CODEWITHSUNDEEP
rif-statementdplyr
Learner_seeker
Learner_seeker

Reputation: 544

change multiple columns based on a condition and column name string

i have a very sparse data set - below is a example of the format. I want to make changes to specific columns based on the logic explained below

# create dummy data set
pb=c('1','0','0','0','0','1','Not_ans','1','0','Not_ans')
qa=c('1','1','0','0','1','0','Not_ans','1','Not_ans','Not_ans')
#zy=c('1','Not_ans','0','1','Not_ans','0','1','1','1','Not_ans')

#sub questions for pb
pb.abr=c('1','0','0','0','0','1','0','1','0','0')
pb.ras=c('0','0','0','0','1','0','0','1','0','0')
pb.sfg=c('1','0','0','0','0','0','0','1','0','0')

#sub questions for qa
qa.fgs=c('1','0','0','0','0','0','0','1','0','0')
qa.sdf=c('0','1','0','0','0','0','0','0','0','0')
qa.tyu=c('0','0','0','0','1','0','0','1','0','0')

df=data.frame(pb,qa,pb.abr,pb.ras,pb.sfg,qa.fgs,qa.sdf,qa.tyu)
df

        pb      qa pb.abr pb.ras pb.sfg qa.fgs qa.sdf qa.tyu
1        1       1      1      0      1      1      0      0
2        0       1      0      0      0      0      1      0
3        0       0      0      0      0      0      0      0
4        0       0      0      0      0      0      0      0
5        0       1      0      1      0      0      0      1
6        1       0      1      0      0      0      0      0
7  Not_ans Not_ans      0      0      0      0      0      0
8        1       1      1      1      1      1      0      1
9        0 Not_ans      0      0      0      0      0      0
10 Not_ans Not_ans      0      0      0      0      0      0

The two columns pb and qa are called base columns, and they have further sub columns for with naming convention as pb. and qa. - so we see three sub columns for pa and 3 for qa. I want to make changes to these sub columns based on a condition to the base column ( pa or qa) .

Condition is if column pb =='Not_ans' then make all sub columns (pb.abr,pb.ras and pb.sfg) = 'Not_applicable'

how do i write a function which achieves this? where i specify the base column name i.e. pb and naming of sub columns example 'pb.' below - would it be something like below but it wont give the result

data.frame(ifelse(df['base_q']=='Not_ans',
df[ , grepl( paste('base_q','.') , names(df) )]=='Not_applicable',df[,grepl( 
paste('base_q','.') , names(df)) ])

How do i write a generic function which takes the base column numbers as inputs for example 1,2 here - applies the function i.e whereever pb is Not_ans it changes sub_columns ( pb.abr,pb.ras,pb.sfg) to Not applicable and then moves to column 2 ( qa) and applies the same logic?

Upvotes: 1

Views: 86

Answers (3)

Learner_seeker
Learner_seeker

Reputation: 544

Based on the answer given by @Wen-Ben - the following code worked -

yf=function(df,v,y){ for(i in v:y) { df[df[i]=='Not_ans',][,names(df)[substr(names(df),1,nchar(colnames(df)[i])+1)==paste0(colnames(df)[i],'.')]]='Not_applicable' } return(df) }

Upvotes: 0

jazzurro
jazzurro

Reputation: 23574

One way would be the following. You can specify which columns you want to apply a function (or functions) in var in mutate_at(). Here I used contains() to specify the column names. Then, I replaced the numeric values in the columns when pb == "Not_ans" with "Not_applicable". 

mutate_at(df, 
          vars(contains("pb.")),
          .funs = funs(ifelse(pb == "Not_ans",
                              "Not_applicable",
                              .)))

#        pb      qa         pb.abr         pb.ras         pb.sfg qa.fgs qa.sdf qa.tyu
#1        1       1              2              1              2      1      0      0
#2        0       1              1              1              1      0      1      0
#3        0       0              1              1              1      0      0      0
#4        0       0              1              1              1      0      0      0
#5        0       1              1              2              1      0      0      1
#6        1       0              2              1              1      0      0      0
#7  Not_ans Not_ans Not_applicable Not_applicable Not_applicable      0      0      0
#8        1       1              2              2              2      1      0      1
#9        0 Not_ans              1              1              1      0      0      0
#10 Not_ans Not_ans Not_applicable Not_applicable Not_applicable      0      0      0

If you want to apply the same task for both pb and qa, you can use mutate_at() twice.

mutate_at(df, 
          vars(contains("pb.")),
          .funs = funs(ifelse(pb == "Not_ans",
                              "Not_applicable",
                              .))) %>%
mutate_at(vars(contains("qa.")),
          .funs = funs(ifelse(qa == "Not_ans", "Not_applicable",.)))

Upvotes: 0

BENY
BENY

Reputation: 323226

You can do with 

yf=function(df,v){
   df[df[v]=='Not_ans',][,names(df)[substr(names(df),1,nchar(v)+1)==paste0(v,'.')]]='Not_applicable'
   return(df)
 }
yf(df,'pb')
        pb      qa         pb.abr         pb.ras         pb.sfg qa.fgs qa.sdf qa.tyu
1        1       1              1              0              1      1      0      0
2        0       1              0              0              0      0      1      0
3        0       0              0              0              0      0      0      0
4        0       0              0              0              0      0      0      0
5        0       1              0              1              0      0      0      1
6        1       0              1              0              0      0      0      0
7  Not_ans Not_ans Not_applicable Not_applicable Not_applicable      0      0      0
8        1       1              1              1              1      1      0      1
9        0 Not_ans              0              0              0      0      0      0
10 Not_ans Not_ans Not_applicable Not_applicable Not_applicable      0      0      0

Data input 

df=data.frame(pb,qa,pb.abr,pb.ras,pb.sfg,qa.fgs,qa.sdf,qa.tyu,stringsAsFactors = F) 
# notice stringsAsFactors

Upvotes: 2

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