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6















I have a sequence of variables in a dataframe (over 100) and I would like to create an indicator variable for if particular text patterns are present in any of the variables. Below is an example with three variables. One solution I've found is using tidyr::unite() followed by dplyr::mutate(), but I'm interested in a solution where I do not have to unite the variables.



c1<-c("T1", "X1", "T6", "R5")
c2<-c("R4", "C6", "C7", "X3")
c3<-c("C5", "C2", "X4", "T2")

df<-data.frame(c1, c2, c3)

c1 c2 c3
1 T1 R4 C5
2 X1 C6 C2
3 T6 C7 X4
4 R5 X3 T2

code.vec<-c("T1", "T2", "T3", "T4") #Text patterns of interest
code_regex<-paste(code.vec, collapse="|")

new<-df %>%
unite(all_c, c1:c3, remove=FALSE) %>%
mutate(indicator=if_else(grepl(code_regex, all_c), 1, 0)) %>%
select(-(all_c))

c1 c2 c3 indicator
1 T1 R4 C5 1
2 X1 C6 C2 0
3 T6 C7 X4 0
4 R5 X3 T2 1


Above is an example that produces the desired result, however I feel as if there should be a way of doing this in tidyverse without having to unite the variables. This is something that SAS handles very easily using an ARRAY statement and a DO loop, and I'm hoping R has a good way of handling this.



The real dataframe has many additional variables besides from the "c" fields to search, so a solution that involves searching every column would require subsetting the dataframe to first only contain the variables I want to search, and then joining the data back with the other variables.










share|improve this question

























  • You said you don't want to use unite, but it's worth noting that passing the argument remove = FALSE has unite create a column of the united variables leaving the others intact. Might be convenient in this case.

    – camille
    11 hours ago











  • Yes, it is convenient. And it does work. I just feel like there may be a simpler approach I'm missing that doesn't need to create a united variable.

    – patward5656
    11 hours ago


















6















I have a sequence of variables in a dataframe (over 100) and I would like to create an indicator variable for if particular text patterns are present in any of the variables. Below is an example with three variables. One solution I've found is using tidyr::unite() followed by dplyr::mutate(), but I'm interested in a solution where I do not have to unite the variables.



c1<-c("T1", "X1", "T6", "R5")
c2<-c("R4", "C6", "C7", "X3")
c3<-c("C5", "C2", "X4", "T2")

df<-data.frame(c1, c2, c3)

c1 c2 c3
1 T1 R4 C5
2 X1 C6 C2
3 T6 C7 X4
4 R5 X3 T2

code.vec<-c("T1", "T2", "T3", "T4") #Text patterns of interest
code_regex<-paste(code.vec, collapse="|")

new<-df %>%
unite(all_c, c1:c3, remove=FALSE) %>%
mutate(indicator=if_else(grepl(code_regex, all_c), 1, 0)) %>%
select(-(all_c))

c1 c2 c3 indicator
1 T1 R4 C5 1
2 X1 C6 C2 0
3 T6 C7 X4 0
4 R5 X3 T2 1


Above is an example that produces the desired result, however I feel as if there should be a way of doing this in tidyverse without having to unite the variables. This is something that SAS handles very easily using an ARRAY statement and a DO loop, and I'm hoping R has a good way of handling this.



The real dataframe has many additional variables besides from the "c" fields to search, so a solution that involves searching every column would require subsetting the dataframe to first only contain the variables I want to search, and then joining the data back with the other variables.










share|improve this question

























  • You said you don't want to use unite, but it's worth noting that passing the argument remove = FALSE has unite create a column of the united variables leaving the others intact. Might be convenient in this case.

    – camille
    11 hours ago











  • Yes, it is convenient. And it does work. I just feel like there may be a simpler approach I'm missing that doesn't need to create a united variable.

    – patward5656
    11 hours ago














6












6








6








I have a sequence of variables in a dataframe (over 100) and I would like to create an indicator variable for if particular text patterns are present in any of the variables. Below is an example with three variables. One solution I've found is using tidyr::unite() followed by dplyr::mutate(), but I'm interested in a solution where I do not have to unite the variables.



c1<-c("T1", "X1", "T6", "R5")
c2<-c("R4", "C6", "C7", "X3")
c3<-c("C5", "C2", "X4", "T2")

df<-data.frame(c1, c2, c3)

c1 c2 c3
1 T1 R4 C5
2 X1 C6 C2
3 T6 C7 X4
4 R5 X3 T2

code.vec<-c("T1", "T2", "T3", "T4") #Text patterns of interest
code_regex<-paste(code.vec, collapse="|")

new<-df %>%
unite(all_c, c1:c3, remove=FALSE) %>%
mutate(indicator=if_else(grepl(code_regex, all_c), 1, 0)) %>%
select(-(all_c))

c1 c2 c3 indicator
1 T1 R4 C5 1
2 X1 C6 C2 0
3 T6 C7 X4 0
4 R5 X3 T2 1


Above is an example that produces the desired result, however I feel as if there should be a way of doing this in tidyverse without having to unite the variables. This is something that SAS handles very easily using an ARRAY statement and a DO loop, and I'm hoping R has a good way of handling this.



The real dataframe has many additional variables besides from the "c" fields to search, so a solution that involves searching every column would require subsetting the dataframe to first only contain the variables I want to search, and then joining the data back with the other variables.










share|improve this question
















I have a sequence of variables in a dataframe (over 100) and I would like to create an indicator variable for if particular text patterns are present in any of the variables. Below is an example with three variables. One solution I've found is using tidyr::unite() followed by dplyr::mutate(), but I'm interested in a solution where I do not have to unite the variables.



c1<-c("T1", "X1", "T6", "R5")
c2<-c("R4", "C6", "C7", "X3")
c3<-c("C5", "C2", "X4", "T2")

df<-data.frame(c1, c2, c3)

c1 c2 c3
1 T1 R4 C5
2 X1 C6 C2
3 T6 C7 X4
4 R5 X3 T2

code.vec<-c("T1", "T2", "T3", "T4") #Text patterns of interest
code_regex<-paste(code.vec, collapse="|")

new<-df %>%
unite(all_c, c1:c3, remove=FALSE) %>%
mutate(indicator=if_else(grepl(code_regex, all_c), 1, 0)) %>%
select(-(all_c))

c1 c2 c3 indicator
1 T1 R4 C5 1
2 X1 C6 C2 0
3 T6 C7 X4 0
4 R5 X3 T2 1


Above is an example that produces the desired result, however I feel as if there should be a way of doing this in tidyverse without having to unite the variables. This is something that SAS handles very easily using an ARRAY statement and a DO loop, and I'm hoping R has a good way of handling this.



The real dataframe has many additional variables besides from the "c" fields to search, so a solution that involves searching every column would require subsetting the dataframe to first only contain the variables I want to search, and then joining the data back with the other variables.







r dplyr tidyverse mutate






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited 12 hours ago







patward5656

















asked 12 hours ago









patward5656patward5656

425




425













  • You said you don't want to use unite, but it's worth noting that passing the argument remove = FALSE has unite create a column of the united variables leaving the others intact. Might be convenient in this case.

    – camille
    11 hours ago











  • Yes, it is convenient. And it does work. I just feel like there may be a simpler approach I'm missing that doesn't need to create a united variable.

    – patward5656
    11 hours ago



















  • You said you don't want to use unite, but it's worth noting that passing the argument remove = FALSE has unite create a column of the united variables leaving the others intact. Might be convenient in this case.

    – camille
    11 hours ago











  • Yes, it is convenient. And it does work. I just feel like there may be a simpler approach I'm missing that doesn't need to create a united variable.

    – patward5656
    11 hours ago

















You said you don't want to use unite, but it's worth noting that passing the argument remove = FALSE has unite create a column of the united variables leaving the others intact. Might be convenient in this case.

– camille
11 hours ago





You said you don't want to use unite, but it's worth noting that passing the argument remove = FALSE has unite create a column of the united variables leaving the others intact. Might be convenient in this case.

– camille
11 hours ago













Yes, it is convenient. And it does work. I just feel like there may be a simpler approach I'm missing that doesn't need to create a united variable.

– patward5656
11 hours ago





Yes, it is convenient. And it does work. I just feel like there may be a simpler approach I'm missing that doesn't need to create a united variable.

– patward5656
11 hours ago












3 Answers
3






active

oldest

votes


















3














We can use tidyverse



library(tidyverse)
df %>%
mutate_all(str_detect, pattern = code_regex) %>%
reduce(`+`) %>%
mutate(df, indicator = .)
# c1 c2 c3 indicator
#1 T1 R4 C5 1
#2 X1 C6 C2 0
#3 T6 C7 X4 0
#4 R5 X3 T2 1




Or using base R



Reduce(`+`, lapply(df, grepl, pattern = code_regex))
#[1] 1 0 0 1





share|improve this answer
























  • This tidyverse solution seems to only work in the scenario where all of the columns are being searched. I have other variables in my real dataset, and when using it for that the output is all NA. Does this have something to do with the reduce function?

    – patward5656
    11 hours ago











  • @patward5656 That is an easy fix. df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce("+") %>% mutate(df, indicator = .)

    – akrun
    11 hours ago













  • c1<-c("T1", "X1", "T6", "R5") c2<-c("R4", "C6", "C7", "X3") c3<-c("C5", "C2", "X4", "T2") z1<-c("C5", "C2", "X4", "T2") df<-data.frame(c1, c2, c3, z1) df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+) %>% mutate(df, indicator = .) c1 c2 c3 z1 indicator 1 T1 R4 C5 C5 NA 2 X1 C6 C2 C2 NA 3 T6 C7 X4 X4 NA 4 R5 X3 T2 T2 NA Warning message: In Ops.factor(.x, .y) : ‘+’ not meaningful for factors This produced NAs, it seems.

    – patward5656
    10 hours ago








  • 1





    @patward5656 I would use transmute_at instead of mutate_at df %>% transmute_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+)

    – akrun
    10 hours ago








  • 1





    Thanks. I believe transmute_at() solves it perfectly.

    – patward5656
    10 hours ago





















6














Using base R, we can use sapply and use grepl to find pattern in every column and assign 1 to rows where there is more than 0 matches.



df$indicator <- as.integer(rowSums(sapply(df, grepl, pattern = code_regex)) > 0)

df
# c1 c2 c3 indicator
#1 T1 R4 C5 1
#2 X1 C6 C2 0
#3 T6 C7 X4 0
#4 R5 X3 T2 1


If there are few other columns and we are interested to apply it only for columns which start with "c" we can use grep to filter them.



cols <- grep("^c", names(df))
as.integer(rowSums(sapply(df[cols], grepl, pattern = code_regex)) > 0)




Using dplyr we can do



library(dplyr)

df$indicator <- as.integer(df %>%
mutate_at(vars(c1:c3), ~grepl(code_regex, .)) %>%
rowSums() > 0)





share|improve this answer


























  • This is a good solution, but in the real data there are additional variables that I do not want to pattern search, so this would require me to index the dataframe to include only the columns I want to search first. Will edit my original post to include this information.

    – patward5656
    12 hours ago











  • The purr solution looks like what I was looking for--one line of code that doesn't involve uniting the variables.

    – patward5656
    12 hours ago











  • @patward5656 I think the purrr solution would not give you the expected output. I changed it to use mutate_at which should work on range of columns. Moreover, you can use column numbers directly in cols for sapply ., say columns 3:5 or 1:3 to find pattern in those column.

    – Ronak Shah
    12 hours ago





















1














Base R with apply



apply(df[cols], 1, function(x) sum(grepl(code_regex, x)))
# [1] 1 0 0 1





share|improve this answer
























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    3 Answers
    3






    active

    oldest

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    3 Answers
    3






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    3














    We can use tidyverse



    library(tidyverse)
    df %>%
    mutate_all(str_detect, pattern = code_regex) %>%
    reduce(`+`) %>%
    mutate(df, indicator = .)
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1




    Or using base R



    Reduce(`+`, lapply(df, grepl, pattern = code_regex))
    #[1] 1 0 0 1





    share|improve this answer
























    • This tidyverse solution seems to only work in the scenario where all of the columns are being searched. I have other variables in my real dataset, and when using it for that the output is all NA. Does this have something to do with the reduce function?

      – patward5656
      11 hours ago











    • @patward5656 That is an easy fix. df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce("+") %>% mutate(df, indicator = .)

      – akrun
      11 hours ago













    • c1<-c("T1", "X1", "T6", "R5") c2<-c("R4", "C6", "C7", "X3") c3<-c("C5", "C2", "X4", "T2") z1<-c("C5", "C2", "X4", "T2") df<-data.frame(c1, c2, c3, z1) df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+) %>% mutate(df, indicator = .) c1 c2 c3 z1 indicator 1 T1 R4 C5 C5 NA 2 X1 C6 C2 C2 NA 3 T6 C7 X4 X4 NA 4 R5 X3 T2 T2 NA Warning message: In Ops.factor(.x, .y) : ‘+’ not meaningful for factors This produced NAs, it seems.

      – patward5656
      10 hours ago








    • 1





      @patward5656 I would use transmute_at instead of mutate_at df %>% transmute_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+)

      – akrun
      10 hours ago








    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      10 hours ago


















    3














    We can use tidyverse



    library(tidyverse)
    df %>%
    mutate_all(str_detect, pattern = code_regex) %>%
    reduce(`+`) %>%
    mutate(df, indicator = .)
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1




    Or using base R



    Reduce(`+`, lapply(df, grepl, pattern = code_regex))
    #[1] 1 0 0 1





    share|improve this answer
























    • This tidyverse solution seems to only work in the scenario where all of the columns are being searched. I have other variables in my real dataset, and when using it for that the output is all NA. Does this have something to do with the reduce function?

      – patward5656
      11 hours ago











    • @patward5656 That is an easy fix. df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce("+") %>% mutate(df, indicator = .)

      – akrun
      11 hours ago













    • c1<-c("T1", "X1", "T6", "R5") c2<-c("R4", "C6", "C7", "X3") c3<-c("C5", "C2", "X4", "T2") z1<-c("C5", "C2", "X4", "T2") df<-data.frame(c1, c2, c3, z1) df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+) %>% mutate(df, indicator = .) c1 c2 c3 z1 indicator 1 T1 R4 C5 C5 NA 2 X1 C6 C2 C2 NA 3 T6 C7 X4 X4 NA 4 R5 X3 T2 T2 NA Warning message: In Ops.factor(.x, .y) : ‘+’ not meaningful for factors This produced NAs, it seems.

      – patward5656
      10 hours ago








    • 1





      @patward5656 I would use transmute_at instead of mutate_at df %>% transmute_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+)

      – akrun
      10 hours ago








    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      10 hours ago
















    3












    3








    3







    We can use tidyverse



    library(tidyverse)
    df %>%
    mutate_all(str_detect, pattern = code_regex) %>%
    reduce(`+`) %>%
    mutate(df, indicator = .)
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1




    Or using base R



    Reduce(`+`, lapply(df, grepl, pattern = code_regex))
    #[1] 1 0 0 1





    share|improve this answer













    We can use tidyverse



    library(tidyverse)
    df %>%
    mutate_all(str_detect, pattern = code_regex) %>%
    reduce(`+`) %>%
    mutate(df, indicator = .)
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1




    Or using base R



    Reduce(`+`, lapply(df, grepl, pattern = code_regex))
    #[1] 1 0 0 1






    share|improve this answer












    share|improve this answer



    share|improve this answer










    answered 11 hours ago









    akrunakrun

    424k13209287




    424k13209287













    • This tidyverse solution seems to only work in the scenario where all of the columns are being searched. I have other variables in my real dataset, and when using it for that the output is all NA. Does this have something to do with the reduce function?

      – patward5656
      11 hours ago











    • @patward5656 That is an easy fix. df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce("+") %>% mutate(df, indicator = .)

      – akrun
      11 hours ago













    • c1<-c("T1", "X1", "T6", "R5") c2<-c("R4", "C6", "C7", "X3") c3<-c("C5", "C2", "X4", "T2") z1<-c("C5", "C2", "X4", "T2") df<-data.frame(c1, c2, c3, z1) df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+) %>% mutate(df, indicator = .) c1 c2 c3 z1 indicator 1 T1 R4 C5 C5 NA 2 X1 C6 C2 C2 NA 3 T6 C7 X4 X4 NA 4 R5 X3 T2 T2 NA Warning message: In Ops.factor(.x, .y) : ‘+’ not meaningful for factors This produced NAs, it seems.

      – patward5656
      10 hours ago








    • 1





      @patward5656 I would use transmute_at instead of mutate_at df %>% transmute_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+)

      – akrun
      10 hours ago








    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      10 hours ago





















    • This tidyverse solution seems to only work in the scenario where all of the columns are being searched. I have other variables in my real dataset, and when using it for that the output is all NA. Does this have something to do with the reduce function?

      – patward5656
      11 hours ago











    • @patward5656 That is an easy fix. df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce("+") %>% mutate(df, indicator = .)

      – akrun
      11 hours ago













    • c1<-c("T1", "X1", "T6", "R5") c2<-c("R4", "C6", "C7", "X3") c3<-c("C5", "C2", "X4", "T2") z1<-c("C5", "C2", "X4", "T2") df<-data.frame(c1, c2, c3, z1) df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+) %>% mutate(df, indicator = .) c1 c2 c3 z1 indicator 1 T1 R4 C5 C5 NA 2 X1 C6 C2 C2 NA 3 T6 C7 X4 X4 NA 4 R5 X3 T2 T2 NA Warning message: In Ops.factor(.x, .y) : ‘+’ not meaningful for factors This produced NAs, it seems.

      – patward5656
      10 hours ago








    • 1





      @patward5656 I would use transmute_at instead of mutate_at df %>% transmute_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+)

      – akrun
      10 hours ago








    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      10 hours ago



















    This tidyverse solution seems to only work in the scenario where all of the columns are being searched. I have other variables in my real dataset, and when using it for that the output is all NA. Does this have something to do with the reduce function?

    – patward5656
    11 hours ago





    This tidyverse solution seems to only work in the scenario where all of the columns are being searched. I have other variables in my real dataset, and when using it for that the output is all NA. Does this have something to do with the reduce function?

    – patward5656
    11 hours ago













    @patward5656 That is an easy fix. df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce("+") %>% mutate(df, indicator = .)

    – akrun
    11 hours ago







    @patward5656 That is an easy fix. df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce("+") %>% mutate(df, indicator = .)

    – akrun
    11 hours ago















    c1<-c("T1", "X1", "T6", "R5") c2<-c("R4", "C6", "C7", "X3") c3<-c("C5", "C2", "X4", "T2") z1<-c("C5", "C2", "X4", "T2") df<-data.frame(c1, c2, c3, z1) df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+) %>% mutate(df, indicator = .) c1 c2 c3 z1 indicator 1 T1 R4 C5 C5 NA 2 X1 C6 C2 C2 NA 3 T6 C7 X4 X4 NA 4 R5 X3 T2 T2 NA Warning message: In Ops.factor(.x, .y) : ‘+’ not meaningful for factors This produced NAs, it seems.

    – patward5656
    10 hours ago







    c1<-c("T1", "X1", "T6", "R5") c2<-c("R4", "C6", "C7", "X3") c3<-c("C5", "C2", "X4", "T2") z1<-c("C5", "C2", "X4", "T2") df<-data.frame(c1, c2, c3, z1) df %>% mutate_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+) %>% mutate(df, indicator = .) c1 c2 c3 z1 indicator 1 T1 R4 C5 C5 NA 2 X1 C6 C2 C2 NA 3 T6 C7 X4 X4 NA 4 R5 X3 T2 T2 NA Warning message: In Ops.factor(.x, .y) : ‘+’ not meaningful for factors This produced NAs, it seems.

    – patward5656
    10 hours ago






    1




    1





    @patward5656 I would use transmute_at instead of mutate_at df %>% transmute_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+)

    – akrun
    10 hours ago







    @patward5656 I would use transmute_at instead of mutate_at df %>% transmute_at(vars(starts_with("c")), str_detect, pattern = code_regex) %>% reduce(+)

    – akrun
    10 hours ago






    1




    1





    Thanks. I believe transmute_at() solves it perfectly.

    – patward5656
    10 hours ago







    Thanks. I believe transmute_at() solves it perfectly.

    – patward5656
    10 hours ago















    6














    Using base R, we can use sapply and use grepl to find pattern in every column and assign 1 to rows where there is more than 0 matches.



    df$indicator <- as.integer(rowSums(sapply(df, grepl, pattern = code_regex)) > 0)

    df
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1


    If there are few other columns and we are interested to apply it only for columns which start with "c" we can use grep to filter them.



    cols <- grep("^c", names(df))
    as.integer(rowSums(sapply(df[cols], grepl, pattern = code_regex)) > 0)




    Using dplyr we can do



    library(dplyr)

    df$indicator <- as.integer(df %>%
    mutate_at(vars(c1:c3), ~grepl(code_regex, .)) %>%
    rowSums() > 0)





    share|improve this answer


























    • This is a good solution, but in the real data there are additional variables that I do not want to pattern search, so this would require me to index the dataframe to include only the columns I want to search first. Will edit my original post to include this information.

      – patward5656
      12 hours ago











    • The purr solution looks like what I was looking for--one line of code that doesn't involve uniting the variables.

      – patward5656
      12 hours ago











    • @patward5656 I think the purrr solution would not give you the expected output. I changed it to use mutate_at which should work on range of columns. Moreover, you can use column numbers directly in cols for sapply ., say columns 3:5 or 1:3 to find pattern in those column.

      – Ronak Shah
      12 hours ago


















    6














    Using base R, we can use sapply and use grepl to find pattern in every column and assign 1 to rows where there is more than 0 matches.



    df$indicator <- as.integer(rowSums(sapply(df, grepl, pattern = code_regex)) > 0)

    df
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1


    If there are few other columns and we are interested to apply it only for columns which start with "c" we can use grep to filter them.



    cols <- grep("^c", names(df))
    as.integer(rowSums(sapply(df[cols], grepl, pattern = code_regex)) > 0)




    Using dplyr we can do



    library(dplyr)

    df$indicator <- as.integer(df %>%
    mutate_at(vars(c1:c3), ~grepl(code_regex, .)) %>%
    rowSums() > 0)





    share|improve this answer


























    • This is a good solution, but in the real data there are additional variables that I do not want to pattern search, so this would require me to index the dataframe to include only the columns I want to search first. Will edit my original post to include this information.

      – patward5656
      12 hours ago











    • The purr solution looks like what I was looking for--one line of code that doesn't involve uniting the variables.

      – patward5656
      12 hours ago











    • @patward5656 I think the purrr solution would not give you the expected output. I changed it to use mutate_at which should work on range of columns. Moreover, you can use column numbers directly in cols for sapply ., say columns 3:5 or 1:3 to find pattern in those column.

      – Ronak Shah
      12 hours ago
















    6












    6








    6







    Using base R, we can use sapply and use grepl to find pattern in every column and assign 1 to rows where there is more than 0 matches.



    df$indicator <- as.integer(rowSums(sapply(df, grepl, pattern = code_regex)) > 0)

    df
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1


    If there are few other columns and we are interested to apply it only for columns which start with "c" we can use grep to filter them.



    cols <- grep("^c", names(df))
    as.integer(rowSums(sapply(df[cols], grepl, pattern = code_regex)) > 0)




    Using dplyr we can do



    library(dplyr)

    df$indicator <- as.integer(df %>%
    mutate_at(vars(c1:c3), ~grepl(code_regex, .)) %>%
    rowSums() > 0)





    share|improve this answer















    Using base R, we can use sapply and use grepl to find pattern in every column and assign 1 to rows where there is more than 0 matches.



    df$indicator <- as.integer(rowSums(sapply(df, grepl, pattern = code_regex)) > 0)

    df
    # c1 c2 c3 indicator
    #1 T1 R4 C5 1
    #2 X1 C6 C2 0
    #3 T6 C7 X4 0
    #4 R5 X3 T2 1


    If there are few other columns and we are interested to apply it only for columns which start with "c" we can use grep to filter them.



    cols <- grep("^c", names(df))
    as.integer(rowSums(sapply(df[cols], grepl, pattern = code_regex)) > 0)




    Using dplyr we can do



    library(dplyr)

    df$indicator <- as.integer(df %>%
    mutate_at(vars(c1:c3), ~grepl(code_regex, .)) %>%
    rowSums() > 0)






    share|improve this answer














    share|improve this answer



    share|improve this answer








    edited 12 hours ago

























    answered 12 hours ago









    Ronak ShahRonak Shah

    49k104370




    49k104370













    • This is a good solution, but in the real data there are additional variables that I do not want to pattern search, so this would require me to index the dataframe to include only the columns I want to search first. Will edit my original post to include this information.

      – patward5656
      12 hours ago











    • The purr solution looks like what I was looking for--one line of code that doesn't involve uniting the variables.

      – patward5656
      12 hours ago











    • @patward5656 I think the purrr solution would not give you the expected output. I changed it to use mutate_at which should work on range of columns. Moreover, you can use column numbers directly in cols for sapply ., say columns 3:5 or 1:3 to find pattern in those column.

      – Ronak Shah
      12 hours ago





















    • This is a good solution, but in the real data there are additional variables that I do not want to pattern search, so this would require me to index the dataframe to include only the columns I want to search first. Will edit my original post to include this information.

      – patward5656
      12 hours ago











    • The purr solution looks like what I was looking for--one line of code that doesn't involve uniting the variables.

      – patward5656
      12 hours ago











    • @patward5656 I think the purrr solution would not give you the expected output. I changed it to use mutate_at which should work on range of columns. Moreover, you can use column numbers directly in cols for sapply ., say columns 3:5 or 1:3 to find pattern in those column.

      – Ronak Shah
      12 hours ago



















    This is a good solution, but in the real data there are additional variables that I do not want to pattern search, so this would require me to index the dataframe to include only the columns I want to search first. Will edit my original post to include this information.

    – patward5656
    12 hours ago





    This is a good solution, but in the real data there are additional variables that I do not want to pattern search, so this would require me to index the dataframe to include only the columns I want to search first. Will edit my original post to include this information.

    – patward5656
    12 hours ago













    The purr solution looks like what I was looking for--one line of code that doesn't involve uniting the variables.

    – patward5656
    12 hours ago





    The purr solution looks like what I was looking for--one line of code that doesn't involve uniting the variables.

    – patward5656
    12 hours ago













    @patward5656 I think the purrr solution would not give you the expected output. I changed it to use mutate_at which should work on range of columns. Moreover, you can use column numbers directly in cols for sapply ., say columns 3:5 or 1:3 to find pattern in those column.

    – Ronak Shah
    12 hours ago







    @patward5656 I think the purrr solution would not give you the expected output. I changed it to use mutate_at which should work on range of columns. Moreover, you can use column numbers directly in cols for sapply ., say columns 3:5 or 1:3 to find pattern in those column.

    – Ronak Shah
    12 hours ago













    1














    Base R with apply



    apply(df[cols], 1, function(x) sum(grepl(code_regex, x)))
    # [1] 1 0 0 1





    share|improve this answer




























      1














      Base R with apply



      apply(df[cols], 1, function(x) sum(grepl(code_regex, x)))
      # [1] 1 0 0 1





      share|improve this answer


























        1












        1








        1







        Base R with apply



        apply(df[cols], 1, function(x) sum(grepl(code_regex, x)))
        # [1] 1 0 0 1





        share|improve this answer













        Base R with apply



        apply(df[cols], 1, function(x) sum(grepl(code_regex, x)))
        # [1] 1 0 0 1






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 11 hours ago









        nsinghsnsinghs

        1,262621




        1,262621






























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