This vignette introduces the data.table syntax, its general form, how to subset rows, select and compute on columns and perform aggregations by group. Familiarity with data.frame data structure from base R is useful, but not essential to follow this vignette.
Data analysis using data.table
Data manipulation operations such as subject,group,update,joinetc.,are all inherently related.Keeping these related operations together allows for：
Briefly,if you are interested in reducing programming and compute time tremendously,then this package is for you.
The philosophy that data.table adheres to makes this possible. Our goal is to illustrate it through this series of vignettes.
Data {#data}
We can use data.table's fast file reader fread to load flights directly as follows:
library(data.table)
getwd()
flights <- fread("flights14.csv") #默认
flights
dim(flights)
Aside: fread accepts http and https URLs directly as well as operating system commands such as sed and awk output. See ?fread for examples.
Introduction
In this vignette,we will
1.start with basics - what is a data.table,its general form,how to subset rows,select and compute on columns.
2.and then we will look at performing data aggregatons by group.
- Basics {#basics-1}
a) What is data.table? {#what-is-datatable-1a}
data.table is an R package that provides an enhanced version of data.frames.
In the Data section, we already created a data.table using fread(). We can also create one using the data.table() function.
Here is an example:
DT <- data.table(ID = c("b","b","b","a","a","c"),a = 1:6,b = 7:12,c = 13:18)
DT
class(DT$ID)
You can also convert existing objects to a data.table using as.data.table.
Note that: {.bs-callout .bs-callout-info}
- Unlike data.frames, columns of character type are never converted to factors by default.
- Row numbers are printed with a : in order to visually separate the row number from the first column.
- When the number of rows to print exceeds the global option datatable.print.nrows (default = 100), it automatically prints only the top 5 and bottom 5 rows (as can be seen in the Data section).
getOption("datatable.print.nrows")
- data.table doesn't set or use row names, ever. We will see as to why in “Keys and fast binary search based subset” vignette.
b) General form - in what way is a data.table enhanced? {#enhanced-1b}
In contrast to a data.frame, you can do a lot more than just subsetting rows and selecting columns within the frame of a data.table, i.e., within [ ... ]. To understand it we will have to first look at the general form of data.table syntax, as shown below:
DT[i, j, by]
## R: i j by
## SQL: where select | update group by
Users who have a SQL background might perhaps inmediately relate to this syntax.
The way to read it (out loud) is: {.bs-callout .bs-callout-info}
Take DT, subset rows using i, then calculate j, grouped by by.
Let's begin by looking at i and j first - subsetting rows and operating on columns.
c) Subset rows in i {#subset-i-1c}
– Get all the flights with “JFK” as the origin airport in the month of June.
ans <- flights[origin == "JFK" & month == 6L] #,不是必需的
head(ans)
{.bs-callout .bs-callout-info}
.Within the frame of a data.table, columns can be referred to as if they are variables. Therefore, we simply refer to dest and month as if they are variables. We do not need to add the prefix flights$ each time. However using flights$dest and flights$month would work just fine.
.The row indices that satisfies the condition origin == "JFK" & month == 6L are computed, and since there is nothing else left to do, a data.table all columns from flights corresponding to those row indices are simply returned.
.A comma after the condition is also not required in i. But flights[dest == "JFK" & month == 6L, ] would work just fine. In data.frames however, the comma is necessary.
– Get the first two rows from flights. {#subset-rows-integer}
ans <- flights[1:2]
ans
class(ans)
{.bs-callout .bs-callout-info}
- .In this case, there is no condition. The row indices are already provided in i. We therefore return a data.table with all columns from flight for those row indices.
– Sort flights first by column origin in ascending order, and then by dest in descending order:
We can use the base R function order() to accomplish this:
## 使用order函数
ans <- flights[order(origin,-dest)]
head(ans)
order() is internally optimised {.bs-callout .bs-callout-info}
- .We can use “-” on a character columns within the frame of a data.table to sort in decreasing order.
- .In addition, order(...) within the frame of a data.table uses data.table's internal fast radix order forder(), which is much faster than base::order. Here's a small example to highlight the difference.
odt = data.table(col = sample(1e7))
(t1 <- system.time(ans1 <- odt[base::order(col)]))
(t2 <- system.time(ans2 <- odt[order(col)])) ## 但是包 fsort()
(identical(ans1,ans2))
d) Select column(s) in j {#select-j-1d}
– Select arr_delay column, but return it as a vector.
ans <- flights[,arr_delay]
head(ans)
{.bs-callout .bs-callout-info}
.Since columns can be referred to as if they are variables within the frame of data.tables, we directly refer to the variable we want to subset. Since we want all the rows, we simply skip i.
.It returns all the rows for the column arr_delay.
– Select arr_delay column, but return as a data.table instead.
ans <- flights[,list(arr_delay)]
head(ans)
mode(ans)
anss <- flights[, .(arr_delay)]
head(anss)
mode(anss)
{.bs-callout .bs-callout-info}
.We wrap the variables (column names) within list(), which ensures that a data.table is returned. In case of a single column name, not wrapping with list() returns a vector instead, as seen in the previous example.
.data.table also allows using .() to wrap columns with. It is an alias to list(); they both mean the same. Feel free to use whichever you prefer.
We will continue to use .() from here on.
data.tables(and data.frames) are internally lists as well,but with all its columns of equal length and with a class attribute.Allowing j to return a list enables converting and returning a data.table very efficiently.
Tip: {.bs-callout .bs-callout-warning #tip-1}
As long as j-expression returns a list, each element of the list will be converted to a column in the resulting data.table. This makes j quite powerful, as we will see shortly.
– Select both arr_delay and dep_delay columns.
ans <- flights[,.(arr_delay,dep_delay)]
head(ans)
## alternatively
## ans <- flights[,list(arr_delay,dep_delay)]
{.bs-callout .bs-callout-info}
Wrap both columns within .(), or list(). That's it.
– Select both arr_delay and dep_delay columns and rename them to delay_arr and delay_dep.
Since .() is just an alias for list(), we can name columns as we would while creating a list.
ans <- flights[, .(delay_arr = arr_delay,delay_dep = dep_delay)]
head(ans)
e) Compute or do in j
– How many trips have had total delay < 0?
ans <- flights[, sum((arr_delay + dep_delay) < 0)]
ans
What's happening here? {.bs-callout .bs-callout-info}
- .data.table's[j] can handle more than just selecting columns - it can handle expressions,i.e.,compute on columns.This shouldn't be surprising,as columns can be referred to as if they are variables.THen we should be able to compute by calling functions on those variables.And that's what precisely happens here.
f) Subset in i and do in j
– Calculate the average arrival and departure delay for all flights with “JFK” as the origin airport in the month of June.
ans <- flights[origin == "JFK" & month == 6L,
.(m_arr = mean(arr_delay),m_dep = mean(dep_delay))]
ans
{.bs-callout .bs-callout-info}
We first subset in i to find matching row indices where origin airport equals “JFK”, and month equals 6. At this point, we do not subset the entire data.table corresponding to those rows.
Now, we look at j and find that it uses only two columns. And what we have to do is to compute their mean(). Therefore we subset just those columns corresponding to the matching rows, and compute their mean().
Because the three main components of the query(i,j and by)are together inside[...],data.table can see all three and optimise the query altogether before evaluation,not each separately.We are able to therefore avoid the entire subset,for both speed and memory efficiency.
– How many trips have been made in 2014 from “JFK” airport in the month of June?
ans <- flights[origin == "JFK" & month == 6L,length(dest)]
ans
The function length() requires an input argument. We just needed to compute the number of rows in the subset. We could have used any other column as input argument to length() really.
This type of operation occurs quite frequently, especially while grouping as we will see in the next section, that data.table provides a special symbol .N for it.
Special symbol .N: {.bs-callout .bs-callout-info #special-N}
.N is a special in-built variable that holds the number of observations in the current group. It is particularly useful when combined with by as we'll see in the next section. In the absence of group by operations, it simply returns the number of rows in the subset.
– So how can we directly order by all the grouping variables?# So we can now accomplish the same task by using .N as follows:
ans <- flights[origin == "JFK" & month == 6L, .N]
ans
We could have accomplished the same operation by doing nrow(flights[origin == "JFK" & month == 6L]). However, it would have to subset the entire data.table first corresponding to the row indices in i and then return the rows using nrow(), which is unnecessary and inefficient. We will cover this and other optimisation aspects in detail under the data.table design vignette.
g) Great! But how can I refer to columns by names in j (like in a data.frame)?
you can refer to column names the data.frame way using with = FALSE.
- .select both
arr_delayanddep_delaycolumns the data.frame way.
ans <- flights[,c("arr_delay","dep_delay"),with = FALSE]
head(ans)
The argument is named with after the R function with() because of similar functionality. Suppose you've a data.frame DF and you'd like to subset all rows where x > 1.
DF <- data.frame(x = c(1,1,1,1,2,3,3,4),y = 1:8)
## Normal way
DF[DF$x >1 ,] ## data.frame needs that ',' as well
## using with
DF[with(DF,x > 1),]
## with(DF,x >2) 返回逻辑值
{.bs-callout .bs-callout-info #with_false}
Using with() in (2) allows using DF's column x as if it were a variable.
Hence the argument name with in data.table. Setting with = FALSE disables the ability to refer to columns as if they are variables, thereby restoring the “data.frame mode”.
We can also deselect columns using - or !. For example:
# returns all columns except arr_delay and dep_delay
ans <- flights[, !c("arr_delay", "dep_delay"), with = FALSE]
# or
ans <- flights[, -c("arr_delay", "dep_delay"), with = FALSE]
with = TRUE is default in data.table because we can do much more by allowing j to handle expressions - especially when combined with by as we'll see in a moment.
2.Aggregations
We've already seen i and j from data.table's general form in the previous section. In this section, we'll see how they can be combined together with by to perform operations by group. Let's look at some examples.
a) Grouping using by
– How can we get the number of trips corresponding to each origin airport?
ans <- flights[, .(.N) ,by = .(origin)] ##直接.N也可以
ans
mode(ans)
{.bs-callout .bs-callout-info}
We know .N is a special variable that holds the number of rows in the current group. Grouping by origin obtains the number of rows, .N, for each group.
By doing head(flights) you can see that the origin airports occur in the order “JFK”, “LGA” and “EWR”. The original order of grouping variables is preserved in the result.
Since we did not provide a name for the column returned in j, it was named Nautomatically by recognising the special symbol .N.
by also accepts character vector of column names. It is particularly useful to program with, for e.g., designing a function with the columns to be group by as a function argument.
When there's only one column or expression to refer to in j and by, we can drop the .() notation. This is purely for convenience. We could instead do:
ans <- flights[, .N, by = origin] # .(origin)
ans
## 重命名
ans <- flights[, .(count =.N), by = origin]
ans
We'll use this convenient form wherever applicable hereafter.
– How can we calculate the number of trips for each origin airport for carrier code “AA”? {#origin-.N}
The unique carrier code “AA” corresponds to American Airlines Inc.
ans <- flights[carrier == "AA", .N ,by = origin]
ans
We first obtain the row indices for the expression carrier == "AA" from i.
Using those row indices, we obtain the number of rows while grouped by origin. Once again no columns are actually materialised here, because the j-expression does not require any columns to be actually subsetted and is therefore fast and memory efficient.
– How can we get the total number of trips for each origin, dest pair for carrier code “AA”? {#origin-dest-.N}
ans <- flights[carrier == "AA", .N, by = .(origin,dest)]
head(ans)
{.bs-callout .bs-callout-info}
by accepts multiple columns. We just provide all the columns by which to group by.
– How can we get the average arrival and departure delay for each orig,dest pair for each month for carrier code “AA”? {#origin-dest-month}
ans <- flights[carrier == "AA",
.(mean(arr_delay),mean(dep_delay)),
by = .(origin,dest,month)]
ans
Now what if we would like to order the result by those grouping columns origin, dest and month?
b) keyby
data.table retaining the original order of groups is intentional and by design. There are cases when preserving the original order is essential. But at times we would like to automatically sort by the variables we grouped by.
– So how can we directly order by all the grouping variables?
ans <- flights[carrier == "AA",
.(mean(arr_delay),mean(dep_delay)),
keyby = .(origin,dest,month)]
ans
All we did was to change by to keyby. This automatically orders the result by the grouping variables in increasing order. Note that keyby() is applied after performing the operation, i.e., on the computed result.
Keys: Actually keyby does a little more than just ordering. It also sets a key after ordering by setting an attribute called sorted. But we'll learn more about keys in the next vignette.
For now, all you've to know is you can use keyby to automatically order by the columns specified in by.
c) Chaining
– How can we order ans using the columns origin in ascending order, and dest in descending order?
We can store the intermediate result in a variable, and then use order(origin, -dest) on that variable. It seems fairly straightforward.
ans <- ans[order(origin,-dest)]
head(ans)
Recall that we can use “-” on a character column in order() within the frame of a data.table. This is possible to due data.table's internal query optimisation.
Also recall that order(...) with the frame of a data.table is automatically optimised to use data.table's internal fast radix order forder() for speed. So you can keep using the already familiar base R functions without compromising in speed or memory efficiency that data.table offers. We will cover this in more detail in the data.table internals vignette.
But this requires having to assign the intermediate result and then overwriting that result. We can do one better and avoid this intermediate assignment on to a variable altogether by chaining expressions.
ans <- flights[carrier == "AA", .N ,by = .(origin,dest)][order(origin,-dest)] # order(-N)
head(ans,10)
{.bs-callout .bs-callout-info}
We can tack expressions one after another, forming a chain of operations, i.e., DT[ ... ][ ... ][ ... ].
Or you can also chain them vertically:
DT[ ...
][ ...
][ ...
]
d) Expressions in by
– Can by accept expressions as well or just take columns?
Yes it does. As an example, if we would like to find out how many flights started late but arrived early (or on time), started and arrived late etc…
ans <- flights[, .N , .(dep_delay>0,arr_delay>0)] ##逻辑值
ans
The last row corresponds to dep_delay > 0 = TRUE and arr_delay > 0 = FALSE. We can see that 26593 flights started late but arrived early (or on time).
Note that we did not provide any names to by-expression. And names have been automatically assigned in the result.
You can provide other columns along with expressions, for example: DT[, .N, by = .(a, b>0)].
e) Multiple columns in j - .SD
– Do we have to compute mean() for each column individually?
It is of course not practical to have to type mean(myCol) for every column one by one.What if you had a 100 columns to compute mean() of ?
How can we do this efficiently? To get there, refresh on this tip - “As long as j-expression returns a list, each element of the list will be converted to a column in the resulting data.table”. Suppose we can refer to the data subset for each group as a variable while grouping, then we can loop through all the columns of that variable using the already familiar base function lapply(). We don't have to learn any new function.
Special symbol .SD: {.bs-callout .bs-callout-info #special-SD}
data.table provides a special symbol, called .SD. It stands for Subset of Data. It by itself is a data.table that holds the data for the current group defined using by.
Recall that a data.table is internally a list as well with all its columns of equal length.
Let's use the data.table DT from before to get a glimpse of what .SD looks like.
DT[,print(.SD),by = ID]
## DT[, .SD, by = ID] 这样写也可以
{.bs-callout .bs-callout-info}
.SD contains all the columns except the grouping columns by default.
It is also generated by preserving the original order - data corresponding to ID = "b", then ID = "a", and then ID = "c".
To compute on (multiple) columns, we can then simply use the base R function lapply().
DT[,lapply(.SD, mean), by = ID]
.SD holds the rows corresponding to columns a, b and c for that group. We compute the mean() on each of these columns using the already familiar base function lapply().
Each group returns a list of three elements containing the mean value which will become the columns of the resulting data.table.
Since lapply() returns a list, there is no need to wrap it with an additional .() (if necessary, refer to this tip).
We are almost there. There is one little thing left to address. In our flights data.table, we only wanted to calculate the mean() of two columns arr_delay and dep_delay. But .SD would contain all the columns other than the grouping variables by default.
– How can we specify just the columns we would like to compute the mean() on?
.SDcols {.bs-callout .bs-callout-info}
Using the argument .SDcols. It accepts either column names or column indices. For example, .SDcols = c("arr_delay", "dep_delay") ensures that .SD contains only these two columns for each group.
Similar to the with = FALSE section, you can also provide the columns to remove instead of columns to keep using - or ! sign as well as select consecutive columns as colA:colB and deselect consecutive columns as !(colA:colB) or-(colA:colB).
Now let us try to use .SD along with .SDcols to get the mean() of arr_delay and dep_delay columns grouped by origin, dest and month.
flights[carrier == "AA", ##Only on trips with carrier "AA"
lapply(.SD, mean), ## compute the mean
by = .(origin,dest,month), ##for wvery 'origin dest month'
.SDcols = c("arr_delay","dep_delay")] ## for just those specified in .SDcols
f) Subset .SD for each group:
– How can we return the first two rows for each month?
ans <- flights[,head(.SD,2),by = month]
head(ans)
{.bs-callout .bs-callout-info}
.SD is a data.table that holds all the rows for that group. We simply subset the first two rows as we have seen here already.
For each group, head(.SD, 2) returns the first two rows as a data.table which is also a list. So we do not have to wrap it with .().
g) Why keep j so flexible?
So that we have consistent syntax and keep using already existing (and familiar) base functions instead of learning new functions.To illustrate, let us use the data.table DT we created at the very beginning under What is a data.table? section.
– How can we concatenate columns a and b for each group in ID?
DT
DT[, .(val = c(a,b)) ,b = ID]
{.bs-callout .bs-callout-info}
That's it. There is no special syntax required. All we need to know is the base function c() which concatenates vectors and the tip from before.
– What if we would like to have all the values of column a and b concatenated, but returned as a list column?
DT[, .(val = list(c(a,b))),by = ID]
{.bs-callout .bs-callout-info}
Here, we first concatenate the values with c(a,b) for each group, and wrap that with list(). So for each group, we return a list of all concatenated values.
Note those commas are for display only. A list column can contain any object in each cell, and in this example, each cell is itself a vector and some cells contain longer vectors than others.
Once you start internalising usage in j, you will realise how powerful the syntax can be. A very useful way to understand it is by playing around, with the help of print().
For example:
## (1)look at the difference between
DT[,print(c(a,b)),by = ID]
##(2)
DT[,print(list(c(a,b))),by = ID]
In (1), for each group, a vector is returned, with length = 6,4,2 here. However (2) returns a list of length 1 for each group, with its first element holding vectors of length 6,4,2. Therefore (1) results in a length of 6+4+2 = 12, whereas (2) returns 1+1+1=3.
Summary
The general form of data.table syntax is:
DT[i,j,by]
We have seen so far that,
Using i: {.bs-callout .bs-callout-info}
We can subset rows similar to a data.frame - except you don't have to use DT$ repetitively since columns within the frame of a data.table are seen as if they are variables.
We can also sort a data.table using order(), which internally uses data.table's fast order for performance.
We can do much more in i by keying a data.table, which allows blazing fast subsets and joins. We will see this in the “Keys and fast binary search based subsets” and “Joins and rolling joins” vignette.
Using j: {.bs-callout .bs-callout-info}
Select columns the data.table way:
DT[, .(colA, colB)].
Select columns the data.frame way:
DT[, c("colA", "colB"), with = FALSE].
Compute on columns:
DT[, .(sum(colA), mean(colB))].
Provide names if necessary:
DT[, .(sA =sum(colA), mB = mean(colB))].
Combine with i:
DT[colA > value, sum(colB)].
Using by: {.bs-callout .bs-callout-info}
Using by, we can group by columns by specifying a list of columns or a character vector of column names or even expressions. The flexibility of j, combined with by and imakes for a very powerful syntax.
by can handle multiple columns and also expressions.
We can keyby grouping columns to automatically sort the grouped result.
We can use.SD and .SDcols inj to operate on multiple columns using already familiar base functions.
Here are some examples:
DT[, lapply(.SD, fun), by = ..., .SDcols = ...]
appliesfunto all columns specified in.SDcolswhile grouping by the columns specified inby.DT[, head(.SD, 2), by = ...]
return the first two rows for each group.DT[col > val, head(.SD, 1), by = ...]
combineialong withjandby.
And remember the tip: {.bs-callout .bs-callout-warning}
As long as j returns a list, each element of the list will become a column in the resulting data.table.
We will see how to add/update/delete columns by reference and how to combine them with i and by in the next vignette.
