Story starts from this Stackoverflow question.

Mrflick gave an answer to help the OP drew the plot using ggplot2, and I was curious that the way how he came up with data frame looks so unique. Honestly, I have never seen anyone so far using the structure function with nested list object to create a data frame. Then I suggested to him using `read.table` to import the data directly into R, like this.

library(plyr)
library(reshape2)

datatext=”
8192 2 1 1 1
65536 10 5 4 4
1048576 81 60 63 52
8388608 675 555 572 464
16777216 1334 1124 1171 953
33554432 2780 2348 2438 2014
67108864 5853 5229 4957 4238
134217728 12437 10303 10521 8921
”
mydata <- read.table(text=datatext, col.names=c(“size”, “v1”, “v2”, “v3”, “v4”))

Clearly, I guess you should never tell a guy with 28K stackoverflow credits what is the right way to read in data. 🙂 I guess when he read in the data, it is probably in a much smarted way than I imagine, but clearly, he was using the `dput` and `dget` function because he is SHARING CODE.

So basically, you can use dput to “Writes an ASCII text representation of an R object to a file or connection, or uses one to recreate the object.”, say you have a small data frame `mtcars` that you want to send to your coworker through Skype.

You can just type `dput(mtcars)` and it will print a long string to the standard output and you can just cp to Skype, then they can read in simply by copy and paste the string to reconstruct the object in one line by running `data <- <skypestring>`.  This not only works for data but also for functions.

`dget` is only used to read from a file which contains the output from dput.