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left_join in loop with changing conditions in R

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-1

I have a dataset of approx 15000 individual causes of death (CoD) from the 19th century, where the aim is to a) standardize each word/cause and b) apply codes for later analysis. My question relates to standardization.
I have two data frames:

I - oriCoD1. This data contains the original CoD, and 32 additional columns where each CoD is parsed into multiple columns, separated by space, and named in alphabetic order; 'a', 'b', 'c', etc.

II - standard: Standard has two columns; the original word and the standardized word, where the original words only appear once. The standardized words match any possible spelling from any column 'a' to 'ae' in oriCoD. The joint column is the original word in standard and 'a' to 'ae' in oriCoD.

I have been able to do the joint column by column, like this:

 oriCoD2<-left_join(oriCoD1, standard, by=c('a'))

 standard<-rename(standard, c("a"="b"))
 oriCoD3<-left_join(oriCoD2, standard, by=c('b'))

 standard<-rename(standard, c("b"="c"))
 oriCoD4<-left_join(oriCoD3, standard, by=c('c'))

 [...]

Instead of writing this out 32 times, Is there a way to do this in a loop?

r for-loop dplyr left-join

share|improve this question

edited Mar 22 at 15:06

Sommerseth

asked Mar 21 at 21:27

SommersethSommerseth

42

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  Please include a Minimal, Complete, and Verifiable example of your problem with toy versions of each data set (use dput to get them into a format you can paste into the text of the question. This will let us understand your problem and find a solution.
  
  – divibisan
  Mar 21 at 21:36
  

  
  
  
  

add a comment | 

-1

I have a dataset of approx 15000 individual causes of death (CoD) from the 19th century, where the aim is to a) standardize each word/cause and b) apply codes for later analysis. My question relates to standardization.
I have two data frames:

I - oriCoD1. This data contains the original CoD, and 32 additional columns where each CoD is parsed into multiple columns, separated by space, and named in alphabetic order; 'a', 'b', 'c', etc.

II - standard: Standard has two columns; the original word and the standardized word, where the original words only appear once. The standardized words match any possible spelling from any column 'a' to 'ae' in oriCoD. The joint column is the original word in standard and 'a' to 'ae' in oriCoD.

I have been able to do the joint column by column, like this:

 oriCoD2<-left_join(oriCoD1, standard, by=c('a'))

 standard<-rename(standard, c("a"="b"))
 oriCoD3<-left_join(oriCoD2, standard, by=c('b'))

 standard<-rename(standard, c("b"="c"))
 oriCoD4<-left_join(oriCoD3, standard, by=c('c'))

 [...]

Instead of writing this out 32 times, Is there a way to do this in a loop?

r for-loop dplyr left-join

share|improve this question

edited Mar 22 at 15:06

Sommerseth

asked Mar 21 at 21:27

SommersethSommerseth

42

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  Please include a Minimal, Complete, and Verifiable example of your problem with toy versions of each data set (use dput to get them into a format you can paste into the text of the question. This will let us understand your problem and find a solution.
  
  – divibisan
  Mar 21 at 21:36
  

  
  
  
  

add a comment | 

I have a dataset of approx 15000 individual causes of death (CoD) from the 19th century, where the aim is to a) standardize each word/cause and b) apply codes for later analysis. My question relates to standardization.
I have two data frames:

I - oriCoD1. This data contains the original CoD, and 32 additional columns where each CoD is parsed into multiple columns, separated by space, and named in alphabetic order; 'a', 'b', 'c', etc.

II - standard: Standard has two columns; the original word and the standardized word, where the original words only appear once. The standardized words match any possible spelling from any column 'a' to 'ae' in oriCoD. The joint column is the original word in standard and 'a' to 'ae' in oriCoD.

I have been able to do the joint column by column, like this:

 oriCoD2<-left_join(oriCoD1, standard, by=c('a'))

 standard<-rename(standard, c("a"="b"))
 oriCoD3<-left_join(oriCoD2, standard, by=c('b'))

 standard<-rename(standard, c("b"="c"))
 oriCoD4<-left_join(oriCoD3, standard, by=c('c'))

 [...]

Instead of writing this out 32 times, Is there a way to do this in a loop?

r for-loop dplyr left-join

share|improve this question

edited Mar 22 at 15:06

Sommerseth

asked Mar 21 at 21:27

SommersethSommerseth

42

 oriCoD2<-left_join(oriCoD1, standard, by=c('a'))

 standard<-rename(standard, c("a"="b"))
 oriCoD3<-left_join(oriCoD2, standard, by=c('b'))

 standard<-rename(standard, c("b"="c"))
 oriCoD4<-left_join(oriCoD3, standard, by=c('c'))

 [...]

share|improve this question

edited Mar 22 at 15:06

Sommerseth

asked Mar 21 at 21:27

SommersethSommerseth

42

share|improve this question

edited Mar 22 at 15:06

Sommerseth

asked Mar 21 at 21:27

SommersethSommerseth

42

asked Mar 21 at 21:27

SommersethSommerseth

42

asked Mar 21 at 21:27

SommersethSommerseth

42