Only read limited number of columns

Question

Can anyone please tell me how to read only the first 6 months (7 columns) for each year of the data below, for example by using read.table()?

Year   Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec   
2009   -41  -27  -25  -31  -31  -39  -25  -15  -30  -27  -21  -25
2010   -41  -27  -25  -31  -31  -39  -25  -15  -30  -27  -21  -25 
2011   -21  -27   -2   -6  -10  -32  -13  -12  -27  -30  -38  -29

Answer 1

To read a specific set of columns from a dataset you, there are several other options:

1) With freadfrom the data.table-package:

You can specify the desired columns with the select parameter from fread from the data.table package. You can specify the columns with a vector of column names or column numbers.

For the example dataset:

library(data.table)
dat <- fread("data.txt", select = c("Year","Jan","Feb","Mar","Apr","May","Jun"))
dat <- fread("data.txt", select = c(1:7))

Alternatively, you can use the drop parameter to indicate which columns should not be read:

dat <- fread("data.txt", drop = c("Jul","Aug","Sep","Oct","Nov","Dec"))
dat <- fread("data.txt", drop = c(8:13))

All result in:

> data
  Year Jan Feb Mar Apr May Jun
1 2009 -41 -27 -25 -31 -31 -39
2 2010 -41 -27 -25 -31 -31 -39
3 2011 -21 -27  -2  -6 -10 -32

UPDATE: When you don't want fread to return a data.table, use the data.table = FALSE-parameter, e.g.: fread("data.txt", select = c(1:7), data.table = FALSE)

2) With read.csv.sql from the sqldf-package:

Another alternative is the read.csv.sql function from the sqldf package:

library(sqldf)
dat <- read.csv.sql("data.txt",
                    sql = "select Year,Jan,Feb,Mar,Apr,May,Jun from file",
                    sep = "\t")

3) With the read_*-functions from the readr-package:

library(readr)
dat <- read_table("data.txt",
                  col_types = cols_only(Year = 'i', Jan = 'i', Feb = 'i', Mar = 'i',
                                        Apr = 'i', May = 'i', Jun = 'i'))
dat <- read_table("data.txt",
                  col_types = list(Jul = col_skip(), Aug = col_skip(), Sep = col_skip(),
                                   Oct = col_skip(), Nov = col_skip(), Dec = col_skip()))
dat <- read_table("data.txt", col_types = 'iiiiiii______')

From the documentation an explanation for the used characters with col_types:

each character represents one column: c = character, i = integer, n = number, d = double, l = logical, D = date, T = date time, t = time, ? = guess, or _/- to skip the column

Answer 2

Say the data are in file data.txt, you can use the colClasses argument of read.table() to skip columns. Here the data in the first 7 columns are "integer" and we set the remaining 6 columns to "NULL" indicating they should be skipped

> read.table("data.txt", colClasses = c(rep("integer", 7), rep("NULL", 6)), 
+            header = TRUE)
  Year Jan Feb Mar Apr May Jun
1 2009 -41 -27 -25 -31 -31 -39
2 2010 -41 -27 -25 -31 -31 -39
3 2011 -21 -27  -2  -6 -10 -32

Change "integer" to one of the accepted types as detailed in ?read.table depending on the real type of data.

data.txt looks like this:

$ cat data.txt 
"Year" "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
2009 -41 -27 -25 -31 -31 -39 -25 -15 -30 -27 -21 -25
2010 -41 -27 -25 -31 -31 -39 -25 -15 -30 -27 -21 -25
2011 -21 -27 -2 -6 -10 -32 -13 -12 -27 -30 -38 -29

and was created by using

write.table(dat, file = "data.txt", row.names = FALSE)

where dat is

dat <- structure(list(Year = 2009:2011, Jan = c(-41L, -41L, -21L), Feb = c(-27L, 
-27L, -27L), Mar = c(-25L, -25L, -2L), Apr = c(-31L, -31L, -6L
), May = c(-31L, -31L, -10L), Jun = c(-39L, -39L, -32L), Jul = c(-25L, 
-25L, -13L), Aug = c(-15L, -15L, -12L), Sep = c(-30L, -30L, -27L
), Oct = c(-27L, -27L, -30L), Nov = c(-21L, -21L, -38L), Dec = c(-25L, 
-25L, -29L)), .Names = c("Year", "Jan", "Feb", "Mar", "Apr", 
"May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), class = "data.frame",
row.names = c(NA, -3L))

If the number of columns is not known beforehand, the utility function count.fields will read through the file and count the number of fields in each line.

## returns a vector equal to the number of lines in the file
count.fields("data.txt", sep = "\t")
## returns the maximum to set colClasses
max(count.fields("data.txt", sep = "\t"))

Answer 3

You could also use JDBC to achieve this. Let's create a sample csv file.

write.table(x=mtcars, file="mtcars.csv", sep=",", row.names=F, col.names=T) # create example csv file

Download and save the the CSV JDBC driver from this link: http://sourceforge.net/projects/csvjdbc/files/latest/download

> library(RJDBC)

> path.to.jdbc.driver <- "jdbc//csvjdbc-1.0-18.jar"
> drv <- JDBC("org.relique.jdbc.csv.CsvDriver", path.to.jdbc.driver)
> conn <- dbConnect(drv, sprintf("jdbc:relique:csv:%s", getwd()))

> head(dbGetQuery(conn, "select * from mtcars"), 3)
   mpg cyl disp  hp drat    wt  qsec vs am gear carb
1   21   6  160 110  3.9  2.62 16.46  0  1    4    4
2   21   6  160 110  3.9 2.875 17.02  0  1    4    4
3 22.8   4  108  93 3.85  2.32 18.61  1  1    4    1

> head(dbGetQuery(conn, "select mpg, gear from mtcars"), 3)
   MPG GEAR
1   21    4
2   21    4
3 22.8    4