The data.table R package is considered as the fastest package for data manipulation. This tutorial includes various examples and practice questions to make you familiar with the data.table package.

Analysts generally call R programming not compatible with big datasets (> 10 GB) as it is not memory efficient and loads everything into RAM. To change their perception, 'data.table' package comes into play. This package was designed to be concise and painless. There are many benchmarks done in the past to compare dplyr vs data.table. In every benchmark, data.table wins. The efficiency of this package was also compared with python' package (panda). And data.table wins. In CRAN, there are more than 200 packages that are dependent on data.table which makes it listed in the top 5 R's package.

data.table Tutorial |

## data.table Syntax

The syntax of data.table is shown in the image below :

data.table Syntax |

DT[ i , j , by]

- The first parameter of data.table
**i**refers to rows. It implies subsetting rows. It is equivalent to**WHERE**clause in SQL - The second parameter of data.table
**j**refers to columns. It implies subsetting columns (dropping / keeping). It is equivalent to**SELECT**clause in SQL. - The third parameter of data.table
**by**refers to adding a group so that all calculations would be done within a group. Equivalent to SQL's**GROUP BY**clause.

**The data.table syntax is NOT RESTRICTED to only 3 parameters.** There are other arguments that can be added to data.table syntax. The list is as follows -

- allow.cartesian
- roll, rollends
- .SD, .SDcols
- on, mult, nomatch

The above arguments will be explained in the latter part of this tutorial.

install.packages("data.table")

#load required library library(data.table)

In data.table package, **fread() function** is used to read data from your computer or from a web page. It is equivalent to the read.csv() function of base R.

mydata = fread("https://github.com/arunsrinivasan/satrdays-workshop/raw/master/flights_2014.csv")

This dataset contains 253K observations and 17 columns. It constitutes information about flights' arrival or departure time, delays, flight cancellation and destination in year 2014.

nrow(mydata) [1] 253316

ncol(mydata) [1] 17

names(mydata) [1] "year" "month" "day" "dep_time" "dep_delay" "arr_time" "arr_delay" [8] "cancelled" "carrier" "tailnum" "flight" "origin" "dest" "air_time" [15] "distance" "hour" "min"

head(mydata) year month day dep_time dep_delay arr_time arr_delay cancelled carrier tailnum flight 1: 2014 1 1 914 14 1238 13 0 AA N338AA 1 2: 2014 1 1 1157 -3 1523 13 0 AA N335AA 3 3: 2014 1 1 1902 2 2224 9 0 AA N327AA 21 4: 2014 1 1 722 -8 1014 -26 0 AA N3EHAA 29 5: 2014 1 1 1347 2 1706 1 0 AA N319AA 117 6: 2014 1 1 1824 4 2145 0 0 AA N3DEAA 119 origin dest air_time distance hour min 1: JFK LAX 359 2475 9 14 2: JFK LAX 363 2475 11 57 3: JFK LAX 351 2475 19 2 4: LGA PBI 157 1035 7 22 5: JFK LAX 350 2475 13 47 6: EWR LAX 339 2454 18 24

## Convert to Data.Table Format

The function `is.data.table()`

checks whether the object is a data.table. If it is not a data.table, you can convert it into a data.table using the `as.data.table()`

function.

is.data.table(mydata) mydata = as.data.table(mydata)

## Selecting or Keeping Columns

Suppose you need to select only 'origin' column. You can use the code below -

dat1 = mydata[ , origin] # returns a vector

*The above line of code returns a vector not data.table.*To get result in data.table format, run the code below :

dat1 = mydata[ ,It can also be written like.(origin)] # returns a data.table

**data.frame way**

dat1 = mydata[, c("origin")]

dat2 =mydata[, 2]In this code, we are selecting

**second column**from mydata

**.**

The following code tells R to select 'origin', 'year', 'month', 'hour' columns.

dat3 = mydata[, .(origin, year, month, hour)]

You can keep second through fourth columns using the code below -

dat4 = mydata[, c(2:4)]

Suppose you want to include all the variables except one column, say. 'origin'. It can be easily done by adding **! sign** (implies negation in R).

dat5 = mydata[, !c("origin")]

dat6 = mydata[, !c("origin", "year", "month")]

You can use **%like%** operator to find pattern. It is same as **base R's grepl() function**, **SQL's LIKE **operator and **SAS's CONTAINS **function.

dat7 = mydata[,names(mydata)%like%"dep"]

## Rename Variables

You can rename variables with **setnames()** function. In the following code, we are renaming the variable 'dest' to 'destination'.

setnames(mydata, c("dest"), c("Destination"))

To rename multiple variables, you can simply add variables in both the sides.

setnames(mydata, c("dest","origin"), c("Destination", "origin.of.flight"))

## Filtering Data

The following code shows how you can subset rows. Suppose you are asked to find all the flights whose origin is 'JFK'.

# Filter based on one variable

dat8 = mydata[origin == "JFK"]

Filter all the flights whose origin is either 'JFK' or 'LGA'

dat9 = mydata[origin %in% c("JFK", "LGA")]

The following program selects all the flights whose origin is not equal to 'JFK' and 'LGA'

# Exclude Values

dat10 = mydata[!origin %in% c("JFK", "LGA")]

If you need to select all the flights whose origin is equal to 'JFK' and carrier = 'AA'

dat11 = mydata[origin == "JFK" & carrier == "AA"]

## Faster Data Manipulation with Indexing

data.table uses **binary search algorithm** that makes data manipulation faster.

Binary search is an efficient algorithm for finding a value from aSuppose you have the following values in a variable :sortedlist of values. It involves repeatedly splitting in half the portion of the list that contains values, until you found the value that you were searching for.

5, 10, 7, 20, 3, 13, 26You are searching the value

**20**in the above list. See how binary search algorithm works -

- First, we sort the values
- We would calculate the middle value i.e. 10.
- We would check whether 20 = 10? No. 20 < 10.
- Since 20 is greater than 10, it should be somewhere after 10. So we can ignore all the values that are lower than or equal to 10.
- We are left with 13, 20, 26. The middle value is 20.
- We would again check whether 20=20. Yes. the match found.

If we do not use this algorithm, we would have to search 5 in the whole list of seven values.

It is important to set

**key**in your dataset which tells system that data is sorted by the key column. For example, you have employee’s name, address, salary, designation, department, employee ID. We can use 'employee ID' as a key to search a particular employee.

In this case, we are setting

**'origin'**as a key in the dataset

**mydata**.

# Indexing (Set Keys)

setkey(mydata, origin)

**Note :**It makes the data table

**sorted**by the column 'origin'.

You don't need to refer the key column when you apply filter.

data12 = mydata[c("JFK", "LGA")]

You can compare performance of the filtering process **(With or Without KEY).**

system.time(mydata[origin %in% c("JFK", "LGA")])

system.time(mydata[c("JFK", "LGA")])

Performance - With or without KEY |

We can also set keys to multiple columns like we did below to columns 'origin' and 'dest'. See the example below.

setkey(mydata, origin, dest)

# First key column 'origin' matches "JFK" and second key column 'dest' matches "MIA" mydata[.("JFK", "MIA")]

**It is equivalent to the following code :**mydata[origin == "JFK" & dest == "MIA"]

key(mydata)

**Result :**It returns origin and dest as these are columns that are set keys.

## Sorting Data

We can sort data using

**setorder()**function, By default, it sorts data on ascending order.

mydata01 = setorder(mydata, origin)

In this case, we are sorting data by 'origin' variable on descending order.

mydata02 = setorder(mydata,-origin)

In this example, we tells R to reorder data first by origin on ascending order and then variable 'carrier'on descending order.

mydata03 = setorder(mydata, origin, -carrier)

## Adding Columns

You can do any operation on rows by adding **:= operator**. In this example, we are subtracting 'dep_delay' variable from 'dep_time' variable to compute scheduled departure time.

mydata[, dep_sch:=dep_time - dep_delay]

mydata[, c("dep_sch","arr_sch"):=list(dep_time - dep_delay, arr_time - arr_delay)]

If you don't want to make changes (addition of columns) in the original data, you can make a copy of it.

mydata_C <- copy(mydata) mydata_C[, c("dep_sch","arr_sch"):=list(dep_time - dep_delay, arr_time - arr_delay)]

## IF THEN ELSE

The 'IF THEN ELSE' conditions are very popular for recoding values. In data.table package, it can be done with the following methods :

The following code sets flag= 1 if min is less than 50. Otherwise, set flag =0.

Method 1 : mydata[, flag:= ifelse(min < 50, 1,0)]

Method 2 : mydata[, flag:= 1*(min < 50)]

## How to write Sub Queries (like SQL)

We can use this format - **DT[ ] [ ] [ ] **to build a chain in data.table. It is like sub-queries like SQL.

mydata[, dep_sch:=dep_time - dep_delay][,.(dep_time,dep_delay,dep_sch)]First, we are computing scheduled departure time and then selecting only relevant columns.

## Summarize or Aggregate Columns

It's easy to summarize data with data.table package. We can generate summary statistics of specific variables. In this case, we are calculating mean, median, minimum and maximum value of variable arr_delay.

mydata[, .(mean = mean(arr_delay, na.rm = TRUE),

median = median(arr_delay, na.rm = TRUE),

min = min(arr_delay, na.rm = TRUE),

max = max(arr_delay, na.rm = TRUE))]

Summarize with data.table package |

To summarize multiple variables, we can simply write all the summary statistics function in a bracket. See the command below-

mydata[, .(mean(arr_delay), mean(dep_delay))]If you need to calculate summary statistics for a larger list of variables, you can use

**.SD and .SDcols**operators. The

**.SD**operator implies

**'Subset of Data'.**

mydata[, lapply(.SD, mean), .SDcols = c("arr_delay", "dep_delay")]In this case, we are calculating mean of two variables - arr_delay and dep_delay.

By default,

**.SD**takes all continuous variables (excluding grouping variables)

mydata[, lapply(.SD, mean)]

mydata[, sapply(.SD, function(x) c(mean=mean(x), median=median(x)))]

## Summarize by Group

The following code calculates the mean arrival delay calculated for each unique value in the "origin" column.

mydata[, .(mean_arr_delay = mean(arr_delay, na.rm = TRUE)), by = origin]

Summary by group |

Instead of 'by', you can use **keyby= **operator.

mydata[, .(mean_arr_delay = mean(arr_delay, na.rm = TRUE)),keyby= origin]

mydata[, .(mean(arr_delay, na.rm = TRUE), mean(dep_delay, na.rm = TRUE)), by = origin]Or it can be written like below -

mydata[, lapply(.SD, mean, na.rm = TRUE), .SDcols = c("arr_delay", "dep_delay"), by = origin]

## Remove Duplicates

You can remove duplicate values with **unique()** function. Suppose you want to delete duplicates based on a variable, say. carrier.

setkey(mydata, "carrier")

unique(mydata)

Suppose you want to remove duplicated based on all the variables. You can use the command below -

setkey(mydata, NULL)

unique(mydata)

**Note :**Setting key to NULL is not required if no key is already set.

The following command selects first and second values from a categorical variable carrier.

mydata[, .SD[1:2], by=carrier]

The following code is used to extract the last row within each group defined by the carrier column in the mydata data.table.

mydata[, .SD[.N], by=carrier]

## Ranking within Groups

In SQL, Window functions are very useful for solving complex data problems. RANK OVER PARTITION is the most popular window function. It assigns a unique rank within each partition defined by the specified column, ordered by another column. It can be easily translated in data.table with the help of **frank() **function. frank() is similar to base R's rank() function but much faster.

In this case, we are calculating rank of variable 'distance' by 'carrier'. We are assigning rank 1 to the highest value of 'distance' within unique values of 'carrier'.

dt = mydata[, rank:=frank(-distance,ties.method = "min"), by=carrier]

## Cumulative SUM by GROUP

We can calculate cumulative sum by using **cumsum()** function.

dat = mydata[, cum:=cumsum(distance), by=carrier]

## Lag and Lead

The lag and lead of a variable can be calculated with shift() function. The syntax of shift() function is as follows -

**shift(variable_name, number_of_lags, type=c("lag", "lead"))**

DT <- data.table(A=1:5)

DT[ , X :=shift(A, 1, type="lag")]

DT[ , Y :=shift(A, 1, type="lead")]

Lag and Lead Function |

## Between and LIKE Operator

We can use %between% operator to define a range. It is inclusive of the values of both the ends.

DT = data.table(x=6:10)The %like% is mainly used to find all the values that matches a pattern.

DT[x%between%c(7,9)]

DT = data.table(Name=c("dep_time","dep_delay","arrival"), ID=c(2,3,4))

DT[Name%like%"dep"]

## Joins

The merging in data.table is very similar to base R merge() function. The only difference is data.table by default takes common key variable as a primary key to merge two datasets. Whereas, data.frame takes common variable name as a primary key to merge the datasets.

(dt1 <- data.table(A = letters[rep(1:3, 2)], X = 1:6, key = "A"))

(dt2 <- data.table(A = letters[rep(2:4, 2)], Y = 6:1, key = "A"))

It returns all the matching observations in both the datasets.

merge(dt1, dt2, by="A")

It returns all observations from the left dataset and the matched observations from the right dataset.

merge(dt1, dt2, by="A", all.x = TRUE)

It returns all observations from the right dataset and the matched observations from the left dataset.

merge(dt1, dt2, by="A", all.y = TRUE)

It return all rows when there is a match in one of the datasets.

merge(dt1, dt2, all=TRUE)

## Convert a data.table to data.frame

You can use **setDF()** function to accomplish this task.

setDF(mydata)

Similarly, you can use **setDT() **function to convert data frame to data table.

set.seed(123) X = data.frame(A=sample(3, 10, TRUE), B=sample(letters[1:3], 10, TRUE)) setDT(X, key = "A")

The data.table package includes several useful functions which makes data cleaning easy and smooth. To reshape or transpose data, you can use **dcast.data.table() **and **melt.data.table()** functions. These functions are sourced from reshape2 package and make them efficient. It also add some new features in these functions.

It supports rolling joins. They are commonly used for analyzing time series data. A very R packages supports these kind of joins.

Here are a few questions you can use to practice using the data.table package in R :

Q1. Calculate total number of rows by month and then sort on descending order.

mydata[, .N, by = month] [order(-N)]

The **.N operator** is used to find count.

Q2. Find top 3 months with high mean arrival delay.

mydata[, .(mean_arr_delay = mean(arr_delay, na.rm = TRUE)), by = month][order(-mean_arr_delay)][1:3]

Q3. Find origin of flights having average total delay is greater than 20 minutes.

mydata[, lapply(.SD, mean, na.rm = TRUE), .SDcols = c("arr_delay", "dep_delay"), by = origin][(arr_delay + dep_delay) > 20]

Q4. Extract average of arrival and departure delays for carrier == 'DL' by 'origin' and 'dest' variables.

mydata[carrier == "DL", lapply(.SD, mean, na.rm = TRUE), by = .(origin, dest), .SDcols = c("arr_delay", "dep_delay")]

Q5. Pull first value of 'air_time' by 'origin' and then sum the returned values when it is greater than 300

mydata[, .SD[1], .SDcols="air_time", by=origin][air_time > 300, sum(air_time)]

This package provides a one-stop solution for data wrangling in R. It offers two main benefits - less coding and lower computing time. However, it's not a first choice of some of R programmers. Some prefer **dplyr** package for its simplicity. I would recommend learn both the packages. Check out **dplyr tutorial**. If you are working on data having size less than 1 GB, you can use dplyr package. It offers decent speed but slower than data.table package.

extraordinary explanation. Thanks a lot to the author for making the subject so clear.

ReplyDeletenice

ReplyDeletevery nice, thx

ReplyDeleteNice Tutorial

ReplyDeleteOne of the best website to learn R

ReplyDeletethank you...helped tons

ReplyDeleteGood One, helped me during my data analysis work!

ReplyDeleteVery nice... thank u so much... Request to post more other R usefull packages

ReplyDeleteThank you!

ReplyDeleteNice intro to data.table!

ReplyDeleteI did not see any example of filtering by grouped stats. Like below, to keep only customers who made at least 10 orders:

DT3 <- DT2[, if(max(order_no) >=10) .SD, by = customer_number]

Will do what dplyr does with.

DT2 %>% group_by(customer_number) %>% filter(max(order_no) >= 10) %>% ungroup

Thanks. Will add this example!

DeleteDeepanshu Sir,

ReplyDeleteThis is an excellent R tutorial I have ever seen.

Previously I went to one of Data Science coaching centre in hyderabad and paid Rs.20K but, couldn't get this type of knowledge. I am happy to say this is helping so many people to learn R step by step easily. Thanks a lot.

Sir I have one dought.

ReplyDeleteWhy the newly added columns "Dep_Sch" in md22 and "dep_sch", "arv_sch" in md23 are being included in the master dataset "md" as given below.

# Adding Multiple Columns

> md23= md[ , c("dep_sch", "arv_sch") := list(dep_time - dep_delay, arr_time - arr_delay)]

> names(md23)

[1] "year" "month" "day" "dep_time" "dep_delay" "arr_time" "arr_delay"

[8] "cancelled" "carrier" "tailnum" "flight" "origin" "dest" "air_time"

[15] "distance" "hour" "min" "Dep_Sch" "dep_sch" "arv_sch"

> names(md)

[1] "year" "month" "day" "dep_time" "dep_delay" "arr_time" "arr_delay"

[8] "cancelled" "carrier" "tailnum" "flight" "origin" "dest" "air_time"

[15] "distance" "hour" "min" "Dep_Sch" "dep_sch" "arv_sch"

data.table was designed for big tables so it always try to save memory. If you don't want to make changes in the original data, make a copy of it like mydata_C <- copy(mydata).

DeleteOK Sir,

ReplyDeleteThanks for clarification.

One of the best tutorial that i have seen !

ReplyDeleteIs there a way to find max value or last day value in a column(for a subset of values) using shift function

ReplyDelete