loops - applying R script prepared for single file to multiple files in the directory

Question

I have prepared a r script and trying to apply the same codes in multiple files currently working with 5 sample files (but trying to learn to work with files over 100) in the directory to learn how to work with multiple files. Sorry for the quality of codes I have written as I am currently learning R and I am sure there are much better and organised way to write then what I have prepared. (please see below for sample data and my codes)

What I am trying to acheive is to run my codes in all files and write them back to the same directory or a different directory with slightly altered name.

I have tried to read each files using the following and add all my codes within the {} bracekts:

filenames = dir(pattern=".csv") 

for( i in 1:length(filenames) ){}

but its not working, I thing I am doing this step all wrong, I am just wondering if you could give me some guidence about how should I approch to work with multiple files?

I have prepared a sample dataset so that I can show you the codes I have got, the following two pictures shows the dataset after I read and after I run all codes how the dataset look like:

My sample datafile:

> dput (df)
structure(list(X = structure(c(3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "w", "wo"), class = "factor"), 
    X.1 = structure(c(1L, 11L, 18L, 9L, 26L, 30L, 22L, 5L, 14L, 
    15L, 6L, 23L, 27L, 19L, 2L, 10L, 16L, 7L, 24L, 28L, 20L, 
    3L, 12L, 17L, 8L, 25L, 29L, 21L, 4L, 13L), .Label = c("Fri 1 Jan", 
    "Fri 15 Jan", "Fri 22 Jan", "Fri 29 Jan", "Fri 8 Jan", "Mon 11 Jan", 
    "Mon 18 Jan", "Mon 25 Jan", "Mon 4 Jan", "Sat 16 Jan", "Sat 2 Jan", 
    "Sat 23 Jan", "Sat 30 Jan", "Sat 9 Jan", "Sun 10 Jan", "Sun 17 Jan", 
    "Sun 24 Jan", "Sun 3 Jan", "Thu 14 Jan", "Thu 21 Jan", "Thu 28 Jan", 
    "Thu 7 Jan", "Tue 12 Jan", "Tue 19 Jan", "Tue 26 Jan", "Tue 5 Jan", 
    "Wed 13 Jan", "Wed 20 Jan", "Wed 27 Jan", "Wed 6 Jan"), class = "factor"), 
    X1 = c(322L, 89L, 242L, NA, 136L, 113L, 70L, 134L, 232L, 
    NA, 127L, 124L, 120L, 162L, 179L, 374L, 477L, NA, 147L, 136L, 
    152L, 196L, 384L, 491L, 136L, NA, 143L, 172L, 226L, 509L), 
    X2 = c(409L, 71L, 206L, NA, 104L, 57L, NA, 98L, 201L, NA, 
    74L, 94L, 69L, 98L, 117L, 277L, 445L, NA, 131L, 90L, 83L, 
    NA, 329L, 473L, 73L, NA, 104L, 113L, 136L, 427L), X3 = c(367L, 
    54L, 211L, NA, 107L, 69L, 51L, 63L, 157L, NA, 56L, 115L, 
    96L, 100L, 118L, 250L, 388L, NA, 124L, 85L, 96L, 118L, 313L, 
    431L, 79L, NA, 93L, 135L, 134L, 290L), X4 = c(343L, 60L, 
    183L, NA, 110L, 53L, 32L, 77L, 123L, NA, 37L, 100L, 64L, 
    68L, 99L, 199L, 333L, NA, 107L, 71L, 81L, 89L, 219L, 393L, 
    43L, NA, 72L, 96L, 127L, NA), X5 = c(231L, 42L, 79L, NA, 
    74L, 58L, 48L, 59L, 78L, NA, 62L, 74L, 63L, 59L, 74L, 110L, 
    134L, NA, 74L, 82L, 59L, 73L, 135L, 170L, 53L, NA, 61L, 72L, 
    67L, 186L), X6 = c(140L, 41L, 57L, NA, 104L, 92L, 89L, 94L, 
    68L, NA, 116L, 131L, NA, 110L, 125L, 89L, 89L, NA, 113L, 
    124L, 115L, 116L, 95L, 77L, 126L, NA, 110L, 122L, 119L, 122L
    ), X7 = c(90L, 104L, 82L, NA, 368L, 258L, NA, 289L, 117L, 
    NA, 395L, 416L, 397L, 391L, 400L, 132L, 101L, NA, 397L, 426L, 
    418L, 411L, 151L, 66L, 396L, NA, 457L, 437L, 428L, 128L)), .Names = c("X", 
"X.1", "X1", "X2", "X3", "X4", "X5", "X6", "X7"), class = "data.frame", row.names = c(NA, 
-30L))

The codes I have prepared:

# example codes for sample data

## step 1 - read file

filename <- '101_E45_N66.csv'
df <- read.csv(filename, header = TRUE,skip =5, nrow =
    length(count.fields(filename)) - 12)

## step 2 - Change coloumn name

colnames(df) = c("type","date","v1","v2","v3","v4","v5","v6","v7")

## step 3 - spliting name "101_E45_N66.csv" to create 3 new coloumn within dataframe

s = strsplit(filename,"_",,fixed=TRUE)[[1]] 
df1= cbind(df[,c("type","date")],ID=s[1],name1=s[2],name2=s[3],df[,3:ncol(df)])

## step 4 - changing type coloumn for weekday/weekend

f = c("wd", "we", "we", "wd", "wd", "wd", "wd") 
df1$type = rep(f,52, length.out = 30)

## creating a backup file 

df2 = df1

## step 5 - subsetting for weekday and weekend

df3 = df2[df2$type == "wd",] ## weekday
df4 = df2[df2$type == "we",] ## weekend

## step 16 - adding new rows in df1 with total, weekday and weeknd sum and number of missing values

df2[31,(6:12)] <- colSums(df1[,6:12], na.rm = T) ## all
df2[32,(6:12)] <- colSums(df3[,6:12], na.rm = T) ## weekday
df2[33,(6:12)] <- colSums(df4[,6:12], na.rm = T) ## weekend

df2[34,(6:12)] = colSums(is.na(df1[,6:12])) ## all missing
df2[35,(6:12)] = colSums(is.na(df3[,6:12]))## weekday missing
df2[36,(6:12)] = colSums(is.na(df4[,6:12]))## weekend missing
df2

Answer 1

To read a set of csv files into a data.frame, I often use ldply from the plyr package:

library(plyr)
all_data = ldply(list.files(pattern = "csv"), function(fname) {
    dum = read.csv(fname)
    dum$fname = fname  # adds the filename it was read from as a column
    return(dum)
  })

If you need more specific things, you extend the function you call with ldply.