Chaining allows for executing multiple data manipulation operations in a single expression.
It makes code more readable and concise.
Achieved using the
%>%
operator in other packages, butdata.table
uses its syntax.
2024
Chaining allows for executing multiple data manipulation operations in a single expression.
It makes code more readable and concise.
Achieved using the %>%
operator in other packages, but data.table
uses its syntax.
Example data.table
DT <- data.table(ID = 1:5, Name = c("Alice", "Bob", "Charlie", "David", "Eva"), Score = c(85, 92, 88, 94, 90))
DT[<row ops>][, <col ops>, by=<group ops>][<result ops>]
DT[order(-Score)][1:3][, .(TopScore = max(Score))]
## TopScore ## <num> ## 1: 94
DT[Score >= 90, .(Name, Score)][order(-Score)]
## Name Score ## <char> <num> ## 1: David 94 ## 2: Bob 92 ## 3: Eva 90
DT[, .(MeanScore = mean(Score))][, c("AdjustedScore") := .(MeanScore * 1.05)]
DT[, .(AverageScore = mean(Score)), by = Name][order(-AverageScore)]
## Name AverageScore ## <char> <num> ## 1: David 94 ## 2: Bob 92 ## 3: Eva 90 ## 4: Charlie 88 ## 5: Alice 85
data.table
s and further manipulate the result.DT1 <- data.table(ID = 1:3, Dept = c("HR", "IT", "Finance")) DT2 <- DT[1:3, .(ID, Name)] DT1[DT2, on = "ID"][, .(ID, Name, Dept)]
## ID Name Dept ## <int> <char> <char> ## 1: 1 Alice HR ## 2: 2 Bob IT ## 3: 3 Charlie Finance
data.table
within a chain by using :=
in the j
expression.DT[, NewColumn := Score * 1.1][]
## ID Name Score NewColumn ## <int> <char> <num> <num> ## 1: 1 Alice 85 93.5 ## 2: 2 Bob 92 101.2 ## 3: 3 Charlie 88 96.8 ## 4: 4 David 94 103.4 ## 5: 5 Eva 90 99.0
DT[Score > 85][, .(MeanScore = mean(Score)), by = Name][, Grade := ifelse(MeanScore > 90, "A", "B")][]
## Name MeanScore Grade ## <char> <num> <char> ## 1: Bob 92 A ## 2: Charlie 88 B ## 3: David 94 A ## 4: Eva 90 B
Efficiency: Reduces memory overhead by avoiding intermediate copies.
Readability: Makes complex operations more readable by structuring them into a single logical flow.
Productivity: Speeds up data manipulation tasks by consolidating steps.
data.table
chain that filters for rows where the Score
is greater than 85 and then orders the results by Score
in descending order. Assume your data table is named DT
.DT[Score > 85][order(-Score)]This chain first filters rows where
Score
is greater than 85, then orders these filtered results by Score
in descending order.
Score
by Dept
for a data.table
named DT
. Assume DT
includes columns for Dept
and Score
.DT[, .(AvgScore = mean(Score)), by = Dept]This chain groups the data by
Dept
and then calculates the average Score
for each group, returning a data table with each Dept
and its corresponding AvgScore
.
AdjustedScore
(which is Score
multiplied by 1.05) to DT
and then filters for rows where AdjustedScore
is greater than 90.DT[, AdjustedScore := Score * 1.05][AdjustedScore > 90]This chain first adds a new column
AdjustedScore
by multiplying each Score
by 1.05. It then filters the resulting data table for rows where AdjustedScore
is greater than 90.