Background
I'm doing some data manipulation (joins, etc.) on a very large dataset in R, so I decided to use a local installation of Apache Spark and sparklyr to be able to use my dplyr code to manipulate it all. (I'm running Windows 10 Pro; R is 64-bit.) I've done the work needed, and now want to output the sparklyr table to a .csv file.
The Problem
Here's the code I'm using to output a .csv file to a folder on my hard drive:
spark_write_csv(d1, "C:/d1.csv")
When I navigate to the directory in question, though, I don't see a single csv file d1.csv. Instead I see a newly created folder called d1, and when I click inside it I see ~10 .csv files all beginning with "part". Here's a screenshot:
The folder also contains the same number of .csv.crc files, which I see from Googling are "used to store CRC code for a split file archive".
What's going on here? Is there a way to put these files back together, or to get spark_write_csv to output a single file like write.csv?
Edit
A user below suggested that this post may answer the question, and it nearly does, but it seems like the asker is looking for Scala code that does what I want, while I'm looking for R code that does what I want.
I had the exact same issue.
In simple terms, the partitions are done for computational efficiency. If you have partitions, multiple workers/executors can write the table on each partition. In contrast, if you only have one partition, the csv file can only be written by a single worker/executor, making the task much slower. The same principle applies not only for writing tables but also for parallel computations.
For more details on partitioning, you can check this link.
Suppose I want to save table as a single file with the path path/to/table.csv. I would do this as follows
table %>% sdf_repartition(partitions=1)
spark_write_csv(table, path/to/table.csv,...)
You can check full details of sdf_repartition in the official documentation.
Data will be divided into multiple partitions. When you save the dataframe to CSV, you will get file from each partition. Before calling spark_write_csv method you need to bring all the data to single partition to get single file.
You can use a method called as coalese to achieve this.
coalesce(df, 1)
Related
I am using openxlsx in order to write the outputs of my data.
I have used the following code to read my data using readxl.
df1=read_excel("C:/my_data.xlsx",skip=2);
Now I want to write the output and keep the original Excel file using any possible package. I have used the following codes, but it does not keep the original Excel file. Can we do it it in R packages?
write.xlsx(df1, 'C:/mydata.xlsx',skip=2)
Given your code, you should nhave two different data files in your working directory:
"my_data.xlsx" (the one that you loaded), and "mydata.xlsx" (the one that you created through R). R shouldn't overwrite your files if you give them different names.
If there's only one file, are you sure you didn't use the same name for both files? If so, then everything should work fine if you give the files different names (e.g. "my_file1.xlsx" and "my_file2.xlsx")!
Also, in general, it's a good idea to give data files an informative name so that you don't accidentally delete/overwrite files that you need. For example, if the original excel data is you raw data, consider naming it "data_raw.xlsx", and make sure that you only read it, and whenever you make some changes to it, save it under a different name (e.g. "data_processed1.xlsx").
You can also save data files in the native R format .rds using the save_rds() function, this is especially helpful if you want to keep special attributes of variables such as factors, etc...
Hope this helps!
I have a list whose elements are several dataframes, which looks like this
Because it is hard for another user to use these data by re-running my original code. Hence, I would like to export it. As the graph shows, the dataframes in that list have different number of rows. I am wondering if there is any method to export it as file without damaging any information, and make it be able to be used by Rstudio. I have tried to save it as RData, but I don't know how to save the information.
Thanks a lot
To output objects in R, here are 4 common methods:
dput() writes a text representation of an R object
This is very convenient if you want to allow someone to get your object by copying and pasting text (for instance on this site), without having to email or upload and download a file. The downside however is that the output is long and re-reading the object into R (simply by assigning the copied text to an object) can hang R for large objects. This works best to create reproducible examples. For a list of data frames, this would not be a very good option.
You can print an object to a .csv, .xlsx, etc. file with write.table(), write.csv(), readr::write_csv(), xlsx::write.xlsx(), etc.
While the file can then be used by other software (and re-imported into R with read.csv(), readr::read_csv(), readxl::read_excel(), etc.), the data can be transformed in the process and some objects cannot be printed in a single file without prior modifications. So this is not ideal in your case either.
save.image() saves your entire workspace (objects + environment)
The workspace can then be recreated with load(). This can be useful, but you are here only interested in saving one object. In that case, it is preferable to use:
saveRDS() which allows to write one object to file
The object can then be re-created with readRDS(). This is the best option to save an R object to file, without any modification and then re-create it.
In your situation, this is definitely the best solution.
I'm no R-programmer (because of the problem I started learning it), I'm using Python, In a forcasting task I got a dataset signalList.rdata of a pheomenen called partial discharge.
I tried some commands to load, open and view, Hardly got a glimps
my_data <- get(load('C:/Users/Zack-PC/Desktop/Study/Data Sets/pdCluster/signalList.Rdata'))
but, since i lack deep knowledge about R, I wanted to convert it into a csv file, or any type that I can deal with in python.
or, explore it and copy-paste manually.
so, i'm asking for any solution whether using R or Python or any tool to get what's in the .rdata file.
Have you managed to load the data successfully into your working environment?
If so, write.csv is the function you are looking for.
If not,
setwd("C:/Users/Zack-PC/Desktop/Study/Data Sets/pdCluster/")
signalList <- load("signalList.Rdata")
write.csv(signalList, "signalList.csv")
should do the trick.
If you would like to remove signalList from your working directory,
rm(signalList)
will accomplish this.
Note: changing your working directory isn't necessary, it just makes it easier to read in a comment I feel. You may also specify another path for saving your csv to within the second argument of write.csv.
I am using the following R script to write the data. table into the excel file in my set directory. However, the size of the file is in GB's as the total rows are 50 million+. Hence upon opening the file, I just see a blank grey screen and nothing else.
How can I see the contents in the file?
The first line is just for illustration purpose.
Final1 <- rep(iris, times = 1000000)
fwrite(Final1,"data2.csv")
You mention that this is part of the report. I would be willing to bet good money that whoever will be reading this report will not check all or most of the values by hand. In which case, you don't need a format that is easily browsable, e.g. xlsx or even csv. If this indeed is the case, you might want to try a (relational) database. If you do not have anything centralized, you might want to give SQLite a try. You save everything into one file which acts as a database. There are packages that handle this interaction in R. You can try with sqldf or RSQLite.
I apologize in advance if this has a simple answer somewhere. It seems like the kind of thing that would, but I can't seem to locate it in the help files, by searching SO, or by Googling.
I'm working with some datasets that are several GB right now. It's enough to fit in memory on one of the cluster nodes I have access to, but takes quite a bit of time to load. For many debugging/programming activities with this data, I don't need the entire file loaded, just the first few thousand observations to have a dataset on which to test code. I can of course just read the whole file in and subset, but I was wondering if there's a way to tell read.dta() to only read in the first N rows? This would of course be much faster.
I could also use a proper format like .csv and then use read.csv()'s nrows argument, but then I'd lose the factor labels in the Stata dataset (and have to recreate quite a few GB of data from someone else's code that's feeding in to this project. So a direct solution on .dta files is preferred.
Stata's binary files are written row-by-row, so you could change the R_LoadStataData function in stataread.c to limit the number of rows read in. However, this will only work if you do not need the value labels because they are written at the end of the file and would require you to read the entire file--which wouldn't save any time.
That's going to be a difficult one, as the do_readStata function under the hood is compiled code, only capable of taking in the whole file. I believe that in general binary files are hard to read line by line, and .dta is a binary format. Also the native binary format of R doesn't allow to select a number of lines from the dataset while reading in.
In my humble opinion, you can better just create a set of test files from within Stata ( eg the Stata code sample 1000, count will give you a sample of 1000 observations from the loaded dataset), and work with them. And if you have no access to Stata, someone else in the project should be able to do that for you.
To follow up on Joris Meys: For this kind of thing, I use a "test" data set and the "real" data set, each in separate folders. I keep a macro at the top of the .do file (with if/then statements below) to (1) take a sample of the data and (2) point input/output to the right folder containing one or the other. I probably do it different for every project, but something like this:
data creation .do file
blah blah blah
save using data/myfile.dta
save if uniform()<.05 using test_data/myfile.dta // or bsample, then save for panel data
analysis .do file
local test = "test_"
// when you're ready to run the file with all the data, use the following
// local test = ""
use `test'data/myfile.dta
blah blah blah
outreg2 ... using `test'output/mytable.txt