diff --git a/DESCRIPTION b/DESCRIPTION
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@@ -8,3 +8,4 @@ Description: Funciones internas para el laboratorio CIT
 License: GPL
 Encoding: UTF-8
 LazyData: true
+VignetteBuilder: knitr
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diff --git a/vignettes/citfuns.Rmd b/vignettes/citfuns.Rmd
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@@ -0,0 +1,129 @@
+---
+title: "CitFuns"
+output: rmarkdown::html_vignette
+description: |
+  How to use CitFuns functions.
+vignette: >
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, message = F, warning = F, error = F)
+```
+
+## Installation
+
+In order to install CitFuns package from Git reppository, you must install `devtools` package:
+
+```{r, eval=F}
+install.packages('devtools')
+```
+
+In order to install it, you will have to install (if not already done) [Rtools](https://cran.r-project.org/bin/windows/Rtools/rtools40.html).
+
+You will also need [Git](https://git-scm.com/downloads) installed in your computer.
+
+Now we are ready to install CitFuns package:
+```{r, eval=F}
+devtools::install_git("https://git.ratg.cat/marcelcosta/CitFuns.git")
+```
+
+## Update
+
+Any time you want to update the package, you must *reinstall* it:
+
+```{r, eval=F}
+detach("package:CitFuns", unload = TRUE) # Only required if you have loaded the package in this session.
+devtools::install_git("https://git.ratg.cat/marcelcosta/CitFuns.git")
+```
+
+## ggheatmap
+
+**ggheatmap** generates ggplot heatmaps easily.
+
+We start by loading required packages, including *CitFuns*
+
+```{r}
+library(tidyverse)
+library(CitFuns)
+```
+
+Now we will create an example dataframe:
+
+```{r}
+df<-data.frame("pats"=paste0("PAT", 1:20), "CytA"=rnorm(20,5), "CytB"=rnorm(20,5),
+"CytC"=c(rnorm(5,10),rnorm(5,5),rnorm(5,10),rnorm(5,5)),"CytD"=rnorm(20,5),
+"CytE"=c(rnorm(5,10),rnorm(5,5),rnorm(5,10),rnorm(5,5)),"CytF"=rnorm(20,5),
+"CytG"=c(rnorm(5,10),rnorm(5,5),rnorm(5,10),rnorm(5,5)))
+
+df<-gather(df, Cyt, Value,-pats)
+head(df)
+```
+
+Usually, this package works with the dataframes in **Long** format, as it is intended in ggplot workflow.
+
+And now we will generate the heatmap.
+
+```{r, fig.width=8}
+ggheatmap(df)
+```
+
+As we can observe, X and Y axis are sorted by cluster detection (using *hclust*).
+
+*ggheatmap* groups the results if more there is more than one observation for each X-Y coordinate. By default, it calculates the mean, but the median can also be used instead. To show this, we will create another variable, "Met", and we plot Cytokine expression *versus* Met status.
+
+```{r, fig.width=8}
+clinics<-data.frame("pats"=df$pats %>% unique, "Met"=rep(c("0","1"), 10))
+df<-merge(df, clinics)
+
+ggheatmap(df, x="Cyt",y="Met", grouping = "median")
+```
+
+By default, *other variables not used are eliminated* during the grouping process. However, if you want to further use them (for faceting, for example), you can use the *exclude_group* parameter to keep them in the data.frame.
+
+```{r, fig.width=8}
+ggheatmap(df, exclude_group = "Met")+facet_grid(.~Met, scales = "free")
+```
+
+Finally, you can scale the heatmap either by rows or by columns:
+
+```{r, fig.width=8}
+ggheatmap(df, scale="rows")
+```
+
+It is worth noticing that ggheatmap outputs a ggplot object, so you can further modify it as you are used to:
+
+```{r, fig.width=8, fig.height=5}
+ggheatmap(df)+
+  scale_fill_gradient(low = "black", high = "yellow")+
+  scale_y_discrete(position = "right")+
+  theme(legend.position = "bottom")
+```
+
+## ggcorrplot
+
+ggcorrplot generates a correlation matrix. Using the same example dataframe, you have to specify which variable and value columns will be used to test correlation.
+
+```{r, fig.width=5, fig.height=4}
+ggcorrplot(df, var = "Cyt", value = "Value")
+```
+
+You can specify a color for the tile lines, transparent by default:
+
+```{r, fig.width=5, fig.height=4}
+ggcorrplot(df, var = "Cyt", value = "Value", color = "white")
+```
+
+By default, ggcorrplot converts the p-value into *star* significance equivalence. You can show the pvalue or nothing ("none").
+
+```{r, fig.width=5, fig.height=4}
+ggcorrplot(df, var = "Cyt", value = "Value", color = "white", stat="pval")
+```
+
+Finally, you can show only the upper part or the lower part of the specular matrix.
+
+```{r, fig.width=5, fig.height=4}
+ggcorrplot(df, var = "Cyt", value = "Value", color = "black", tri="lower")
+```
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