how to regress unemployment on minimum wage in r

Question

I have two different data sets, one that has the annual unemployment rate by state (listed under a single column) and the second data set has the minimum wage for each state. Both have only have data between 2003-2020.

The problem is

1. They are in different data sets
2. The X variable (minimum wage) spans over 17 different columns

Questions

1. How can I regress data from 2 different data sets
2. How can I regress 17 columns without having to type minwage$2003 + minwage$2004 + . . . + minwage$2020

I tried this, but again, it's very inefficient.

unemp_minwage <- lm(unemployment_03_20$`U-3` ~ minwage$`2003` + minwage$`2004` + minwage$`2005` + minwage$`2006` + minwage$`2007` + minwage$`2008` + minwage$`2009` + minwage$`2010` + minwage$`2011` + minwage$`2012` + minwage$`2013` + minwage$`2014` + minwage$`2015` + minwage$`2016` + minwage$`2017` + minwage$`2018` + minwage$`2019` + minwage$`2020`)

Not to mention I got this error code: Error in model.frame.default(formula = unemployment_03_20$U-3 ~ minwage$2003 + : variable lengths differ (found for 'minwage$2003')

Then I tried just regressing on one year of minimum wage, but got a similar error.

Suggestions?

Answer 1

To get the exact formula in your question:

as.formula(paste("unemployment_03_20$`U-3` ~", paste(paste0("minwage$`", 2003:2020, "`"), collapse = " + ")))

So you can do something like this (for clarity):

model <- as.formula(paste("unemployment_03_20$`U-3` ~", paste(paste0("minwage$`", 2003:2020, "`"), collapse = " + ")))

unemp_minwage2015 <- lm(model)

I strongly suggest merging the data first so you don't inadvertently make an error, and then supplying lm() with that data (rather than individual vectors from multiple datasets.