conditional lagging variables

[Hk_AI]

Dear all, I have a question in R studio regarding conditional lagging variables. The data structure is as following: paired variables of S&P1500 CEO characteristics according to a company key and financial year. For one company you can have multiple values of the same financial year (multiple CEO's in that year). I would like to lookup the value of a third variable (called AT) of the last value of the previous financial year within the same key (same company). Any idea how I can best solve this problem in Rstudio? Best, Hossein

[pieterjanvc]

Hi,

Welcome to the RStudio community!

In order for us to help you with your question, please provide us a minimal reproducible example where you provide a minimal (dummy) dataset and code that can recreate the issue.
A screenshot is not useful as we cannot extract any data from it (unless be manually recreating the whole table).

Once we have a reprex, we can go from there. For help on creating a Reprex, see this guide:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

Good luck!
PJ

[Hk_AI]

Thank you for your reply Pieterjanvc. Attached a sample dataset.
I would like to lookup the value of the third variable (called AT) of the last value of the previous financial year within the same key (same company).
Any help would be appreciated.
Thank you.

Kind regards,
Hossein

|GVKEYNEW|YEARNEW|at|
|10884|2009|20.448.260|
|10884|2009|20.448.260|
|10884|2009|20.448.260|
|10884|2009|20.448.260|
|10884|2009|20.448.260|
|10884|2009|20.448.260|
|10884|2010|20.671.460|
|10884|2010|20.671.460|
|10884|2010|20.671.460|
|10884|2010|20.671.460|
|10884|2010|20.671.460|
|10884|2011|19.379.590|
|10884|2011|19.379.590|
|10884|2011|19.379.590|
|10884|2011|19.379.590|
|10884|2011|19.379.590|
|10884|2011|19.379.590|
|10884|2012|16.977.620|
|10884|2012|16.977.620|
|10884|2012|16.977.620|
|10884|2012|16.977.620|
|10884|2012|16.977.620|
|10884|2012|16.977.620|
|10884|2012|16.977.620|
|10884|2013|16.591.380|
|10884|2013|16.591.380|
|10884|2013|16.591.380|
|10884|2013|16.591.380|
|10884|2013|16.591.380|
|140760|2009|178.770.000|
|140760|2009|178.770.000|
|140760|2009|178.770.000|
|140760|2009|178.770.000|
|140760|2009|178.770.000|
|140760|2009|178.770.000|
|140760|2010|178.520.000|
|140760|2010|178.520.000|
|140760|2010|178.520.000|
|140760|2010|178.520.000|
|140760|2010|178.520.000|
|140760|2011|198.440.000|
|140760|2011|198.440.000|
|140760|2011|198.440.000|
|140760|2011|198.440.000|
|140760|2011|198.440.000|
|140760|2011|198.440.000|
|140760|2012|202.240.000|
|140760|2012|202.240.000|
|140760|2012|202.240.000|
|140760|2012|202.240.000|
|140760|2012|202.240.000|
|140760|2013|206.640.000|
|140760|2013|206.640.000|
|140760|2013|206.640.000|
|140760|2013|206.640.000|
|140760|2013|206.640.000|
|140760|2013|206.640.000|
|140760|2014|219.810.000|
|140760|2014|219.810.000|
|140760|2014|219.810.000|
|140760|2014|219.810.000|
|140760|2014|219.810.000|
|140760|2014|219.810.000|
|140760|2015|219.200.000|
|140760|2015|219.200.000|
|140760|2015|219.200.000|
|140760|2015|219.200.000|
|140760|2015|219.200.000|
|140760|2016|197.360.000|
|140760|2016|197.360.000|
|140760|2016|197.360.000|
|140760|2016|197.360.000|
|140760|2016|197.360.000|
|140760|2016|197.360.000|
|140760|2016|197.360.000|
|140760|2017|213.330.000|
|140760|2017|213.330.000|
|140760|2017|213.330.000|
|140760|2017|213.330.000|
|140760|2017|213.330.000|
|24539|2009|16.274.040|
|24539|2009|16.274.040|
|24539|2009|16.274.040|
|24539|2009|16.274.040|
|24539|2009|16.274.040|
|24539|2010|17.664.220|
|24539|2010|17.664.220|
|24539|2010|17.664.220|
|24539|2010|17.664.220|
|24539|2010|17.664.220|
|24539|2011|19.953.680|
|24539|2011|19.953.680|
|24539|2011|19.953.680|
|24539|2011|19.953.680|
|24539|2011|19.953.680|
|24539|2012|20.915.800|
|24539|2012|20.915.800|
|24539|2012|20.915.800|
|24539|2012|20.915.800|
|24539|2012|20.915.800|
|24539|2013|22.377.810|
|24539|2013|22.377.810|
|24539|2013|22.377.810|
|24539|2013|22.377.810|
|24539|2013|22.377.810|
|24539|2014|23.320.200|
|24539|2014|23.320.200|
|24539|2014|23.320.200|
|24539|2014|23.320.200|
|24539|2014|23.320.200|
|24539|2015|25.072.650|
|24539|2015|25.072.650|
|24539|2015|25.072.650|
|24539|2015|25.072.650|
|24539|2015|25.072.650|
|24539|2016|33.733.280|
|24539|2016|33.733.280|
|24539|2016|33.733.280|
|24539|2016|33.733.280|
|24539|2016|33.733.280|

[ddscience]

Very rudimentary tidyverse solution using the slice_ functions in conjunction with groups. The example data above was saved to a data.frame called 'df' in my code below.

The max_year column is just a helper column to make the logic a little clearer; get the max year for each key so that you can get the previous year as max_year - 1, then use a filter to only keep rows showing the previous year for that key.

Assuming your actual data is sorted in a meaningful order (newest dates at the top, oldest at bottom), then slice_head will return the most recent row from the prev year and the at along with it.

df %>% 
    group_by(GVKEYNEW) %>% 
    mutate(max_year = max(YEARNEW)) %>% 
    filter(YEARNEW == max_year-1) %>% 
    slice_head(n=1)

[Hk_AI]

Thank you very much for your help. I will try it and let you know how it went.
Kind regards,
Hossein