Update README.md

README.md

@@ -71,4 +71,4 @@ USER ${NB_USER}
 
 The original [py-rocket 1.0](https://github.com/NASA-Openscapes/py-rocket) was developed by Luis Lopez and was built off a Rocker base image. Carl Boettiger and Eli Holmes later altered the image (py-rocket 2.0) so that the Python environment matched the Pangeo image structure but the image was still built off a Rocker image. Subsequently, Carl Boettiger developed [repo2docker-r](https://github.com/boettiger-lab/repo2docker-r) that creates a JupyterHub-compatible image that uses a [Jupyter docker stack image](https://jupyter-docker-stacks.readthedocs.io/en/latest/) as base. For py-rocker 3.0, Eli Holmes used Carl's ideas but used [repo2docker](https://repo2docker.readthedocs.io/en/latest/) and [repo2docker-action](https://github.com/jupyterhub/repo2docker-action) to build the base image. To do this, the [CryoCloud hub image](https://github.com/CryoInTheCloud/hub-image) repo was used for the basic structure and approach. Eli added the `rocker.sh` script and `appendix` modifications to install R and RStudio via the Rocker scripts (rather than using a Rocker image as base). Yuvi Panda (repo2docker) gave input throughout the process as snags were hit.
 
-**Why Rocker for the R/RStudio environment?** The Rocker images are the standard for R/RStudio contanier images. They are heavily tested and regularly updated. There is a large developer community that fixes problems and bugs. The stack has gone through major revisions to improve modularity and they constantly innovating (integration for machine-learning, CUDA, BLAS, spatial, etc., etc.). py-rocker is building off that work without using the images directly. Instead it uses the Dockerfile code and the installation scripts.  There are many other approaches to adding R and RStudio to images that work in JupyterHubs. See [repo2docker-r](https://github.com/boettiger-lab/repo2docker-r) that Carl developed and [r-conda](https://github.com/binder-examples/r-conda) for a conda native approach using repo2docker. py-rocket is not intended to create small images; it is intended to create images that emulate Rocker in the `/rstudio` environment on a JupyterHub.
+**Why Rocker for the R/RStudio environment?** The Rocker images are the standard for R/RStudio contanier images. They are heavily tested and regularly updated. There is a large developer community that fixes problems and bugs. The stack has gone through major revisions to improve modularity and they constantly innovating (integration for machine-learning, CUDA, BLAS, spatial, etc., etc.). py-rocket is building off that work without using the images directly. Instead it uses the Docker file code and the installation scripts.  There are many other approaches to adding R and RStudio to images that work in JupyterHubs. See [repo2docker-r](https://github.com/boettiger-lab/repo2docker-r) that Carl developed and [r-conda](https://github.com/binder-examples/r-conda) for a conda native approach using repo2docker. py-rocket is not intended to create small images; it is intended to create images that emulate Rocker in the `/rstudio` environment on a JupyterHub.