diff --git a/README.md b/README.md
index f186fa3..902a877 100644
--- a/README.md
+++ b/README.md
@@ -8,9 +8,9 @@
 
 [infoelectoral](https://ropenspain.github.io/infoelectoral/) is a R library that helps retrieve official electoral results for Spain from the [Ministry of the Interior](https://infoelectoral.interior.gob.es/opencms/es/inicio/). It allows you to download the results of general, european and municipal elections of any year at the polling station and municipality level.
 
-# Installation
+## Installation
 
-``` R
+``` r
 # To install the latest stable version from CRAN:
 install.packages("infoelectoral")
 
@@ -18,7 +18,7 @@ install.packages("infoelectoral")
 devtools::install_github("ropenspain/infoelectoral")
 ```
 
-# Overview
+## Overview
 
 [infoelectoral](https://ropenspain.github.io/infoelectoral/) has four functions:
 
@@ -29,14 +29,14 @@ devtools::install_github("ropenspain/infoelectoral")
 
 The package also includes some [datasets](https://ropenspain.github.io/infoelectoral/reference/index.html#datasets) with the official names of the territorial units and the election dates.
 
-# Basic usage
+## Basic usage
 
-``` R
+``` r
 library(infoelectoral)
 df <- municipios(tipo_eleccion = "congreso", anno = 1982, mes = "10")
 ```
 
-# Learn more
+## Learn more
 
 For a a extended example of how all functions work please check the vignettes:
 
diff --git a/vignettes/municipios.Rmd b/vignettes/municipios.Rmd
index 8044e11..7800573 100644
--- a/vignettes/municipios.Rmd
+++ b/vignettes/municipios.Rmd
@@ -42,7 +42,7 @@ shp_ccaa <- mapSpain::esp_get_ccaa()
 ```
 
 
-## Recode the party names
+## Recode party names
 Since most parties have different names throughout the country, you will need to recode them to group their results. You can use the column `codigo_partido_nacional` included in the resulting `data.frame` that indicates the grouping party code at the national level. After that you'll have to create the complete municipality code (`LAU_CODE`) for the merge with the `sf` object and transform the data from long to wide format.
 
 First, let's group the parties by `codigo_partido_nacional` and `siglas` and sum the votes to see which party codes correspond to the main parties.
@@ -80,8 +80,8 @@ results <-
 ```
 
 
-## Join the voting data to the geometries
-With the `LAU_CODE` column merge the data with the geomtries of the municipalities.
+## Join data and sf object
+With the `LAU_CODE` column merge the data with the geometries of the municipalities.
 
 ```{r}
 shp <- left_join(shp, results, by = "LAU_CODE")
@@ -111,7 +111,7 @@ maps <-
       ) +
       geom_sf(
         data = shp_ccaa, fill = NA, color = "black",
-        linewidth = 0.01
+        linewidth = 0.1
       ) +
       facet_wrap(~siglas_r) +
       scale_fill_gradient(