String as formula

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I've tried searching through the forums and unable to find help. I'm quite new to R, and am having limited success in loading in some strings to be used as a formula.



I have a csv with the following format:



 Sam, Frank, Dennis, Steven, Robert, Georgia
Region_1 218, 763, 811, 812, 797, 574
Region_2 474, 983, 343, 697, 310, 335
Region_3 700, 813, 133, 212, 906, 680
Region_4 212, 581, 893, 514, 530, 795


And am able to load this in and add additional columns to my data frame that adds specific columns (ie. Sam + Frank, Dennis + Georgia).



I can do it if I hard code the teams into my code, eg: 



temp <- mutate(temp, team_1 = Sam + Robert)


My issue is that I have another csv that contains a list of 2 columns, and these are teams that changes daily, eg:



Team 1 Sam + Robert
Team 2 Frank + Georgia
Team 3 Frank + Steven
Team 4 Robert + Dennis
Team 5 Frank + Sam


Is there a way that I can load this csv list into R and add these team member columns together.



But I can't see to add this list in from the csv file and have a new data frame with the total of team members dynamically.



Is anyone able to help me with this?



I'm looking to end up with something like this (with the sum of the team members (team 1, team 2, etc from the second csv file) as additional columns:



 Sam, Frank, Dennis, Steven, Robert, Georgia, Sam + Robert, Frank + Georgia, Frank + Steven, Robert + Dennis, Frank + Sam
 Region_1 218, 763, 811, 812, 797, 574, 981, 1337, 1575, 1608, 981
 Region_2 474, 983, 343, 697, 310, 335, 1457, 1318, 1680, 653, 1457
 Region_3 700, 813, 133, 212, 906, 680, 1513, 1493, 1025, 1039, 1513
 Region_4 212, 581, 893, 514, 530, 795, 793, 1376, 1095, 1423, 793


With much thanks.






r dplyr







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    up vote
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    favorite
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    I've tried searching through the forums and unable to find help. I'm quite new to R, and am having limited success in loading in some strings to be used as a formula.



    I have a csv with the following format:



     Sam, Frank, Dennis, Steven, Robert, Georgia
    Region_1 218, 763, 811, 812, 797, 574
    Region_2 474, 983, 343, 697, 310, 335
    Region_3 700, 813, 133, 212, 906, 680
    Region_4 212, 581, 893, 514, 530, 795


    And am able to load this in and add additional columns to my data frame that adds specific columns (ie. Sam + Frank, Dennis + Georgia).



    I can do it if I hard code the teams into my code, eg: 



    temp <- mutate(temp, team_1 = Sam + Robert)


    My issue is that I have another csv that contains a list of 2 columns, and these are teams that changes daily, eg:



    Team 1 Sam + Robert
    Team 2 Frank + Georgia
    Team 3 Frank + Steven
    Team 4 Robert + Dennis
    Team 5 Frank + Sam


    Is there a way that I can load this csv list into R and add these team member columns together.



    But I can't see to add this list in from the csv file and have a new data frame with the total of team members dynamically.



    Is anyone able to help me with this?



    I'm looking to end up with something like this (with the sum of the team members (team 1, team 2, etc from the second csv file) as additional columns:



     Sam, Frank, Dennis, Steven, Robert, Georgia, Sam + Robert, Frank + Georgia, Frank + Steven, Robert + Dennis, Frank + Sam
     Region_1 218, 763, 811, 812, 797, 574, 981, 1337, 1575, 1608, 981
     Region_2 474, 983, 343, 697, 310, 335, 1457, 1318, 1680, 653, 1457
     Region_3 700, 813, 133, 212, 906, 680, 1513, 1493, 1025, 1039, 1513
     Region_4 212, 581, 893, 514, 530, 795, 793, 1376, 1095, 1423, 793


    With much thanks.






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      up vote
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      1





      I've tried searching through the forums and unable to find help. I'm quite new to R, and am having limited success in loading in some strings to be used as a formula.



      I have a csv with the following format:



       Sam, Frank, Dennis, Steven, Robert, Georgia
      Region_1 218, 763, 811, 812, 797, 574
      Region_2 474, 983, 343, 697, 310, 335
      Region_3 700, 813, 133, 212, 906, 680
      Region_4 212, 581, 893, 514, 530, 795


      And am able to load this in and add additional columns to my data frame that adds specific columns (ie. Sam + Frank, Dennis + Georgia).



      I can do it if I hard code the teams into my code, eg: 



      temp <- mutate(temp, team_1 = Sam + Robert)


      My issue is that I have another csv that contains a list of 2 columns, and these are teams that changes daily, eg:



      Team 1 Sam + Robert
      Team 2 Frank + Georgia
      Team 3 Frank + Steven
      Team 4 Robert + Dennis
      Team 5 Frank + Sam


      Is there a way that I can load this csv list into R and add these team member columns together.



      But I can't see to add this list in from the csv file and have a new data frame with the total of team members dynamically.



      Is anyone able to help me with this?



      I'm looking to end up with something like this (with the sum of the team members (team 1, team 2, etc from the second csv file) as additional columns:



       Sam, Frank, Dennis, Steven, Robert, Georgia, Sam + Robert, Frank + Georgia, Frank + Steven, Robert + Dennis, Frank + Sam
       Region_1 218, 763, 811, 812, 797, 574, 981, 1337, 1575, 1608, 981
       Region_2 474, 983, 343, 697, 310, 335, 1457, 1318, 1680, 653, 1457
       Region_3 700, 813, 133, 212, 906, 680, 1513, 1493, 1025, 1039, 1513
       Region_4 212, 581, 893, 514, 530, 795, 793, 1376, 1095, 1423, 793


      With much thanks.






      r dplyr







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      nicshah is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I've tried searching through the forums and unable to find help. I'm quite new to R, and am having limited success in loading in some strings to be used as a formula.



      I have a csv with the following format:



       Sam, Frank, Dennis, Steven, Robert, Georgia
      Region_1 218, 763, 811, 812, 797, 574
      Region_2 474, 983, 343, 697, 310, 335
      Region_3 700, 813, 133, 212, 906, 680
      Region_4 212, 581, 893, 514, 530, 795


      And am able to load this in and add additional columns to my data frame that adds specific columns (ie. Sam + Frank, Dennis + Georgia).



      I can do it if I hard code the teams into my code, eg: 



      temp <- mutate(temp, team_1 = Sam + Robert)


      My issue is that I have another csv that contains a list of 2 columns, and these are teams that changes daily, eg:



      Team 1 Sam + Robert
      Team 2 Frank + Georgia
      Team 3 Frank + Steven
      Team 4 Robert + Dennis
      Team 5 Frank + Sam


      Is there a way that I can load this csv list into R and add these team member columns together.



      But I can't see to add this list in from the csv file and have a new data frame with the total of team members dynamically.



      Is anyone able to help me with this?



      I'm looking to end up with something like this (with the sum of the team members (team 1, team 2, etc from the second csv file) as additional columns:



       Sam, Frank, Dennis, Steven, Robert, Georgia, Sam + Robert, Frank + Georgia, Frank + Steven, Robert + Dennis, Frank + Sam
       Region_1 218, 763, 811, 812, 797, 574, 981, 1337, 1575, 1608, 981
       Region_2 474, 983, 343, 697, 310, 335, 1457, 1318, 1680, 653, 1457
       Region_3 700, 813, 133, 212, 906, 680, 1513, 1493, 1025, 1039, 1513
       Region_4 212, 581, 893, 514, 530, 795, 793, 1376, 1095, 1423, 793


      With much thanks.






      r dplyr




      r dplyr






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      edited 5 hours ago









      alistaire

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      asked 5 hours ago









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          4 Answers
          4






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          Working with code as strings is sort of complicated. Here, it's simpler to turn the second data frame into a lookup table which can be joined to the first reshaped to long form. The result can then be summarized and reshaped into whatever form you'd like.





          library(tidyverse)

          people <- data.frame(
           region = c("Region_1", "Region_2", "Region_3", "Region_4"),
           Sam = c(218L, 474L, 700L, 212L),
           Frank = c(763L, 983L, 813L, 581L),
           Dennis = c(811L, 343L, 133L, 893L),
           Steven = c(812L, 697L, 212L, 514L),
           Robert = c(797L, 310L, 906L, 530L),
           Georgia = c(574L, 335L, 680L, 795L), 
           stringsAsFactors = FALSE
          )

          teams <- data.frame(
           team = c("Team 1", "Team 2", "Team 3", "Team 4", "Team 5"),
           members = c("Sam + Robert", "Frank + Georgia", "Frank + Steven", "Robert + Dennis", "Frank + Sam"), 
           stringsAsFactors = FALSE
          )

          teams_long <- teams %>% 
           mutate(team = members) %>% # set team name
           separate_rows(members) # separate teams to names

          people %>% 
           gather(member, value, -region) %>% # reshape to long form
           left_join(teams_long, c('member' = 'members')) %>% # join by names
           group_by(region, team) %>% 
           summarise(value = sum(value)) %>% # aggregate
           spread(team, value) %>% # reshape to wide form
           left_join(people, .) # join back to original data
          #> region Sam Frank Dennis Steven Robert Georgia Frank + Georgia
          #> 1 Region_1 218 763 811 812 797 574 1337
          #> 2 Region_2 474 983 343 697 310 335 1318
          #> 3 Region_3 700 813 133 212 906 680 1493
          #> 4 Region_4 212 581 893 514 530 795 1376
          #> Frank + Sam Frank + Steven Robert + Dennis Sam + Robert
          #> 1 981 1575 1608 1015
          #> 2 1457 1680 653 784
          #> 3 1513 1025 1039 1606
          #> 4 793 1095 1423 742





          share|improve this answer






















          • Thanks - that's really helpful. Much appreciated.
            – nicshah
            3 hours ago

















          up vote
          2
          down vote













          Here is an option using rlang::parse_expr to directly parse the expression from the second data.frame



          df1 %>% bind_cols(map_dfc(df2$V2, ~df1 %>%
           transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.x)))))
          # Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
          #1 218 763 811 812 797 574 1015 1337
          #2 474 983 343 697 310 335 784 1318
          #3 700 813 133 212 906 680 1606 1493
          #4 212 581 893 514 530 795 742 1376
          # Frank + Steven Robert + Dennis Frank + Sam
          #1 1575 1608 981
          #2 1680 653 1457
          #3 1025 1039 1513
          #4 1095 1423 793


          Or you can name the new columns according to the team:



          df1 %>% bind_cols(map2_dfc(df2$V1, df2$V2, ~df1 %>%
           transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.y)))))
          # Sam Frank Dennis Steven Robert Georgia Team 1 Team 2 Team 3 Team 4 Team 5
          #1 218 763 811 812 797 574 1015 1337 1575 1608 981
          #2 474 983 343 697 310 335 784 1318 1680 653 1457
          #3 700 813 133 212 906 680 1606 1493 1025 1039 1513
          #4 212 581 893 514 530 795 742 1376 1095 1423 793


          The idea is that we use map (or map2) to apply the "formula" stored in df2 within a transmute context of df1. The expression gets evaluated with !!parse_expr(...).



          Sample data



          df1 <- read.table(text =
           "Sam Frank Dennis Steven Robert Georgia
          Region_1 218 763 811 812 797 574
          Region_2 474 983 343 697 310 335
          Region_3 700 813 133 212 906 680
          Region_4 212 581 893 514 530 795", header = T)


          df2 <- read.table(text =
           "'Team 1' 'Sam + Robert'
          'Team 2' 'Frank + Georgia'
          'Team 3' 'Frank + Steven'
          'Team 4' 'Robert + Dennis'
          'Team 5' 'Frank + Sam'", header = F)





          share|improve this answer






















          • You are brilliant! That's exactly what I've been trying to do.
            – nicshah
            3 hours ago










          • You're very welcome @nicshah; glad it was helpful.
            – Maurits Evers
            42 mins ago

















          up vote
          2
          down vote













          Background: You can access data.frame columns stored in vector x with people[, x], and you can use strsplit to get a vector of the columns you need.



          Answer:



          # function to get the sum of two columns from 'people'
          get_sum <- function(m) 
           members <- unlist(strsplit(m, ' + ', fixed = T))
           rowSums(people[, members])


          # "Sam + Robert"
          get_sum(teams$members[1])
          # [1] 1015 784 1606 742

          # apply this for every team, and add results to 'people'
          cbind(people,
           sapply(teams$members, get_sum))

          # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia Frank + Steven Robert + Dennis Frank + Sam
          # 1 Region_1 218 763 811 812 797 574 1015 1337 1575 1608 981
          # 2 Region_2 474 983 343 697 310 335 784 1318 1680 653 1457
          # 3 Region_3 700 813 133 212 906 680 1606 1493 1025 1039 1513
          # 4 Region_4 212 581 893 514 530 795 742 1376 1095 1423 793





          share|improve this answer






















          • I should add, I borrowed the code to create the people and teams objects from @alistaire 's answer -- thx
            – arvi1000
            3 hours ago


















          up vote
          0
          down vote













          (Thank @alistaire for coding the data in a handy format)



          This can be done easily with base R functions (eval + parse) in a simple for-loop:



          for(i in teams$members)
           people[[i]] <- eval(parse(text = i), envir = people)


          # > people
          # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
          # 1 Region_1 218 763 811 812 797 574 1015 1337
          # 2 Region_2 474 983 343 697 310 335 784 1318
          # 3 Region_3 700 813 133 212 906 680 1606 1493
          # 4 Region_4 212 581 893 514 530 795 742 1376
          # Frank + Steven Robert + Dennis Frank + Sam
          # 1 1575 1608 981
          # 2 1680 653 1457
          # 3 1025 1039 1513
          # 4 1095 1423 793





          share|improve this answer




















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            4 Answers
            4






            active

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            4 Answers
            4






            active

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            active

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            active

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            up vote
            4
            down vote













            Working with code as strings is sort of complicated. Here, it's simpler to turn the second data frame into a lookup table which can be joined to the first reshaped to long form. The result can then be summarized and reshaped into whatever form you'd like.





            library(tidyverse)

            people <- data.frame(
             region = c("Region_1", "Region_2", "Region_3", "Region_4"),
             Sam = c(218L, 474L, 700L, 212L),
             Frank = c(763L, 983L, 813L, 581L),
             Dennis = c(811L, 343L, 133L, 893L),
             Steven = c(812L, 697L, 212L, 514L),
             Robert = c(797L, 310L, 906L, 530L),
             Georgia = c(574L, 335L, 680L, 795L), 
             stringsAsFactors = FALSE
            )

            teams <- data.frame(
             team = c("Team 1", "Team 2", "Team 3", "Team 4", "Team 5"),
             members = c("Sam + Robert", "Frank + Georgia", "Frank + Steven", "Robert + Dennis", "Frank + Sam"), 
             stringsAsFactors = FALSE
            )

            teams_long <- teams %>% 
             mutate(team = members) %>% # set team name
             separate_rows(members) # separate teams to names

            people %>% 
             gather(member, value, -region) %>% # reshape to long form
             left_join(teams_long, c('member' = 'members')) %>% # join by names
             group_by(region, team) %>% 
             summarise(value = sum(value)) %>% # aggregate
             spread(team, value) %>% # reshape to wide form
             left_join(people, .) # join back to original data
            #> region Sam Frank Dennis Steven Robert Georgia Frank + Georgia
            #> 1 Region_1 218 763 811 812 797 574 1337
            #> 2 Region_2 474 983 343 697 310 335 1318
            #> 3 Region_3 700 813 133 212 906 680 1493
            #> 4 Region_4 212 581 893 514 530 795 1376
            #> Frank + Sam Frank + Steven Robert + Dennis Sam + Robert
            #> 1 981 1575 1608 1015
            #> 2 1457 1680 653 784
            #> 3 1513 1025 1039 1606
            #> 4 793 1095 1423 742





            share|improve this answer






















            • Thanks - that's really helpful. Much appreciated.
              – nicshah
              3 hours ago














            up vote
            4
            down vote













            Working with code as strings is sort of complicated. Here, it's simpler to turn the second data frame into a lookup table which can be joined to the first reshaped to long form. The result can then be summarized and reshaped into whatever form you'd like.





            library(tidyverse)

            people <- data.frame(
             region = c("Region_1", "Region_2", "Region_3", "Region_4"),
             Sam = c(218L, 474L, 700L, 212L),
             Frank = c(763L, 983L, 813L, 581L),
             Dennis = c(811L, 343L, 133L, 893L),
             Steven = c(812L, 697L, 212L, 514L),
             Robert = c(797L, 310L, 906L, 530L),
             Georgia = c(574L, 335L, 680L, 795L), 
             stringsAsFactors = FALSE
            )

            teams <- data.frame(
             team = c("Team 1", "Team 2", "Team 3", "Team 4", "Team 5"),
             members = c("Sam + Robert", "Frank + Georgia", "Frank + Steven", "Robert + Dennis", "Frank + Sam"), 
             stringsAsFactors = FALSE
            )

            teams_long <- teams %>% 
             mutate(team = members) %>% # set team name
             separate_rows(members) # separate teams to names

            people %>% 
             gather(member, value, -region) %>% # reshape to long form
             left_join(teams_long, c('member' = 'members')) %>% # join by names
             group_by(region, team) %>% 
             summarise(value = sum(value)) %>% # aggregate
             spread(team, value) %>% # reshape to wide form
             left_join(people, .) # join back to original data
            #> region Sam Frank Dennis Steven Robert Georgia Frank + Georgia
            #> 1 Region_1 218 763 811 812 797 574 1337
            #> 2 Region_2 474 983 343 697 310 335 1318
            #> 3 Region_3 700 813 133 212 906 680 1493
            #> 4 Region_4 212 581 893 514 530 795 1376
            #> Frank + Sam Frank + Steven Robert + Dennis Sam + Robert
            #> 1 981 1575 1608 1015
            #> 2 1457 1680 653 784
            #> 3 1513 1025 1039 1606
            #> 4 793 1095 1423 742





            share|improve this answer






















            • Thanks - that's really helpful. Much appreciated.
              – nicshah
              3 hours ago












            up vote
            4
            down vote










            up vote
            4
            down vote









            Working with code as strings is sort of complicated. Here, it's simpler to turn the second data frame into a lookup table which can be joined to the first reshaped to long form. The result can then be summarized and reshaped into whatever form you'd like.





            library(tidyverse)

            people <- data.frame(
             region = c("Region_1", "Region_2", "Region_3", "Region_4"),
             Sam = c(218L, 474L, 700L, 212L),
             Frank = c(763L, 983L, 813L, 581L),
             Dennis = c(811L, 343L, 133L, 893L),
             Steven = c(812L, 697L, 212L, 514L),
             Robert = c(797L, 310L, 906L, 530L),
             Georgia = c(574L, 335L, 680L, 795L), 
             stringsAsFactors = FALSE
            )

            teams <- data.frame(
             team = c("Team 1", "Team 2", "Team 3", "Team 4", "Team 5"),
             members = c("Sam + Robert", "Frank + Georgia", "Frank + Steven", "Robert + Dennis", "Frank + Sam"), 
             stringsAsFactors = FALSE
            )

            teams_long <- teams %>% 
             mutate(team = members) %>% # set team name
             separate_rows(members) # separate teams to names

            people %>% 
             gather(member, value, -region) %>% # reshape to long form
             left_join(teams_long, c('member' = 'members')) %>% # join by names
             group_by(region, team) %>% 
             summarise(value = sum(value)) %>% # aggregate
             spread(team, value) %>% # reshape to wide form
             left_join(people, .) # join back to original data
            #> region Sam Frank Dennis Steven Robert Georgia Frank + Georgia
            #> 1 Region_1 218 763 811 812 797 574 1337
            #> 2 Region_2 474 983 343 697 310 335 1318
            #> 3 Region_3 700 813 133 212 906 680 1493
            #> 4 Region_4 212 581 893 514 530 795 1376
            #> Frank + Sam Frank + Steven Robert + Dennis Sam + Robert
            #> 1 981 1575 1608 1015
            #> 2 1457 1680 653 784
            #> 3 1513 1025 1039 1606
            #> 4 793 1095 1423 742





            share|improve this answer














            Working with code as strings is sort of complicated. Here, it's simpler to turn the second data frame into a lookup table which can be joined to the first reshaped to long form. The result can then be summarized and reshaped into whatever form you'd like.





            library(tidyverse)

            people <- data.frame(
             region = c("Region_1", "Region_2", "Region_3", "Region_4"),
             Sam = c(218L, 474L, 700L, 212L),
             Frank = c(763L, 983L, 813L, 581L),
             Dennis = c(811L, 343L, 133L, 893L),
             Steven = c(812L, 697L, 212L, 514L),
             Robert = c(797L, 310L, 906L, 530L),
             Georgia = c(574L, 335L, 680L, 795L), 
             stringsAsFactors = FALSE
            )

            teams <- data.frame(
             team = c("Team 1", "Team 2", "Team 3", "Team 4", "Team 5"),
             members = c("Sam + Robert", "Frank + Georgia", "Frank + Steven", "Robert + Dennis", "Frank + Sam"), 
             stringsAsFactors = FALSE
            )

            teams_long <- teams %>% 
             mutate(team = members) %>% # set team name
             separate_rows(members) # separate teams to names

            people %>% 
             gather(member, value, -region) %>% # reshape to long form
             left_join(teams_long, c('member' = 'members')) %>% # join by names
             group_by(region, team) %>% 
             summarise(value = sum(value)) %>% # aggregate
             spread(team, value) %>% # reshape to wide form
             left_join(people, .) # join back to original data
            #> region Sam Frank Dennis Steven Robert Georgia Frank + Georgia
            #> 1 Region_1 218 763 811 812 797 574 1337
            #> 2 Region_2 474 983 343 697 310 335 1318
            #> 3 Region_3 700 813 133 212 906 680 1493
            #> 4 Region_4 212 581 893 514 530 795 1376
            #> Frank + Sam Frank + Steven Robert + Dennis Sam + Robert
            #> 1 981 1575 1608 1015
            #> 2 1457 1680 653 784
            #> 3 1513 1025 1039 1606
            #> 4 793 1095 1423 742






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited 5 hours ago

























            answered 5 hours ago









            alistaire

            30.2k43358




            30.2k43358











            • Thanks - that's really helpful. Much appreciated.
              – nicshah
              3 hours ago
















            • Thanks - that's really helpful. Much appreciated.
              – nicshah
              3 hours ago















            Thanks - that's really helpful. Much appreciated.
            – nicshah
            3 hours ago




            Thanks - that's really helpful. Much appreciated.
            – nicshah
            3 hours ago












            up vote
            2
            down vote













            Here is an option using rlang::parse_expr to directly parse the expression from the second data.frame



            df1 %>% bind_cols(map_dfc(df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.x)))))
            # Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
            #1 218 763 811 812 797 574 1015 1337
            #2 474 983 343 697 310 335 784 1318
            #3 700 813 133 212 906 680 1606 1493
            #4 212 581 893 514 530 795 742 1376
            # Frank + Steven Robert + Dennis Frank + Sam
            #1 1575 1608 981
            #2 1680 653 1457
            #3 1025 1039 1513
            #4 1095 1423 793


            Or you can name the new columns according to the team:



            df1 %>% bind_cols(map2_dfc(df2$V1, df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.y)))))
            # Sam Frank Dennis Steven Robert Georgia Team 1 Team 2 Team 3 Team 4 Team 5
            #1 218 763 811 812 797 574 1015 1337 1575 1608 981
            #2 474 983 343 697 310 335 784 1318 1680 653 1457
            #3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            #4 212 581 893 514 530 795 742 1376 1095 1423 793


            The idea is that we use map (or map2) to apply the "formula" stored in df2 within a transmute context of df1. The expression gets evaluated with !!parse_expr(...).



            Sample data



            df1 <- read.table(text =
             "Sam Frank Dennis Steven Robert Georgia
            Region_1 218 763 811 812 797 574
            Region_2 474 983 343 697 310 335
            Region_3 700 813 133 212 906 680
            Region_4 212 581 893 514 530 795", header = T)


            df2 <- read.table(text =
             "'Team 1' 'Sam + Robert'
            'Team 2' 'Frank + Georgia'
            'Team 3' 'Frank + Steven'
            'Team 4' 'Robert + Dennis'
            'Team 5' 'Frank + Sam'", header = F)





            share|improve this answer






















            • You are brilliant! That's exactly what I've been trying to do.
              – nicshah
              3 hours ago










            • You're very welcome @nicshah; glad it was helpful.
              – Maurits Evers
              42 mins ago














            up vote
            2
            down vote













            Here is an option using rlang::parse_expr to directly parse the expression from the second data.frame



            df1 %>% bind_cols(map_dfc(df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.x)))))
            # Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
            #1 218 763 811 812 797 574 1015 1337
            #2 474 983 343 697 310 335 784 1318
            #3 700 813 133 212 906 680 1606 1493
            #4 212 581 893 514 530 795 742 1376
            # Frank + Steven Robert + Dennis Frank + Sam
            #1 1575 1608 981
            #2 1680 653 1457
            #3 1025 1039 1513
            #4 1095 1423 793


            Or you can name the new columns according to the team:



            df1 %>% bind_cols(map2_dfc(df2$V1, df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.y)))))
            # Sam Frank Dennis Steven Robert Georgia Team 1 Team 2 Team 3 Team 4 Team 5
            #1 218 763 811 812 797 574 1015 1337 1575 1608 981
            #2 474 983 343 697 310 335 784 1318 1680 653 1457
            #3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            #4 212 581 893 514 530 795 742 1376 1095 1423 793


            The idea is that we use map (or map2) to apply the "formula" stored in df2 within a transmute context of df1. The expression gets evaluated with !!parse_expr(...).



            Sample data



            df1 <- read.table(text =
             "Sam Frank Dennis Steven Robert Georgia
            Region_1 218 763 811 812 797 574
            Region_2 474 983 343 697 310 335
            Region_3 700 813 133 212 906 680
            Region_4 212 581 893 514 530 795", header = T)


            df2 <- read.table(text =
             "'Team 1' 'Sam + Robert'
            'Team 2' 'Frank + Georgia'
            'Team 3' 'Frank + Steven'
            'Team 4' 'Robert + Dennis'
            'Team 5' 'Frank + Sam'", header = F)





            share|improve this answer






















            • You are brilliant! That's exactly what I've been trying to do.
              – nicshah
              3 hours ago










            • You're very welcome @nicshah; glad it was helpful.
              – Maurits Evers
              42 mins ago












            up vote
            2
            down vote










            up vote
            2
            down vote









            Here is an option using rlang::parse_expr to directly parse the expression from the second data.frame



            df1 %>% bind_cols(map_dfc(df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.x)))))
            # Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
            #1 218 763 811 812 797 574 1015 1337
            #2 474 983 343 697 310 335 784 1318
            #3 700 813 133 212 906 680 1606 1493
            #4 212 581 893 514 530 795 742 1376
            # Frank + Steven Robert + Dennis Frank + Sam
            #1 1575 1608 981
            #2 1680 653 1457
            #3 1025 1039 1513
            #4 1095 1423 793


            Or you can name the new columns according to the team:



            df1 %>% bind_cols(map2_dfc(df2$V1, df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.y)))))
            # Sam Frank Dennis Steven Robert Georgia Team 1 Team 2 Team 3 Team 4 Team 5
            #1 218 763 811 812 797 574 1015 1337 1575 1608 981
            #2 474 983 343 697 310 335 784 1318 1680 653 1457
            #3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            #4 212 581 893 514 530 795 742 1376 1095 1423 793


            The idea is that we use map (or map2) to apply the "formula" stored in df2 within a transmute context of df1. The expression gets evaluated with !!parse_expr(...).



            Sample data



            df1 <- read.table(text =
             "Sam Frank Dennis Steven Robert Georgia
            Region_1 218 763 811 812 797 574
            Region_2 474 983 343 697 310 335
            Region_3 700 813 133 212 906 680
            Region_4 212 581 893 514 530 795", header = T)


            df2 <- read.table(text =
             "'Team 1' 'Sam + Robert'
            'Team 2' 'Frank + Georgia'
            'Team 3' 'Frank + Steven'
            'Team 4' 'Robert + Dennis'
            'Team 5' 'Frank + Sam'", header = F)





            share|improve this answer














            Here is an option using rlang::parse_expr to directly parse the expression from the second data.frame



            df1 %>% bind_cols(map_dfc(df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.x)))))
            # Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
            #1 218 763 811 812 797 574 1015 1337
            #2 474 983 343 697 310 335 784 1318
            #3 700 813 133 212 906 680 1606 1493
            #4 212 581 893 514 530 795 742 1376
            # Frank + Steven Robert + Dennis Frank + Sam
            #1 1575 1608 981
            #2 1680 653 1457
            #3 1025 1039 1513
            #4 1095 1423 793


            Or you can name the new columns according to the team:



            df1 %>% bind_cols(map2_dfc(df2$V1, df2$V2, ~df1 %>%
             transmute(!!as.character(.x) := !!rlang::parse_expr(as.character(.y)))))
            # Sam Frank Dennis Steven Robert Georgia Team 1 Team 2 Team 3 Team 4 Team 5
            #1 218 763 811 812 797 574 1015 1337 1575 1608 981
            #2 474 983 343 697 310 335 784 1318 1680 653 1457
            #3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            #4 212 581 893 514 530 795 742 1376 1095 1423 793


            The idea is that we use map (or map2) to apply the "formula" stored in df2 within a transmute context of df1. The expression gets evaluated with !!parse_expr(...).



            Sample data



            df1 <- read.table(text =
             "Sam Frank Dennis Steven Robert Georgia
            Region_1 218 763 811 812 797 574
            Region_2 474 983 343 697 310 335
            Region_3 700 813 133 212 906 680
            Region_4 212 581 893 514 530 795", header = T)


            df2 <- read.table(text =
             "'Team 1' 'Sam + Robert'
            'Team 2' 'Frank + Georgia'
            'Team 3' 'Frank + Steven'
            'Team 4' 'Robert + Dennis'
            'Team 5' 'Frank + Sam'", header = F)






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited 4 hours ago

























            answered 5 hours ago









            Maurits Evers

            23.1k41532




            23.1k41532











            • You are brilliant! That's exactly what I've been trying to do.
              – nicshah
              3 hours ago










            • You're very welcome @nicshah; glad it was helpful.
              – Maurits Evers
              42 mins ago
















            • You are brilliant! That's exactly what I've been trying to do.
              – nicshah
              3 hours ago










            • You're very welcome @nicshah; glad it was helpful.
              – Maurits Evers
              42 mins ago















            You are brilliant! That's exactly what I've been trying to do.
            – nicshah
            3 hours ago




            You are brilliant! That's exactly what I've been trying to do.
            – nicshah
            3 hours ago












            You're very welcome @nicshah; glad it was helpful.
            – Maurits Evers
            42 mins ago




            You're very welcome @nicshah; glad it was helpful.
            – Maurits Evers
            42 mins ago










            up vote
            2
            down vote













            Background: You can access data.frame columns stored in vector x with people[, x], and you can use strsplit to get a vector of the columns you need.



            Answer:



            # function to get the sum of two columns from 'people'
            get_sum <- function(m) 
             members <- unlist(strsplit(m, ' + ', fixed = T))
             rowSums(people[, members])


            # "Sam + Robert"
            get_sum(teams$members[1])
            # [1] 1015 784 1606 742

            # apply this for every team, and add results to 'people'
            cbind(people,
             sapply(teams$members, get_sum))

            # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia Frank + Steven Robert + Dennis Frank + Sam
            # 1 Region_1 218 763 811 812 797 574 1015 1337 1575 1608 981
            # 2 Region_2 474 983 343 697 310 335 784 1318 1680 653 1457
            # 3 Region_3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            # 4 Region_4 212 581 893 514 530 795 742 1376 1095 1423 793





            share|improve this answer






















            • I should add, I borrowed the code to create the people and teams objects from @alistaire 's answer -- thx
              – arvi1000
              3 hours ago















            up vote
            2
            down vote













            Background: You can access data.frame columns stored in vector x with people[, x], and you can use strsplit to get a vector of the columns you need.



            Answer:



            # function to get the sum of two columns from 'people'
            get_sum <- function(m) 
             members <- unlist(strsplit(m, ' + ', fixed = T))
             rowSums(people[, members])


            # "Sam + Robert"
            get_sum(teams$members[1])
            # [1] 1015 784 1606 742

            # apply this for every team, and add results to 'people'
            cbind(people,
             sapply(teams$members, get_sum))

            # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia Frank + Steven Robert + Dennis Frank + Sam
            # 1 Region_1 218 763 811 812 797 574 1015 1337 1575 1608 981
            # 2 Region_2 474 983 343 697 310 335 784 1318 1680 653 1457
            # 3 Region_3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            # 4 Region_4 212 581 893 514 530 795 742 1376 1095 1423 793





            share|improve this answer






















            • I should add, I borrowed the code to create the people and teams objects from @alistaire 's answer -- thx
              – arvi1000
              3 hours ago













            up vote
            2
            down vote










            up vote
            2
            down vote









            Background: You can access data.frame columns stored in vector x with people[, x], and you can use strsplit to get a vector of the columns you need.



            Answer:



            # function to get the sum of two columns from 'people'
            get_sum <- function(m) 
             members <- unlist(strsplit(m, ' + ', fixed = T))
             rowSums(people[, members])


            # "Sam + Robert"
            get_sum(teams$members[1])
            # [1] 1015 784 1606 742

            # apply this for every team, and add results to 'people'
            cbind(people,
             sapply(teams$members, get_sum))

            # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia Frank + Steven Robert + Dennis Frank + Sam
            # 1 Region_1 218 763 811 812 797 574 1015 1337 1575 1608 981
            # 2 Region_2 474 983 343 697 310 335 784 1318 1680 653 1457
            # 3 Region_3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            # 4 Region_4 212 581 893 514 530 795 742 1376 1095 1423 793





            share|improve this answer














            Background: You can access data.frame columns stored in vector x with people[, x], and you can use strsplit to get a vector of the columns you need.



            Answer:



            # function to get the sum of two columns from 'people'
            get_sum <- function(m) 
             members <- unlist(strsplit(m, ' + ', fixed = T))
             rowSums(people[, members])


            # "Sam + Robert"
            get_sum(teams$members[1])
            # [1] 1015 784 1606 742

            # apply this for every team, and add results to 'people'
            cbind(people,
             sapply(teams$members, get_sum))

            # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia Frank + Steven Robert + Dennis Frank + Sam
            # 1 Region_1 218 763 811 812 797 574 1015 1337 1575 1608 981
            # 2 Region_2 474 983 343 697 310 335 784 1318 1680 653 1457
            # 3 Region_3 700 813 133 212 906 680 1606 1493 1025 1039 1513
            # 4 Region_4 212 581 893 514 530 795 742 1376 1095 1423 793






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited 3 hours ago

























            answered 3 hours ago









            arvi1000

            5,6801534




            5,6801534











            • I should add, I borrowed the code to create the people and teams objects from @alistaire 's answer -- thx
              – arvi1000
              3 hours ago

















            • I should add, I borrowed the code to create the people and teams objects from @alistaire 's answer -- thx
              – arvi1000
              3 hours ago
















            I should add, I borrowed the code to create the people and teams objects from @alistaire 's answer -- thx
            – arvi1000
            3 hours ago





            I should add, I borrowed the code to create the people and teams objects from @alistaire 's answer -- thx
            – arvi1000
            3 hours ago











            up vote
            0
            down vote













            (Thank @alistaire for coding the data in a handy format)



            This can be done easily with base R functions (eval + parse) in a simple for-loop:



            for(i in teams$members)
             people[[i]] <- eval(parse(text = i), envir = people)


            # > people
            # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
            # 1 Region_1 218 763 811 812 797 574 1015 1337
            # 2 Region_2 474 983 343 697 310 335 784 1318
            # 3 Region_3 700 813 133 212 906 680 1606 1493
            # 4 Region_4 212 581 893 514 530 795 742 1376
            # Frank + Steven Robert + Dennis Frank + Sam
            # 1 1575 1608 981
            # 2 1680 653 1457
            # 3 1025 1039 1513
            # 4 1095 1423 793





            share|improve this answer
























              up vote
              0
              down vote













              (Thank @alistaire for coding the data in a handy format)



              This can be done easily with base R functions (eval + parse) in a simple for-loop:



              for(i in teams$members)
               people[[i]] <- eval(parse(text = i), envir = people)


              # > people
              # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
              # 1 Region_1 218 763 811 812 797 574 1015 1337
              # 2 Region_2 474 983 343 697 310 335 784 1318
              # 3 Region_3 700 813 133 212 906 680 1606 1493
              # 4 Region_4 212 581 893 514 530 795 742 1376
              # Frank + Steven Robert + Dennis Frank + Sam
              # 1 1575 1608 981
              # 2 1680 653 1457
              # 3 1025 1039 1513
              # 4 1095 1423 793





              share|improve this answer






















                up vote
                0
                down vote










                up vote
                0
                down vote









                (Thank @alistaire for coding the data in a handy format)



                This can be done easily with base R functions (eval + parse) in a simple for-loop:



                for(i in teams$members)
                 people[[i]] <- eval(parse(text = i), envir = people)


                # > people
                # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
                # 1 Region_1 218 763 811 812 797 574 1015 1337
                # 2 Region_2 474 983 343 697 310 335 784 1318
                # 3 Region_3 700 813 133 212 906 680 1606 1493
                # 4 Region_4 212 581 893 514 530 795 742 1376
                # Frank + Steven Robert + Dennis Frank + Sam
                # 1 1575 1608 981
                # 2 1680 653 1457
                # 3 1025 1039 1513
                # 4 1095 1423 793





                share|improve this answer












                (Thank @alistaire for coding the data in a handy format)



                This can be done easily with base R functions (eval + parse) in a simple for-loop:



                for(i in teams$members)
                 people[[i]] <- eval(parse(text = i), envir = people)


                # > people
                # region Sam Frank Dennis Steven Robert Georgia Sam + Robert Frank + Georgia
                # 1 Region_1 218 763 811 812 797 574 1015 1337
                # 2 Region_2 474 983 343 697 310 335 784 1318
                # 3 Region_3 700 813 133 212 906 680 1606 1493
                # 4 Region_4 212 581 893 514 530 795 742 1376
                # Frank + Steven Robert + Dennis Frank + Sam
                # 1 1575 1608 981
                # 2 1680 653 1457
                # 3 1025 1039 1513
                # 4 1095 1423 793






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 3 hours ago









                mt1022

                9,32021641




                9,32021641




















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