diff --git a/r-package/vignettes/intro_to_geobr.Rmd b/r-package/vignettes/intro_to_geobr.Rmd
index 759684ce..11d5999b 100644
--- a/r-package/vignettes/intro_to_geobr.Rmd
+++ b/r-package/vignettes/intro_to_geobr.Rmd
@@ -25,21 +25,21 @@ You can install geobr from CRAN or the development version to use the latest fea
 
 ```{r eval=FALSE, message=FALSE, warning=FALSE}
 # From CRAN
-  install.packages("geobr")
+install.packages("geobr")
 
 # Development version
-  utils::remove.packages('geobr')
-  devtools::install_github("ipeaGIT/geobr", subdir = "r-package")
+utils::remove.packages('geobr')
+devtools::install_github("ipeaGIT/geobr", subdir = "r-package")
 
 ```
 
 Now let's load the libraries we'll use in this vignette.
 
 ```{r eval=TRUE, message=FALSE, warning=FALSE, results='hide'}
-  library(geobr)
-  library(ggplot2)
-  library(sf)
-  library(dplyr)
+library(geobr)
+library(ggplot2)
+library(sf)
+library(dplyr)
 ```
 
 
@@ -66,14 +66,18 @@ The syntax of all *geobr* functions operate one the same logic, so the code to d
 Download an specific geographic area at a given year
 ```{r eval=TRUE, message=FALSE, warning=FALSE, results='hide'}
 # State of Sergige
-  state <- read_state(code_state="SE", 
-                      year=2018,
-                      showProgress = FALSE)
+state <- read_state(
+  code_state="SE",
+  year=2018,
+  showProgress = FALSE
+  )
 
 # Municipality of Sao Paulo
-  muni <- read_municipality(code_muni = 3550308, 
-                            year=2010, 
-                            showProgress = FALSE)
+muni <- read_municipality(
+  code_muni = 3550308, 
+  year=2010, 
+  showProgress = FALSE
+  )
 
 ```
 
@@ -81,14 +85,16 @@ Download an specific geographic area at a given year
 Download all geographic areas within a state at a given year
 ```{r eval=FALSE, message=FALSE, warning=FALSE, results='hide'}
 # All municipalities in the state of Minas Gerais
-  muni <- read_municipality(code_muni= "MG", 
-                            year=2007,
-                            showProgress = FALSE)
+muni <- read_municipality(code_muni= "MG", 
+                          year=2007,
+                          showProgress = FALSE)
 
 # All census tracts in the state of Rio de Janeiro
-  cntr <- read_census_tract(code_tract = "RJ", 
-                            year = 2010,
-                            showProgress = FALSE)
+cntr <- read_census_tract(
+  code_tract = "RJ", 
+  year = 2010,
+  showProgress = FALSE
+  )
 
 ```
 
@@ -96,12 +102,16 @@ If the parameter `code_` is not passed to the function, geobr returns the data f
 
 ```{r eval=TRUE, message=FALSE, warning=FALSE, results='hide'}
 # read all intermediate regions
-inter <- read_intermediate_region(year=2017,
-                                  showProgress = FALSE)
+inter <- read_intermediate_region(
+  year=2017,
+  showProgress = FALSE
+  )
 
 # read all states
-states <- read_state(year=2019, 
-                     showProgress = FALSE)
+states <- read_state(
+  year=2019, 
+  showProgress = FALSE
+  )
 
 ```
 
@@ -119,18 +129,18 @@ Once you've downloaded the data, it is really simple to plot maps using `ggplot2
 
 ```{r eval=TRUE, message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"}
 # Remove plot axis
-  no_axis <- theme(axis.title=element_blank(),
-                   axis.text=element_blank(),
-                   axis.ticks=element_blank())
+no_axis <- theme(axis.title=element_blank(),
+                 axis.text=element_blank(),
+                 axis.ticks=element_blank())
 
 
 
 # Plot all Brazilian states
-  ggplot() +
-    geom_sf(data=states, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
-    labs(subtitle="States", size=8) +
-    theme_minimal() +
-    no_axis
+ggplot() +
+  geom_sf(data=states, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
+  labs(subtitle="States", size=8) +
+  theme_minimal() +
+  no_axis
 
 ```
 
@@ -144,17 +154,19 @@ Plot all the municipalities of a particular state, such as Rio de Janeiro:
 ```{r eval=TRUE, message=FALSE, warning=FALSE, results='hide', fig.height = 8, fig.width = 8, fig.align = "center"}
 
 # Download all municipalities of Rio
-  all_muni <- read_municipality( code_muni = "RJ", 
-                                 year= 2010,
-                                 showProgress = FALSE)
+all_muni <- read_municipality(
+  code_muni = "RJ", 
+  year= 2010,
+  showProgress = FALSE
+  )
 
 
 # plot
-  ggplot() +
-    geom_sf(data=all_muni, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
-    labs(subtitle="Municipalities of Rio de Janeiro, 2000", size=8) +
-    theme_minimal() +
-    no_axis
+ggplot() +
+  geom_sf(data=all_muni, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
+  labs(subtitle="Municipalities of Rio de Janeiro, 2000", size=8) +
+  theme_minimal() +
+  no_axis
 ```
 
 ```{r munis rio, eval=FALSE, echo=FALSE, message=FALSE, out.width='100%'}
@@ -185,12 +197,12 @@ states <- dplyr::left_join(states, df, by = c("name_state" = "uf"))
 #### Plot thematic map
 
 ```{r eval=TRUE, message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center" }
-  ggplot() +
-    geom_sf(data=states, aes(fill=ESPVIDA2017), color= NA, size=.15) +
-      labs(subtitle="Life Expectancy at birth, Brazilian States, 2014", size=8) +
-      scale_fill_distiller(palette = "Blues", name="Life Expectancy", limits = c(65,80)) +
-      theme_minimal() +
-      no_axis
+ggplot() +
+  geom_sf(data=states, aes(fill=ESPVIDA2017), color= NA, size=.15) +
+    labs(subtitle="Life Expectancy at birth, Brazilian States, 2014", size=8) +
+    scale_fill_distiller(palette = "Blues", name="Life Expectancy", limits = c(65,80)) +
+    theme_minimal() +
+    no_axis
 
 ```