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Soil Analysis

This document provides the latest results of a few analysis run by Celesta lab. It also showcases what’s possible to do with lumo, a minimalist quarto template.

Author: Holtz Yan

Date: October 17, 2024

Introduction

Quarto is probably my favorite tool in the R/Python universe. Transforming your code and thoughts into a stunning report in one click always blows my mind.

While the default Quarto appearance looks great, there are a few tweaks I always apply to elevate the report even further. Rather than manually adding them each time, I bundled everything into a custom format called lumo.

Btw, you can learn how to master Quarto thanks to my online course: Productive R Workflow

This document aims at showcasing how versatile the lumo format is. With a few tweaks, I made it fit the brand of a specific company.

Load Packages

Make sure to load all your packages at the start of your document for clarity and consistency.

Notice that the code chunks are folded by default. You can adjust this behavior with the code-fold option in the document’s YAML header.

Code 
library(tidyverse) 
library(hrbrthemes)
library(viridis)
library(patchwork)
1
this package provides my favorite ggplot2 theme: theme_ipsum()
2
because I was too lazy to find something better in the R Color Finder!

By the way, you should open the code chunk that is folded above. ⬆️ There is a little stunning surprise on its right hand side.

Dataviz theme

I strongly advise to create a dataviz theme that fits your company’s brand. Here is one:

Code 
scale_color_brand <- function() {
  custom_palette <- c( "#00496FFF", "#0F85A0FF", "#EDD746FF", "#ED8B00FF", "#DD4124FF")
  scale_color_manual(values = custom_palette)
}

theme_brand <- function() {
  theme_ipsum() +
    theme(
      plot.title = element_text(color = "red", size = 18, face = "bold"),
      plot.subtitle = element_text(margin = margin(b = 20)),
      axis.text.x = element_text(size = 7),
      axis.text.y = element_text(size = 7),
      plot.background = element_rect(fill = "#faf5f5", color = NA),
      plot.caption = element_text(hjust = 0)
    )
}

And now let’s use it on a graph:

Code 
# Create scatter plot
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3) +
  scale_color_brand() +
  
  # Add title, subtitle, and caption (footer)
  labs(
    title = "Sepal Length vs Sepal Width",
    subtitle = "The relationship between sepal length and sepal width is very strong inside\neach species. It's worth noting that Setosa is very different, with a higher\nsepal width and lower sepal length on average",
    caption = "Author: Yan Holtz\nData: coming from the batch #23 on the 3rd of October"
  ) +
  
  # Add annotation
  annotate("text", x = 7.5, y = 4.2, label = "Highest Sepal Width", color = "red", size = 4, hjust = 0) +
  
  # Customize the theme (optional)
  theme_brand()

Lumo features

For a complete list of features, visit the documentation!

Oh and since we’re here, did you know you can easily include a full width interactive map with leaflet in a quarto doc? 😍

Code 
# Load the library.
library(leaflet)

# Make a map
leaflet() %>%
  addTiles() %>% # Add default OpenStreetMap map tiles
  addMarkers(lng = 174.768, lat = -36.852, popup = "The birthplace of R")

Interactive table

It is very easy to insert an interactive table in your document thanks to the DT package. The output allows to filter rows, search for something and sort using a specific columns!

Install the library with install.packages("DT"). Then, just pass a dataframe to the datatable() function to get a stunning interactive output!

Code 
library(DT)
data(iris)

# Make a table
datatable(iris, filter = "top")







I’ve added a bit of CSS to make the font smaller on DT tables by the way 🙂

A grey section

It’s always good to have a grey section. Makes the document breath a bit. I’ve added a little utility class in Lumo to make sure you can make sections like this very easily. Check the documentation!

Let’s use this space to render a little equation:

\[ x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} \]

Quarto Tricks

Lumo is a great format, but to get the most out of it, you’ll need to master a few Quarto tricks!

The good news? I’ve compiled my 30+ favorite tips in one place. Just spend 30 minutes, and you’ll be up to speed with all of them!

For example, if you’re creating multiple plots, each highlighting different aspects of your dataset, consider using pills to organize them!

This is a good old scatterplot of the iris dataset. If you want to learn more about how to make graphs in R, visit my R graph gallery!

Code 
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + 
    geom_point() +
    theme_brand() +
    scale_color_brand() +
    labs(
      title = "Sepal Length vs Sepal Width",
      subtitle = "Relationship between 2 numerical variables.",
      caption = "Author: Yan Holtz\nData: coming from the batch #44 on the 3rd of October"
    )

You can also make a boxplot, but please do not hide the underlying distribution! This is widely explain in my data-to-viz.com project in case you’re not convinced 😋. Check the next tab to get convinced!

Code 
# create a dataset
data <- data.frame(
  name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100)  ),
  value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )
)

# Plot
data %>%
  ggplot( aes(x=name, y=value, fill=name)) +
    geom_boxplot() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    xlab("") +
    theme_brand() +
    scale_color_brand() +
    labs(
      title = "A boxplot that hides the underlying distribution",
      subtitle = "Never hide the underlying samples on a boxplot. You never know what's behind!?\n(check the next tab: with jittering)",
      caption = "Author: Yan Holtz\nData: coming from the batch #44 on the 3rd of October"
    )

See what’s happening now that the individual data points are visible! Would be a shame to miss this.

Code 
data %>%
  ggplot( aes(x=name, y=value, fill=name)) +
    geom_boxplot() +
    geom_jitter(color="black", size=0.4, alpha=0.9) +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    xlab("") +
    theme_brand() +
    scale_color_brand() +
    labs(
      title = "A boxplot with jitter",
      subtitle = "This boxplot does NOT hide the underlying distribution.\nIt is much more powerful and reveals some massive differences between groups!",
      caption = "Author: Yan Holtz\nData: coming from the batch #44 on the 3rd of October"
    )

More Information

Learn how to use and how to customize Lumo here.

Session Info

When creating a Quarto document, it’s a good practice to include details about your working environment.

This is easy to do—just call sessionInfo(), and it will print out all your R and package versions. This simple step greatly enhances reproducibility.

Code 
sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Paris
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] DT_0.33           leaflet_2.2.2     patchwork_1.2.0   viridis_0.6.5    
 [5] viridisLite_0.4.2 hrbrthemes_0.8.7  lubridate_1.9.3   forcats_1.0.0    
 [9] stringr_1.5.1     dplyr_1.1.4       purrr_1.0.2       readr_2.1.5      
[13] tidyr_1.3.1       tibble_3.2.1      ggplot2_3.5.1     tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.5            bslib_0.7.0             xfun_0.43              
 [4] htmlwidgets_1.6.4       tzdb_0.4.0              crosstalk_1.2.1        
 [7] vctrs_0.6.5             tools_4.4.0             generics_0.1.3         
[10] curl_5.2.1              fansi_1.0.6             pkgconfig_2.0.3        
[13] lifecycle_1.0.4         farver_2.1.1            compiler_4.4.0         
[16] munsell_0.5.1           httpuv_1.6.15           fontquiver_0.2.1       
[19] fontLiberation_0.1.0    sass_0.4.9              htmltools_0.5.8.1      
[22] yaml_2.3.8              Rttf2pt1_1.3.12         jquerylib_0.1.4        
[25] pillar_1.9.0            later_1.3.2             crayon_1.5.2           
[28] extrafontdb_1.0         gfonts_0.2.0            cachem_1.0.8           
[31] mime_0.12               fontBitstreamVera_0.1.1 tidyselect_1.2.1       
[34] digest_0.6.35           stringi_1.8.4           labeling_0.4.3         
[37] extrafont_0.19          fastmap_1.1.1           grid_4.4.0             
[40] colorspace_2.1-0        cli_3.6.2               magrittr_2.0.3         
[43] crul_1.4.2              utf8_1.2.4              withr_3.0.0            
[46] gdtools_0.3.7           scales_1.3.0            promises_1.3.0         
[49] timechange_0.3.0        rmarkdown_2.26          gridExtra_2.3          
[52] hms_1.1.3               shiny_1.8.1.1           evaluate_0.23          
[55] knitr_1.46              rlang_1.1.3             Rcpp_1.0.12            
[58] xtable_1.8-4            glue_1.7.0              httpcode_0.3.0         
[61] rstudioapi_0.16.0       jsonlite_1.8.8          R6_2.5.1               
[64] systemfonts_1.0.6

This document was made using Quarto and the Lumo format.