Gavin Rhys Lloyd authored on 06/02/2023 13:15:07
| ... | ... |
@@ -202,7 +202,7 @@ setMethod(f="chart_plot", |
| 202 | 202 |
} |
| 203 | 203 |
|
| 204 | 204 |
# build the plot |
| 205 |
- A <- data.frame (group=groups,x=x, y=y,slabels=slabels) |
|
| 205 |
+ A <- data.frame (group=groups,x=x, y=y,slabels=slabels,shape=shapes) |
|
| 206 | 206 |
|
| 207 | 207 |
out = ggplot() |
| 208 | 208 |
|
| ... | ... |
@@ -210,10 +210,11 @@ setMethod(f="chart_plot", |
| 210 | 210 |
out = out+geom_point(data=A,aes_string(x='x',y='y'),alpha=0,show.legend=FALSE) |
| 211 | 211 |
|
| 212 | 212 |
if (length(shapes)>1) {
|
| 213 |
- out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group,shape=~shapes)) |
|
| 213 |
+ out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group,shape=~shape)) |
|
| 214 | 214 |
} else {
|
| 215 |
- out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group),shape=shapes) |
|
| 215 |
+ out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group),shape=shapes) |
|
| 216 | 216 |
} |
| 217 |
+ |
|
| 217 | 218 |
out=out+ |
| 218 | 219 |
|
| 219 | 220 |
geom_point(na.rm=TRUE) + |
| ... | ... |
@@ -227,16 +228,23 @@ setMethod(f="chart_plot", |
| 227 | 228 |
} |
| 228 | 229 |
|
| 229 | 230 |
if (obj$ellipse %in% c('all','group')) {
|
| 230 |
- out = out +stat_ellipse(data=A, aes_(x=~x,y=~y,colour=~group),type=obj$ellipse_type, |
|
| 231 |
+ if (is.factor(groups)) {
|
|
| 232 |
+ out = out +stat_ellipse(data=A, aes_(x=~x,y=~y,colour=~group,group=~group),type=obj$ellipse_type, |
|
| 231 | 233 |
level=obj$ellipse_confidence) # ellipse for individual groups |
| 234 |
+ } else {
|
|
| 235 |
+ if (is.factor(shapes)) {
|
|
| 236 |
+ out = out +stat_ellipse(data=A, aes_(x=~x,y=~y,group=~shape),color="#C0C0C0",type=obj$ellipse_type, |
|
| 237 |
+ level=obj$ellipse_confidence) # ellipse for individual groups |
|
| 238 |
+ } |
|
| 239 |
+ } |
|
| 232 | 240 |
} |
| 233 | 241 |
|
| 234 | 242 |
if (is(groups,'factor')) { # if a factor then plot by group using the colours from pmp package
|
| 235 | 243 |
out=out+scale_colour_manual(values=plotClass$manual_colors, |
| 236 | 244 |
name=obj$factor_name[[1]]) |
| 237 |
- }else {# assume continuous and use the default colour gradient
|
|
| 245 |
+ } else {# assume continuous and use the default colour gradient
|
|
| 238 | 246 |
out=out+scale_colour_viridis_c(limits=quantile(groups, |
| 239 |
- c(0.05,0.95),na.rm = TRUE),oob=squish,name=obj$factor_name[[1]]) |
|
| 247 |
+ c(0.05,0.95),na.rm = TRUE),oob=scales::squish,name=obj$factor_name[[1]]) |
|
| 240 | 248 |
} |
| 241 | 249 |
out=out+theme_Publication(base_size = 12) |
| 242 | 250 |
# add ellipse for all samples (ignoring group) |
Gavin Rhys Lloyd authored on 06/02/2023 11:19:56
| ... | ... |
@@ -176,15 +176,14 @@ setMethod(f="chart_plot", |
| 176 | 176 |
} else {
|
| 177 | 177 |
slabels = dobj$sample_meta[[obj$label_factor]] |
| 178 | 178 |
} |
| 179 |
- obj$factor_name=obj$factor_name[[1]] # only use the first factor from now on |
|
| 180 |
- |
|
| 179 |
+ |
|
| 181 | 180 |
x=dobj$data[,obj$xcol] |
| 182 | 181 |
y=dobj$data[,obj$ycol] |
| 183 | 182 |
xlabel=obj$xcol |
| 184 | 183 |
ylabel=obj$ycol |
| 185 | 184 |
|
| 186 | 185 |
# get the factor from meta data |
| 187 |
- groups=dobj$sample_meta[[obj$factor_name]] |
|
| 186 |
+ groups=dobj$sample_meta[[obj$factor_name[[1]]]] |
|
| 188 | 187 |
|
| 189 | 188 |
# add a space to the front of the labels to offset them from the points, because nudge_x is in data units |
| 190 | 189 |
for (i in 1:length(slabels)) {
|
| ... | ... |
@@ -210,24 +209,21 @@ setMethod(f="chart_plot", |
| 210 | 209 |
# add invisible sample points for ellipse |
| 211 | 210 |
out = out+geom_point(data=A,aes_string(x='x',y='y'),alpha=0,show.legend=FALSE) |
| 212 | 211 |
|
| 213 |
- |
|
| 214 |
- if (length(obj$factor_name)==2) {
|
|
| 212 |
+ if (length(shapes)>1) {
|
|
| 215 | 213 |
out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group,shape=~shapes)) |
| 216 |
- } else {
|
|
| 217 |
- out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group)) |
|
| 214 |
+ } else {
|
|
| 215 |
+ out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group),shape=shapes) |
|
| 218 | 216 |
} |
| 219 |
- |
|
| 220 |
- |
|
| 221 | 217 |
out=out+ |
| 222 | 218 |
|
| 223 | 219 |
geom_point(na.rm=TRUE) + |
| 224 | 220 |
xlab(xlabel) + |
| 225 | 221 |
ylab(ylabel) |
| 226 | 222 |
|
| 227 |
- if (length(obj$factor_name)==2) {
|
|
| 223 |
+ if (length(shapes)>1) {
|
|
| 228 | 224 |
out=out+labs(shape=obj$factor_name[[2]],colour=obj$factor_name[[1]]) |
| 229 | 225 |
} else {
|
| 230 |
- out=out+labs(shape=obj$factor_name[[1]]) |
|
| 226 |
+ out=out+labs(color=obj$factor_name[[1]]) |
|
| 231 | 227 |
} |
| 232 | 228 |
|
| 233 | 229 |
if (obj$ellipse %in% c('all','group')) {
|
| ... | ... |
@@ -237,10 +233,10 @@ setMethod(f="chart_plot", |
| 237 | 233 |
|
| 238 | 234 |
if (is(groups,'factor')) { # if a factor then plot by group using the colours from pmp package
|
| 239 | 235 |
out=out+scale_colour_manual(values=plotClass$manual_colors, |
| 240 |
- name=obj$factor_name) |
|
| 236 |
+ name=obj$factor_name[[1]]) |
|
| 241 | 237 |
}else {# assume continuous and use the default colour gradient
|
| 242 | 238 |
out=out+scale_colour_viridis_c(limits=quantile(groups, |
| 243 |
- c(0.05,0.95),na.rm = TRUE),oob=squish,name=obj$factor_name) |
|
| 239 |
+ c(0.05,0.95),na.rm = TRUE),oob=squish,name=obj$factor_name[[1]]) |
|
| 244 | 240 |
} |
| 245 | 241 |
out=out+theme_Publication(base_size = 12) |
| 246 | 242 |
# add ellipse for all samples (ignoring group) |
* add selectivity ratio
* replace vip_summary with feature_importance
renamed and now allows vip, sr and sr_pvalues to be plotted
* add equal_split model
random subsets so generate training sets with equal group numbers
* plot 1 - p-value
to conform with the "best" feature being a maximum value
* add resample iterator
subsample at random over a number of iterations. Option to use
different kinds of splitting methods. Corresponding chart.
* allow use of list() for factor_name
* force apply not to simplify output to guarantee returning a list
* update example
* add correct parameter
collect will collect the requested model output over all iterations in a list WORK IN PROGRESS
* add collection of multiple outputs of model sequence
* plot reg coeff on rhs
* match outputs of xval for use with grid search etc
* specify levels when converting predictions to factor
* change PLSDA to inherit from PLSR
rename some charts to be compatible with both PLSR and PLSDA
* allow y-block column selection
* re-assign y output after PLSR with factor
* update vignettes wrt PLS changes
* update documentation
* update R version to 4.1
* update documentation
* update documentation
* update scatter plot
- new scatter chart object
- used by PCA scores, PLSR/PLSDA scores
- other charts updated to reflect changes in scores plots where necessary
- added ycol param to plots for when y-block is a matrix
* add url to github
* add plsda scores alias
- plsda_scores_plot and pls_scores_plot do that same thing
Included for backwards compatability
- added components back as parameter for scores plots for backwards compatibility
* fix broken example
* fix broken tests
- scores is now returned as a DatasetExperiment object not a data.frame
* Update data_analysis_omics_using_the_structtoolbox.Rmd
- wrt changes in scores plots
* update documentation
* fix colnames for Y matrix
Gavin Rhys Lloyd authored on 28/02/2022 12:38:08
• GitHub committed on 28/02/2022 12:38:08
| 1 | 1 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,284 @@ |
| 1 |
+#' @eval get_description('scatter_chart')
|
|
| 2 |
+#' @import struct |
|
| 3 |
+#' @export scatter_chart |
|
| 4 |
+#' @examples |
|
| 5 |
+#' D = iris_DatasetExperiment() |
|
| 6 |
+#' C = scatter_chart( |
|
| 7 |
+#' xcol = 'Petal.Width', |
|
| 8 |
+#' ycol = 'Sepal.Width', |
|
| 9 |
+#' factor_name = 'Species' |
|
| 10 |
+#' ) |
|
| 11 |
+#' chart_plot(C,D) |
|
| 12 |
+#' |
|
| 13 |
+scatter_chart = function( |
|
| 14 |
+ xcol=1, |
|
| 15 |
+ ycol=2, |
|
| 16 |
+ points_to_label='none', |
|
| 17 |
+ factor_name='none', |
|
| 18 |
+ ellipse='all', |
|
| 19 |
+ ellipse_type='norm', |
|
| 20 |
+ ellipse_confidence=0.95, |
|
| 21 |
+ label_filter=character(0), |
|
| 22 |
+ label_factor='rownames', |
|
| 23 |
+ label_size=3.88, |
|
| 24 |
+ ...) {
|
|
| 25 |
+ out=struct::new_struct('scatter_chart',
|
|
| 26 |
+ xcol=xcol, |
|
| 27 |
+ ycol=ycol, |
|
| 28 |
+ points_to_label=points_to_label, |
|
| 29 |
+ factor_name=factor_name, |
|
| 30 |
+ ellipse=ellipse, |
|
| 31 |
+ label_filter=label_filter, |
|
| 32 |
+ label_factor=label_factor, |
|
| 33 |
+ label_size=label_size, |
|
| 34 |
+ ellipse_type=ellipse_type, |
|
| 35 |
+ ellipse_confidence=ellipse_confidence, |
|
| 36 |
+ ...) |
|
| 37 |
+ return(out) |
|
| 38 |
+} |
|
| 39 |
+ |
|
| 40 |
+ |
|
| 41 |
+.scatter_chart<-setClass( |
|
| 42 |
+ "scatter_chart", |
|
| 43 |
+ contains='chart', |
|
| 44 |
+ slots=c( |
|
| 45 |
+ # INPUTS |
|
| 46 |
+ xcol='entity', |
|
| 47 |
+ ycol='entity', |
|
| 48 |
+ points_to_label='enum', |
|
| 49 |
+ factor_name='entity', |
|
| 50 |
+ ellipse='enum', |
|
| 51 |
+ ellipse_type='enum', |
|
| 52 |
+ ellipse_confidence='entity', |
|
| 53 |
+ label_filter='entity', |
|
| 54 |
+ label_factor='entity', |
|
| 55 |
+ label_size='entity' |
|
| 56 |
+ ), |
|
| 57 |
+ |
|
| 58 |
+ prototype = list(name='Group scatter chart', |
|
| 59 |
+ description='Plots a 2d scatter plot of the input data.', |
|
| 60 |
+ type="scatter", |
|
| 61 |
+ .params=c('xcol','ycol','points_to_label','factor_name','ellipse',
|
|
| 62 |
+ 'label_filter','label_factor','label_size','ellipse_type', |
|
| 63 |
+ 'ellipse_confidence'), |
|
| 64 |
+ |
|
| 65 |
+ xcol=entity( |
|
| 66 |
+ name='x-axis column name', |
|
| 67 |
+ value=1, |
|
| 68 |
+ type=c('numeric','integer','character'),
|
|
| 69 |
+ description=paste0('The column name, or index, of data to plot on the x-axis'),
|
|
| 70 |
+ max_length=1 |
|
| 71 |
+ ), |
|
| 72 |
+ |
|
| 73 |
+ ycol=entity( |
|
| 74 |
+ name='y-axis column name', |
|
| 75 |
+ value=2, |
|
| 76 |
+ type=c('numeric','integer','character'),
|
|
| 77 |
+ description=paste0('The column name, or index, of data to plot on the y-axis'),
|
|
| 78 |
+ max_length=1 |
|
| 79 |
+ ), |
|
| 80 |
+ |
|
| 81 |
+ points_to_label=enum(name='Points to label', |
|
| 82 |
+ value='none', |
|
| 83 |
+ type='character', |
|
| 84 |
+ description=c( |
|
| 85 |
+ 'none' = 'No samples labels are displayed.', |
|
| 86 |
+ "all" = 'The labels for all samples are displayed.', |
|
| 87 |
+ "outliers" = 'Labels for for potential outlier samples are displayed.' |
|
| 88 |
+ ), |
|
| 89 |
+ allowed=c('none','all','outliers')
|
|
| 90 |
+ ), |
|
| 91 |
+ factor_name=ents$factor_name, |
|
| 92 |
+ ellipse=enum( |
|
| 93 |
+ name = 'Plot ellipses', |
|
| 94 |
+ description=c( |
|
| 95 |
+ "all" = paste0('Ellipses are plotted for all groups and all samples.'),
|
|
| 96 |
+ "group" = 'Ellipses are plotted for all groups.', |
|
| 97 |
+ "none" = 'Ellipses are not included on the plot.', |
|
| 98 |
+ "sample" = 'An ellipse is plotted for all samples (ignoring group)'), |
|
| 99 |
+ allowed=c('all','group','none','sample'),
|
|
| 100 |
+ value='all' |
|
| 101 |
+ ), |
|
| 102 |
+ |
|
| 103 |
+ ellipse_type=enum( |
|
| 104 |
+ name='Type of ellipse', |
|
| 105 |
+ description=c( |
|
| 106 |
+ 'norm' = paste0('Multivariate normal (p = 0.95)'),
|
|
| 107 |
+ 't' = paste0('Multivariate t (p = 0.95)')
|
|
| 108 |
+ ), |
|
| 109 |
+ value='norm', |
|
| 110 |
+ type='character', |
|
| 111 |
+ max_length = 1, |
|
| 112 |
+ allowed=c('norm','t')
|
|
| 113 |
+ ), |
|
| 114 |
+ |
|
| 115 |
+ ellipse_confidence=entity( |
|
| 116 |
+ name='Ellipse confidence level', |
|
| 117 |
+ description='The confidence level for plotting ellipses.', |
|
| 118 |
+ value=0.95, |
|
| 119 |
+ type='numeric', |
|
| 120 |
+ max_length = 1 |
|
| 121 |
+ ), |
|
| 122 |
+ |
|
| 123 |
+ label_filter=entity( |
|
| 124 |
+ name='Label filter', |
|
| 125 |
+ value=character(0), |
|
| 126 |
+ type='character', |
|
| 127 |
+ description=paste0( |
|
| 128 |
+ 'Labels are only plotted for the named groups. If ', |
|
| 129 |
+ 'zero-length then all groups are included.' |
|
| 130 |
+ ) |
|
| 131 |
+ ), |
|
| 132 |
+ label_factor=entity(name='Factor for labels', |
|
| 133 |
+ description=paste0('The column name of sample_meta to use for ',
|
|
| 134 |
+ 'labelling samples on the plot. "rownames" will use the row ', |
|
| 135 |
+ 'names from sample_meta.'), |
|
| 136 |
+ type='character', |
|
| 137 |
+ value='rownames', |
|
| 138 |
+ max_length=1), |
|
| 139 |
+ label_size=entity(name='Text size of labels', |
|
| 140 |
+ description='The text size of labels. Note this is not in Font Units.', |
|
| 141 |
+ type='numeric', |
|
| 142 |
+ value=3.88, |
|
| 143 |
+ max_length=1) |
|
| 144 |
+ ) |
|
| 145 |
+) |
|
| 146 |
+ |
|
| 147 |
+#' @importFrom sp point.in.polygon |
|
| 148 |
+#' @import ggplot2 |
|
| 149 |
+#' @importFrom scales squish |
|
| 150 |
+#' @export |
|
| 151 |
+#' @template chart_plot |
|
| 152 |
+setMethod(f="chart_plot", |
|
| 153 |
+ signature=c("scatter_chart",'DatasetExperiment'),
|
|
| 154 |
+ definition=function(obj,dobj) |
|
| 155 |
+ {
|
|
| 156 |
+ # if provided convert index to names |
|
| 157 |
+ if (is.numeric(obj$xcol)) {
|
|
| 158 |
+ obj$xcol=colnames(dobj)[obj$xcol] |
|
| 159 |
+ } |
|
| 160 |
+ if (is.numeric(obj$ycol)) {
|
|
| 161 |
+ obj$ycol=colnames(dobj)[obj$ycol] |
|
| 162 |
+ } |
|
| 163 |
+ |
|
| 164 |
+ if (obj$points_to_label=='outliers' & !(obj$ellipse %in% c('all','sample'))) {
|
|
| 165 |
+ warning('Outliers are only labelled when plotting the sample ellipse')
|
|
| 166 |
+ } |
|
| 167 |
+ |
|
| 168 |
+ if (length(obj$factor_name)==1) {
|
|
| 169 |
+ shapes = 19 # filled circles for all samples |
|
| 170 |
+ } else {
|
|
| 171 |
+ shapes = factor(dobj$sample_meta[[obj$factor_name[2]]]) |
|
| 172 |
+ } |
|
| 173 |
+ |
|
| 174 |
+ if (obj$label_factor=='rownames') {
|
|
| 175 |
+ slabels = rownames(dobj$sample_meta) |
|
| 176 |
+ } else {
|
|
| 177 |
+ slabels = dobj$sample_meta[[obj$label_factor]] |
|
| 178 |
+ } |
|
| 179 |
+ obj$factor_name=obj$factor_name[[1]] # only use the first factor from now on |
|
| 180 |
+ |
|
| 181 |
+ x=dobj$data[,obj$xcol] |
|
| 182 |
+ y=dobj$data[,obj$ycol] |
|
| 183 |
+ xlabel=obj$xcol |
|
| 184 |
+ ylabel=obj$ycol |
|
| 185 |
+ |
|
| 186 |
+ # get the factor from meta data |
|
| 187 |
+ groups=dobj$sample_meta[[obj$factor_name]] |
|
| 188 |
+ |
|
| 189 |
+ # add a space to the front of the labels to offset them from the points, because nudge_x is in data units |
|
| 190 |
+ for (i in 1:length(slabels)) {
|
|
| 191 |
+ slabels[i]=paste0(' ',slabels[i], ' ')
|
|
| 192 |
+ } |
|
| 193 |
+ |
|
| 194 |
+ # filter by label_filter list if provided |
|
| 195 |
+ if (length(obj$label_filter)>0) {
|
|
| 196 |
+ out=!(as.character(groups) %in% obj$label_filter) |
|
| 197 |
+ slabels[out]='' |
|
| 198 |
+ } |
|
| 199 |
+ |
|
| 200 |
+ if (is(groups,'factor') | is(groups,'character')) {
|
|
| 201 |
+ plotClass= createClassAndColors(groups) |
|
| 202 |
+ groups=plotClass$class |
|
| 203 |
+ } |
|
| 204 |
+ |
|
| 205 |
+ # build the plot |
|
| 206 |
+ A <- data.frame (group=groups,x=x, y=y,slabels=slabels) |
|
| 207 |
+ |
|
| 208 |
+ out = ggplot() |
|
| 209 |
+ |
|
| 210 |
+ # add invisible sample points for ellipse |
|
| 211 |
+ out = out+geom_point(data=A,aes_string(x='x',y='y'),alpha=0,show.legend=FALSE) |
|
| 212 |
+ |
|
| 213 |
+ |
|
| 214 |
+ if (length(obj$factor_name)==2) {
|
|
| 215 |
+ out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group,shape=~shapes)) |
|
| 216 |
+ } else {
|
|
| 217 |
+ out=out+geom_point(data=A, aes_(x=~x,y=~y,colour=~group)) |
|
| 218 |
+ } |
|
| 219 |
+ |
|
| 220 |
+ |
|
| 221 |
+ out=out+ |
|
| 222 |
+ |
|
| 223 |
+ geom_point(na.rm=TRUE) + |
|
| 224 |
+ xlab(xlabel) + |
|
| 225 |
+ ylab(ylabel) |
|
| 226 |
+ |
|
| 227 |
+ if (length(obj$factor_name)==2) {
|
|
| 228 |
+ out=out+labs(shape=obj$factor_name[[2]],colour=obj$factor_name[[1]]) |
|
| 229 |
+ } else {
|
|
| 230 |
+ out=out+labs(shape=obj$factor_name[[1]]) |
|
| 231 |
+ } |
|
| 232 |
+ |
|
| 233 |
+ if (obj$ellipse %in% c('all','group')) {
|
|
| 234 |
+ out = out +stat_ellipse(data=A, aes_(x=~x,y=~y,colour=~group),type=obj$ellipse_type, |
|
| 235 |
+ level=obj$ellipse_confidence) # ellipse for individual groups |
|
| 236 |
+ } |
|
| 237 |
+ |
|
| 238 |
+ if (is(groups,'factor')) { # if a factor then plot by group using the colours from pmp package
|
|
| 239 |
+ out=out+scale_colour_manual(values=plotClass$manual_colors, |
|
| 240 |
+ name=obj$factor_name) |
|
| 241 |
+ }else {# assume continuous and use the default colour gradient
|
|
| 242 |
+ out=out+scale_colour_viridis_c(limits=quantile(groups, |
|
| 243 |
+ c(0.05,0.95),na.rm = TRUE),oob=squish,name=obj$factor_name) |
|
| 244 |
+ } |
|
| 245 |
+ out=out+theme_Publication(base_size = 12) |
|
| 246 |
+ # add ellipse for all samples (ignoring group) |
|
| 247 |
+ if (obj$ellipse %in% c('all','sample')) {
|
|
| 248 |
+ out=out+stat_ellipse(type=obj$ellipse_type,mapping=aes(x=x,y=y), |
|
| 249 |
+ colour="#C0C0C0",linetype='dashed',data=A, |
|
| 250 |
+ level=obj$ellipse_confidence) |
|
| 251 |
+ } |
|
| 252 |
+ |
|
| 253 |
+ if (obj$ellipse %in% c('all','sample')) { # only do this if we plotted the sample ellipse
|
|
| 254 |
+ # identify samples outside the ellipse |
|
| 255 |
+ build=ggplot_build(out)$data |
|
| 256 |
+ points=build[[1]] |
|
| 257 |
+ ell=build[[length(build)]] |
|
| 258 |
+ # outlier for DatasetExperiment ellipse |
|
| 259 |
+ points$in.ell=as.logical(sp::point.in.polygon(points$x,points$y,ell$x,ell$y)) |
|
| 260 |
+ |
|
| 261 |
+ # label outliers if |
|
| 262 |
+ if (obj$points_to_label=='outliers') |
|
| 263 |
+ {
|
|
| 264 |
+ if (!all(points$in.ell)) |
|
| 265 |
+ {
|
|
| 266 |
+ temp=subset(points,!points$in.ell) |
|
| 267 |
+ temp$group=groups[!points$in.ell] |
|
| 268 |
+ temp$label=slabels[!points$in.ell] |
|
| 269 |
+ out=out+geom_text(data=temp,aes_(x=~x,y=~y,label=~label,colour=~group),size=obj$label_size,vjust="inward",hjust="inward",show.legend=FALSE) |
|
| 270 |
+ |
|
| 271 |
+ } |
|
| 272 |
+ } |
|
| 273 |
+ # add a list of outliers to the plot object |
|
| 274 |
+ out$outliers=trimws(slabels[!points$in.ell]) |
|
| 275 |
+ } |
|
| 276 |
+ |
|
| 277 |
+ # label all points if requested |
|
| 278 |
+ if (obj$points_to_label=='all') {
|
|
| 279 |
+ out=out+geom_text(data=A,aes_string(x='x',y='y',colour='group',label='slabels'),vjust="inward",hjust="inward",show.legend=FALSE) |
|
| 280 |
+ } |
|
| 281 |
+ |
|
| 282 |
+ return(out) |
|
| 283 |
+ } |
|
| 284 |
+) |
|
| 0 | 285 |
\ No newline at end of file |