Image & Audio Processing

General purpose TIFF file I/O for R users. Currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from ImageJ and write TIFF files than can be correctly read by ImageJ https://imagej.nih.gov/ij/. Also supports text image I/O.

1. Nolan, R., & Padilla-Parra, S. (2018). ijtiff: An R package providing TIFF I/O for ImageJ users. Journal of Open Source Software, 3(23), 633. https://doi.org/10.21105/joss.00633
2. Hoffman, M. M., Zylla, J. S., Bhattacharya, S., Calar, K., Hartman, T. W., Bhardwaj, R. D., … Messerli, S. M. (2020). Analysis of Dual Class I Histone Deacetylase and Lysine Demethylase Inhibitor Domatinostat (4SC-202) on Growth and Cellular and Genomic Landscape of Atypical Teratoid/Rhabdoid. Cancers, 12(3), 756. https://doi.org/10.3390/cancers12030756
3. Germani, E., Lelouard, H., & Fallet, M. (2020). SAPHIR: a Shiny application to analyze tissue section images. F1000Research, 9, 1276. https://doi.org/10.12688/f1000research.27062.1

Experimenting with computer vision and machine learning in R. This package exposes some of the available OpenCV https://opencv.org/ algorithms, such as edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.

Bindings to ImageMagick: the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. The latest version of the package includes a native graphics device for creating in-memory graphics or drawing onto images using pixel coordinates.

1. Stachelek, J., Ford, C., Kincaid, D., King, K., Miller, H., & Nagelkirk, R. (2017). The National Eutrophication Survey: lake characteristics and historical nutrient concentrations. Earth System Science Data Discussions, 1–11. https://doi.org/10.5194/essd-2017-52
2. Mendez, P. K., Lee, S., & Venter, C. E. (2018). Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys, 795, 49–65. https://doi.org/10.3897/zookeys.795.28416
3. Weishäupl, D., Schneider, J., Peixoto Pinheiro, B., Ruess, C., Dold, S. M., von Zweydorf, F., … Schmidt, T. (2018). Physiological and pathophysiological characteristics of ataxin-3 isoforms. Journal of Biological Chemistry, jbc.RA118.005801. https://doi.org/10.1074/jbc.ra118.005801
4. Evans, L. K., & Nishioka, J. (2018). Accumulation processes of trace metals into Arctic sea ice: distribution of Fe, Mn and Cd associated with ice structure. Marine Chemistry. https://doi.org/10.1016/j.marchem.2018.11.011
5. Maia, R., Gruson, H., Endler, J. A., & White, T. E. (2018). pavo 2: new tools for the spectral and spatial analysis of colour in R. https://doi.org/10.1101/427658
6. Salazar, P. C., Navarro-Cerrillo, R. M., Cruz, G., Grados, N., & Villar, R. (2019). Variability in growth and biomass allocation and the phenotypic plasticity of seven Prosopis pallida populations in response to water availability. Trees. https://doi.org/10.1007/s00468-019-01868-9
7. Logemann, A., Schafberg, M., & Brockmeyer, B. (2019). Using the HPTLC-bioluminescence bacteria assay for the determination of acute toxicities in marine sediments and its eligibility as a monitoring assessment tool. Chemosphere. https://doi.org/10.1016/j.chemosphere.2019.05.246
8. Upham, N. S., Esselstyn, J. A., & Jetz, W. (2019). Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLOS Biology, 17(12), e3000494. https://doi.org/10.1371/journal.pbio.3000494
9. Mowinckel, A. M., & Vidal-Piñeiro, D. (2019). Visualisation of Brain Statistics with R-packages ggseg and ggseg3d. arXiv preprint arXiv:1912.08200 https://arxiv.org/abs/1912.08200
10. Schwalb‐Willmann, J., Remelgado, R., Safi, K., & Wegmann, M. (2020). moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13374
11. Michaels, I. H., Pirani, S. J., & Carrascal, A. (2020). Visualizing 50 Years of Cancer Mortality Rates Across the US at Multiple Geographic Levels Using a Synchronized Map and Graph Animation. Preventing Chronic Disease, 17. https://doi.org/10.5888/pcd17.190286
12. Feldmann, M. J., Hardigan, M. A., Famula, R. A., López, C. M., Tabb, A., Cole, G. S., & Knapp, S. J. (2020). Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry. GigaScience, 9(5). https://doi.org/10.1093/gigascience/giaa030
13. Biber‐Freudenberger, L., Ergeneman, C., Förster, J. J., Dietz, T., & Börner, J. (2020). Bioeconomy futures: Expectation patterns of scientists and practitioners on the sustainability of bio‐based transformation. Sustainable Development. https://doi.org/10.1002/sd.2072
14. Berkel, C., & Cacan, E. (2020). GAB2 and GAB3 are expressed in a tumor stage-, grade- and histotype-dependent manner and are associated with shorter progression-free survival in ovarian cancer. Journal of Cell Communication and Signaling. https://doi.org/10.1007/s12079-020-00582-3
15. Sodhi, K., Wang, X., Chaudhry, M. A., Lakhani, H. V., Zehra, M., Pratt, R., … Shapiro, J. I. (2020). Central Role for Adipocyte Na,K-ATPase Oxidant Amplification Loop in the Pathogenesis of Experimental Uremic Cardiomyopathy. Journal of the American Society of Nephrology, 31(8), 1746–1760. https://doi.org/10.1681/asn.2019101070
16. Pregla, D., Lissón, P., Vasishth, S., Burchert, F., & Stadie, N. (2020, September 18). Variability in sentence comprehension in aphasia in German. https://doi.org/10.31234/osf.io/7hfpx
17. Ostrop, J., Zwiggelaar, R., Pedersen, M. T., Gerbe, F., Bösl, K., Lindholm, H. T., … Oudhoff, M. J. (2020). A semi-automated organoid screening method demonstrates epigenetic control of intestinal epithelial differentiation. https://doi.org/10.1101/2020.07.23.217414
18. Ingenloff, K., & Peterson, A. T. (2020). Incorporating time into the traditional correlational distributional modelling framework: A proof‐of‐concept using the Wood Thrush Hylocichla mustelina. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13523

Bindings to Tesseract https://opensource.google.com/projects/tesseract: a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.

1. Stachelek, J., Ford, C., Kincaid, D., King, K., Miller, H., & Nagelkirk, R. (2017). The National Eutrophication Survey: lake characteristics and historical nutrient concentrations. Earth System Science Data Discussions, 1–11. https://doi.org/10.5194/essd-2017-52
2. Bayer, D., & Michael, S. (2019). Exploring the Daschle Collection using Text Mining. arXiv preprint arXiv:1904.12623 https://arxiv.org/pdf/1904.12623
3. Tennant, W. S. D., Tildesley, M. J., Spencer, S. E. F., & Keeling, M. J. (2020). Climate drivers of plague epidemiology in British India, 1898–1949. Proceedings of the Royal Society B: Biological Sciences, 287(1928), 20200538. https://doi.org/10.1098/rspb.2020.0538

Bindings to FFmpeg http://www.ffmpeg.org/ AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. Also offers high performance tools for reading raw audio, creating spectrograms, and converting between countless audio / video formats. This package interfaces directly to the C API and does not require any command line utilities.

Use morphological image processing and edge detection algorithms to automatically measure tree ring widths on digital images. Users can also manually mark tree rings on species with complex anatomical structures. The arcs of inner-rings and angles of successive inclined ring boundaries are used to correct ring-width series. The package provides a Shiny-based application, allowing R beginners to easily analyze tree ring images and export ring-width series in standard file formats.

1. Guiterman, C. H., Lynch, A. M., & Axelson, J. N. (2020). dfoliatR: An R package for detection and analysis of insect defoliation signals in tree rings. Dendrochronologia, 63, 125750. https://doi.org/10.1016/j.dendro.2020.125750
2. Makela, K., Ophelders, T., Quigley, M., Munch, E., Chitwood, D., & Dowtin, A. (2020). Automatic Tree Ring Detection using Jacobi Sets. arXiv preprint arXiv:2010.08691 https://arxiv.org/abs/2010.08691.