Data manipulation with R – Week #3 Update (Jay)

Hello all, I did a couple of things in the past week: Learned about the dplyr library in R, which helps get data frames and matrices in a useable format and wrote an R script to merge some data files from a plant trait database. Learned about parallelizing matrix operations with mpi4py. Learned about remote sensing and geospatial mapping with LIDAR (which also maybe were self-driving car technology may go as LIDAR gets cheaper). Learned about data visualization with ggplot2 utilizing the Grammar of Graphics. Read some cool articles about predicting missing data values from sparsely populated data sets. If you are using R and need to convert or transform data in a particular format, then dpylr is extremely useful. Here's some documentation: http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf http://r4ds.had.co.nz/transform.html

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