diff --git a/README.md b/README.md
index 4aadba15..f9715ddb 100644
--- a/README.md
+++ b/README.md
@@ -39,10 +39,10 @@ The package is documented in the [online manual](https://jgcri.github.io/gcamfao
 
 ### Quick Start in R (> 4.0) & Rstudio
 
-#### 1. Download and install:
+#### 1. Download and install (size < 1 GB):
 
-* Using `devtools` to download and install (The size < 1 GB):
-* `devtools::install_github("jgcri/gcamfaostat")`
+* On the command line: navigate to your desired folder location and then enter 
+* `git clone https://github.com/JGCRI/gcamfaostat.git`
 
 #### 2. Load and run the gcamdata package
 
diff --git a/man/FF_download_FAOSTAT.Rd b/man/FF_download_FAOSTAT.Rd
index 7a3372fb..48ba1898 100644
--- a/man/FF_download_FAOSTAT.Rd
+++ b/man/FF_download_FAOSTAT.Rd
@@ -11,7 +11,7 @@ FF_download_FAOSTAT(
 )
 }
 \arguments{
-\item{DATASETCODE}{Dataset code in FAO metadata, e.g., QCL is the production dataset.}
+\item{DATASETCODE}{Single dataset code in FAO metadata, e.g., QCL is the production dataset.}
 
 \item{DATA_FOLDER}{Destination folder for storing raw data}
 
diff --git a/man/FF_download_RemoteArchive.Rd b/man/FF_download_RemoteArchive.Rd
index 60a2c30b..077434f2 100644
--- a/man/FF_download_RemoteArchive.Rd
+++ b/man/FF_download_RemoteArchive.Rd
@@ -12,7 +12,7 @@ FF_download_RemoteArchive(
 )
 }
 \arguments{
-\item{DATASETCODE}{Dataset code in FAO metadata, e.g., QCL is the production dataset.}
+\item{DATASETCODE}{Single dataset code in FAO metadata, e.g., QCL is the production dataset.}
 
 \item{RemoteArchiveURL}{Zenodo URL, default (GCAM v7) = "https://zenodo.org/record/8260225/files/"}
 
diff --git a/vignettes/vignette_getting_started.Rmd b/vignettes/vignette_getting_started.Rmd
index 4ee3269b..28a84f4c 100644
--- a/vignettes/vignette_getting_started.Rmd
+++ b/vignettes/vignette_getting_started.Rmd
@@ -60,21 +60,24 @@ Nonetheless, the FAOSTAT data employed within gcamdata has traditionally involve
 
 * R version 4.0 or higher and RStudio are recommended. 
 
-#### Clone this repository
+#### Clone this repository (size < 1 GB)
 
-* On the command line: navigate to your desired folder location and then enter `git clone https://github.com/JGCRI/gcamfaostat.git` 
+* On the command line: navigate to your desired folder location and then enter 
+```{r eval = FALSE}
+git clone https://github.com/JGCRI/gcamfaostat.git
+```
+
+* Or using `devtools` to download and install in r environment (into `.libPaths()`)
+```{r eval = FALSE}
+install.packages("devtools") # install devtools package if not already
+devtools::install_github("jgcri/gcamfaostat")
+```
 
-* If using GitHub Desktop: Go to https://github.com/jgcri/gcamfaostat and click the green "Code" button. Then click "Open with GitHub Desktop".
+* Note that `gcamfaostat` is not dependent on GCAM, so it can be installed to folders as desired by the user.
 
 #### Loading the `gcamfaostat` package
 
 * Open the `gcamfaostat` folder you just cloned and double-click the `gcamfaostat.Rproj` file. RStudio should open the project.
-
-* If you have not installed `devtools` before, first enter the following into the R console  
-```{r eval = FALSE}
-install.packages("devtools")
-```
-
 * Then to load the `gcamfaostat` package:
 
 ```{r eval = FALSE}
@@ -82,14 +85,10 @@ devtools::load_all()
 ```
 
 #### Package dependencies
-* You may also need to install package dependencies, for example using:
-```R
-devtools::install_deps()
-```
-* Note that could be issues with some recent package dependencies. Users can consider using `renv`. 
+Users can consider using `renv`. 
 
-  * `renv` is an R package for dependency management. 
-  * Using it will save a private R library with the correct versions of any package dependencies. 
+  * `renv` has been initialized and a `renv.lock` file was included.
+  * Using `renv` will save a private R library with the correct versions of any package dependencies. 
   * Please find more details [here](https://jgcri.github.io/gcamdata/articles/getting-started/getting-started.html#optional-renv-currently-included-in-gcam-6-0-and-gcamdata-release) in `gcamdata` manual.
 
 
diff --git a/vignettes/vignette_preparing_data.Rmd b/vignettes/vignette_preparing_data.Rmd
index 5b338368..7e1d0dc0 100644
--- a/vignettes/vignette_preparing_data.Rmd
+++ b/vignettes/vignette_preparing_data.Rmd
@@ -73,15 +73,38 @@ Note that if Exist_Prebuilt is `TRUE` for a dataset, it suggests the raw data wa
  
 Two functions above are created for downloading the raw data from a remote archive or the FAOSTAT API (latest data). The dataset code variable in the two functions, if including all, can be generated using `gcamfaostat_metadata(OnlyReturnDatasetCodeRequired = T)`. 
 
+Example:
+``` {r, eval =FALSE}
+# Dataset PP, producer prices, is downloaded from RemoteArchiveURL to DATA_FOLDER
+# RemoteArchiveURL = "https://zenodo.org/record/8260225/files/"
+# DATA_FOLDER = "inst/extdata/aglu/FAO/FAOSTAT"
+FF_download_RemoteArchive( 
+  DATASETCODE = "PP",   
+  OverWrite = TRUE     # overwrite existing PP dataset 
+  )
+
+# Dataset OA, population, is downloaded from FAOSTAT to DATA_FOLDER
+FF_download_FAOSTAT(DATASETCODE = "OA", OverWrite = TRUE)
+
+# Note that single DATASETCODE is allowed in both functions. 
+```
 
 ### 4. Check local raw data info
 
 [`FF_rawdata_info()`](https://jgcri.github.io/gcamfaostat/reference/FF_rawdata_info.html)  
 
-* Providing more detailed metadata information, similar to `gcamfaostat_metadata`(which calls `FF_rawdata_info`).  
+* Providing more detailed metadata information, similar to `gcamfaostat_metadata` (which calls `FF_rawdata_info`).  
 * Indicate whether an update from FAOSTAT is potentially needed.
 * Can be used to download data if not exist.
 
+Example:
+``` {r, eval =FALSE}
+# Provide detailed metadata of "PP" and "OA" in DATA_FOLDER ("inst/extdata/aglu/FAO/FAOSTAT")
+FF_rawdata_info(DATASETCODE = c("PP", "OA"), DOWNLOAD_NONEXIST = FALSE)
+
+# If "PP" or "OA" does not exist, download from remote archive
+FF_rawdata_info(DATASETCODE = c("PP", "OA"), DOWNLOAD_NONEXIST = TRUE, FAOSTAT_or_Archive = "Archive")
+```
 
 ### 5. Load raw data into package  
 
@@ -90,6 +113,12 @@ Two functions above are created for downloading the raw data from a remote archi
 * Loading FAOSTAT raw data, e.g., used in `xfaostat_L191_RawDataPreProc*` modules. 
 * Note that `.` in column name is substituted with `_`.
 
+Example:
+``` {r, eval =FALSE}
+# Read raw data of "PP" and "OA" from DATA_FOLDER ("inst/extdata/aglu/FAO/FAOSTAT") to .GlobalEnv
+FAOSTAT_load_raw_data(DATASETCODE = c("PP", "OA"), .Envir = .GlobalEnv)
+```
+
 ### 6. Update the Prebulit data
 
 If users updated the FAOSTAT raw data, and run `driver_drake` is run with `Process_Raw_FAO_Data <- TRUE` in `constants.R`, the updated preprecessed data are stored in drake cache. And they can be used to update `Prebulit data` by sourcing `data-raw/generate_package_data.R`.
diff --git a/vignettes/vignette_use_cases.Rmd b/vignettes/vignette_use_cases.Rmd
index b52781ea..bd51df21 100644
--- a/vignettes/vignette_use_cases.Rmd
+++ b/vignettes/vignette_use_cases.Rmd
@@ -26,9 +26,10 @@ knitr::opts_chunk$set(
 To update the output data by including new data years, e.g., for model base year updates, the user needs to implement the following steps:  
   
 1. Download the latest FAOSTAT data using the `FF_download_FAOSTAT` function.  
-2. In the configuration file (`constants.R`), adjust the year variables and set `Process_Raw_FAO_Data` to `TRUE`.
-3. Verify and update the output formats and names in the data exporting modules.
-4. Execute the `driver_drake` function to initiate the data processing.
+2. In the configuration file (`R/constants.R`), adjust the year variables and set `Process_Raw_FAO_Data` to `TRUE`.
+3. If more recent data is available the maximum year in `FAOSTAT_Hist_Year_*` can be updated accordingly.  
+4. Verify and update the output formats and names in the data exporting modules.
+5. Execute the `driver_drake` function to initiate the data processing.
 
 
 ## Adding new data processing modules
@@ -51,7 +52,9 @@ If all the necessary FAOSTAT raw data is already incorporated into gcamfaostat,
 
 ## Country aggregation and disaggregation
 
-Since the 1970s, the number of countries in the world has increased due to the dissolution of regions. In other words, when a region dissolves, the country associated with it ceases to exist, and new countries emerge in its place, see [**Figure 1**](FigCountryChange). We included functions to deal with changes in the country classifications.  
+Since the 1970s, the number of countries in the world has increased due to the dissolution of regions. In other words, when a region dissolves, the country associated with it ceases to exist, and new countries emerge in its place, see [**Figure 1**](FigCountryChange). We included functions to deal with changes in the country classifications. Note the functions rely on the `area_code` ID included in FAO dataset. They are also consistently defined in the function. 
+
+ 
 
 [`FAOSTAT_AREA_RM_NONEXIST`](https://jgcri.github.io/gcamfaostat/reference/FAOSTAT_AREA_RM_NONEXIST.html)