Fertility is something people don’t typically discuss openly in the US, which isn’t a surprise because it is an incredibly personal topic. In fact, it’s really difficult to even write a blog post about, I wrote this over a year ago and I’m only getting around to posting it now. It took us roughly 7 months to conceive a baby, and I’m proud to say we now have a happy baby boy!
However, every negative pregnancy test you see takes an emotional toll on you (and can even put strain on some marriages). During that time, I found that research online wasn’t extremely helpful. My wife and I found it relatively difficult to find answers to two very important questions:
What are the odds of a couple conceiving each month?
How much of a factor does age play?
I need to start this off by saying, I am not a doctor (nor do I play one on TV). In fact, I’m just going to start my exploration of this topic by first reading some blogs on the topic. This isn’t typically a great option, but then again, I’m writing a blog as well… What could go wrong, a blog based off of other blogs which might be discussed in another blog? I digress.
The disastrous impact of recent hurricanes, Harvey and Irma, generated a large influx of data within the online community. I was curious about the history of hurricanes and tropical storms so I found a data set on data.world and started some basic Exploratory data analysis (EDA).
EDA is crucial to starting any project. Through EDA you can start to identify errors & inconsistencies in your data, find interesting patterns, see correlations and start to develop hypotheses to test. For most people, basic spreadsheets and charts are handy and provide a great place to start. They are an easy-to-use method to manipulate and visualize your data quickly. Data scientists may cringe at the idea of using a graphical user interface (GUI) to kick-off the EDA process but those tools are very effective and efficient when used properly. However, if you’re reading this, you’re probably trying to take EDA to the next level. The best way to learn is to get your hands dirty, let’s get started.
In the coming months I’ll be digging into the immigration enforcement data posted on data.world. I encourage anyone to take this data and either add to the project or to do something on their own. I will be bringing in external data sources to merge as well (which I did for this first plot).
If you’re only here for a “high-level nugget” of information, the basic thing you can see is:
About two weeks ago I created an extremely rough version of an R Shiny Application surrounding Medicare data. Right after publishing the blog post, I received a lot of input for improvement and help from others.
I was traveling for two weeks and had very little time to do any work on it. After creating a GitHub Repository for it, the user Ginberg played a huge role in cleaning it up and adding a lot more functionality. I found it incredible that a complete stranger to me would put in such effort to something like this. In fact, he isn’t even a resident of the USA – so Medicare probably isn’t on his radar as often as it is for some of us. Fantastic generosity!
Ultimately, I will be looking to keep this project alive and grow it to fully utilize a lot more of the Medicare data available. The infections data set was very simple and easy to use, so I started off with it but there are a lot more tables listed on data.gov. The purpose of this application is to allow people who don’t want to spend time digging through tables to utilize the information available. This isn’t necessarily just for people seeking care to make a decision but this could perhaps be utilized for others doing research regarding hospitals in the US.
The R Shiny App allows you to filter by location and infection information. These are important in helping to actually find information on what you care about.
Three key tabs were created by (@Ginberg):
Sorting hospitals by infection score
Maps of hospitals in the area
Data table of hospital data
Sorting hospital data by score:
This is a tricky plot because “score” is different for each type of metric
Higher “scores” aren’t necessarily bad because they can be swayed by more heavily populated areas (or density)
Hello R community. if you’re up for some fun tinkering with a Shiny App please join me on a new project. I would love to see some collaboration in designing a Shiny Application which will help people make a decision about a healthcare provider. I have only just begun on this project but would to work with others.
This is just a quick look at the data, the roughest shiny app you’ve ever seen can be located on my shinyapps.io page
The first goal is to help people find a provider based off of City and State (or perhaps zipcode and latitude/longitude). This can take the form of a list, map, etc. I would also like people to be able to glean some information about the place they are going in comparison to the surrounding locations.
I was only able to put a an hour or so into this (and that was months ago) but have decided that it would be fun to start collaborating with anyone who is interested. Please make any pull requests and I’ll get to them!