Merge branch 'master' of github.com:stencila/dockter

README.md

@@ -93,7 +93,7 @@ If the folder contains any 🦄 [#9](https://github.com/stencila/dockter/issues/
 
 ### Efficiently handling of updates to project code
 
-Docker layered filesystem has advantages but it can cause real delays when you are updating your project dependencies. For example, see [this issue](https://github.com/npm/npm/issues/11446) for the workarounds used by Node.js developers to prevent long waits when they update their `package.json`. The reason this happens is that when you update a requirements file Docker throws away all the susequent layers, including the one where you install all your package dependencies.
+Docker layered filesystem has advantages but it can cause real delays when you are updating your project dependencies. For example, see [this issue](https://github.com/npm/npm/issues/11446) for the workarounds used by Node.js developers to prevent long waits when they update their `package.json`. The reason this happens is that when you update a requirements file Docker throws away all the subsequent layers, including the one where you install all your package dependencies.
 
 Here's a simple motivating [example](fixtures/tests/py-pandas) of a Dockerized Python project. It's got a [`pip`](https://pypi.org/project/pip/) `requirements.txt` file which specifies that the project requires `pandas` which, to ensure reproducibility, is pinned to version `0.23.0`,
 
@@ -106,7 +106,7 @@ The project has also got a `Dockerfile` that specifies which Python version we w
 ```Dockerfile
 FROM python:3.7.0
 
-COPY requirements.xt .
+COPY requirements.txt .
 RUN pip install -r requirements.txt
 ```
 
@@ -125,7 +125,7 @@ pandas==0.23.0
 matplotlib==3.0.0
 ```
 
-When we do `docker build .` again Docker notices that the `requirements.txt` file has changed and so throws away that layer and all subsequant ones. This means that it will download and install **all** the necesary packages again, including the ones that we previously installed - and takes longer than the first install. For a more contrieved illustration of this, simply add a space to a line in the `requirements.txt` file and notice how the package install gets repeated all over again.
+When we do `docker build .` again Docker notices that the `requirements.txt` file has changed and so throws away that layer and all subsequent ones. This means that it will download and install **all** the necessary packages again, including the ones that we previously installed - and takes longer than the first install. For a more contrived illustration of this, simply add a space to a line in the `requirements.txt` file and notice how the package install gets repeated all over again.
 
 Now, let's add a special `# dockter` comment to the Dockerfile before the `COPY` directive,
 
@@ -175,7 +175,7 @@ Dockter is designed to make it easier to get started creating Docker images for
 
 - *Dockerfile generation*: Dockter aims to generate readable Dockerfiles that conform to best practices. They're a good place to start learning how to write your own Dockerfiles. To stop Dockter generating a `.Dockerfile`, and start editing it yourself, just rename it to `Dockerfile`.
 
-- *Image build*: Dockter manage builds use a special comment in the `Dockerfile`, so you can stop using Dockter alltogether and build the same image using Docker (it will just take longer if you change you project dependencies).
+- *Image build*: Dockter manage builds use a special comment in the `Dockerfile`, so you can stop using Dockter altogether and build the same image using Docker (it will just take longer if you change you project dependencies).
 
 
 ## Install

doc/how-to.md

@@ -108,7 +108,40 @@ Dockter can create a software environment file which reflects the requirements t
 The software environment file is a file in `JSON` or `yaml` format with comprehensive meta data about the project.
 Dockter parses the folder with the Python project for `py` files and extracts the relevant information.
 
+#### 4. Real-life example
 
+To demonstrate convenience of using Dockter, try running it for one of the [Software Carpentry modules](https://github.com/swcarpentry/r-novice-inflammation)
+for learning R. The repository with the module contains a range of source code files, including 'python`, `JS`, `Rmd` and `R`. 
+First, clone the repository on your machine:
+
+```
+git clone https://github.com/swcarpentry/r-novice-inflammation.git
+```
+
+Run `dockter compile` on the whole directory:
+
+```
+dockter compile r-novice-inflammation
+```
+
+Then, `dockter build` to create a `Docker` image so that the module can be run (note, `dockter` will likely have to download and install a number
+of packages, this may take a while):
+
+```
+dockter build r-novice-inflammation
+```
+
+Check if the image is there:
+
+```
+docker images
+```
+
+And you should see the image on the list:
+
+```                                 TAG                 IMAGE ID            CREATED             SIZE
+rnoviceinflammation                        latest              149d3dc3fc73        20 minutes ago      1.12GB
+```
 
 #### Resources