cleanup: spelling

CONTRIBUTORS.md

@@ -16,11 +16,11 @@ If you are interested in making a code contribution and would like to learn more
 13. Make changes to the pull request if the reviewing maintainer recommends them.
 14. Celebrate your success after your pull request is merged!
 
-## Steps to create a new visulization
+## Steps to create a new visualization
 1. Make a new file for the visualization and put it in the folder of the [page](https://github.com/sandiego-rh/explorer/tree/main/pages) you want to add it to
 2. Once the file is created, import the card to the respective page file and put it into the layout
 IF you need to add a new query:
-3. add a ddc.store componet to the (app.py)[https://github.com/sandiego-rh/explorer/blob/main/app.py]
+3. add a ddc.store component to the (app.py)[https://github.com/sandiego-rh/explorer/blob/main/app.py]
 4. Create the call back in the (app_callback.py)[https://github.com/sandiego-rh/explorer/blob/main/app_callbacks.py]for the data query in a similar format to the 3 at the bottom
 
 ## Steps to create a new page

README.md

@@ -16,10 +16,10 @@ The number of repositories and projects that GitHub hosts is staggering. Even mo
 Projects on GitHub range from student projects to technological monoliths like the
 Linux Kernel. Understanding the growth trajectory, contributor behavior, progress blockers, etc. intuitively is no longer feasible for individual
 large projects, and is even less so for groups of projects. It is necessary that community architects and contributors alike have an accessible,
-correct, and extendible resource that can aggregate and interpret the data describing these communities.
+correct, and extendable resource that can aggregate and interpret the data describing these communities.
 
 The goal of this application is to serve Open Source community mangers, stakeholders, contributors, and enthusiasts by
-providing an insightful and convenient interface to the Open Source project dataset collected by the project [Augur](https://github.com/chaoss/augur) (CHAOSS).
+providing an insightful and convenient interface to the Open Source project data set collected by the project [Augur](https://github.com/chaoss/augur) (CHAOSS).
 
 ---
 

job_manager/docs/architecture.md

@@ -12,7 +12,7 @@ This is highly performant because we can arbitrarily scale our application by du
 
 To bolster the responsiveness of this app, it is ideal for the resources dedicated to serving user requests to be allocated separately from those used to do any hard work.
 
-For our purposes this is a solved problem with a well-documented design pattern: if we know that a task would take more than a reasonable amount of time to run and would consume relatively more than minimal resources, we can add that task to a queue datastructure to be processed later.
+For our purposes this is a solved problem with a well-documented design pattern: if we know that a task would take more than a reasonable amount of time to run and would consume relatively more than minimal resources, we can add that task to a queue data structure to be processed later.
 
 Workers, with resources allocated separately from those that serve client requests, take jobs from this queue in order and publish the results from these jobs in a cache.
 
@@ -24,15 +24,15 @@ We implement a scalable and minimalist job queue that resolves previous applicat
 
 ### Queries
 
-The longest-running tasks our app runs are SQL queries to our instance of an Augur database. These can take as little as 500ms to resolve but in the worst case it can take up to 15 minutes to get a response. These are I/O-bound tasks so they could be solved by implementing naive multithreading, but a job-queue design is far more easily scalable.
+The longest-running tasks our app runs are SQL queries to our instance of an Augur database. These can take as little as 500ms to resolve but in the worst case it can take up to 15 minutes to get a response. These are I/O-bound tasks so they could be solved by implementing naive multi-threading, but a job-queue design is far more easily scalable.
 
 ### RQ
 
 We enqueue our long-running queries in a Queue object implemented by the RQ (Redis Queue) python library. RQ executes a specified function (passed by reference) in its own Python process (with a separate GIL). Arguments to this function are passed via pickling. This can be a problem for non-picklable objects but there are simple workarounds.
 
 ### Redis
 
-Redis is an in-RAM datastructure store library. RQ implements its queue structure using Redis, and the results of a worker's task are cached in the Redis datastore, accessible by reference.
+Redis is an in-RAM data structure store library. RQ implements its queue structure using Redis, and the results of a worker's task are cached in the Redis data store, accessible by reference.
 
 ### Dash Server
 
@@ -46,4 +46,4 @@ The server routinely polls the job it created for its results. When the job's me
 
 ## Conclusion
 
-This architecture is minimally configured and low-overhead, likely requiring very little maintenance. It is likely that worker management by the Supervisor module will be effective in the future if Openshift scaling isn't a satisfying solution.
+This architecture is minimally configured and low-overhead, likely requiring very little maintenance. It is likely that worker management by the Supervisor module will be effective in the future if OpenShift scaling isn't a satisfying solution.

new_fig_guidance.md

@@ -18,7 +18,7 @@ Please try to understand the architecture by reading the code before you begin
 any of your own development work. There are few "gotchas" but some are unavoidable.
 A recommended way of doing this is to start by looking at /pages/overview.py.
 This file sketches the markup of the Overview page. You'll see that we import
-the files that have the appropriate callback functions for the pages's figures from
+the files that have the appropriate callback functions for the pages' figures from
 /pages/visualizations/overview/. In this respect, we aim to isolate larger logical
 "chunks" of our application- the page's structure is higher in the hierarchy, and 
 individual visualizations are imported and further organized below.