> For the complete documentation index, see [llms.txt](https://ondrej-kvasnovsky-2.gitbook.io/handbook-of-hidden-data-scientist/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ondrej-kvasnovsky-2.gitbook.io/handbook-of-hidden-data-scientist/r/data_table.md).

# Data Table

Facts about `data.table`.

* Extends from `data.frame` and therefore should provide the same API.
* Is written in C and is really fast.
* Much faster at subsetting, grouping and updating.

## Hello world

```r
install.packages("data.table")
library(data.table)

years = c(2012, 2013)
average = c(250, 275)
table.values <- data.table(year = years, averageBeerConsumption = average)
```

See all `data.table` tables created in memory.

```r
tables()
```

## Subsetting rows.

Access row on specific index.

```r
table.values[2]
table.values[c(1,2)]
```

Access rows that fulfil a condition.

```r
table.values[table.values$year==2012]
```

## Calculate values from columns

```r
table.values[, sum(averageBeerConsumption)]

table.values[, list(mean(year), sum(averageBeerConsumption))]
```

## Return table of values for a column

```r
table.values[, table(year)]
```

## Add new column

```r
table.values[, volume:=averageBeerConsumption*0.5]
```

## Multiple operations.

```r
table.values[,
    x:={temp <- averageBeerConsumption*year;
log2(temp)
}]
```

## Plyr like operations

```r
table.values[, y:= year<2013]
```

## Grouping by

```r
table.values[, sum:= sum(averageBeerConsumption), by= year]
```

## Count number of occurrences

```r
table.values[, .N, by=year]
```

## Keys

Making table faster by setting the keys

```r
setkey(table.values, year)
```

Then we can join tables by keys.

```r
setkey(table1.values, year)
setkey(table2.values, year)

merge(table1.values, table2.values)
```

## Fast reading

First we create a file that we can use to test speed of reading.

```r
big.file <- data.frame(x=rnorm(1E6), y=rnorm(1E6))

file <- tempfile()

write.table(big.file, file=file, row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE)
```

Slow approach using `read.table` function.

```r
system.time(read.table(file, header=TRUE, sep="\t"))
```

Faster approach using `fread` function.

```r
system.time(fread(file))
```


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