in describing quantitatively the shape, and its (co)variations, of the objects they study.
In the last decade, R has become the open-source lingua franca for statistics, and morphometrics known its so-called "revolution". Nevertheless, morphometric analyses still have to be carried out using various software packages either dedicated to a particular morphometric and/or for which source code is mostly unavailable and/or copyrighted. Moreover, most of existing software packages cannot be extended and their bugs are hard to detect and thus correct. This situation is detrimental to morphometrics: time is wasted, analyses are restricted to available methods, and last but not least, are poorly reproducible. This impedes collaborative effort both in software development and in morphometric studies.
By gathering the common morphometric approaches in an open-source environment and welcoming contributions, Momocs is an attempt to solve this twofold problem.
Momocs hinges on the core functions published in the must-have Morphometrics with R by Julien Claude (2008), but has been further extended to allow other shape description systems. So far, configurations of landmarks, outlines and open outline analyses, along with some facilities for traditional morphometrics are implemented.
Prior to analysis, Momocs can be used to acquire and manipulate data or to import/export from/to other formats. Momocs also has the facility for a wide range of multivariate analyses and production of the companion graphics. Thus a researcher will find that just a few lines of code will provide initial results, but the methods implemented can be finely tuned and extended according to the user's needs.
citation("Momocs")
.?efourier
.Momocs_help("efourier")
.[email protected]
.First, of all, let's download the last version of Momocs. You will need to install the devtools
package to get it from my GitHub repository :
install.packages("devtools") devtools::install_github("vbonhomme/Momocs")
The typical install_packages("Momocs")
will get you the last CRAN version of Momocs, but the GitHub version is preferred as Momocs is still under active development.
We can start using Momocs, as long as it has been loaded using:
library(Momocs)
Keywords used all accross Momocs are introduced here in bold.
Morphometrics is the ugly job of turning beautiful shapes into quantitative variables. Just kidding, that's pretty exciting.
A shape is defined as a collection of (x; y)
coordinates. No 3D yet but different families can be handled: outlines, here in a first-quarter moon ; open outlines, here is the sterile valve of an olive stone; configuration of landmarks; here, hologous points from a mosquito wing.
shapes[18] %>% coo_sample(60) %>% coo_plot(points=TRUE) olea[11] %>% coo_sample(32) %>% coo_plot(points=TRUE) wings[1] %>% coo_plot(points=TRUE)
They are all single shapes defined by a matrix of (x; y)
coordinates; here are the first points of the moon:
shapes[18] %>% head()
A few dozens of operations on single shapes are implemented such as: plotting, centering, calculating areas, etc. These 70+ operations can be accessed with apropos("coo_")
. But working on single shapes is quite boring.
Shapes can be organized into a collection of coordinates: a Coo
object that carries:
$coo
, a list
of shapes (as matrix
.ces);$fac
, a data.frame
to store covariates, either factor
s or numeric
s;One can do several things with a Coo
object: visualize it, apply morphometric operations, handle the data it contains, but in the end, a morphometric method will turn coordinates into coefficients.
Such morphometric operation on coordinates produce a collection of coefficients: a Coe
object that carries:
$coe
, a matrix
of coefficients;Coo
, $fac
is inherited;This can be summarized as follows:
Coo
| + | Morphometric method | = | Coe
---------------------------|----|----------------------|----|---------------
(x; y)
coordinates | + | appropriate method | = |quantitative variables
Coo
objects are collections of coordinates that become Coe
objects when an appropriate morphometric method is applied on them.
Some operations on Coo
/Coe
are generic in that they do not depend of the nature of the shape. For instance, centering a configuration of landmarks or an outline, or calculating their centroid size is, mathematically, the same generic operation. But some operations on shapes are specific to a peculiar family. For instance, calculating elliptical Fourier transforms on a configuration of landmarks would make no sense.
Momocs implement this desirable behavior and defines classes and subclasses, as S3 objects.
Coo
| Morphometrics methods | Coe
-------------------------------------|------------------------------------|----------
OutCoo
(outlines) | efourier
, rfourier
, sfourier
, tfourier
| OutCoe
OpnCoo
(open outlines) | npoly
, opoly
, dfourier
| OpnCoe
LdkCoo
(configuration of landmarks)|fgProcrustes
, slide
| LdkCoe
You can see this table as a scheme there.
In other words:
{OutCoo, OpnCoo, LdkCoo}
and is also a Coo
object;{OutCoe, OpnCoe, LdkCoe}
and is also a Coe
object.Finally, generic and specific operations can be applied to the Coe
objects, chiefly multivariate methods, capitalicized: PCA
, LDA
, CLUST
, MANOVA
(and MANCOVA), MSHAPES
, KMEANS
, etc.
Overall, Momocs implements a simple and consistent grammar that is detailed below.
Also, if you're familiar with modern R and the Hadley-verse, you should feel home as ggplot2
graphics, dplyr
verbs and magrittr
pipes are implemented.
Let's load one of the Momocs datasets, some various outlines (an Out
object):
shapes # prints a brief summary panel(shapes, names=TRUE) # base graphics panel2(shapes) # ggplot2 graphics
shapes
is one of the datasets bundled with Momocs. It's ("lazy") loaded in memory as soon as you call it, no need for data(shapes)
. To see all Momocs' datasets, try data(package="Momocs")
. These datasets are all Coo
objects (try class(bot)
), ie collection of shapes.
One can do many things on a Coo
object, as above, e.g. printing a summary of it (just by typing its name in the console), plotting a family picture with panel
or panel2
. Note the 2
that refers to a ggplot2
variant of a given plot.
So far, we're interested in single shapes so
let's extract the 4th shape from shapes
, using the traditional syntax. We plot it with coo_plot
that comes with several options for plotting all families of shapes.
shp <- shapes[4] coo_plot(shp) # coo_plot is the base plotter for shapes # but it can be finely customized, see ?coo_plot coo_plot(shp, col="grey80", border=NA, centroid=FALSE, main="Meow")
Let's now do some basic operations on this shape. They all named coo_*
and you can have the full list with apropos("coo_")
. coo_*
family encompasses:
coo_plot(coo_center(shp), main="centered Meow") coo_plot(coo_sample(shp, 64), points=TRUE, pch=20, main="64-pts Meow")
Momocs makes use of maggritr's pipe operators. A nice introduction can be found there. magrittr
requires a (very small) cerebral gymnastics at the beginning but the benefits are huge, for defining moprhometric pipelines in Momocs but also for R as a whole. It makes things clearer, it: saves typing; reduces intermediate variable assignation; reads from left to right; substantiates the pipe we (should) have in mind. magrittr
's pipes are already loaded with Momocs.
shapes[4] %>% coo_smooth(5) %>% coo_sample(64) %>% coo_scale() %>% coo_plot() # pipes can be turned into custom function cs64 <- function(x) x %>% coo_sample(64) %>% coo_scale() %>% coo_center() shapes[4] %>% cs64 %>% coo_plot() # note the axes
The most familiar operation can directly be applied on Coo
objects:
bot %>% coo_center %>% coo_scale %>% coo_alignxax() %>% coo_slidedirection("N") %T>% print() %>% stack()
A word about data import: you can extract outlines from a list of black masks over a white background, as .jpg
images with import_jpg
. Have a look to helpfiles (import_jpg
and import_jpg1
) for more details. Here we do not bother with import since we will use the bot
tles outlines dataset bundled with Momocs.
data(bot) bot panel(bot, fac="type", names=TRUE) stack(bot) ```` Here we will illustrate outline analysis with some elliptical Fourier transforms (but the less used - and tested - radii variation Fourier transforms, its variant used by Renaud et al., and tangent angle Fourier transforms are also implemented with `rfourier`, `sfourier` and `tfourier` respectively). The idea behind elliptical Fourier transforms is to fit the x and y coordinates separately, that is the blue and red curves below: ```r coo_oscillo(bot[1], "efourier")
Graphically, this is equivalent to fitting Ptolemaic ellipses on the plane:
Ptolemy(bot[1])
Let's calibrate the number of harmonics required. More details can be found in their respective help files.
calibrate_harmonicpower(bot) calibrate_deviations(bot) calibrate_reconstructions(bot) ```` Here, 10 harmonics gather 99% of the harmonic power. If you're happy with this criterium, you can even omit `nb.h` in `efourier`: that's the default parameter, returned with a message. ```r bot.f <- efourier(bot, nb.h=10) bot.f
bot.f
is a Coe
object (and even an OutCoe
), you have have a look to the help files to go deeper into Momocs classes.
Let's have a look to the amplitude of fitted coefficients.
hist(bot.f, drop=0) boxplot(bot.f, drop=1)
Now, we can calculate a PCA on the Coe
object and plot it, along with morphospaces, calculated on the fly.
bot.p <- PCA(bot.f) class(bot.p) # a PCA object, let's plot it plot(bot.p)
Amazing but we will do much better afterwards.
The question of normalization in elliptical Fourier transforms is central. Normalization can either be done beforehand, with geometric operations, or afterhand, directly on the matrix of Fourier coefficients and consuming the first harmonic. In brief, I'm not a big fan of the "use the first harmonics and see what happens" strategy, as some biases can be introduced (and actually quite hard to detect), particularly on rounded/ellipsoid shapes. More can be found in ?efourier
.
Here is an example of such bias from the molars
dataset generously shared by Cornu and Detroit.
# raw molars dataset stack(molars, title = "Non-aligned molars") # Procrustes-aligned and slided molars mol.al <- fgProcrustes(molars, tol = 1e-4) %>% coo_slidedirection("W") stack(mol.al, title="Aligned molars") # Now compare PCA and morphospace using the 1st harmonic alignment molars %>% efourier(norm=TRUE) %>% PCA() %>% plot("type") # and the a priori normalization molars %>% efourier(norm=FALSE) %>% PCA() %>% plot("type")
Finally, you can drop some harmonics with rm_harm
. And methods that removes the bilateral (a)symmetry are implemented: rm_asym
and rm_sym
, while symmetry
calculates some related indices.
Open outlines are curves. Methods actually implemented are:
npoly
that fit natural polynomials;opoly
that fit orthogonal (also called Legendre's) polynomials;dfourier
for the discrete cosine transform.Note that opoly
and npoly
can only be used on simple curves, curves that have at most one y
for any x
coordinates, at least under a given orientation. dfourier
can fit complex curves, curves "that back on their feets".
Here, we will work on the fertile valves of olive stones, a (very partial) dataset provided by my colleagues Terral, Ivorra, and others.
They have two orthogonal views (a lateral and a dorsal view). See the paper cited in ?olea
for more details. Let's explore it a bit:
olea stack(olea, fac="view") # already aligned \o/ panel(olea, names="ind") # another family picture
Now, we gonna calculate opoly
on it and plot the result of the PCA. Notice how consistent is the grammar and the objects obtained:
op <- opoly(olea) # orthogonal polynomials class(op) # an OpnCoe, but also a Coe op.p <- PCA(op) # we calculate a PCA on it class(op.p) # a PCA object plot(PCA(op), ~domes+var) # notice the formula interface to combine factors
But this is perfectly wrong! We merged the two views are if they were different individuals. Momocs can first chop
or filter
the whole dataset to separate the two views, do morphometrics on them, and combine
them afterwards.
table(olea, "view", "var") # we drop 'Cypre' since there is no VL for 'Cypre' var olea %>% filter(var != "Cypre") %>% # split, do morphometrics, combine chop(view) %>% lapply(opoly) %>% combine() %T>% # we print the OpnCoe object, then resume to the pipe print() %>% # note the two views in the morphospace PCA() %>% plot("var")
Now the PCA is done on the combination of two OpnCoe
objects, each one resulting from an independant opoly
call. That is the meaning of the [ combined: opoly + opoly analyses ]
printed by the pipe above. Momocs can combine up to four different views.
_Landmarks methods are still quite experimental (i.e. not tested extensively)
Let's have a look to graphics facilities and apply a full generalized Procrustes adjustment:
stack(wings, title = "Raw wings") w.al <- fgProcrustes(wings) stack(w.al, title = "Aligned wings") ldk_chull(w.al$coo) ldk_labels(mshapes(w.al$coo)) # try those as well #ldk_confell(w.al$coo, col = "red") #ldk_contour(w.al$coo) # PCA PCA(w.al) %>% plot(1)
Sliding landmarks are supported and rely on geomorph
package by D.C. Adams and E. Otarola-Castillo.
stack(chaff, title="Raw chaff") chaff.al <- fgsProcrustes(chaff) stack(chaff.al, title="Aligned chaff") chaff.al %>% PCA() %>% plot(~taxa, chull.filled=TRUE)
Again, the grammar is consistent for landmarks.
Traditional morphometrics lose geometries: from the variables, you can't unambiguously reconstruct the shape. Every shape is described by a combination of measurements, (inter landmark distance, quantitative variables, scalar descriptor, etc.)
Momocs provides some basics utilities to work with such objects in the TraCoe
class. There is not TraCoo
per se but it can be obtained from any Coo
with the measure
method. Let's take the hearts
dataset that comes from handdrawn heart shapes from my former colleagues at the French Intitute of Pondicherry:
hearts panel(hearts, fac="aut", names="aut")
Notice that there are 4 landmarks defined on them. Such landmarks on outlines can be: defined withdef_ldk()
, retrieved with get_ldk()
, and overall used to align outlines with fgProcrustes()
. You can compare: hearts %>% stack()
with hearts %>% fgProcrustes() %>% coo_slide(ldk=1) %>% stack()
.
Let's describe these hearts with scalar descriptors: area, circularity and the distance between the 1st and the 3rd bumps of the hearts. measure
is of great help. Note the loadings.
ht <- measure(hearts, coo_area, coo_circularity, d(1, 3)) ht %>% PCA() %>% plot("aut", pch=20, ellipsesax=F, ellipse=T, loadings=T)
Again, there are plenty of scalar descriptors of shape, which names starts with coo_*
, apropos("coo_")
.
Such a TraCoe
is provided in the flower
dataset which is simply a rearranged iris
. Once again, note the grammar consistency.
flower flower %>% PCA() %>% plot("sp", loadings=TRUE, contour=TRUE, lev.contour=5)
Note that, by default, PCA on TraCoe
object first centers and scales variables. This can be changed, see ?PCA
.
This section will mainly be illustrated with bot
, and consequently outline analysis, but it works the same on any Coe
object, resulting from open outlines, configuration of landmarks, traditional morphometrics, etc.
bot.f <- efourier(bot)
PCA
: Principal Component AnalysisLet's see the main components of shape variability with a Principal Component Analysis.
bot.p <- PCA(bot.f) plot(bot.p) ```` Morphological spaces are reconstructed on the fly with `plot.PCA`. We call it `plot.PCA` because it uses the familiar `plot` but on the particular `PCA` class (type `class(bot.p)`). We may want to display the two groups saved in `bot$fac`. Just type the id of the column or its name. ```r plot(bot.p, "type") # equivalent to plot(bot.p, 1)
plot.PCA
has many arguments and examples below will give a glimpse, but see ?plot.PCA
for the full list.
plot(bot.p, 1, ellipses=TRUE, ellipsesax = FALSE, pch=c(4, 5)) plot(bot.p, 1, chull=TRUE, pos.shp = "full_axes", abbreviate.labelsgroups = TRUE, points=FALSE, labelspoints = TRUE) plot(bot.p, 1, pos.shp="circle", stars=TRUE, chull.filled=TRUE, palette=col_spring)
You have a ggplot2
alternative, that is not completely consistent:
# plot2(bot.p, "type") # deprecated for the moment
And other helper functions for the PCA
class:
scree(bot.p)
scree_plot(bot.p)
boxplot(bot.p, 1)
PCcontrib(bot.p)
By the way, you can use Momocs plotters to plot non-morphometric datasets. Using a TraCoe
object is an option, but PCA
also works fine. Let's see an example with iris
dataset:
TraCoe(iris[, -5], fac=data.frame(sp=iris$Species)) %>% PCA() %>% plot("sp", loadings=TRUE) # or PCA(iris[, -5], fac=data.frame(sp=iris$Species)) %>% plot("sp", chull=TRUE, ellipses=TRUE, conf_ellipses = 0.9)
LDA
: Linear Discriminant AnalysisWe can also calculate a Linear Discriminant Analysis on the PCA scores, or on the Coe
object, directly on the matrix of coefficients (and results may be better yet we may encounter collinearity between variables). Try the following:
#LDA(bot.f, 1) # we work on PCA scores bot.l <- LDA(bot.p, 1) # print a summary, along with the leave-one-out cross-validation table. bot.l # a much more detailed summary bot.l %>% summary # plot.LDA works pretty much with the same grammar as plot.PCA # here we only have one LD plot(bot.l) # plot the cross-validation table plot_CV(bot.l) # tabular version plot_CV(bot.l, freq=TRUE) # frequency table plot_CV2(bot.l) # arrays version
Finally, if you need to classify using LDA methods, see classify
.
MANOVA
: Multivariate Analysis of (co)variaceWe can test for a difference in the distribution of PC scores with:
MANOVA(bot.p, "type")
We can also calculate pairwise combination between every levels of a fac
. Here we just have two levels, so a single pairwise combination but the syntax is:
MANOVA_PW(bot.p, "type")
If we want a MANCOVA instead :
bot %<>% mutate(cs=coo_centsize(.)) bot %>% efourier %>% PCA %>% MANOVA("cs")
CLUST
: Hierarchical clusteringA hierarchical classification now. It relies on dist
+ hclust
+ ape::plot.phylo
.
CLUST(bot.p, 1)
Monophyly is plotted by default. Many options can be found in ?CLUST
KMEANS
: K-means clusteringA very minimal k-means clustering is implemented:
KMEANS(bot.p, centers = 5)
mshapes
: Mean shapesWe can retrieve the mean shapes, group wise (if a fac
is specified), or the global mean shape (if omitted). It works from the Coe
object:
# mean shape bot.f %>% mshapes() %>% coo_plot() # mean shape, per group bot.ms <- mshapes(bot.f, 1) beer <- bot.ms$shp$beer %T>% coo_plot(border="blue") whisky <- bot.ms$shp$whisky %T>% coo_draw(border="red") legend("topright", lwd=1, col=c("blue", "red"), legend=c("beer", "whisky"))
We can also plot a pairwise comparison of them:
leaves <- shapes %>% slice(grep("leaf", names(shapes))) %$% coo leaves %>% plot_mshapes(col2="#0000FF") # or from mshapes directly bot %>% efourier(6) %>% mshapes("type") %>% plot_mshapes
dplyr
verbsOne common yet boring task of morphometrics consists in handling datasets: add new information, remove some individuals, etc.
Momocs adapts dplyr verbs to its objects, and add new ones. If you have never heard of dplyr, let's have a look to its introduction there, this may change your (R) life.
Basics verbs are implemented; try the following:
data(olea) olea # slice: select individuals based on their position slice(olea, 1:5) slice(olea, -(1:100)) # filter: select individual based on a logical condition filter(olea, domes=="cult", view!="VD") # select: pick, reorder columns from the $fac select(olea, 1, Ind=ind) # rename: rename columns (select can also do it) rename(olea, domesticated=domes) # mutate: add new columns mutate(olea, fake=factor(rep(letters[1:2], each=105)))
And you can pipe those operations: say, we only want dorsal views from domesticated individuals, for a (renamed) 'status' column, and drop the 'ind' column:
olea %>% filter(domes=="cult", view=="VD") %>% rename(domesticated=domes) %>% select(-ind)
This should save some headaches.
dplyr
New verbs are implemented: for instance, you can chop
(a rougher slicing) according to a condition: this will create a list, on which you can apply further operations, then combine
it back. This is particularly useful when you want to apply independant treatments to different partitions, eg orthogonal views of your model. Prior to this, we can use table
to cross-tabulate data from $fac
. We could have done the first step of what follows with rm_uncomplete
that drops (if any) missing data.
table(olea, "view", "var") # we drop 'Cypre' since there is no VL for 'Cypre' var olea %>% filter(var != "Cypre") %>% # split, do morphometrics, combine chop(view) %>% lapply(opoly) %>% combine() %>% # note the two views in the morphospace PCA() %>% plot("var")
Also if you need some group_by
operation, you can use the fac
data.frame
. For instance if you want to arrange
based on a column and retain the top 10 shapes, then you can:
retain <- x$fac %>% # here we sort and create an id arrange(your_column) %>% mutate(.id=1:n()) %>% dplyr::group_by(taxa) %>% slice(1:10) %$% .id # it worked! x %>% slice(retain) %$% table(fac$taxa)
Some methods help, on Coe
objects to:
* select groups with at least a certain number of individuals in them: at_least
* removes outliers : which_out
* sample a given number: sample_n
;
* sample a given proportion: sample_frac
;
* generate new individuals based on calibrated Gaussian coefficient generation: breed
;
* generate new individuals based on permutations: perm
.
Several shortcuts are implemented on Coo
and Coe
objects:
names
returns shape names;
length
returns their number;
table
cross-tabulates the$fac
component;
Ntable
does the same job and plots a confusion matrix;
[]
extracts one (or more) shape;
$
can access either a shape name or a column name for the $fac
.
Try the following:
names(bot) length(bot) table(olea, "var", "domes") Ntable(olea, "var", "domes") bot[1] bot[1:5] bot$brahma bot$type
There are various morphometrics formats in the wild, almost as much as softwares. Momocs tries to create bridges between them, all gathered in the Babel family.
You can convert from/to array, matrix, list or data.frame with the functions {a, m, l, d}2{a, m, l, d}
. For instance, l2a
converts a list into an array that you can use with geomorph
; a2l
does the inverse operation.
Imagine you want to import pupfish from geomorph
as a Ldk
object:
library(geomorph) data(pupfish) str(pupfish) # so $coords will become $coo, and # all other components will be turned into a data.frame to feed $fac # with a single line Ldk(coo=pupfish$coords %>% a2l, fac=pupfish[-1] %>% as.data.frame())
If you use this excellent package by Olsen to digitize landmarks and curves, you can import them, from the files produced with the functions import_StereoMorph_ldk
and import_StereoMorph_curve
.
.tps
files can be read with import_tps
.nts
files an be read with nts2Coo
(will be turned into import_nts
soon)Coo
objects. For instance if you have a matrix of coefficients, you can directly build an OutCoe
with the builder (see below). Same approach for OpnCoe
and TraCoe
; have a look to the help files of these builders.# we simulate an imported matrix of coordinates, eg from a .csv coeffs_from_the_wild <- bot %>% efourier(6) %$% coe coeffs_in_Momocs <- OutCoe(coe=coeffs_from_the_wild, method="efourier", norm=TRUE) coeffs_in_Momocs %>% PCA %>% plot
spA_group7_ind4_VL.{txt|jpg|etc.}
, use lf_structure
;$fac
, rw_rule
is your friend;rescale
;.txt
(eg coordinates of landmarks on them), see tie_jpg_txt
.The best way to save a Momocs object is probably to use the base save
function. You can call it back afterwards with load
:
save(bot, file="Bottles.rda") # closing R, going to the beach, back at work load("Bottles.rda") # bot is back
Any Momocs object, Coo
s, Coe
s, PCA
s, etc. can be turned into a data.frame
with as_df
. Work with dplyr
, ggplot2
is made easy and you can export it as .txt
, .csv
"by hand" or use the export
function:
bot %>% as_df # then %>% write.table bot %>% efourier %>% export bot %>% efourier %>% PCA %>% export
But, of course, you can directly access information within the Momocs objects; try the following:
# from Coo objects bot$coo bot$fac # from Coe objects bot.f$coe bot.f$fac as_df(bot.f) # from PCA objects bot.p$x # scores bot.p$rotation # rotation matrix
The most common graphics have been already presented in previous sections. But there are many more of them, and they are gathered in the Graphics family that can be accessed with ... TODO.
tps_*
: Thin Plate SplinesTPS have not been presented before but here there are:
tps_grid(beer, whisky) tps_arr(beer, whisky) tps_iso(beer, whisky)
Again, plenty options in ?tps_*
.
You may also like lolliplots and friends:
coo_lolli(beer, whisky); title("coo_lolli") coo_arrows(beer, whisky); title("coo_arrow") # an example with coo_ruban coo_plot(beer) # to get the first plot coo_ruban(beer, edm(beer, whisky), lwd=8) # we add ruban based from deviations coo_draw(whisky) title("coo_ruban")
ggplot2
Many graphical function have their ggplot2
counterpart:
panel(bot) panel2(bot)
And the classical ggplot2
grammar applies:
library(ggplot2) gg <- panel2(bot) gg + theme_minimal()
Otherwise, you can build your own plots. Let's begin with a simple example:
# we build a ggplot object from a shape turned into a data.frame shapes[4] %>% m2d() %>% ggplot() + aes(x, y) + geom_path() + coord_equal() + labs(title="ggplot2 Meow") + theme_minimal()
Here is a more complicated plot that takes profit of as_df
methods that turns objects into ggplot2
-friendly data.frame
s:
bot.p %>% as_df() %>% ggplot() + aes(x=PC1, y=PC2, col=type) + coord_equal() + geom_point() + geom_density2d() + theme_light()
?Palettes
.How to access all methods for a given class?
methods(class="your_class")
, eg methods(class="Opn")
How to access methods defined for a particular function?
methods("your_function")
, eg methods("fgProcrustes")
How can I access help for a particular methods?
function.class
, eg ?plot.PCA
How can I access the code of a method?
lf_structure
; for methods add its class, eg plot.PCA
To be continued
<[email protected]>
.Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.