Loop over strings in r -


i'd know wrong code rather solution. wish loop on strings data follows: 

id    source    transaction   1     > b       6 > 0  2     j > k       5  3     b > c       4 > 0

i have list , wish go on list , find rows contains element , compute average.

mylist <- c ("a", "b")

so desired output 1 of element in list 

source  avg        6  b        2

i not know loop on list , send them csv file. tried this

mylist <- c( "a", "b" )  for(i in mylist) {    keepdata <- df [grepl(i, df$source), ]    keepdata <- csplit(keepdata, "transaction", ">", "long")    avg<- mean(keepdata$transactions)   result <- list(i,avg )    write.table(result ,file="c:/users.csv", append=true,sep=",",col.names=false,row.names=false)  }

but gives me "na" result following warning

warning messages: 1: in mean.default(keepdata$transactions) :
argument not numeric or logical: returning na 2: in mean.default(keepdata$transactions) : argument not numeric or logical: returning na

we can use csplit split 'source' , convert dataset 'long' format, specify 'i', grouped 'source', mean of 'transaction' (using data.table methods)

library(splitstackshape) csplit(df1, "source", " > ", "long")[source %in% mylist, .(avg = mean(transaction)), source] #   source avg #1:        6 #2:      b   5

or option separate_rows tidyr convert 'long' format, use dplyr methods summarise after grouping 'source'

library(tidyr) library(dplyr) separate_rows(df1, source) %>%         filter(source %in% mylist) %>%         group_by(source) %>%          summarise(avg  = mean(transaction))

update

for new dataset ('df2'), need split both columns 'long' format, , mean of 'transaction' grouped 'source'

csplit(df2, 2:3,  " > ", "long")[source %in% my_list, .(avg = mean(transaction)), source] #   source avg #1:        6 #2:      b   2

the for loop can modified to

for(i in mylist) {    keepdata <-  csplit(df2, 2:3,  ">", "long")    keepdata <- keepdata[grepl(i, source)]    avg<- mean(keepdata$transaction)    result <- list(i,avg )    print(result)    write.table(result ,file="c:/users.csv",               append=true,sep=",",col.names=false,row.names=false)  } #[[1]] #[1] "a"  #[[2]] #[1] 6  #[[1]] #[1] "b"  #[[2]] #[1] 2

data

df1 <- structure(list(id = 1:3, source = c("a > b", "j > k", "b > c"  ), transaction = c(6l, 5l, 4l)), .names = c("id", "source", "transaction" ), class = "data.frame", row.names = c(na, -3l))   df2 <- structure(list(id = 1:3, source = c("a > b", "j > k", "b > c" ), transaction = c("6 > 0", "5", "4 > 0")), .names = c("id",  "source", "transaction"), class = "data.frame", row.names = c(na,  -3l))

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