## 13.2data.table— enhanced data.frame

What is data.table? It is an R package that provides an enhanced version of data.frame. The most used object in R is data frame. Before we move on, let’s briefly review some basic characters and manipulations of data.frame:

• It is a set of rows and columns.
• Each row is of the same length and data type
• Every column is of the same length but can be of differing data types
• It has characteristics of both a matrix and a list
• It uses [] to subset data

We will use the clothes customer data to illustrate. There are two dimensions in []. The first one indicates the row and second one indicates column. It uses a comma to separate them.

# read data
sim.dat <- readr::read_csv("http://bit.ly/2P5gTw4")
## Rows: 1000 Columns: 19
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (3): gender, house, segment
## dbl (16): age, income, store_exp, online_exp, store_trans, online_trans, Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10
##
## ℹ Use spec() to retrieve the full column specification for this data.
## ℹ Specify the column types or set show_col_types = FALSE to quiet this message.
# subset the first two rows
sim.dat[1:2, ]
# subset the first two rows and column 3 and 5
sim.dat[1:2, c(3, 5)]
# get all rows with age>70
sim.dat[sim.dat$age > 70, ] # get rows with age> 60 and gender is Male select column 3 and 4 sim.dat[sim.dat$age > 68 & sim.dat$gender == "Male", 3:4] Remember that there are usually different ways to conduct the same manipulation. For example, the following code presents three ways to calculate an average number of online transactions for male and female: tapply(sim.dat$online_trans, sim.dat$gender, mean) aggregate(online_trans ~ gender, data = sim.dat, mean) sim.dat %>% group_by(gender) %>% summarise(Avg_online_trans = mean(online_trans)) There is no gold standard to choose a specific function to manipulate data. The goal is to solve the real problem, not the tool itself. So just use whatever tool that is convenient for you. The way to use [] is straightforward. But the manipulations are limited. If you need more complicated data reshaping or aggregation, there are other packages to use such as dplyr, reshape2, tidyr etc. But the usage of those packages are not as straightforward as []. You often need to change functions. Keeping related operations together, such as subset, group, update, join etc, will allow for: • concise, consistent and readable syntax irrespective of the set of operations you would like to perform to achieve your end goal • performing data manipulation fluidly without the cognitive burden of having to change among different functions • by knowing precisely the data required for each operation, you can automatically optimize operations effectively data.table is the package for that. If you are not familiar with other data manipulating packages and are interested in reducing programming time tremendously, then this package is for you. Other than extending the function of [], data.table has the following advantages: • Offers fast import, subset, grouping, update, and joins for large data files • It is easy to turn data frame to data table • Can behave just like a data frame You need to install and load the package: Use data.table() to convert the existing data frame sim.dat to data table: dt <- data.table(sim.dat) class(dt) ## [1] "data.table" "data.frame" Calculate mean for counts of online transactions: dt[, mean(online_trans)] ## [1] 13.55 You can’t do the same thing using data frame: sim.dat[,mean(online_trans)] Error in mean(online_trans) : object 'online_trans' not found If you want to calculate mean by group as before, set “by =” argument: dt[ , mean(online_trans), by = gender] ## gender V1 ## 1: Female 15.38 ## 2: Male 11.26 You can group by more than one variables. For example, group by “gender” and “house”: dt[ , mean(online_trans), by = .(gender, house)] ## gender house V1 ## 1: Female Yes 11.312 ## 2: Male Yes 8.772 ## 3: Female No 19.146 ## 4: Male No 16.486 Assign column names for aggregated variables: dt[ , .(avg = mean(online_trans)), by = .(gender, house)] ## gender house avg ## 1: Female Yes 11.312 ## 2: Male Yes 8.772 ## 3: Female No 19.146 ## 4: Male No 16.486 data.table can accomplish all operations that aggregate() and tapply()can do for data frame. • General setting of data.table Different from data frame, there are three arguments for data table: It is analogous to SQL. You don’t have to know SQL to learn data table. But experience with SQL will help you understand data table. In SQL, you select column j (use command SELECT) for row i (using command WHERE). GROUP BY in SQL will assign the variable to group the observations. Let’s review our previous code: dt[ , mean(online_trans), by = gender] The code above is equal to the following SQL: SELECT gender, avg(online_trans) FROM sim.dat GROUP BY gender R code: dt[ , .(avg = mean(online_trans)), by = .(gender, house)] is equal to SQL: SELECT gender, house, avg(online_trans) AS avg FROM sim.dat GROUP BY gender, house R code: dt[ age < 40, .(avg = mean(online_trans)), by = .(gender, house)] is equal to SQL: SELECT gender, house, avg(online_trans) AS avg FROM sim.dat WHERE age < 40 GROUP BY gender, house You can see the analogy between data.table and SQL. Now let’s focus on operations in data table. • select row # select rows with age<20 and income > 80000 dt[age < 20 & income > 80000] ## age gender income house store_exp online_exp store_trans online_trans Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 segment ## 1: 19 Female 83535 No 227.7 1491 1 22 2 1 1 2 4 1 4 2 4 1 Style ## 2: 18 Female 89416 Yes 209.5 1926 3 28 2 1 1 1 4 1 4 2 4 1 Style ## 3: 19 Female 92813 No 186.7 1042 2 18 3 1 1 2 4 1 4 3 4 1 Style # select the first two rows dt[1:2] ## age gender income house store_exp online_exp store_trans online_trans Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 segment ## 1: 57 Female 120963 Yes 529.1 303.5 2 2 4 2 1 2 1 4 1 4 2 4 Price ## 2: 63 Female 122008 Yes 478.0 109.5 4 2 4 1 1 2 1 4 1 4 1 4 Price • select column Selecting columns in data.table don’t need $:

# select column “age” but return it as a vector
# the argument for row is empty so the result
# will return all observations
ans <- dt[, age]
head(ans)
## [1] 57 63 59 60 51 59

To return data.table object, put column names in list():

# Select age and online_exp columns
# and return as a data.table instead
ans <- dt[, list(age, online_exp)]
head(ans)

Or you can also put column names in .():

ans <- dt[, .(age, online_exp)]

To select all columns from “age” to “income”:

ans <- dt[, age:income, with = FALSE]
head(ans,2)
##    age gender income
## 1:  57 Female 120963
## 2:  63 Female 122008

Delete columns using - or !:

# delete columns from  age to online_exp
ans <- dt[, -(age:online_exp), with = FALSE]
ans <- dt[, !(age:online_exp), with = FALSE]
• tabulation

In data table. .N means to count。

# row count
dt[, .N] 
## [1] 1000

If you assign the group variable, then it will count by groups:

# counts by gender
dt[, .N, by= gender]  
##    gender   N
## 1: Female 554
## 2:   Male 446
# for those younger than 30, count by gender
dt[age < 30, .(count=.N), by= gender] 
##    gender count
## 1: Female   292
## 2:   Male    86

Order table:

# get records with the highest 5 online expense:
head(dt[order(-online_exp)],5) 
   age gender   income house store_exp online_exp store_trans ...
1:  40 Female 217599.7    No  7023.684   9479.442          10
2:  41 Female       NA   Yes  3786.740   8638.239          14
3:  36   Male 228550.1   Yes  3279.621   8220.555           8
4:  31 Female 159508.1   Yes  5177.081   8005.932          11
5:  43 Female 190407.4   Yes  4694.922   7875.562           6
...

Since data table keep some characters of data frame, they share some operations:

dt[order(-online_exp)][1:5]

You can also order the table by more than one variable. The following code will order the table by gender, then order within gender by online_exp:

dt[order(gender, -online_exp)][1:5]
• Use fread() to import dat

Other than read.csv in base R, we have introduced ‘read_csv’ in ‘readr’. read_csv is much faster and will provide progress bar which makes user feel much better (at least make me feel better). fread() in data.table further increase the efficiency of reading data. The following are three examples of reading the same data file topic.csv. The file includes text data scraped from an agriculture forum with 209670 rows and 6 columns:

system.time(topic <- read.csv("http://bit.ly/2zam5ny"))
   user  system elapsed
3.561   0.051   4.888 
system.time(topic <- readr::read_csv("http://bit.ly/2zam5ny"))
   user  system elapsed
0.409   0.032   2.233 
system.time(topic <- data.table::fread("http://bit.ly/2zam5ny"))
   user  system elapsed
0.276   0.096   1.117 

It is clear that read_csv() is much faster than read.csv(). fread() is a little faster than read_csv(). As the size increasing, the difference will become for significant. Note that fread() will read file as data.table by default.