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Superindex and subindex in ggpairs axes labels
I was having problems on the syntax to get the axis labels with subindices and superindices, as it didn’t work as in ggplot2. The trick was to use a single expression() and specifying labeller = label_parsed)
Example:
resin_bv |>
ggpairs(columns = c("canal_size_bv", "canal_area_bv", "canal_density_bv"),
columnLabels = c("Canal~size~(mm^2)", "Canal~area~('%')", "Canal~density~(n~cm^{-2})"),
upper = "blank",
labeller = label_parsed) +
theme_bw(base_size = 14)
greenR: Green spaces in R
Yesterday I was attending the Urban Forest Futures conference and there were several interesting presentations. Here is a couple of links to work that was mentioned:
- greenR: “an R package that enables the quantification, analysis, and visualization of urban greenness within city networks” https://github.com/sachit27/greenR/
- Wildcounts: “supports and celebrates people documenting the changes in wild nature” https://wildcounts.org/
duckplyr: dplyr + DuckDB
DuckDB released a new R package – duckplyr, which enables running dplyr functions using the DuckDB engine on the backend (for large datasets) https://github.com/duckdblabs/duckplyr
SQL: I always need a refresh
The Querynomicon by “Mad Arab” Abdul Alhazred or Greg Wilson. Your pick. https://gvwilson.github.io/sql-tutorial/
I haven’t done an animation in R in ages
So I needed to remember how to do it. Thie post “Building an animation step-by-step with gganimate” is pretty helpful:
https://www.alexcookson.com/post/2020-10-18-building-an-animation-step-by-step-with-gganimate/
Genomic data analysis for beginners
Video series by Gábor Mészáros on genomic data analysis.
Water stress, Dothistroma and remote sensing
Long-term effects of water stress on hyperspectral remote sensing indicators in young radiata pine https://www.sciencedirect.com/science/article/abs/pii/S0378112721007970
Prediction of the severity of Dothistroma needle blight in radiata pine using plant based traits and narrow band indices derived from UAV hyperspectral imagery https://www.sciencedirect.com/science/article/abs/pii/S0168192322004804
Experiments in glasshouse
Blocking and re-arrangement of pots in greenhouse experiments: which approach is more effective?
https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0527-4
Plant image analysis
The pliman (plant image analysis) package is designed to analyze plant images, particularly for leaf and seed analysis. It offers a range of functionalities to assist with various tasks such as measuring disease severity, counting lesions, obtaining lesion shapes, counting objects in an image, extracting object characteristics, performing Fourier Analysis, obtaining RGB values, extracting object coordinates and outlines, isolating objects, and plotting object measurements.