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Creating one variable from a list of variables in R?


R dplyr/tidyr: “mutate” new columns with data from other observationsFunction for Tidy chisq.test Output for Visualizing or Filtering P-ValuesShiny: Create reactive filter using different variables.Create a Table with Alternating Total Rows Followed by Sub-Rows Using Dplyr and TidyverseUsing switch statement within dplyr's mutateConditional Recoding - Using a Vector of Columns within Mutate_at Together with If_else and Dplyr::RecodeCreating and using new variables in function in R: NSE programing error in the tidyversedplyr mutate-ifelse combination not creating correct conditional variableTidyverse — integrating mutate select and case when to likert scalesCan I create a new numerical variable using dplyr and <= and >= operators to subset values from an existing vector?






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;








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
    Apr 22 at 14:55











  • 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
    Apr 22 at 15:06

















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
    Apr 22 at 14:55











  • 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
    Apr 22 at 15:06













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 Apr 22 at 14:38







patward5656

















asked Apr 22 at 14:23









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
    Apr 22 at 14:55











  • 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
    Apr 22 at 15:06

















  • 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
    Apr 22 at 14:55











  • 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
    Apr 22 at 15:06
















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
Apr 22 at 14:55





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
Apr 22 at 14:55













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
Apr 22 at 15:06





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
Apr 22 at 15:06












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
    Apr 22 at 15:40











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

    – akrun
    Apr 22 at 15:41












  • 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
    Apr 22 at 15:59







  • 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
    Apr 22 at 16:08







  • 1





    Thanks. I believe transmute_at() solves it perfectly.

    – patward5656
    Apr 22 at 16:10



















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
    Apr 22 at 14:37











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

    – patward5656
    Apr 22 at 14:42











  • @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
    Apr 22 at 14:52



















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

    votes








    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
      Apr 22 at 15:40











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

      – akrun
      Apr 22 at 15:41












    • 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
      Apr 22 at 15:59







    • 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
      Apr 22 at 16:08







    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      Apr 22 at 16:10
















    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
      Apr 22 at 15:40











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

      – akrun
      Apr 22 at 15:41












    • 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
      Apr 22 at 15:59







    • 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
      Apr 22 at 16:08







    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      Apr 22 at 16:10














    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 Apr 22 at 15:09









    akrunakrun

    426k13209287




    426k13209287












    • 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
      Apr 22 at 15:40











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

      – akrun
      Apr 22 at 15:41












    • 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
      Apr 22 at 15:59







    • 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
      Apr 22 at 16:08







    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      Apr 22 at 16:10


















    • 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
      Apr 22 at 15:40











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

      – akrun
      Apr 22 at 15:41












    • 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
      Apr 22 at 15:59







    • 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
      Apr 22 at 16:08







    • 1





      Thanks. I believe transmute_at() solves it perfectly.

      – patward5656
      Apr 22 at 16:10

















    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
    Apr 22 at 15:40





    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
    Apr 22 at 15:40













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

    – akrun
    Apr 22 at 15:41






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

    – akrun
    Apr 22 at 15:41














    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
    Apr 22 at 15:59






    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
    Apr 22 at 15:59





    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
    Apr 22 at 16:08






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

    – akrun
    Apr 22 at 16:08





    1




    1





    Thanks. I believe transmute_at() solves it perfectly.

    – patward5656
    Apr 22 at 16:10






    Thanks. I believe transmute_at() solves it perfectly.

    – patward5656
    Apr 22 at 16:10














    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
      Apr 22 at 14:37











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

      – patward5656
      Apr 22 at 14:42











    • @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
      Apr 22 at 14:52
















    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
      Apr 22 at 14:37











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

      – patward5656
      Apr 22 at 14:42











    • @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
      Apr 22 at 14:52














    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 Apr 22 at 14:50

























    answered Apr 22 at 14:27









    Ronak ShahRonak Shah

    50k104370




    50k104370












    • 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
      Apr 22 at 14:37











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

      – patward5656
      Apr 22 at 14:42











    • @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
      Apr 22 at 14:52


















    • 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
      Apr 22 at 14:37











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

      – patward5656
      Apr 22 at 14:42











    • @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
      Apr 22 at 14:52

















    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
    Apr 22 at 14:37





    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
    Apr 22 at 14:37













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

    – patward5656
    Apr 22 at 14:42





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

    – patward5656
    Apr 22 at 14:42













    @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
    Apr 22 at 14:52






    @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
    Apr 22 at 14:52












    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 Apr 22 at 15:13









        nsinghsnsinghs

        1,264721




        1,264721



























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