Merge pull request #3 from computational-metabolomics/update_documation

Updates  for CMD and BiocCheck

Author: grlloyd

.Rbuildignore

@@ -14,3 +14,4 @@ LICENSE
 README.rst
 ^binder/
 ^notebooks/
+man-roxygen

.gitignore

@@ -13,3 +13,4 @@ structtoolbox.Rproj
 *.tiff
 vignettes/.build.timestamp
 *.xml
+doc

.travis.yml

@@ -3,7 +3,6 @@ r: bioc-devel
 sudo: false
 cache: packages
 warnings_are_errors: false
-r_github_packages: r-lib/remotes#340
 after_success:
     - Rscript -e 'covr::codecov()'
 addons:

DESCRIPTION

@@ -9,6 +9,7 @@ License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Collate:
+    'entity_objects.R'
     'anova_class.R'
     'HSD_class.R'
     'mixed_effect_class.R'
@@ -23,6 +24,8 @@ Collate:
     'autoscale_class.R'
     'balanced_accuracy_class.R'
     'blank_filter_class.R'
+    'calculate_doc.R'
+    'chart_plot_doc.R'
     'classical_lsq_class.R'
     'confounders_clsq_class.R'
     'corr_coef_class.R'
@@ -47,6 +50,10 @@ Collate:
     'linear_model_class.R'
     'log_transform.R'
     'mean_centre_class.R'
+    'model_apply_doc.R'
+    'model_predict_doc.R'
+    'model_reverse_doc.R'
+    'model_train_doc.R'
     'mv_feature_filter_class.R'
     'mv_sample_filter_class.R'
     'pairs_filter_class.R'
@@ -56,6 +63,7 @@ Collate:
     'prop_na_class.R'
     'r_squared_class.R'
     'rsd_filter.R'
+    'run_doc.R'
     'sb_corr.R'
     'sbcms_dataset_class.R'
     'split_data_class.R'
@@ -64,6 +72,7 @@ Collate:
     'ttest_class.R'
     'vec_norm_class.R'
     'wilcox_test_class.R'
+    'zzz.R'
 Depends: struct
 Imports: ggplot2,
  pmp,
@@ -81,15 +90,18 @@ Imports: ggplot2,
  nlme,
  ggthemes,
  ggdendro,
- Rtsne
+ Rtsne,
+ datasets
 RoxygenNote: 6.1.1
 Suggests: 
  testthat,
  covr,
  knitr,
  rmarkdown,
- BiocStyle
+ BiocStyle,
+ sbcms
 Remotes: computational-metabolomics/pmp,
-  computational-metabolomics/struct
+  computational-metabolomics/struct,
+  computational-metabolomics/sbcms
 VignetteBuilder: knitr
 biocViews: WorkflowStep

NAMESPACE

@@ -89,7 +89,6 @@ export(wilcox_p_hist)
 export(wilcox_test)
 exportMethods(calculate)
 exportMethods(chart.plot)
-exportMethods(evaluate)
 exportMethods(method.apply)
 exportMethods(model.predict)
 exportMethods(model.reverse)

R/HSDEM_class.R

@@ -60,6 +60,7 @@ HSDEM<-setClass(
 )
 
 #' @export
+#' @template method_apply
 setMethod(f="method.apply",
     signature=c("HSDEM",'dataset'),
     definition=function(M,D) {
@@ -106,7 +107,10 @@ setMethod(f="method.apply",
             for (k in 1:length(FF)) {
                 if (!is.na(testlm[[1]])) {
                     testhsd=tryCatch({
-                        output2[[k]]=as.data.frame(pairs(emmeans(LM,FF[[k]],data=temp)))
+                        output2[[k]]=as.data.frame(
+                                pairs(
+                                emmeans::emmeans(LM,FF[[k]],
+                                data=temp)))
                     }, warning  = function(w) {
                         NA
                     } , message = function(m) {

R/HSD_class.R

@@ -70,6 +70,7 @@ HSD<-setClass(
 )
 
 #' @export
+#' @template method_apply
 setMethod(f="method.apply",
     signature=c("HSD",'dataset'),
     definition=function(M,D) {

R/PCA_class.R

@@ -39,6 +39,7 @@ PCA<-setClass(
 )
 
 #' @export
+#' @template model_train
 setMethod(f="model.train",
     signature=c("PCA",'dataset'),
     definition=function(M,D)
@@ -82,6 +83,7 @@ setMethod(f="model.train",
 )
 
 #' @export
+#' @template model_predict
 setMethod(f="model.predict",
     signature=c("PCA",'dataset'),
     definition=function(M,D)

R/PCA_plotfcns.R

@@ -28,6 +28,7 @@ pca_correlation_plot<-setClass(
 )
 
 #' @export
+#' @template chart_plot
 setMethod(f="chart.plot",
     signature=c("pca_correlation_plot",'PCA'),
     definition=function(obj,dobj)
@@ -70,7 +71,9 @@ pca_scores_plot<-setClass(
         params.factor_name='entity',
         params.ellipse='enum',
         params.label_filter='entity',
-        params.groups='ANY' # will be deprecated
+        params.groups='ANY', # will be deprecated
+        params.label_factor='entity',
+        params.label_size='entity'
     ),
 
     prototype = list(name='PCA scores plot',
@@ -93,7 +96,8 @@ pca_scores_plot<-setClass(
         params.factor_name=entity(name='Factor name',
             value='factor',
             type='character',
-            description='The column name of sample meta to use for plotting.'
+            description='The column name of sample meta to use for plotting. A second column can be included to plot using symbols.',
+            max_length=2
         ),
         params.ellipse=enum(name = 'Plot ellipses',description=c(
             '"all" will plot all ellipses',
@@ -106,7 +110,15 @@ pca_scores_plot<-setClass(
             value=character(0),
             type='character',
             description='Only include the param.group labels included in params.label_filter. If zero length then all labels will be included.'
-        )
+        ),
+        params.label_factor=entity(names='Factor for labels',
+            description='The column name of sample_meta to use as labels. "rownames" will use the row names from sample_meta.',
+            type='character',
+            value='rownames'),
+        params.label_size=entity(names='Text size of labels',
+            description='The text size of labels. Note this is not in Font Units. Default 3.88.',
+            type='numeric',
+            value=3.88)
     )
 )
 
@@ -116,6 +128,7 @@ pca_scores_plot<-setClass(
 #' @import ggplot2
 #' @importFrom scales squish
 #' @export
+#' @template chart_plot
 setMethod(f="chart.plot",
     signature=c("pca_scores_plot",'PCA'),
     definition=function(obj,dobj)
@@ -126,10 +139,22 @@ setMethod(f="chart.plot",
         }
         opt=param.list(obj)
         scores=output.value(dobj,'scores')$data
-        pvar=(colSums(scores*scores)/output.value(dobj,'ssx'))*100 # percent variance
-        pvar=round(pvar,digits = 2) # round to 2 decimal places
-        shapes <- rep(19,nrow(scores)) # filled circles for all samples
-        slabels <- rownames(scores)
+        pvar = (colSums(scores*scores)/output.value(dobj,'ssx'))*100 # percent variance
+        pvar = round(pvar,digits = 2) # round to 2 decimal places
+
+        if (length(obj$factor_name)==1) {
+            shapes = 19 # filled circles for all samples
+        } else {
+            shapes = factor(dobj$scores$sample_meta[[obj$factor_name[2]]])
+        }
+
+        if (obj$label_factor=='rownames') {
+            slabels = rownames(dobj$scores$sample_meta)
+        } else {
+            slabels = dobj$scores$sample_meta[[obj$label_factor]]
+        }
+        opt$factor_name=opt$factor_name[[1]] # only use the first factor from now on
+
         x=scores[,opt$components[1]]
         y=scores[,opt$components[2]]
         xlabel=paste("PC",opt$components[[1]],' (',sprintf("%.1f",pvar[opt$components[[1]]]),'%)',sep='')
@@ -158,16 +183,27 @@ setMethod(f="chart.plot",
         # build the plot
         A <- data.frame (group=opt$groups,x=x, y=y)
 
-        out=ggplot (data=A, aes_(x=~x,y=~y,colour=~group,label=~slabels,shapes=~shapes)) +
+        if (length(obj$factor_name)==2) {
+            out=ggplot (data=A, aes_(x=~x,y=~y,colour=~group,label=~slabels,shape=~shapes))
+        }   else {
+            out=ggplot (data=A, aes_(x=~x,y=~y,colour=~group,label=~slabels))
+        }
+        out=out+
 
             geom_point(na.rm=TRUE) +
             xlab(xlabel) +
             ylab(ylabel) +
-            ggtitle('PCA Scores', subtitle=NULL) +
+            ggtitle('PCA Scores', subtitle=NULL)
 
-            if (obj$ellipse %in% c('all','group')) {
-                stat_ellipse(type='norm') # ellipse for individual groups
-            }
+        if (length(obj$factor_name)==2) {
+            out=out+labs(shape=obj$factor_name[[2]],colour=obj$factor_name[[1]])
+        } else {
+            out=out+labs(shape=obj$factor_name[[1]])
+        }
+
+        if (obj$ellipse %in% c('all','group')) {
+            out = out +stat_ellipse(type='norm') # ellipse for individual groups
+        }
 
         if (is(opt$groups,'factor')) { # if a factor then plot by group using the colours from pmp package
             out=out+scale_colour_manual(values=plotClass$manual_colors,name=opt$factor_name)
@@ -194,10 +230,9 @@ setMethod(f="chart.plot",
             {
                 if (!all(points$in.ell))
                 {
-
                     temp=subset(points,!points$in.ell)
                     temp$group=opt$groups[!points$in.ell]
-                    out=out+geom_text(data=temp,aes_(x=~x,y=~y,label=~label,colour=~group),vjust="inward",hjust="inward")
+                    out=out+geom_text(data=temp,aes_(x=~x,y=~y,label=~label,colour=~group),size=obj$label_size,vjust="inward",hjust="inward")
 
                 }
             }
@@ -248,7 +283,7 @@ pca_biplot_plot<-setClass(
             type='numeric',
             description='the components to be plotted e.g. c(1,2) plots component 1 on the x axis and component 2 on the y axis.',
             max_length=2
-            ),
+        ),
         params.points_to_label=entity(name='points_to_label',
             value='none',
             type='character',
@@ -285,6 +320,7 @@ pca_biplot_plot<-setClass(
 )
 
 #' @export
+#' @template chart_plot
 setMethod(f="chart.plot",
     signature=c("pca_biplot_plot",'PCA'),
     definition=function(obj,dobj)
@@ -405,6 +441,7 @@ pca_loadings_plot<-setClass(
 
 
 #' @export
+#' @template chart_plot
 setMethod(f="chart.plot",
     signature=c("pca_loadings_plot",'PCA'),
     definition=function(obj,dobj)
@@ -466,6 +503,7 @@ PCA.scree<-setClass(
 )
 
 #' @export
+#' @template chart_plot
 setMethod(f="chart.plot",
     signature=c("PCA.scree",'PCA'),
     definition=function(obj,dobj)
@@ -522,6 +560,7 @@ PCA.dstat<-setClass(
 )
 
 #' @export
+#' @template chart_plot
 setMethod(f="chart.plot",
     signature=c("PCA.dstat",'PCA'),
     definition=function(obj,dobj)

R/PLSDA_charts.R

@@ -8,6 +8,8 @@
 #' @import struct
 #' @export plsda_scores_plot
 #' @include PLSDA_class.R
+#' @examples
+#' C = plsda_scores_plot()
 plsda_scores_plot<-setClass(
     "plsda_scores_plot",
     contains='chart',
@@ -47,6 +49,7 @@ plsda_scores_plot<-setClass(
 )
 
 #' @export
+#' @template chart_plot
 setMethod(f="chart.plot",
     signature=c("plsda_scores_plot",'PLSDA'),
     definition=function(obj,dobj)