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Reading large data files in R
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big files.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reading big files in R\n",
"\n",
"## tl;dr\n",
"\n",
"Jump to our [takeaway messages](#Takeaway-messages) right away.\n",
"\n",
"## Intro\n",
"\n",
"R is known to have difficulties with large files. Here we will explore some best practices that make working with large data files less painfull.\n",
"\n",
"First we will need to load some packages."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Attaching package: ‘pryr’\n",
"\n",
"The following object is masked from ‘package:data.table’:\n",
"\n",
" address\n",
"\n",
"\n",
"Attaching package: ‘reshape’\n",
"\n",
"The following object is masked from ‘package:data.table’:\n",
"\n",
" melt\n",
"\n",
"Loading required package: bit\n",
"Attaching package bit\n",
"package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)\n",
"creators: bit bitwhich\n",
"coercion: as.logical as.integer as.bit as.bitwhich which\n",
"operator: ! & | xor != ==\n",
"querying: print length any all min max range sum summary\n",
"bit access: length<- [ [<- [[ [[<-\n",
"for more help type ?bit\n",
"\n",
"Attaching package: ‘bit’\n",
"\n",
"The following object is masked from ‘package:data.table’:\n",
"\n",
" setattr\n",
"\n",
"The following object is masked from ‘package:base’:\n",
"\n",
" xor\n",
"\n",
"Attaching package ff\n",
"- getOption(\"fftempdir\")==\"/var/folders/pn/v14jsq692h32nq5jrbyk5k0rtqc4k7/T//RtmpOSmLFk\"\n",
"\n",
"- getOption(\"ffextension\")==\"ff\"\n",
"\n",
"- getOption(\"ffdrop\")==TRUE\n",
"\n",
"- getOption(\"fffinonexit\")==TRUE\n",
"\n",
"- getOption(\"ffpagesize\")==65536\n",
"\n",
"- getOption(\"ffcaching\")==\"mmnoflush\" -- consider \"ffeachflush\" if your system stalls on large writes\n",
"\n",
"- getOption(\"ffbatchbytes\")==16777216 -- consider a different value for tuning your system\n",
"\n",
"- getOption(\"ffmaxbytes\")==536870912 -- consider a different value for tuning your system\n",
"\n",
"\n",
"Attaching package: ‘ff’\n",
"\n",
"The following objects are masked from ‘package:bit’:\n",
"\n",
" clone, clone.default, clone.list\n",
"\n",
"The following objects are masked from ‘package:utils’:\n",
"\n",
" write.csv, write.csv2\n",
"\n",
"The following objects are masked from ‘package:base’:\n",
"\n",
" is.factor, is.ordered\n",
"\n",
"Loading required package: gsubfn\n",
"Loading required package: proto\n",
"Loading required package: RSQLite\n",
"Loading required package: DBI\n"
]
}
],
"source": [
"library(\"data.table\")\n",
"library(\"ggplot2\")\n",
"library(\"pryr\")\n",
"library(\"reshape\")\n",
"library(\"ff\")\n",
"library(\"sqldf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use fread\n",
"\n",
"If you really need to read an entire csv in memory, by default, R users use the `read.table` method or variations thereof (such as `read.csv`). However, `fread` from the `data.table` package is supposed to be a lot faster. Let's measure the time to read in the data using these two different methods."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>method</th><th scope=col>timing</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>read.table</td><td>125.648</td></tr>\n",
"\t<tr><th scope=row>2</th><td>fread</td><td>4.215</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" & method & timing\\\\\n",
"\\hline\n",
"\t1 & read.table & 125.648\\\\\n",
"\t2 & fread & 4.215\\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
" method timing\n",
"1 read.table 125.648\n",
"2 fread 4.215"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datafile = \"bird_tracking.csv\"\n",
"read.table.timing = system.time(read.table(datafile, header=TRUE, sep=\",\"))\n",
"data.table.timing = system.time(allData <- fread(datafile, showProgress=FALSE))\n",
"data = data.frame(method=c('read.table', 'fread'), timing=c(read.table.timing[3], data.table.timing[3]))\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`fread` is indeed much faster then the default `read.table`. However, the result of `fread` is a `data.table`, not a `data.frame`. The `data.table` package describes the `data.table` object as a more performant replacement for the `data.frame`. This means that selecting, filterin and aggregating data is much faster on a `data.table` compared to the standard `data.frame` but it requires you to use a slightly different syntax.\n",
"\n",
"You can find the `data.table` package [on CRAN](https://cran.r-project.org/web/packages/data.table/index.html). A good place to learn this package are the package vignettes. The [introduction to data.table](https://cran.r-project.org/web/packages/data.table/vignettes/datatable-intro-vignette.html) should be enough to get started.\n",
"\n",
"## Data that doesn't fit in memory\n",
"\n",
"If you are not able to read in the data file, because it does not fit in memory (or because R becomes slow when you load the entire dataset), you will need to limit the amount of data that will actually be stored in memory. There are a couple of options which we will investigate:\n",
"\n",
"1. limit the number of lines you're trying to read to do some exploratory analysis. Once you're happy with the analysis you want to run on the entire dataset, move to another machine.\n",
"1. limit the number of columns you’re reading to reduce the memory required to store the data.\n",
"1. stream the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Limit the number of lines you read\n",
"\n",
"Limiting the number of lines you read is easy. Just use the `nrows` and/or the `skip` option (available to both `read.table` and `fread`). `skip` can be used to skip a number of rows, but you can also pass a string to this parameter causing `fread` to only start reading lines from the first line matching that string. Let's say we only want to start reading lines after we find a line matching the pattern `801,2014-06-29`. We can do that like this:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"'Number of lines in full data set: 1326969'"
],
"text/latex": [
"'Number of lines in full data set: 1326969'"
],
"text/markdown": [
"'Number of lines in full data set: 1326969'"
],
"text/plain": [
"[1] \"Number of lines in full data set: 1326969\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"'Number of lines in data set with skipped lines: 1313177'"
],
"text/latex": [
"'Number of lines in data set with skipped lines: 1313177'"
],
"text/markdown": [
"'Number of lines in data set with skipped lines: 1313177'"
],
"text/plain": [
"[1] \"Number of lines in data set with skipped lines: 1313177\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sprintf(\"Number of lines in full data set: %s\", nrow(allData))\n",
"subSet = fread(datafile, skip=\"801,2014-06-29\", showProgress=FALSE)\n",
"sprintf(\"Number of lines in data set with skipped lines: %s\", nrow(subSet))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Skipping rows this way is obviously not giving you the entire dataset. This strategy is actually only useful for doing some exploratory analysis on a subset of your data, and look for other ways to upscale this analysis to the full dataset when you're defined your analysis.\n",
"\n",
"However, you could also skip rows more intelligently. Say you are only interested in records from a certain species or date. For that, look at the [sqldf example](#Another-alternative:-the-sqldf-package)\n",
"\n",
"### 2. Limit the number of columns you read\n",
"\n",
"Limiting the number of columns you read can be a more useful strategy. If you only need 4 columns of the 21 columns present in the file, you can tell `fread` to only select those 4. This can have a major impact on the memory footprint of your data. The option you need for this is: `select`. With this, you can specify a number of columns to keep. The opposite - specifying the columns you want to drop - can be accomplished with the `drop` option."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"'Size of total data in memory: 426.434016 MB'"
],
"text/latex": [
"'Size of total data in memory: 426.434016 MB'"
],
"text/markdown": [
"'Size of total data in memory: 426.434016 MB'"
],
"text/plain": [
"[1] \"Size of total data in memory: 426.434016 MB\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"'Size of only four columns in memory: 129.216776 MB'"
],
"text/latex": [
"'Size of only four columns in memory: 129.216776 MB'"
],
"text/markdown": [
"'Size of only four columns in memory: 129.216776 MB'"
],
"text/plain": [
"[1] \"Size of only four columns in memory: 129.216776 MB\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fourColumns = fread(datafile,\n",
" select=c(\"device_info_serial\", \"date_time\", \"latitude\", \"longitude\"),\n",
" showProgress=FALSE)\n",
"sprintf(\"Size of total data in memory: %s MB\", object_size(allData)/1000000)\n",
"sprintf(\"Size of only four columns in memory: %s MB\", object_size(fourColumns)/1000000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The difference might not be as large as you would expect. R objects claim more memory then needed just to store the data alone. (they keep pointers, and other object attributes) But still, the difference could save you.\n",
"\n",
"### 3. Stream the file\n",
"\n",
"Bottom line: streaming a file in R is a bad idea. If you are interested why, read [this section](#Why-streaming-is-a-bad-idea)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Limiting both the number of rows and the number of columns using `sqldf`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This package allows you to run SQL like queries on a file, resulting in only a selection of the file being read. It allows you to limit both the number of lines and the number of rows at the same time. Let's try to select rows where the device id matches a given value, and the date time is between two given timestamps. For our analysis, we only need `date_time`, `latitude`, `longitude` and `altitude` so we only select those."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: tcltk\n"
]
},
{
"data": {
"text/html": [
"<strong>elapsed:</strong> 32.077"
],
"text/latex": [
"\\textbf{elapsed:} 32.077"
],
"text/markdown": [
"**elapsed:** 32.077"
],
"text/plain": [
"elapsed \n",
" 32.077 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minDate <- strptime(\"2014-03-01\", format=\"%Y-%m-%d\")\n",
"maxDate <- strptime(\"2014-07-01\", format=\"%Y-%m-%d\")\n",
"\n",
"sqlTiming <- system.time(\n",
" selData <- read.csv.sql(file=datafile, sep=\",\",\n",
" sql=\"select date_time,latitude,longitude,altitude from file where device_info_serial = 860 and\n",
" date_time < '2014-07-01' and date_time > '2014-03-01'\"\n",
" )\n",
")\n",
"sqlTiming[3]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"'Size of total data in memory: 2.88812 MB'"
],
"text/latex": [
"'Size of total data in memory: 2.88812 MB'"
],
"text/markdown": [
"'Size of total data in memory: 2.88812 MB'"
],
"text/plain": [
"[1] \"Size of total data in memory: 2.88812 MB\""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sprintf(\"Size of total data in memory: %s MB\", object_size(selData)/1000000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading data this way is not very fast (32 seconds compared to 4 seconds using `fread`) but the amount of data in memory is much lower (2.8 MB while the entire dataset was 426 MB). This makes working with this dataset easier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How about dplyr?\n",
"\n",
"`dplyr` is a commonly used R package that provides a more convenient syntax to filtering, selecting and aggregating data. However, dplyr works on a `data.frame` (or a `data.table`) so you still need to read the entire file in memory first. There is an alternative however. See the section [Interacting with SQLite databases using dplyr](#Interacting-with-SQLite-databases-using-dplyr)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with SQLite databases\n",
"\n",
"SQLite databases are single file databases meaning you can simply download them, store them in a folder or share them with colleages. Similar to a csv. They are however more powerful than csv's because of two important features:\n",
"\n",
"* Support for SQL: this allows you to execute intelligent filters on your data, similar to the `sqldf` package. That way, you can reduce the amount of data that's stored in memory by filtering out rows or columns.\n",
"* Indexes: SQLite databases contain indexes. An index is something like an ordered version of a column. When enabled on a column, you can search through the column very fast. We'll demonstrate this below.\n",
"\n",
"Here we've created a second file `bird_tracking.db` that contains the same data as the `bird_tracking.csv` file, but as a sqlite database. Furthermore, the database contains indexes which will dramatically drop the time needed to perform search queries. If you don't have a SQLite database containing your data, try creating one as described [here](https://cran.r-project.org/web/packages/dplyr/vignettes/databases.html).\n",
"\n",
"Remeber we used `sqldf` to limit the amount of data stored in memory. It was not amazingly fast:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<strong>elapsed:</strong> 29.7970000000005"
],
"text/latex": [
"\\textbf{elapsed:} 29.7970000000005"
],
"text/markdown": [
"**elapsed:** 29.7970000000005"
],
"text/plain": [
"elapsed \n",
" 29.797 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqlTiming <- system.time(\n",
" selData <- read.csv.sql(file=datafile, sep=\",\",\n",
" sql=\"select date_time,latitude,longitude,altitude from file where device_info_serial = 860 and\n",
" date_time < '2014-07-01' and date_time > '2014-03-01'\"\n",
" )\n",
")\n",
"sqlTiming[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is not extremely fast, because R needs to read every line of the csv file. With a SQLite database containing indexes, that is not needed.\n",
"\n",
"Let's first connect to the database and list the available tables."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"'bird_tracking'"
],
"text/latex": [
"'bird_tracking'"
],
"text/markdown": [
"'bird_tracking'"
],
"text/plain": [
"[1] \"bird_tracking\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db <- dbConnect(SQLite(), dbname=\"bird_tracking.db\")\n",
"\n",
"# show the tables in this database\n",
"dbListTables(db)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's perform the same query and return the result."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"In sqliteSendQuery(conn, statement): Closing result set with pending rows"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"elapsed \n",
" 0.004 \n",
"elapsed \n",
" 0.001 \n"
]
}
],
"source": [
"sqlTiming1 <- system.time(res <- dbSendQuery(conn = db,\n",
" \"select date_time,latitude,longitude,altitude\n",
" from bird_tracking where device_info_serial = 860 and date_time < '2014-07-01' and date_time > '2014-03-01'\"\n",
")) # This does not execute the query yet\n",
"sqlTiming2 <- system.time(\n",
" data <- dbFetch(res)\n",
")\n",
"print(sqlTiming1[3])\n",
"print(sqlTiming2[3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's a lot faster than performing this query on the csv file. The reason is that SQLite uses the indexes available to quickly select the rows you're looking for.\n",
"\n",
"# Interacting with SQLite databases using dplyr\n",
"\n",
"If you're not comfortable with writing queries in SQL, R has a great alternative: `dplyr`. Dplyr can connect to a SQLite database and you can perform the same operations on it that you would do on a dataframe. However, dplyr will translate your commands to SQL allowing you to take advantage of the indexes in the SQLite database."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<strong>elapsed:</strong> 0.0190000000000055"
],
"text/latex": [
"\\textbf{elapsed:} 0.0190000000000055"
],
"text/markdown": [
"**elapsed:** 0.0190000000000055"
],
"text/plain": [
"elapsed \n",
" 0.019 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"library(dplyr)\n",
"my_db <- src_sqlite(\"bird_tracking.db\", create = F)\n",
"bird_tracking <- tbl(my_db, \"bird_tracking\")\n",
"sqlTiming3 <- system.time(\n",
" results <- bird_tracking %>%\n",
" filter(device_info_serial==860) %>%\n",
" select(date_time, latitude, longitude, altitude)\n",
")\n",
"sqlTiming3[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That is slightly slower than sending SQL queries directly to the SQLite database as demonstrated before using `sqldf`. This is probably due to the fact that `dplyr` needs to make a translation to SQL first. However, it is still way faster than performing this query on a csv file so it demonstrates the advantage of a SQLite database without the need to know SQL.\n",
"\n",
"If you want to learn more about how to use `dplyr` with a SQLite database, head over to the [vignette](https://cran.r-project.org/web/packages/dplyr/vignettes/databases.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Takeaway messages\n",
"\n",
"* If you can comfortably work with the entire dataset in memory, but reading the file is rather slow, consider using the `data.table` package and read the file with its `fread` function.\n",
"* If you need to limit the amount of data stored in memory, we would suggest two options:\n",
" * If you're stuck to a csv, [use `sqldf`](#Limiting-both-the-number-of-rows-and-the-number-of-columns-using-sqldf)\n",
" * If you can, use a SQLite database and query it using either [SQL queries](#Working-with-SQLite-databases) or [`dplyr`](#Interacting-with-SQLite-databases-using-dplyr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Other options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `ff` for large files and fast access\n",
"\n",
"You see that the way R handles streams results in an exponentially growing execution time. There is an alternative however, and that is R's [`ff` package](http://ff.r-forge.r-project.org/). The ff package lets you work with large data objects without the need to load these objects entirely in memory. Part of the object stays on disk, but `ff` provides fast access to the data in those parts."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<strong>elapsed:</strong> 208.923"
],
"text/latex": [
"\\textbf{elapsed:} 208.923"
],
"text/markdown": [
"**elapsed:** 208.923"
],
"text/plain": [
"elapsed \n",
"208.923 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ffTiming = system.time(ffObj <- read.table.ffdf(file=datafile, sep=\",\"))\n",
"ffTiming[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`read.table.ffdf` is not amazingly fast, but it should be able to read files that `fread` or `read.table` can't handle."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Bigmemory`. Geeks only\n",
"\n",
"`Bigmemory` is another package for working with large data files. However, there are no binaries of this package available for Windows. I'd say, this is a geeks only package."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Why streaming is a bad idea\n",
"\n",
"Streaming a file means reading it line by line and only keep the lines you need or do stuff with the lines while you read through the file. It turns out that R is really not very efficient in streaming a file. The main reason is the memory allocation process that has difficulties with a constantly growing object (which can be a dataframe containing only the selected lines).\n",
"\n",
"In the next code block, we will read parts of our data file once using the `fread` function, and once line by line. You'll appreciate the performance issue with the streaming solution."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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],
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"allowedDevices = c(753, 801, 852)\n",
"minDate = strptime('1/3/2014',format='%d/%m/%Y')\n",
"maxDate = strptime('1/10/2014',format='%d/%m/%Y')\n",
"\n",
"streamFile <- function(limit) {\n",
" con = file(datafile, open = \"r\")\n",
" selectedRecords = list()\n",
" i<- 0\n",
" file.streaming.timing = system.time(\n",
" while (i < limit) {\n",
" oneLine <- readLines(con, n=1, warn=FALSE)\n",
" vec = (strsplit(oneLine, \",\"))\n",
" selectedRecords <- c(selectedRecords, vec)\n",
" i <- i+1\n",
" }\n",
" )\n",
" close(con)\n",
" return(file.streaming.timing[[3]])\n",
"}\n",
"\n",
"freadFile <- function(limit) {\n",
" file.fread.timing = system.time(\n",
" d <- fread(datafile, showProgress=FALSE, nrows=limit)\n",
" )\n",
" return (file.fread.timing[[3]])\n",
"}\n",
"\n",
"maxLines <- c(5000, 10000, 15000, 20000, 25000, 30000)\n",
"streamingTimes <- sapply(maxLines, streamFile)\n",
"freadTimes <- sapply(maxLines, freadFile)\n",
"data = data.frame(n=maxLines, streaming=streamingTimes, fread=freadTimes)\n",
"pdata = melt(data, id=c(\"n\"))\n",
"colnames(pdata) = c(\"n\", \"algorithm\", \"execTime\")\n",
"qplot(n, execTime, data=pdata, color=algorithm, xlab=\"number of lines read\", ylab=\"execution time (s)\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
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"codemirror_mode": "r",
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"name": "R",
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"version": "3.2.0"
}
},
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"nbformat_minor": 0
}
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