# Create my_first_data_table
> my_first_data_table = data.table(x = c("a", "b", "c", "d", "e"),
y = c(1, 2, 3, 4, 5))
>
# Create a data.table using recycling
> DT = data.table(a = c(1L,2L,1L,2L), b = LETTERS[1:4])
>
# Print the third row to the console
> DT[3]
a b
1: 1 C
>
# Print the second and third row to the console, but do not commas
> DT[2:3]
a b
1: 2 B
2: 1 C
> # Print the penultimate row of DT using .N
> DT[.N -1]
a b
1: 1 C
>
> # Print the column names of DT, and number of rows and number of columns
> names(DT)
[1] "a" "b"
> dim(DT)
[1] 4 2
>
> # Select row 2 twice and row 3
> DT[c(2,2,3)]
a b
1: 2 B
2: 2 B
3: 1 C
> DT[,.(B)]
B
1: a
2: b
3: c
4: d
5: e
> DT
...
A B C
1: 1 a 6
2: 2 b 7
3: 3 c 8
4: 4 d 9
5: 5 e 10
> DT[, B]
[1] "a" "b" "c" "d" "e"
> DT
A B C
1: 1 a 6
2: 2 b 7
3: 3 c 8
4: 4 d 9
5: 5 e 10
> A = 4
> DT[,.(A)]
A
1: 1
2: 2
3: 3
4: 4
5: 5
> # Subset rows 1 and 3, and columns B and C
> DT
A B C
1: 1 a 6
2: 2 b 7
3: 3 c 8
4: 4 d 9
5: 5 e 10
> DT[c(1,3),.(B,C)]
B C
1: a 6
2: c 8
>
> # Assign to ans the correct value
> DT
A B C
1: 1 a 6
2: 2 b 7
3: 3 c 8
4: 4 d 9
5: 5 e 10
> ans = DT[,.(B, val = A*C)]
> ans
B val
1: a 6
2: b 14
3: c 24
4: d 36
5: e 50
>
> # Fill in the blanks such that ans2 equals target
> target <- data.table(B = c("a", "b", "c", "d", "e", "a", "b", "c", "d", "e"),
val = as.integer(c(6:10, 1:5)))
> ans2 <- DT[, .(B, val = target$val )]
> ans2
B val
1: a 6
2: b 7
3: c 8
4: d 9
5: e 10
6: a 1
7: b 2
8: c 3
9: d 4
10: e 5
> target
B val
1: a 6
2: b 7
3: c 8
4: d 9
5: e 10
6: a 1
7: b 2
8: c 3
9: d 4
10: e 5
>
> # Convert iris to a data.table: DT
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
> DT = data.table(iris)
> head(DT)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 5.1 3.5 1.4 0.2 setosa
2: 4.9 3.0 1.4 0.2 setosa
3: 4.7 3.2 1.3 0.2 setosa
4: 4.6 3.1 1.5 0.2 setosa
5: 5.0 3.6 1.4 0.2 setosa
6: 5.4 3.9 1.7 0.4 setosa
>
> # For each Species, print the mean Sepal.Length
> DT[,mean(Sepal.Length), by = Species]
Species V1
1: setosa 5.006
2: versicolor 5.936
3: virginica 6.588
>
> # Print mean Sepal.Length, grouping by first letter of Species
> DT[,mean(Sepal.Length), by = substr(Species,1,1)]
substr V1
1: s 5.006
2: v 6.262
> DT <- as.data.table(iris)
>
> # Group the specimens by Sepal area (to the nearest 10 cm2) and count how many occur in each group.
> DT[,.N, by=10*round(Sepal.Length*Sepal.Width/10)]
round N
1: 20 117
2: 10 29
3: 30 4
>
> # Now name the output columns `Area` and `Count`
> DT[,.(Count=.N), by=.(Area=10*round(Sepal.Length*Sepal.Width/10))]
Area Count
1: 20 117
2: 10 29
3: 30 4
> # Create the data.table DT
> set.seed(1L)
> DT <- data.table(A=rep(letters[2:1], each=4L), B=rep(1:4, each=2L), C=sample(8))
> DT
A B C
1: b 1 3
2: b 1 8
3: b 2 4
4: b 2 5
5: a 3 1
6: a 3 7
7: a 4 2
8: a 4 6
> # Create the new data.table, DT2
> DT2 <- DT[, .(C=cumsum(C)), by=.(A,B)]
> DT2
A B C
1: b 1 3
2: b 1 11
3: b 2 4
4: b 2 9
5: a 3 1
6: a 3 8
7: a 4 2
8: a 4 8
> # Select from DT2 the last two values from C while you group by A
> DT2[, .(C=tail(C,2)), by=A]
A C
1: b 4
2: b 9
3: a 2
4: a 8
Next Exercise
script.R
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# The data.table package has already been loaded
# Build DT
set.seed(1L)
DT <- data.table(A = rep(letters[2:1], each = 4L), B = rep(1:4, each = 2L), C
= sample(8))
DT
# Use chaining:
# Cumsum of C while grouping by A and B, and then select last two values of C
while grouping by A
DT[,.(C = cumsum(C)), by=.(A,B)][,.(C=tail(C,2)),by=A]
Submit Answer
R Console
Slides
> # The data.table package has already been loaded
>
> # Build DT
> set.seed(1L)
> DT <- data.table(A = rep(letters[2:1], each = 4L), B = rep(1:4, each = 2L), C = sample(8))
> DT
A B C
1: b 1 3
2: b 1 8
3: b 2 4
4: b 2 5
5: a 3 1
6: a 3 7
7: a 4 2
8: a 4 6
> # Use chaining:
> # Cumsum of C while grouping by A and B, and then select last two values of C while grouping by A
> DT[,.(C = cumsum(C)), by=.(A,B)][,.(C=tail(C,2)),by=A]
A C
1: b 4
2: b 9
3: a 2
4: a 8
>
> DT <- as.data.table(iris)
> # Perform chained operations on DT
> DT[, .( Sepal.Length=median(Sepal.Length),
Sepal.Width=median(Sepal.Width),
Petal.Length=median(Petal.Length),
Petal.Width=median(Petal.Width)), by=Species][order(-Species)]
Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1: virginica 6.5 3.0 5.55 2.0
2: versicolor 5.9 2.8 4.35 1.3
3: setosa 5.0 3.4 1.50 0.2
>
> DT
x y z
1: 2 1 2
2: 1 3 4
3: 2 5 6
4: 1 7 8
5: 2 9 10
6: 2 11 12
7: 1 13 14
>
> # Mean of columns
> DT[, lapply(.SD, mean), by=x]
x y z
1: 2 6.500000 7.500000
2: 1 7.666667 8.666667
>
> # Median of columns
> DT[, lapply(.SD, median), by=x]
x y z
1: 2 7 8
2: 1 7 8
>
> DT
grp Q1 Q2 Q3 H1 H2
1: 6 2 5 2 3 5
2: 6 2 5 1 4 2
3: 8 3 4 4 5 4
4: 8 5 4 2 2 1
5: 8 2 1 4 4 2
>
> # Calculate the sum of the Q columns
> DT[, lapply(.SD, sum), .SDcols=2:4]
Q1 Q2 Q3
1: 14 19 13
>
> # Calculate the sum of columns H1 and H2
> DT[,lapply(.SD,sum), .SDcols=paste0("H",1:2)]
H1 H2
1: 18 14
>
> # Select all but the first row of groups 1 and 2, returning only the grp column and the Q columns.
> DT[,.SD[-1], by=grp, .SDcols=paste0("Q",1:3)]
grp Q1 Q2 Q3
1: 6 2 5 1
2: 8 5 4 2
3: 8 2 1 4
>
> DT
x y z
1: 2 1 2
2: 1 3 4
3: 2 5 6
4: 1 7 8
5: 2 9 10
6: 2 11 12
7: 1 13 14
> # Sum of all columns and the number of rows
> DT[, c(lapply(.SD, sum), .N), by=x, .SDcols=c("x", "y", "z")]
x x y z N
1: 2 8 26 30 4
2: 1 3 23 26 3
>
> # Cumulative sum of column x and y while grouping by x and z > 8
> DT[, lapply(.SD, cumsum),by=.(by1=x, by2=z>8), .SDcols=c("x", "y")]
by1 by2 x y
1: 2 FALSE 2 1
2: 2 FALSE 4 6
3: 1 FALSE 1 3
4: 1 FALSE 2 10
5: 2 TRUE 2 9
6: 2 TRUE 4 20
7: 1 TRUE 1 13
>
> # Chaining
> DT[, lapply(.SD, cumsum),by=.(by1=x, by2=z>8), .SDcols=1:2][, lapply(.SD, max), by=by1, .SDcols=c("x", "y")]
by1 x y
1: 2 4 20
2: 1 2 13
Submit Answer
R Console
Slides
> # The data.table DT
> DT <- data.table(A = letters[c(1, 1, 1, 2, 2)], B = 1:5)
> DT
A B
1: a 1
2: a 2
3: a 3
4: b 4
5: b 5
>
> # Add column by reference: Total
> DT[,Total:=sum(B),by=A]
A B Total
1: a 1 6
2: a 2 6
3: a 3 6
4: b 4 9
5: b 5 9
>
> # Add 1 to column B
> DT[c(2,4), B:=B+1L]
A B Total
1: a 1 6
2: a 3 6
3: a 3 6
4: b 5 9
5: b 5 9
>
> # Add a new column Total2
> DT[2:4, Total2:=sum(B),by=A]
A B Total Total2
1: a 1 6 NA
2: a 3 6 6
3: a 3 6 6
4: b 5 9 5
5: b 5 9 NA
>
> # Remove the Total column
> DT[, Total:=NULL]
A B Total2
1: a 1 NA
2: a 3 6
3: a 3 6
4: b 5 5
5: b 5 NA
>
> # Select the third column using `[[`
> DT[[3]]
[1] NA 6 6 5 NA
> # Set the seed
> set.seed(1)
>
> # Check the DT that is made available to you
> DT
A B C D
1: 2 2 5 3
2: 2 1 2 3
3: 3 4 4 3
4: 5 2 1 1
5: 2 4 2 5
6: 5 3 2 4
7: 5 4 1 4
8: 4 5 2 1
9: 4 2 5 4
10: 1 4 2 3
>
> # For loop with set
> for(i in 2:4)
set(DT, sample(10,3), i, NA)
>
> # Change the column names to lowercase
> setnames(DT, tolower(names(DT)))
>
> # Print the resulting DT to the console
> DT
a b c d
1: 2 2 5 3
2: 2 1 NA 3
3: 3 NA 4 3
4: 5 NA 1 1
5: 2 NA 2 5
6: 5 3 2 NA
7: 5 4 1 4
8: 4 5 NA 1
9: 4 2 5 NA
10: 1 4 NA NA
>
> DT <- data.table(a = letters[c(1, 1, 1, 2, 2)], b = 1)
> DT
a b
1: a 1
2: a 1
3: a 1
4: b 1
5: b 1
> # Add a postfix "_2" to all column names
> setnames(DT, paste0(names(DT),"_2"))
> DT
a_2 b_2
1: a 1
2: a 1
3: a 1
4: b 1
5: b 1
> # Change column name "a_2" to "A2"
> setnames(DT, "a_2", "A2")
> DT
A2 b_2
1: a 1
2: a 1
3: a 1
4: b 1
5: b 1
> # Reverse the order of the columns
> setcolorder(DT, 2:1)
> DT
b_2 A2
1: 1 a
2: 1 a
3: 1 a
4: 1 b
5: 1 b
> # Convert iris to a data.table
> iris = as.data.table(iris)
>
> # Species is "virginica"
> iris[Species == "virginica"]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.3 3.3 6.0 2.5 virginica
2: 5.8 2.7 5.1 1.9 virginica
3: 7.1 3.0 5.9 2.1 virginica
4: 6.3 2.9 5.6 1.8 virginica
5: 6.5 3.0 5.8 2.2 virginica
6: 7.6 3.0 6.6 2.1 virginica
7: 4.9 2.5 4.5 1.7 virginica
8: 7.3 2.9 6.3 1.8 virginica
9: 6.7 2.5 5.8 1.8 virginica
10: 7.2 3.6 6.1 2.5 virginica
11: 6.5 3.2 5.1 2.0 virginica
12: 6.4 2.7 5.3 1.9 virginica
13: 6.8 3.0 5.5 2.1 virginica
14: 5.7 2.5 5.0 2.0 virginica
15: 5.8 2.8 5.1 2.4 virginica
16: 6.4 3.2 5.3 2.3 virginica
17: 6.5 3.0 5.5 1.8 virginica
18: 7.7 3.8 6.7 2.2 virginica
19: 7.7 2.6 6.9 2.3 virginica
20: 6.0 2.2 5.0 1.5 virginica
21: 6.9 3.2 5.7 2.3 virginica
22: 5.6 2.8 4.9 2.0 virginica
23: 7.7 2.8 6.7 2.0 virginica
24: 6.3 2.7 4.9 1.8 virginica
25: 6.7 3.3 5.7 2.1 virginica
26: 7.2 3.2 6.0 1.8 virginica
27: 6.2 2.8 4.8 1.8 virginica
28: 6.1 3.0 4.9 1.8 virginica
29: 6.4 2.8 5.6 2.1 virginica
30: 7.2 3.0 5.8 1.6 virginica
31: 7.4 2.8 6.1 1.9 virginica
32: 7.9 3.8 6.4 2.0 virginica
33: 6.4 2.8 5.6 2.2 virginica
34: 6.3 2.8 5.1 1.5 virginica
35: 6.1 2.6 5.6 1.4 virginica
36: 7.7 3.0 6.1 2.3 virginica
37: 6.3 3.4 5.6 2.4 virginica
38: 6.4 3.1 5.5 1.8 virginica
39: 6.0 3.0 4.8 1.8 virginica
40: 6.9 3.1 5.4 2.1 virginica
41: 6.7 3.1 5.6 2.4 virginica
42: 6.9 3.1 5.1 2.3 virginica
43: 5.8 2.7 5.1 1.9 virginica
44: 6.8 3.2 5.9 2.3 virginica
45: 6.7 3.3 5.7 2.5 virginica
46: 6.7 3.0 5.2 2.3 virginica
47: 6.3 2.5 5.0 1.9 virginica
48: 6.5 3.0 5.2 2.0 virginica
49: 6.2 3.4 5.4 2.3 virginica
50: 5.9 3.0 5.1 1.8 virginica
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
>
>
> # Species is either "virginica" or "versicolor"
> iris[Species %in% c("virginica","versicolor")]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.3 3.3 6.0 2.5 virginica
2: 5.8 2.7 5.1 1.9 virginica
3: 7.1 3.0 5.9 2.1 virginica
4: 6.3 2.9 5.6 1.8 virginica
5: 6.5 3.0 5.8 2.2 virginica
6: 7.6 3.0 6.6 2.1 virginica
7: 4.9 2.5 4.5 1.7 virginica
8: 7.3 2.9 6.3 1.8 virginica
9: 6.7 2.5 5.8 1.8 virginica
10: 7.2 3.6 6.1 2.5 virginica
11: 6.5 3.2 5.1 2.0 virginica
12: 6.4 2.7 5.3 1.9 virginica
13: 6.8 3.0 5.5 2.1 virginica
14: 5.7 2.5 5.0 2.0 virginica
15: 5.8 2.8 5.1 2.4 virginica
16: 6.4 3.2 5.3 2.3 virginica
17: 6.5 3.0 5.5 1.8 virginica
18: 7.7 3.8 6.7 2.2 virginica
19: 7.7 2.6 6.9 2.3 virginica
20: 6.0 2.2 5.0 1.5 virginica
21: 6.9 3.2 5.7 2.3 virginica
22: 5.6 2.8 4.9 2.0 virginica
23: 7.7 2.8 6.7 2.0 virginica
24: 6.3 2.7 4.9 1.8 virginica
25: 6.7 3.3 5.7 2.1 virginica
26: 7.2 3.2 6.0 1.8 virginica
27: 6.2 2.8 4.8 1.8 virginica
28: 6.1 3.0 4.9 1.8 virginica
29: 6.4 2.8 5.6 2.1 virginica
30: 7.2 3.0 5.8 1.6 virginica
31: 7.4 2.8 6.1 1.9 virginica
32: 7.9 3.8 6.4 2.0 virginica
33: 6.4 2.8 5.6 2.2 virginica
34: 6.3 2.8 5.1 1.5 virginica
35: 6.1 2.6 5.6 1.4 virginica
36: 7.7 3.0 6.1 2.3 virginica
37: 6.3 3.4 5.6 2.4 virginica
38: 6.4 3.1 5.5 1.8 virginica
39: 6.0 3.0 4.8 1.8 virginica
40: 6.9 3.1 5.4 2.1 virginica
41: 6.7 3.1 5.6 2.4 virginica
42: 6.9 3.1 5.1 2.3 virginica
43: 5.8 2.7 5.1 1.9 virginica
44: 6.8 3.2 5.9 2.3 virginica
45: 6.7 3.3 5.7 2.5 virginica
46: 6.7 3.0 5.2 2.3 virginica
47: 6.3 2.5 5.0 1.9 virginica
48: 6.5 3.0 5.2 2.0 virginica
49: 6.2 3.4 5.4 2.3 virginica
50: 5.9 3.0 5.1 1.8 virginica
51: 7.0 3.2 4.7 1.4 versicolor
52: 6.4 3.2 4.5 1.5 versicolor
53: 6.9 3.1 4.9 1.5 versicolor
54: 5.5 2.3 4.0 1.3 versicolor
55: 6.5 2.8 4.6 1.5 versicolor
56: 5.7 2.8 4.5 1.3 versicolor
57: 6.3 3.3 4.7 1.6 versicolor
58: 4.9 2.4 3.3 1.0 versicolor
59: 6.6 2.9 4.6 1.3 versicolor
60: 5.2 2.7 3.9 1.4 versicolor
61: 5.0 2.0 3.5 1.0 versicolor
62: 5.9 3.0 4.2 1.5 versicolor
63: 6.0 2.2 4.0 1.0 versicolor
64: 6.1 2.9 4.7 1.4 versicolor
65: 5.6 2.9 3.6 1.3 versicolor
66: 6.7 3.1 4.4 1.4 versicolor
67: 5.6 3.0 4.5 1.5 versicolor
68: 5.8 2.7 4.1 1.0 versicolor
69: 6.2 2.2 4.5 1.5 versicolor
70: 5.6 2.5 3.9 1.1 versicolor
71: 5.9 3.2 4.8 1.8 versicolor
72: 6.1 2.8 4.0 1.3 versicolor
73: 6.3 2.5 4.9 1.5 versicolor
74: 6.1 2.8 4.7 1.2 versicolor
75: 6.4 2.9 4.3 1.3 versicolor
76: 6.6 3.0 4.4 1.4 versicolor
77: 6.8 2.8 4.8 1.4 versicolor
78: 6.7 3.0 5.0 1.7 versicolor
79: 6.0 2.9 4.5 1.5 versicolor
80: 5.7 2.6 3.5 1.0 versicolor
81: 5.5 2.4 3.8 1.1 versicolor
82: 5.5 2.4 3.7 1.0 versicolor
83: 5.8 2.7 3.9 1.2 versicolor
84: 6.0 2.7 5.1 1.6 versicolor
85: 5.4 3.0 4.5 1.5 versicolor
86: 6.0 3.4 4.5 1.6 versicolor
87: 6.7 3.1 4.7 1.5 versicolor
88: 6.3 2.3 4.4 1.3 versicolor
89: 5.6 3.0 4.1 1.3 versicolor
90: 5.5 2.5 4.0 1.3 versicolor
91: 5.5 2.6 4.4 1.2 versicolor
92: 6.1 3.0 4.6 1.4 versicolor
93: 5.8 2.6 4.0 1.2 versicolor
94: 5.0 2.3 3.3 1.0 versicolor
95: 5.6 2.7 4.2 1.3 versicolor
96: 5.7 3.0 4.2 1.2 versicolor
97: 5.7 2.9 4.2 1.3 versicolor
98: 6.2 2.9 4.3 1.3 versicolor
99: 5.1 2.5 3.0 1.1 versicolor
100: 5.7 2.8 4.1 1.3 versicolor
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
>
> # iris as a data.table
> iris <- as.data.table(iris)
>
> # Remove the "Sepal." prefix
> setnames(iris, gsub("^Sepal[.]", "", names(iris)))
>
> # Remove the two columns starting with "Petal"
> iris <- iris[, grep("^Petal",names(iris)) := NULL]
>
>
> iris
Length Width Species
1: 5.1 3.5 setosa
2: 4.9 3.0 setosa
3: 4.7 3.2 setosa
4: 4.6 3.1 setosa
5: 5.0 3.6 setosa
---
146: 6.7 3.0 virginica
147: 6.3 2.5 virginica
148: 6.5 3.0 virginica
149: 6.2 3.4 virginica
150: 5.9 3.0 virginica
>
> # Rows for which area is greater than 20 sq cm
> iris[ Width*Length > 20 ]
Length Width Species
1: 5.4 3.9 setosa
2: 5.8 4.0 setosa
3: 5.7 4.4 setosa
4: 5.4 3.9 setosa
5: 5.7 3.8 setosa
6: 5.2 4.1 setosa
7: 5.5 4.2 setosa
8: 7.0 3.2 versicolor
9: 6.4 3.2 versicolor
10: 6.9 3.1 versicolor
11: 6.3 3.3 versicolor
12: 6.7 3.1 versicolor
13: 6.7 3.0 versicolor
14: 6.0 3.4 versicolor
15: 6.7 3.1 versicolor
16: 6.3 3.3 virginica
17: 7.1 3.0 virginica
18: 7.6 3.0 virginica
19: 7.3 2.9 virginica
20: 7.2 3.6 virginica
21: 6.5 3.2 virginica
22: 6.8 3.0 virginica
23: 6.4 3.2 virginica
24: 7.7 3.8 virginica
25: 7.7 2.6 virginica
26: 6.9 3.2 virginica
27: 7.7 2.8 virginica
28: 6.7 3.3 virginica
29: 7.2 3.2 virginica
30: 7.2 3.0 virginica
31: 7.4 2.8 virginica
32: 7.9 3.8 virginica
33: 7.7 3.0 virginica
34: 6.3 3.4 virginica
35: 6.9 3.1 virginica
36: 6.7 3.1 virginica
37: 6.9 3.1 virginica
38: 6.8 3.2 virginica
39: 6.7 3.3 virginica
40: 6.7 3.0 virginica
41: 6.2 3.4 virginica
Length Width Species
>
> # Add new boolean column is_large (area greater than 25 sq cm)
> iris[, is_large := Width*Length > 25]
Length Width Species is_large
1: 5.1 3.5 setosa FALSE
2: 4.9 3.0 setosa FALSE
3: 4.7 3.2 setosa FALSE
4: 4.6 3.1 setosa FALSE
5: 5.0 3.6 setosa FALSE
---
146: 6.7 3.0 virginica FALSE
147: 6.3 2.5 virginica FALSE
148: 6.5 3.0 virginica FALSE
149: 6.2 3.4 virginica FALSE
150: 5.9 3.0 virginica FALSE
>
> # Now select rows again where the area is greater than 25 square centimeters
> iris[is_large == TRUE]
Length Width Species is_large
1: 5.7 4.4 setosa TRUE
2: 7.2 3.6 virginica TRUE
3: 7.7 3.8 virginica TRUE
4: 7.9 3.8 virginica TRUE
> iris[(is_large)]
Length Width Species is_large
1: 5.7 4.4 setosa TRUE
2: 7.2 3.6 virginica TRUE
3: 7.7 3.8 virginica TRUE
4: 7.9 3.8 virginica TRUE
> # The 'keyed' data.table DT
> DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9,8 ,8, 6),
C = 6:12)
> setkey(DT,A,B)
>
> # Select the "b" group
> DT["b"]
A B C
1: b 1 8
2: b 5 6
3: b 8 11
>
> # "b" and "c" groups
> DT[c("b","c")]
A B C
1: b 1 8
2: b 5 6
3: b 8 11
4: c 6 12
5: c 9 9
>
> # The first row of the "b" and "c" groups
> DT[c("b","c"), mult="first"]
A B C
1: b 1 8
2: c 6 12
>
> # First and last row of the "b" and "c" groups
> DT[c("b","c"), .SD[c(1,.N)], by=.EACHI]
A B C
1: b 1 8
2: b 8 11
3: c 6 12
4: c 9 9
>
> # Copy and extend code for instruction 4: add printout
> DT[c("b","c"), {print(.SD);.SD[c(1,.N)]}, by=.EACHI]
B C
1: 1 8
2: 5 6
3: 8 11
B C
1: 6 12
2: 9 9
A B C
1: b 1 8
2: b 8 11
3: c 6 12
4: c 9 9
>
Next Exercise
script.R
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# Keyed data.table DT
DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9, 8, 8, 6),
C = 6:12,
key= "A,B")
# Get the key of DT
key(DT)
# Row where A == "b" & B == 6
DT[.("b",6)]
# Return the prevailing row
DT[.("b",6), roll=TRUE]
# Return the nearest row
DT[.("b",6), roll="nearest"]
Submit Answer
R Console
Slides
> # Keyed data.table DT
> DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9, 8, 8, 6),
C = 6:12,
key= "A,B")
>
> # Get the key of DT
> key(DT)
[1] "A" "B"
>
> # Row where A == "b" & B == 6
> DT[.("b",6)]
A B C
1: b 6 NA
>
> # Return the prevailing row
> DT[.("b",6), roll=TRUE]
A B C
1: b 6 6
>
> # Return the nearest row
> DT[.("b",6), roll="nearest"]
A B C
1: b 6 6
>
Next Exercise
script.R
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# Keyed data.table DT
DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9, 8, 8, 6),
C = 6:12,
key= "A,B")
# Print out the sequence (-2):10 for the "b" group
DT[.("b",(-2):10)]
# Add code: carry the prevailing values forwards
DT[.("b",(-2):10), roll=TRUE]
# Add code: carry the first observation backwards
DT[.("b",(-2):10), roll=TRUE, rollends=TRUE]
Submit Answer
R Console
Slides
> # Keyed data.table DT
> DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9, 8, 8, 6),
C = 6:12,
key= "A,B")
>
> # Print out the sequence (-2):10 for the "b" group
> DT[.("b",(-2):10)]
A B C
1: b -2 NA
2: b -1 NA
3: b 0 NA
4: b 1 8
5: b 2 NA
6: b 3 NA
7: b 4 NA
8: b 5 6
9: b 6 NA
10: b 7 NA
11: b 8 11
12: b 9 NA
13: b 10 NA
>
> # Add code: carry the prevailing values forwards
> DT[.("b",(-2):10), roll=TRUE]
A B C
1: b -2 NA
2: b -1 NA
3: b 0 NA
4: b 1 8
5: b 2 8
6: b 3 8
7: b 4 8
8: b 5 6
9: b 6 6
10: b 7 6
11: b 8 11
12: b 9 11
13: b 10 11
>
> # Add code: carry the first observation backwards
> DT[.("b",(-2):10), roll=TRUE, rollends=TRUE]
A B C
1: b -2 8
2: b -1 8
3: b 0 8
4: b 1 8
5: b 2 8
6: b 3 8
7: b 4 8
8: b 5 6
9: b 6 6
10: b 7 6
11: b 8 11
12: b 9 11
13: b 10 11
>
