vignette is back

.gitignore

@@ -7,7 +7,6 @@
 .Rproj.user
 data/phoenix.R
 man/phoenix.Rd
-inst/doc
 *.DS_Store
 r-hub.R
 tmp/.*

inst/doc/Momocs_speed_dating.R

@@ -0,0 +1,334 @@
+## ---- echo=FALSE---------------------------------------------------------
+library(knitr)
+opts_chunk$set(eval = TRUE, message=FALSE, 
+               warnings=FALSE, results="hold")
+
+## ---- eval=FALSE---------------------------------------------------------
+#  install.packages("devtools")
+#  devtools::install_github("vbonhomme/Momocs")
+
+## ---- eval=TRUE, echo=TRUE, message=FALSE--------------------------------
+library(Momocs)
+
+## ---- echo=FALSE, message=FALSE------------------------------------------
+shapes[18] %>% coo_sample(60) %>% coo_plot(points=TRUE)
+olea[11] %>% coo_sample(32) %>% coo_plot(points=TRUE)
+wings[1] %>% coo_plot(points=TRUE)
+
+## ------------------------------------------------------------------------
+shapes[18] %>% head()
+
+## ------------------------------------------------------------------------
+shapes                    # prints a brief summary
+panel(shapes, names=TRUE) # base graphics
+panel2(shapes)            # ggplot2 graphics
+
+## ------------------------------------------------------------------------
+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")
+
+## ------------------------------------------------------------------------
+coo_plot(coo_center(shp), main="centered Meow")
+coo_plot(coo_sample(shp, 64), points=TRUE, pch=20, main="64-pts Meow")
+
+## ------------------------------------------------------------------------
+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
+
+## ------------------------------------------------------------------------
+bot %>% 
+  coo_center %>% coo_scale %>% 
+  coo_alignxax() %>% coo_slidedirection("N") %T>% 
+  print() %>% stack()
+
+## ------------------------------------------------------------------------
+data(bot)
+bot
+panel(bot, fac="type", names=TRUE)
+stack(bot)
+
+## ------------------------------------------------------------------------
+coo_oscillo(bot[1], "efourier")
+
+## ------------------------------------------------------------------------
+Ptolemy(bot[1])
+
+## ------------------------------------------------------------------------
+calibrate_harmonicpower(bot)
+calibrate_deviations(bot)
+calibrate_reconstructions(bot)
+
+## ------------------------------------------------------------------------
+bot.f <- efourier(bot, nb.h=10)
+bot.f
+
+## ------------------------------------------------------------------------
+hist(bot.f, drop=0)
+boxplot(bot.f, drop=1)
+
+## ------------------------------------------------------------------------
+bot.p <- PCA(bot.f)
+class(bot.p)        # a PCA object, let's plot it
+plot(bot.p)
+
+## ---- message=FALSE, error=FALSE, warning=FALSE, results="hide"----------
+# 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")
+
+## ------------------------------------------------------------------------
+olea
+stack(olea, fac="view")     # already aligned \o/
+panel(olea, names="ind")    # another family picture
+
+## ------------------------------------------------------------------------
+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
+
+## ---- message=FALSE------------------------------------------------------
+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")
+
+## ---- message=FALSE, echo=TRUE-------------------------------------------
+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$coo)
+
+# PCA
+PCA(w.al) %>% plot(1)
+
+## ---- message=FALSE, echo=TRUE-------------------------------------------
+stack(chaff, title="Raw chaff")
+chaff.al <- fgsProcrustes(chaff)
+stack(chaff.al, title="Aligned chaff")
+chaff.al %>% PCA() %>% plot(~taxa, chull.filled=TRUE)
+
+## ------------------------------------------------------------------------
+hearts
+panel(hearts, fac="aut", names="aut")
+
+## ------------------------------------------------------------------------
+ht <- measure(hearts, coo_area, coo_circularity, d(1, 3))
+ht %>% PCA() %>% plot("aut", pch=20, ellipsesax=F, ellipse=T, loadings=T)
+
+## ------------------------------------------------------------------------
+flower
+flower %>% PCA() %>% plot("sp", loadings=TRUE, contour=TRUE, lev.contour=5)
+
+## ---- message=FALSE------------------------------------------------------
+bot.f <- efourier(bot)
+
+## ------------------------------------------------------------------------
+bot.p <- PCA(bot.f)
+plot(bot.p)
+
+## ------------------------------------------------------------------------
+plot(bot.p, "type") # equivalent to plot(bot.p, 1)
+
+## ------------------------------------------------------------------------
+plot(bot.p, 1, ellipses=TRUE, ellipsesax = FALSE, pch=c(4, 5))
+plot(bot.p, 1, chull=TRUE, pos.shp = "full_axes", abbreviate.labelsgroups = TRUE, points=FALSE, labelspoints = TRUE)
+plot(bot.p, 1, pos.shp="circle", stars=TRUE, chull.filled=TRUE, palette=col_spring)
+
+## ------------------------------------------------------------------------
+# plot2(bot.p, "type") # deprecated for the moment
+
+## ------------------------------------------------------------------------
+scree(bot.p)
+scree_plot(bot.p)
+boxplot(bot.p, 1)
+PCcontrib(bot.p)
+
+## ------------------------------------------------------------------------
+TraCoe(iris[, -5], fac=data.frame(sp=iris$Species)) %>% 
+  PCA() %>% plot("sp", loadings=TRUE)
+# or
+PCA(iris[, -5], fac=data.frame(sp=iris$Species)) %>%
+  plot("sp", chull=TRUE, ellipses=TRUE, conf_ellipses = 0.9)
+
+## ---- eval=FALSE---------------------------------------------------------
+#  #LDA(bot.f, 1)
+#  # we work on PCA scores
+#  bot.l <- LDA(bot.p, 1)
+#  # print a summary, along with the leave-one-out cross-validation table.
+#  bot.l
+#  # plot.LDA works pretty much with the same grammar as plot.PCA
+#  # here we only have one LD
+#  plot(bot.l)
+#  # plot the cross-validation table
+#  plot_CV(bot.l)  # tabular version
+#  plot_CV(bot.l, freq=TRUE) # frequency table
+#  plot_CV2(bot.l) # arrays version
+
+## ------------------------------------------------------------------------
+MANOVA(bot.p, "type")
+
+## ------------------------------------------------------------------------
+MANOVA_PW(bot.p, "type")
+
+## ------------------------------------------------------------------------
+bot %<>% mutate(cs=coo_centsize(.))
+bot %>% efourier %>% PCA %>% MANOVA("cs")
+
+## ---- eval=FALSE---------------------------------------------------------
+#  CLUST(bot.p, 1)
+
+## ------------------------------------------------------------------------
+KMEANS(bot.p, centers = 5)
+
+## ------------------------------------------------------------------------
+# mean shape
+bot.f %>% mshapes() %>% coo_plot()
+# mean shape, per group
+bot.ms <- mshapes(bot.f, 1)
+beer   <- bot.ms$shp$beer    %T>% coo_plot(border="blue")
+whisky <- bot.ms$shp$whisky  %T>% coo_draw(border="red")
+legend("topright", lwd=1,
+       col=c("blue", "red"), legend=c("beer", "whisky"))
+
+## ------------------------------------------------------------------------
+leaves <- shapes %>% slice(grep("leaf", names(shapes))) %$% coo
+leaves %>% plot_mshapes(col2="#0000FF")
+
+# or from mshapes directly
+bot %>% efourier(6) %>% mshapes("type") %>% plot_mshapes
+
+## ---- results="markup"---------------------------------------------------
+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 $fac
+select(olea, 1, Ind=ind)
+
+# rename: rename columns (select can also do it)
+rename(olea, domesticated=domes)
+
+# mutate: add new columns
+mutate(olea, fake=factor(rep(letters[1:2], each=105)))
+
+# transmute: add new columns and drop others
+transmute(olea, fake=factor(rep(letters[1:2], each=105)))
+
+## ------------------------------------------------------------------------
+olea %>% 
+filter(domes=="cult", view=="VD") %>% 
+rename(domesticated=domes) %>% 
+select(-ind)
+
+## ------------------------------------------------------------------------
+table(olea, "view", "var") 
+# we drop 'Cypre' since there is no VL for 'Cypre' var
+olea %>% filter(var != "Cypre") %>%              
+  # split, do morphometrics, combine
+  chop(view) %>% lapply(opoly) %>% combine() %>% 
+  # note the two views in the morphospace
+  PCA() %>% plot("var")
+
+## ---- eval=FALSE---------------------------------------------------------
+#  names(bot)
+#  length(bot)
+#  table(olea, "var", "domes")
+#  Ntable(olea, "var", "domes")
+#  bot[1]
+#  bot[1:5]
+#  bot$brahma
+#  bot$type
+
+## ---- eval=FALSE---------------------------------------------------------
+#  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())
+
+## ---- eval=FALSE---------------------------------------------------------
+#  save(bot, file="Bottles.rda")
+#  # closing R, going to the beach, back at work
+#  load("Bottles.rda") # bot is back
+
+## ---- eval=FALSE---------------------------------------------------------
+#  bot %>% as_df # then %>% write.table
+#  bot %>% efourier %>% export
+#  bot %>% efourier %>% PCA %>% export
+
+## ---- eval=FALSE---------------------------------------------------------
+#  # 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
+
+## ------------------------------------------------------------------------
+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")
+
+## ------------------------------------------------------------------------
+panel(bot)
+panel2(bot)
+
+## ------------------------------------------------------------------------
+library(ggplot2)
+gg <- panel2(bot)
+gg + theme_minimal()
+
+## ------------------------------------------------------------------------
+# 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()
+
+## ------------------------------------------------------------------------
+bot.p %>% as_df() %>% ggplot() +
+  aes(x=PC1, y=PC2, col=type) + coord_equal() + 
+  geom_point() + geom_density2d() + theme_light()
+