This tutorial describes how to manipulate data with data.table R package. It is considered as the fastest R package for data wrangling. 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. This post includes various examples and practice questions to make you familiar with the package.

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

In data.table package,

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.

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

To get result in data.table format, run the code below :

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

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

Suppose you want to include all the variables except one column, say. 'origin'. It can be easily done by adding

You can use

You can rename variables with

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

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

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

data.table uses

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

In this case, we are setting

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

You can compare performance of the filtering process

If you look at the real time in the image above, setting key makes filtering twice as faster than without using keys.

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

We can sort data using

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

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

You can do any operation on rows by adding

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

We can use this format -

Like SAS PROC MEANS procedure, we can generate summary statistics of specific variables. In this case, we are calculating mean, median, minimum and maximum value of variable arr_delay.

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

By default,

Instead of 'by', you can use

You can remove non-unique / duplicate cases with unique() function. Suppose you want to eliminate duplicates based on a variable, say. carrier.

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

In SQL, Window functions are very useful for solving complex data problems. RANK OVER PARTITION is the most popular window function. It can be easily translated in data.table with the help of

We can calculate cumulative sum by using

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

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

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.

It returns all the matching observations in both the datasets.

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

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

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

You can use

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

Q2. Find top 3 months with high mean arrival delay

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

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

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

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

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 -

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

The above arguments would be explained in the latter part of the post.

**How to Install and load data.table Package**install.packages("data.table")

#load required library

library(data.table)

**Read Data**In data.table package,

**fread() function**is available to read or get data from your computer or from a web page. It is equivalent to read.csv() function of base R.mydata = fread("https://github.com/arunsrinivasan/satrdays-workshop/raw/master/flights_2014.csv")

**Describe Data**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

**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"), with=FALSE]

**Keeping a column based on column position**dat2 =mydata[, 2, with=FALSE]In this code, we are selecting

**second column**from mydata**.****Keeping Multiple Columns**The following code tells R to select 'origin', 'year', 'month', 'hour' columns.

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

**Keeping multiple columns based on column position**You can keep second through fourth columns using the code below -

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

**Dropping a Column**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"), with=FALSE]

**Dropping Multiple Columns**dat6 = mydata[, !c("origin", "year", "month"), with=FALSE]

**Keeping variables that contain 'dep'**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", with=FALSE]

**Rename Variables**You can rename variables with

**setnames()**function. In the following code, we are renaming a 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"))

**Subsetting Rows / Filtering**

**Suppose you are asked to find all the flights whose origin is 'JFK'.**# Filter based on one variable

dat8 = mydata[origin == "JFK"]

**Select Multiple Values**Filter all the flights whose origin is either 'JFK' or 'LGA'

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

**Apply Logical Operator : NOT**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")]

**Filter based on Multiple variables**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 Algorithm**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.

**Set Key**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'.**How to filter when key is turned on.**You don't need to refer the key column when you apply filter.

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

**Performance Comparison**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 |

**Indexing Multiple Columns**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)

**Filtering while setting keys on Multiple Columns**# 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"]

**To identify the column(s) indexed by**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)

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

mydata02 = setorder(mydata,-origin)

**Sorting Data based on multiple variables**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 (Calculation on rows)**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]

**Adding Multiple Columns**mydata002 = mydata[, 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 :

**Method I :**mydata[, flag:= 1*(min < 50)]**Method II :**mydata[, flag:= ifelse(min < 50, 1,0)]
It means to set flag= 1 if min is less than 50. Otherwise, set flag =0.

**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**Like SAS PROC MEANS procedure, 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 |

**Summarize Multiple Columns**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.

**Summarize all numeric Columns**By default,

**.SD**takes all continuous variables (excluding grouping variables)mydata[, lapply(.SD, mean)]

**Summarize with multiple statistics**mydata[, sapply(.SD, function(x) c(mean=mean(x), median=median(x)))]

**GROUP BY (Within Group Calculation)****Summarize by group 'origin**mydata[, .(mean_arr_delay = mean(arr_delay, na.rm = TRUE)), by = origin]

Summary by group |

**Use key column in a by operation**Instead of 'by', you can use

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

**Summarize multiple variables by group '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 non-unique / duplicate cases with unique() function. Suppose you want to eliminate 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.

**Extract values within a group**The following command selects first and second values from a categorical variable carrier.

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

**Select LAST value from a group**mydata[, .SD[.N], by=carrier]

**SQL's RANK OVER PARTITION**

In SQL, Window functions are very useful for solving complex data problems. RANK OVER PARTITION is the most popular window function. 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. See the code below.

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

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'.

**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"]

**Merging / 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.

**Sample Data**(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"))

**Inner Join**It returns all the matching observations in both the datasets.

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

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

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

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

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

**Full Join**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")

**Other Useful Functions****Reshape Data**It includes several useful functions which makes data cleaning easy and smooth. To reshape or transpose data, you can usedcast.data.table()andmelt.data.table()functions. These functions are sourced from reshape2 package and make them efficient. It also add some new features in these functions.

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

**Examples for Practise**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.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)]

**Endnotes**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.
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