Añadida función sem.

R/gglegend.R

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+gglegend<-function(data, x, y, var, stat="median", color="black", ...){
+  if (stat == "median"){
+    data<-data %>% group_by({{var}}) %>% summarise("{{x}}":=median({{x}}, na.rm=T), "{{y}}":=median({{y}}, na.rm=T))
+  }
+  if (stat == "mean"){
+    data<-data %>% group_by({{var}}) %>% summarise("{{x}}":=mean({{x}}, na.rm=T), "{{y}}":=mean({{y}}, na.rm=T))
+  }
+  if (!is.null(color)){
+    return(geom_label(data = data, aes({{x}},{{y}}, label={{var}}), color=color, ...))
+  }else{
+    return(geom_label(data = data, aes({{x}},{{y}}, label={{var}}),...))
+  }
+}

R/perc.R

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+perc<-function(x, per100=T){
+  if (per100==T){
+    return(x*100/sum(x, na.rm = T))
+  }else{
+    return(x/sum(x, na.rm = T))
+  }
+}

R/sem.R

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+sem <- function(x, na.rm=F){
+  sem<-sd(x, na.rm = na.rm)/sqrt(length(x))
+  return(sem)
+}

man/gglegend.Rd

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+\name{gglegend}
+\alias{gglegend}
+\title{gglegend}
+\usage{
+gglegend(data, x, y, var, stat="median", color="black", ...)
+}
+\arguments{
+  \item{data}{A data frame from where to take x and y axis, and the grouping variable.}
+  \item{x}{The variable that will be used for X axis in the heatmap.}
+  \item{y}{The variable that will be used for Y axis in the heatmap.}
+  \item{value}{The variable that will be used for grouping the heatmap. The labels will be taken from it}
+  \item{stat}{The statistical central test that will be used to calculate the label coordinates. "mean" or "median" are posible, defaulting to "median".}
+  \item{color}{The color of the labels, "black" by default. If NULL, nothig will be passed to the geom_label so it may be taken from aesthetics.}
+  \item{...}{Other arguments that will be passed to the geom_label function.}
+}
+\description{
+Generates a geom_label object with labels in the center of each population or agrupation.
+}
+\examples{
+library(tidyverse)
+library(Rtsne)
+library(Rphenograph)
+library(igraph)
+
+## We will generate an example using tSNE and Phenograph algorithms, but can be applied to any other situation.
+
+iris_unique <- unique(iris) # Remove duplicates
+iris_matrix <- as.matrix(iris_unique[,1:4])
+
+# Set a seed if you want reproducible results
+set.seed(42)
+tsne_out <- Rtsne(iris_matrix,pca=FALSE,perplexity=30,theta=0.0) # Run TSNE
+
+rpheno<-Rphenograph(tsne_out$Y)
+df<-data.frame(as.data.frame(tsne_out$Y), "Clust"=factor(membership(rpheno[[2]])))
+head(df)
+
+ggplot(df, aes(V1,V2, color=Clust))+
+  geom_point()+
+  gglegend(df, V1, V2, Clust)+
+  guides(color="none")
+
+}

man/perc.Rd

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+\name{perc}
+\alias{perc}
+\title{perc}
+\usage{
+perc(x, per100=T)
+}
+\arguments{
+  \item{x}{A numeric vector or an R object but not a factor coercible to numeric by as.double(x).}
+  \item{perc}{A logical value indicating whether the result must sum 100 (TRUE) or 1 (FALSE).}
+}
+\description{
+This function the percentages of a numeric vector.
+}
+\examples{
+v<-c(2,5,10,3)
+perc(v)
+
+# It can be used with dplyr tables
+library(tidyverse)
+df<-data.frame("X"=c("A","A","B","B"), "Y"=v)
+df \%>\% group_by(X) \%>\% summarise(Y=perc(Y))
+
+# Or it can be under 1
+df \%>\% group_by(X) \%>\% summarise(Y=perc(Y, per100=F))
+}

man/sem.Rd

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+\name{sem}
+\alias{sem}
+\title{sem}
+\usage{
+sem(x, na.rm=T)
+}
+\arguments{
+  \item{x}{A numeric vector or an R object but not a factor coercible to numeric by as.double(x).}
+  \item{na.rm}{A logical value indicating whether NA values should be stripped before the computation proceeds.}
+}
+\description{
+This function computes the Standard Error of the Mean of the values in x. If na.rm is TRUE then missing values are removed before computation proceeds.
+}
+\examples{
+v<-rnorm(10)
+sem(v)
+}