Le-Huynh Truc-Ly

bookmark collection

Fri, 09 Oct 2026 03:56:18 GMT

A collection of interesting links featuring open-access books, tutorials, and articles on data analysis, data visualization, and reproducible workflow.

R+

Big Book of R by Oscar Baruffa.
R for Data Science by Hadley Wickham and Garrett Grolemund.
R Packages by Hadley Wickham and Jenny Bryan.

Git and GitHub

Happy Git with R by Jenny Bryan.
Notes on Git and GitHub by Daniel Czarnievicz.

lehuynh: Now Available on CRAN!

Fri, 09 Oct 2026 03:56:18 GMT

→ GitHub repository
→ Package Website

I am thrilled to announce that my personal R package, lehuynh, is now officially available on CRAN!

lehuynh is a collection of miscellaneous R functions and templates that I’ve found particularly useful over time. It includes:

Functions for graphics, data importation, data transformation, and general utilities.
Templates for exploratory analysis, Bayesian modeling, and crafting scientific manuscripts.

A Learning Journey

Publishing lehuynh on CRAN has been an incredibly educational experience. Throughout the process, I learned a lot about package development, from structuring the package correctly to ensuring compliance with CRAN’s submission guidelines.

Big thanks to the CRAN team for their invaluable feedback. Their suggestions were instrumental in refining and improving my package. After incorporating their feedback, lehuynh has become much better and more robust.

I also want to acknowledge the numerous references and resources that guided me through the preparation and submission process. These resources saved me a significant amount of time and effort. If you’re considering submitting your own package to CRAN, I highly recommend leveraging the wealth of knowledge available in the R community.

Releasing to CRAN, R Packages by Hadley Wickham and Jenny Bryan
extrachecks by Davis Vaughan and contributors

More Than Just a Collection of Code

For me, lehuynh is more than just a collection of useful code and templates. It’s a personal project that has allowed me to experiment and learn new things. Whether it’s writing templates or understanding the intricacies of publishing on platforms like CRAN and Zenodo, lehuynh has been my playground for exploration and growth.

Installation

I hope you find lehuynh as useful as I have. You can install the package directly from CRAN using the following command:

install.packages("lehuynh")

To get the latest in-development features, install the development version from GitHub:

if(!requireNamespace("devtools", quietly = TRUE)) {
 install.packages("devtools")
}
devtools::install_github("le-huynh/lehuynh")

Additionally, this package is accessible for download via Zenodo at 10.5281/zenodo.11522849.

For a comprehensive overview of the package’s functions, check out the package website at
le-huynh.github.io/lehuynh/.

Now, my beer is ready. Cheers!

Targeted elevation mapping with grayscale contrast using R

Fri, 09 Oct 2026 03:56:18 GMT

This blog post serves as a step-by-step guide to generating stunning elevation maps in R. The elevation map highlights a specific area of interest in vibrant color against a grayscale background of neighboring landscapes. Using Vietnam as featured example, this post will showcase the power of R in visualizing elevation data.
The complete code for this visualization is available on Github.

library(tidyverse)
library(here)
library(elevatr)
library(raster)
library(sf)
library(scales)

Step 1: Specify the boundaries of the map drawing rectangle

Set the plot boundaries using coordinate data.
The coordinate can be obtained from several websites, for example, bboxfinder.com.

bounds_data <- data.frame(x = c(100.854490, 116.147458), # longitude
                          y = c(6.945000, 23.812699) # latitude
                          )

Step 2: Get the elevation of background raster

Note: Change the zoom level if getting Error: cannot allocate vector of size XXX Mb.

# set projection
proj <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"

# get elevation raster
background_raster <- elevatr::get_elev_raster(locations = bounds_data,
                                              # projection
                                              prj = proj,
                                              # set the zoom level to return; the value ranges from 1 to 14
                                              z = 6,
                                              # determine clipping of returned DEM
                                              clip = "locations")

Step 3: Read the polygonal file to outline the boundary of Vietnam

The polygonal data can be downloaded from several websites, for example, diva-gis.

# corresponding file format: SHAPEFILE (.shp)
terrain_extent <- sf::st_read(here::here("data/VNM_gis/VNM_adm0.shp"))

Step 4: Retain the boundary of Vietnam to display the terrain raster

terrain_raster <- raster::mask(background_raster, terrain_extent)

Step 5: Modify RasterLayer data to be compatible with ggplot2

Convert the RasterLayer into a data.frame format
Adjust the value range of the layer (elevation)

# background part
names(background_raster) <- "layer"
background_raster_df <- as.data.frame(background_raster, xy = TRUE) %>%
                                mutate(alpha = rescale(layer, to = c(0.25, 0.75)))

# terrain part
names(terrain_raster) <- "layer"
terrain_raster_df <- as.data.frame(terrain_raster, xy = TRUE) %>%
                                mutate(alpha = ifelse(is.na(layer), 0, 1))

Step 6: Create the map using ‘ggplot2’

# set scale value to control size of text, point, legend, etc. to fit figure size
scale_value <- 2

# initial ggplot
gmap <- ggplot() +
        # plot background layer
        geom_raster(data = background_raster_df,
                    aes(x = x, y = y, alpha = alpha)) +
        # plot terrain layer
        geom_raster(data = terrain_raster_df,
                    aes(x = x, y = y, fill = layer, alpha = alpha)) +
        scale_fill_gradientn(colours = terrain.colors(100, rev = TRUE),
                             # set the legend title for elevation using the 'name' parameter
                             name = "Elevation (m)") +
        # project this plot as a map rather than a standard figure
        coord_quickmap() +
        # modify axis titles
        labs(x = "", y = "") +
        # define axes limits
        # use `expand` parameter to remove the gaps between the rectangular map and axes
        scale_x_continuous(limits = c(100.854490, 116.147458), # longitude
                           expand = c(0, 0),
                           breaks = c(102, 106, 110, 114),
                           labels = c("102\u00B0E",
                                      "106\u00B0E",
                                      "110\u00B0E",
                                      "114\u00B0E")) +
        scale_y_continuous(limits = c(6.945000, 23.812699), # latitude
                           expand = c(0, 0),
                           breaks = c(10, 15, 20),
                           labels = c("10\u00B0N",
                                      "15\u00B0N",
                                      "20\u00B0N")) +
        # remove legend of alpha
        scale_alpha(guide = "none") +
        # modify theme
        theme_minimal() +
        theme(panel.grid = element_blank(),
              panel.background = element_blank(),
              axis.text = element_text(size = 7 * scale_value,
                                       color = "black"),
              legend.position = "top",
              legend.key.width = unit(1 * scale_value, "cm"),
              legend.key.height = unit(0.2 * scale_value, "cm"),
              legend.text = element_text(size = 6 * scale_value),
              legend.title = element_text(size = 7 * scale_value))

Step 7: Incorporate cities and islands into the map

Generate a dataframe containing coordinate data and site names.

site_df <- data.frame(site_name = c("Ha Noi",
                                    "Ho Chi Minh City",
                                    "Paracel Islands \n(Hoang Sa, VIETNAM)",
                                    "Spratly Islands \n(Truong Sa, VIETNAM)"),
                      site_latitude = c(21.028511, 10.823020, 16.83517, 8.64464),
                      site_longitude = c(105.804817, 106.629650, 112.33873, 111.91969))

Incorporate cities and islands into the map using ggplot2

gmap <- gmap +
        # add the areas of islands as large circles on the map
        geom_point(data = site_df %>% filter(str_detect(site_name, "Islands")),
                   mapping = aes(x = site_longitude,
                                 y = site_latitude),
                   size = 8 * scale_value,
                   color = "coral",
                   alpha = 0.3) +
        # add the cities and primary islands as points
        geom_point(mapping = aes(x = site_longitude,
                                 y = site_latitude),
                   data = site_df,
                   size = 2 * scale_value,
                   colour = "coral1") +
        # add names of cities and islands as text labels
        geom_text(mapping = aes(x = site_longitude,
                                y = site_latitude + 0.5,
                                label = site_name),
                  data = site_df,
                  size = 2.5 * scale_value)

Step 8: Add the map title

gmap <- gmap +
        geom_text(mapping = aes(x = 112.1, y = 23, label = "MAP OF VIETNAM"),
                  size = 4 * scale_value,
                  color = "white")

References

Using R for Everything: 使用 ggplot2 绘制 RasterLayer 地图 by Chen Han
Elevation data in R by Ivan Lizarazo

Highlighting regions in terrain maps using ggmap and ggplot2

Fri, 09 Oct 2026 03:56:18 GMT

This blog post serves as a quick tutorial for creating terrain map in R using ggmap and ggplot2. The terrain map accentuates a designated region amid the surrounding landscape. Using Vietnam as an illustrative example, the post underscores the effectiveness of R in visualizing map.
The complete code for this visualization is available on Github.

library(tidyverse)
library(ggmap)
library(sf)

Step 1: Specify the boundaries of the map drawing rectangle

Set the plot boundaries using coordinate data.
The coordinate can be obtained from several websites, for example, bboxfinder.com.

longitude <- c(100.854490, 116.147458)
latitude <- c(6.945000, 23.812699)

Step 2: Get the stamen map of the plot

In this post, a Stamen map hosted by Stadia Maps is used as an example. To utilize Stadia Maps with ggmap, an API key is necessary. A free API key for non-commercial purposes can be obtained without requiring a credit card for sign-up. Quick tips for obtaining the API key can be found here.

Enable Stadia Maps services in R using API key:

ggmap::register_stadiamaps(key = "YOUR-API-KEY")

Note: The API key is a PRIVATE access key and should be handled with caution. Avoid sharing it online, like in a public GitHub repository, or storing it in a shared R script file.

Getting the background map:

background_map <- ggmap::get_stadiamap(bbox = c(longitude[1],
                                                latitude[1],
                                                longitude[2],
                                                latitude[2]),
                                       maptype = "stamen_terrain_background",
                                       # increase `zoom` to get higher resolution
                                       zoom = 7)

Step 3: Read the polygonal file to outline the boundary of Vietnam

The polygonal data can be downloaded from several websites, for example, diva-gis.

target_extent <- sf::st_read("data/VNM_gis/VNM_adm0.shp")

Step 4: Convert shapefile to be compatible with ggplot2

target_shape <- # convert coordinates into correct Coordinate Reference System (CRS)
                st_transform(target_extent, crs = 4326) %>%
                # convert data type to be compatible with 'ggplot2' and 'ggmap'
                as_Spatial()

Step 5: Create the map using ggplot2

map <- ggmap(background_map) +
        geom_polygon(data = target_shape,
                     aes(x = long, y = lat, group = group),
                     colour = 'white',
                     fill = 'gray87',
                     alpha = 0.65,
                     size = 0.5) +
        theme(axis.title = element_blank(),
              axis.text = element_blank(),
              axis.ticks = element_blank(),
              plot.margin = margin(t = 0.1,
                                   r = 0.1,
                                   b = 0.1,
                                   l = 0.1,
                                   unit = "in"))

Step 6: Incorporate cities and islands into the map

Generate a dataframe containing coordinate data and site names.

site_df <- data.frame(site_name = c("Ha Noi",
                                    "Ho Chi Minh City",
                                    "Paracel Islands \n(Hoang Sa, VIETNAM)",
                                    "Spratly Islands \n(Truong Sa, VIETNAM)"),
                      site_latitude = c(21.028511, 10.823020, 16.83517, 8.64464),
                      site_longitude = c(105.804817, 106.629650, 112.33873, 111.91969))

Incorporate cities and islands into the map using ggplot2

map <- map +
        # add the areas of islands as large circles on the map
        geom_point(data = site_df %>% filter(str_detect(site_name, "Islands")),
                   mapping = aes(x = site_longitude,
                                 y = site_latitude),
                   size = 16,
                   color = "coral",
                   alpha = 0.3) +
        # add the cities and primary islands as points
        geom_point(mapping = aes(x = site_longitude,
                                 y = site_latitude),
                   data = site_df,
                   size = 4,
                   colour = "coral1") +
        # add names of cities and islands as text labels
        geom_text(mapping = aes(x = site_longitude,
                                y = site_latitude + 0.5,
                                label = site_name),
                  data = site_df,
                  size = 5)

Step 7: Add the map title

map <- map +
        geom_text(mapping = aes(x = 112.1, y = 23, label = "MAP OF VIETNAM"),
                  size = 10,
                  color = "black")

Further information

Map Style Library by Stadia Maps Documentation
ggmap Documentation

CSV parse error

Fri, 09 Oct 2026 03:56:18 GMT

Error

I got the following error when using arrow::read_csv_arrow() or arrow::open_dataset().

"Error: Invalid: CSV parse error: Row #20241208: Expected 111 columns, got 110: 
"col1","col1","col3","col4","col5"," ..."

Reason

At row 20241208, there might be unexpected characters in one or more columns that interfere with detecting the delimiter of the CSV file. These could include "", \", a blank space, or a combination of punctuation marks.

Solution

I have not found a perfect solution for this error. The approach may vary depending on the goal after importing the data into R. In my case, the CSV file was very large, and I wanted to convert it into multiple Parquet files for storage and efficient querying later. However, the file could not be converted unless it was read correctly.
Here are the steps I followed:

Step 1 (Optional)

Extract row 20241208 to identify the problematic characters.
Big thanks to Dr. Huynh Vu for helping with the Python code. If you’re into Python and data stuff, you should totally check out his blog —he’s got tons of great tips and tricks!

Step 2

Use data.table::fread() to read the CSV file into R, and replace the problematic characters if necessary. This function does not fail due to parsing errors. The downside is that your computer’s RAM must be large enough to handle fread() for in-memory data import. But what if your computer’s RAM isn’t sufficient? No worries at all! You can read the data in chunks, and guess what? The awesome team behind data.table made sure that fread() can do that too.

In my case, I read the data chunk by chunk and converted each one to Parquet format. It took longer than loading the whole file in one go with fread(), but since my RAM could not handle the entire file at once, this was a pretty good workaround.

Note: A nifty function I wrote to make the process easier.

References

Big Data Analytics by Ulrich Matter.

Getting Stadia Maps API for ggmap Rpackage

Fri, 09 Oct 2026 03:56:18 GMT

The ggmap R package is a powerful tool for creating stunning map visualizations in R, making mapping tasks more accessible and efficient. To enable Stadia Maps services in R, an API key is required. A free API key for non-commercial use can be obtained without requiring a credit card for sign-up.

Obtain API key

Sign up for Stadia Maps at https://client.stadiamaps.com/signup/
Navigate to the client dashboard at https://client.stadiamaps.com/dashboard/
Click on Create a Property → insert Property Name → click on Create
Under Authentication Configuration, select Add API key

Set API key for R session

Copy the provided API key and execute the following code in the console

ggmap::register_stadiamaps(key = "YOUR-API-KEY", write = FALSE)

When setting the API key: - Using write = FALSE to configure the key for the current R session only. The API key needs to be provided again when restarting R. - Using write = TRUE to set the API key permanently across R sessions.

Note: The API key is a PRIVATE access key and should be handled with caution. Avoid sharing it online, like in a public GitHub repository, or storing it in a shared R script file.

If the API key is shared inadvertently, access https://client.stadiamaps.com/ → delete the existing API key → generate a new one

Further information

Authentication by Stadia Maps Documentation
ggmap Documentation

Download a single folder from a GitHub repository

Fri, 09 Oct 2026 03:56:18 GMT

Go to the GitHub repo you would like to download the folder.

For example: https://github.com/le-huynh/writing_journal_article_in_rmarkdown

Replace .com with .dev in the URL to access the repository using github.dev web-based editor.

For example: https://github.dev/le-huynh/writing_journal_article_in_rmarkdown

Within the EXPLORER panel (located on the left-hand side or accessed via Ctrl+Shift+E), right-click on the specific file or folder you need and choose the option Download....
When prompted with the Select Folder dialog box, pick the directory on your computer where you’d like the chosen file or folder to be saved.

Undefined global variables

Fri, 09 Oct 2026 03:56:18 GMT

Note

I am creating a new function in my Rpackage. After running devtools::check(), I get this NOTE:

checking R code for possible problems ... NOTE
  function_name1: no visible binding for global variable 'value1'
  function_name2: no visible binding for global variable 'value2'
  Undefined global variables:
    value1 value2

Solution

Create a file globals.R in the R folder, listing undefined global variables:

utils::globalVariables(c("value1", "value2"))

Warning: ’‘::’’ or ’‘:::’’ imports not declared from: package_name

Fri, 09 Oct 2026 03:56:18 GMT

Warning

I am creating a new function in my Rpackage. After running devtools::check(), I get this WARNING:

checking dependencies in R code ... WARNING
'::' or ':::' imports not declared from:
  ‘dplyr’

Solution

Add dplyr to Imports in the DESCRIPTION file.

usethis::use_package("dplyr")
#> Adding dplyr to Imports
#> Refer to functions with dplyr::fun()

Further information

Dependencies: What does your package need?, R Packages by Hadley Wickham and Jenny Bryan

Hugo-creative-portfolio-theme: content page left 1/3 blank for desktop

Fri, 09 Oct 2026 03:56:18 GMT

Problem

Hugo theme Creative Portfolio has a wonderfully clean and elegant style. However, when viewed on a desktop screen, there appears to be a one-third blank space on the right-hand side. I’d like the content to fill the entire available space.

Solution

It was confirmed that the one-third blank space was intentionally designed that way. To modify the display settings:

Locate the file theme/layouts/_default/single.html.
Navigate to line 5 and adjust the code from
to
, where X represents the column size. For a full-page display, use X = 12. You can also experiment with different numbers for X to suit your preference. X = 11 in the screenshot below.

Tips

If you can’t find a solution using common keyword searches, consider navigating to the theme’s GitHub repository page. Sometimes, the answer can be found by exploring the theme’s Issues page.

Customizing snippets for crafting efficiency

Fri, 09 Oct 2026 03:56:18 GMT

Code snippets are pre-defined pieces of code that can be inserted into a script or console with a simple shortcut. RStudio provides built-in snippets for general functionalities. Moreover, you have the flexibility to modify these built-in snippets or introduce your personalized user-defined snippets. These customizable components cater to individual needs, greatly enhancing coding efficiency.

An example of RStudio built-in nippet

Create your own snippets

(or edit built-in snippets if you want)

Tools → Global Options → Code → Edit Snippets

In the Edit Snippets window, select a language → edit built-in snippets or add your own snippets → Save.
Note: Define custom snippets using the snippet keyword and indent the contents using the Tab key.

Try out your snippet and get a beer. Cheers!

Further information

RStudio Snippets

Le-Huynh Truc-Ly

bookmark collection

R+

Git and GitHub

Bayesian data analysis

Data visualization

Reproducible reports

Other

lehuynh: Now Available on CRAN!

A Learning Journey

More Than Just a Collection of Code

Installation

Targeted elevation mapping with grayscale contrast using R

Step 1: Specify the boundaries of the map drawing rectangle

Step 2: Get the elevation of background raster

Step 3: Read the polygonal file to outline the boundary of Vietnam

Step 4: Retain the boundary of Vietnam to display the terrain raster

Step 5: Modify RasterLayer data to be compatible with ggplot2

Step 6: Create the map using ‘ggplot2’

Step 7: Incorporate cities and islands into the map

Step 8: Add the map title

References

Highlighting regions in terrain maps using ggmap and ggplot2

Step 1: Specify the boundaries of the map drawing rectangle

Step 2: Get the stamen map of the plot

Step 3: Read the polygonal file to outline the boundary of Vietnam

Step 4: Convert shapefile to be compatible with ggplot2

Step 5: Create the map using ggplot2

Step 6: Incorporate cities and islands into the map

Step 7: Add the map title

Further information

CSV parse error

Error

Reason

Solution

Step 1 (Optional)

Step 2

References

Getting Stadia Maps API for ggmap Rpackage

Obtain API key

Set API key for R session

Further information

Download a single folder from a GitHub repository

Undefined global variables

Note

Solution

Warning: ’‘::’’ or ’‘:::’’ imports not declared from: package_name

Warning

Solution

Further information

Hugo-creative-portfolio-theme: content page left 1/3 blank for desktop

Problem

Solution

Tips

Customizing snippets for crafting efficiency

Create your own snippets

Further information