Thread by @StatStas

Webinar on @ipums data @popdatatech @nhgis @ipumsi and I am probably forgetting their other accounts - human population data…

… geographic data @dcvanriper …

@ipums meets #rstats via @gregfreedman

#rstats backbone infrastructure of library(ipumsr)

@ipums ipumsr relies on the @DDIAlliance DDI code book metadata format for approximately everything

Some internal structure of the DDI codebook objects in ipumsr

Variable names and labels and value labels are available

Your daily reminder that #rstats factor variables suck big time compared to Stata and SAS

I am not crying, you are crying

Helper functions: replace missing values. These suck in #rstats compared to Stata and SAS, too: in the latter, you can have extended missing values .a:.z, and they all can be labelled with the reasons it is missing (not in pop, refused, not applicable, etc.)

Another helper: recode and label new values. Very helpful for hierarchical classification codes where you can divide by 10 or 100 to obtain the higher level / fewer digits code. Labels are copied from the lowest numbered original category - would need extra work down the line

A more generic function for that is relabel.

Now let’s talk about @ipums @nhgis geographic data - tied to library(sf)

There are helper functions for the geo data management

Coming soon - @popdatatech API - ask to beta test. Sharing extract definitions is important for reproducibility!

Ceci n’est pas une pipe

Respond to #Reviewer2 without leaving #rstats

Everything you need to know about @ipums data and this presentation