How to directly open .R data containing data frame code in R?

Question

Here is an dataset

Environment= c("ENV1","ENV2","ENV3","ENV4","ENV5")
CV1<- c(25.2,26.3,15.2,22.1,21.9)
CV2<- c(37.1,34.5,26.0,29.9,28.3)
CV3<- c(33.0,30.5,25.0,30.2,28.9)
CV4<- c(35.4,37.0,28.0,26.0,34.0)
CV5<- c(34.6,32.7,20.0,32.3,28.6)

dataA= data.frame(Environment,CV1,CV2,CV3,CV4,CV5)

and I want to convert this data table as data frame code

dput(dataA)

structure(list(Environment = c("ENV1", "ENV2", "ENV3", "ENV4", 
                               "ENV5"), CV1 = c(25.2, 26.3, 15.2, 22.1, 21.9), 
               CV2 = c(37.1, 34.5, 26, 29.9, 28.3), 
               CV3 = c(33, 30.5, 25, 30.2, 28.9), 
               CV4 = c(35.4, 37, 28, 26, 34), 
               CV5 = c(34.6, 32.7, 20, 32.3, 28.6)), 
              class = "data.frame", row.names = c(NA, -5L))

Then, I saved this code as .R data for some cases. I saved as data_code.R in my PC.

Later, I want to open this data again, but directly want to open it as data table. For example, I want like

df= C:/Users/Desktop/data_code.R

print(df)
  Environment  CV1  CV2  CV3  CV4  CV5
1        ENV1 25.2 37.1 33.0 35.4 34.6
2        ENV2 26.3 34.5 30.5 37.0 32.7
3        ENV3 15.2 26.0 25.0 28.0 20.0
4        ENV4 22.1 29.9 30.2 26.0 32.3
5        ENV5 21.9 28.3 28.9 34.0 28.6

Is that possible to import data frame code to directly R?

Thanks,

Try saving it as .RDS or .RData and then load it if needed see this. Or just save as .txt/.csv/.csv2 and read. There are a lot of ways to do this in R. — lailaps
– lailaps, Commented Mar 5 at 18:59
@TimG Thank you for your comment. Sometimes I lose my data in my PC and couldn't upload to R. So, I want to save it as data.frame code so that I can directly use data. But sometimes, the code is too long to output in the R script. So, I want to save it as .R data and import from them. This is because of losing excel/csv data as time goes by. — J.K Kim
– J.K Kim, Commented Mar 5 at 19:28
Ahh so that's what I thought. You want to store the dput-text inside an R-Script file and then load this script file and parse it. Still I am wondering where the advantage is of storing the defining Code for a data.frame opposed to storing and restoring the data directly. Unless you create the data.frame using some sort of sampling/ rep that is depending on a seed. — lailaps
– lailaps, Commented Mar 5 at 19:36
I think this question has dozens, if not hundreds, of answers. Each text book about R has an I/O section. — Friede
– Friede, Commented Mar 5 at 19:50
@TimG Yes, it seems less convenient, but I used to lose my excel files, and without data, I cannot do anything. So, first, I planned to save data.frame code directly in my R script so that when I open R file, I can easily use it. However, sometimes I have 100 MB excel file, and when I convert it to data.frame code using dput(), the codes are too long to output in R script. So, I want to save it as code file (.R or .csv whatever) and will save in one folder. Then, I'll never lose it. Because I save excel files in different folders according to projects, and I lose it over time. — J.K Kim
– J.K Kim, Commented Mar 5 at 19:51

G. Grothendieck · Accepted Answer · 2025-03-05 22:03:59Z

Here are a few ways. A few more ways (fread/fwrite, RDS, feather, fst) can be found here.

# 1
dput(dataA, "dataA.R")
dataB <- dget("dataA.R")
identical(dataA, dataB)
## [1] TRUE

# 2
save(dataA, file = "dataA.Rdata")
load("dataA.Rdata", envir = e <- new.env())
identical(dataA, e$dataA)
## [1] TRUE

# 3
write.csv(dataA, "dataA.csv", row.names = FALSE)
dataB <- read.csv("dataA.csv")
identical(dataA, dataB)
## [1] TRUE

# 4
library(arrow)
write_parquet(dataA, normalizePath("dataA.parquet", mustWork = FALSE))
dataB <- read_parquet("dataA_parquet") |> as.data.frame()
identical(dataA, dataB)
## [1] TRUE

# 5
library(writexl)
library(readxl)
write_xlsx(dataA, "dataA.xlsx")
dataB <- read_xlsx("dataA.xlsx") |> as.data.frame()
identical(dataA, dataB)
## [1] TRUE

# 6
library(RSQLite)
con <- dbConnect(SQLite(), "dataA.sqlite")
dbWriteTable(con, "dataA", dataA)
dbDisconnect(con)
con <- dbConnect(SQLite(), "dataA.sqlite")
dataB <- dbReadTable(con, "dataA")
identical(dataA, dataB)
## [1] TRUE

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