Today I read a cute post from Flowing Data on the most trendy names in US history. What caught my attention was a link posted in the article to the source data, which happens to be yearly lists of baby names registered with the US social security agency since 1880
(see here). I thought that it might be good to compile and use these lists at work for two reasons:
(1) I don’t have experience handling file input programmatically in R (ie working with a bunch of files in a directory instead of manually loading one or two) and
(2) It could be useful to have age estimates in the donor files that I work with (using the year when each first name was most popular).
I’ve included the R code in this post at the bottom, after the following explanatory text.
I managed to build a dataframe that contains in each row a name, how many people were registered as having been born with that name in a given year, the year, the total population for that year, and the relative proportion of people with that name in that year.
Once I got that dataframe, I built a function to query that dataframe for the year when a given name was most popular, an estimated age using that year, and the relative proportion of people born with that name from that year.
I don’t have any testing data to check the results against, but I did do an informal check around the office, and it seems okay!
However, I’d like to scale this upwards so that age estimates can be calculated for each row in a vector of first names. As the code stands below, the function I made takes too long to be scaled up effectively.
I’m wondering what’s the best approach to take? Some ideas I have so far follow:
Does anyone have any better ideas for me ?
I’ll also accept any suggestions for cleaning up my code as is