Go to TogaWare.com Home Page. Data Science Desktop Survival Guide
by Graham Williams
Duck Duck Go

Clipboard Data

20200106 A useful trick to quickly ingest a small amount of data into R is to read the data from the clipboard. That is, from a selection that is made in some other application. For example, if a CSV file is being viewed within a text editor, then some number of rows and columns could be highlighted and copied (often by pressing Ctrl-C) to the clipboard. The data can then be read into R using the package readr. The function readr::read_csv() can have as its first argument (file=) readr::clipboard(), which diverts input to come from the clipboard. Another use case is when a small data sample is shared on a web page. The data can be highlighted and copied to the clipboard to then be ingested into R. Note the use below of col_names=. Set to FALSE it indicates that the first row is not the column names, as might typically be the case when selecting different ranges of rows from sample data. Names like X1 and X2 will be generated. If the first row does name the columns then simply remove the option or else set it to TRUE. It can also be a character vector of names to be used for the columns.
library(readr)        # Modern and efficient data reader.

ds <- read_csv(clipboard(), col_names=FALSE)

ds
## # A tibble: 11 x 2
##    X1               X2
##    <chr>         <dbl>
##  1 27 Jun 2020 -2149  
##  2 09 Jun 2020    33.8
##  3 11 Jun 2020    56.3
##  4 11 Jun 2020    12.1
##  5 11 Jun 2020    10.6
##  6 11 Jun 2020    47.4
##  7 11 Jun 2020    41.7
##  8 12 Jun 2020    45.6
##  9 15 Jun 2020  1838. 
## 10 15 Jun 2020    50.3
## 11 17 Jun 2020    58.8

Using base R a similar approach is available with the special string clipboard as the first argument in utils::read.table(), for example. This generally only works on Linux with X11.

ds <- read.table("clipboard")


Support further development by purchasing the PDF version of the book.
Other online resources include the GNU/Linux Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
Popular open source software includes rattle and wajig.
Hosted by Togaware, a pioneer of free and open source software since 1984.
Copyright © 2000-2020 Togaware Pty Ltd. . Creative Commons ShareAlike V4.