Updated for clear execution of RMarkdown

Author: Chris-Schnaufer

sensors/06-list-datasets-by-plot.Rmd

@@ -10,7 +10,7 @@ from the workbench or Globus.
 ## Getting started
 
 After installing terrautils, you should be able to import the `products` module.
-```{python}
+```{python eval=FALSE}
 from terrautils.products import get_sensor_list, unique_sensor_names
 from terrautils.products import get_file_listing, extract_file_paths
 ```
@@ -29,7 +29,7 @@ a plot id number. The utility function `unique_sensor_names` accpets the
 sensor list and provides a list of names suitable for use in the 
 `get_file_listing` function.
 
-```{python}
+```{python eval=FALSE}
 sensors = get_sensor_list(None, url, key)
 names = unique_sensor_names(sensors)
 ```
@@ -48,7 +48,7 @@ The geostreams API can be used to get a list of datasets that overlap a
 specific plot boundary and, optionally, limited by a time range. Iterating 
 over the datasets allows the paths to all the files to be extracted.
 
-```{python}
+```{python eval=FALSE}
 sensor = 'Thermal IR GeoTIFFs Datasets'
 sitename = 'MAC Field Scanner Season 1 Field Plot 101 W'
 datasets = get_file_listing(None, url, key, sensor, sitename)
@@ -58,7 +58,7 @@ files = extract_file_paths(datasets)
 Datasets can be further filtered using the *since* and *until* parameters
 of `get_file_listing` with a date string.
 
-```{python}
+```{python eval=FALSE}
 dataset = get_file_listing(None, url, key, sensor, sitename, 
         since='2016-06-01', until='2016-06-10')
 ```
@@ -131,7 +131,7 @@ curl -o datasets.json -X GET "https://terraref.ncsa.illinois.edu/clowder/api/geo
 After the call succeeds, a file named *datasets.json* is created containing the returned JSON onject. As part of the
 JSON object there are one or more `properties` fields containing *source_dataset* parameters.
 
-```{python}
+```{javascript eval=FALSE}
 properties: {
     dataset_name: "Thermal IR GeoTIFFs - 2016-05-09__12-07-57-990",
     source_dataset: "https://terraref.ncsa.illinois.edu/clowder/datasets/59fc9e7d4f0c3383c73d2905"
@@ -160,7 +160,7 @@ curl -o files.json -X GET "${SOURCE_DATASET}/files"
 As before, we will have a file containing the returned JSON, named *files.json* in this case. The returned JSON consists of a list 
 of the files in the dataset with their IDs, and other data if available:
 
-``` {python}
+``` {javascript eval=FALSE}
 [
     {
         size: "346069",