is too old to have a package for newer versions of Gdal. In this case the package is not coming from Anaconda but the CRAN mirror and the package management in R (or Python for that matter) is notoriously bad for external dependencies - especially on older Linux. Anaconda uses a rather crude method to mitigate that but the package has to come from a conda channel (so you'd install it using conda install from command line. That would download the additional gdal shared library and inject the path to your LD_LIBRARY_PATH). I don't know if conda's support for R packages matches the one for Python's though. As I've said a few times on this forum, in my case we got tired of this problem of maintaining external dependencies by hand (about 900 R packages from CRAN, Bioconductor, Github,... to manage, keep up to date, etc.). See my old post which happens to deal with Gdal. Note that our approach is not for everybody - the learning curve is rather steep.
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