Welcome to our new blog, More into data.table. We hope you enjoy reading what we have to say!
Data table syntax is of the form DT[i, j, by]
i: on which rowj: what to doby: group by whatirisDT = as.data.table(iris)
tables() # show loaded tables
NAME NROW NCOL MB
1: irisDT 150 5 0
COLS KEY
1: Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species
Total: 0MBSorting / ordering rows
setorder(data.table, …)
-: to sort a variable in descending order
setorder(irisDT, Sepal.Length, Sepal.Width)
irisDT
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 4.3 3.0 1.1 0.1 setosa
2: 4.4 2.9 1.4 0.2 setosa
3: 4.4 3.0 1.3 0.2 setosa
4: 4.4 3.2 1.3 0.2 setosa
5: 4.5 2.3 1.3 0.3 setosa
---
146: 7.7 2.6 6.9 2.3 virginica
147: 7.7 2.8 6.7 2.0 virginica
148: 7.7 3.0 6.1 2.3 virginica
149: 7.7 3.8 6.7 2.2 virginica
150: 7.9 3.8 6.4 2.0 virginicasetorder(irisDT, -Species, Sepal.Width)
irisDT
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
---
146: 5.4 3.9 1.3 0.4 setosa
147: 5.8 4.0 1.2 0.2 setosa
148: 5.2 4.1 1.5 0.1 setosa
149: 5.5 4.2 1.4 0.2 setosa
150: 5.7 4.4 1.5 0.4 setosaDT[i, j, by]
i: on which rows?irisDT[3:4]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 5.7 2.5 5 2.0 virginica
2: 6.3 2.5 5 1.9 virginicairisDT[3:4,] #row 3 and 4
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 5.7 2.5 5 2.0 virginica
2: 6.3 2.5 5 1.9 virginicairisDT[-(1:5)] #delete rows 1:5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.1 2.6 5.6 1.4 virginica
2: 7.7 2.6 6.9 2.3 virginica
3: 5.8 2.7 5.1 1.9 virginica
4: 5.8 2.7 5.1 1.9 virginica
5: 6.3 2.7 4.9 1.8 virginica
---
141: 5.4 3.9 1.3 0.4 setosa
142: 5.8 4.0 1.2 0.2 setosa
143: 5.2 4.1 1.5 0.1 setosa
144: 5.5 4.2 1.4 0.2 setosa
145: 5.7 4.4 1.5 0.4 setosairisDT[!(1:5)] # using false to delete as well
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.1 2.6 5.6 1.4 virginica
2: 7.7 2.6 6.9 2.3 virginica
3: 5.8 2.7 5.1 1.9 virginica
4: 5.8 2.7 5.1 1.9 virginica
5: 6.3 2.7 4.9 1.8 virginica
---
141: 5.4 3.9 1.3 0.4 setosa
142: 5.8 4.0 1.2 0.2 setosa
143: 5.2 4.1 1.5 0.1 setosa
144: 5.5 4.2 1.4 0.2 setosa
145: 5.7 4.4 1.5 0.4 setosairisDT[.N] # last row
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 5.7 4.4 1.5 0.4 setosairisDT[1:(.N-10)] # all but the last 10 rows
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
---
136: 4.9 3.6 1.4 0.1 setosa
137: 5.0 3.6 1.4 0.2 setosa
138: 5.1 3.7 1.5 0.4 setosa
139: 5.3 3.7 1.5 0.2 setosa
140: 5.4 3.7 1.5 0.2 setosairisDT[Species == "virginica"] # based on conditions fulfilled in columns
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
6: 6.1 2.6 5.6 1.4 virginica
7: 7.7 2.6 6.9 2.3 virginica
8: 5.8 2.7 5.1 1.9 virginica
9: 5.8 2.7 5.1 1.9 virginica
10: 6.3 2.7 4.9 1.8 virginica
11: 6.4 2.7 5.3 1.9 virginica
12: 5.6 2.8 4.9 2.0 virginica
13: 5.8 2.8 5.1 2.4 virginica
14: 6.2 2.8 4.8 1.8 virginica
15: 6.3 2.8 5.1 1.5 virginica
16: 6.4 2.8 5.6 2.1 virginica
17: 6.4 2.8 5.6 2.2 virginica
18: 7.4 2.8 6.1 1.9 virginica
19: 7.7 2.8 6.7 2.0 virginica
20: 6.3 2.9 5.6 1.8 virginica
21: 7.3 2.9 6.3 1.8 virginica
22: 5.9 3.0 5.1 1.8 virginica
23: 6.0 3.0 4.8 1.8 virginica
24: 6.1 3.0 4.9 1.8 virginica
25: 6.5 3.0 5.8 2.2 virginica
26: 6.5 3.0 5.5 1.8 virginica
27: 6.5 3.0 5.2 2.0 virginica
28: 6.7 3.0 5.2 2.3 virginica
29: 6.8 3.0 5.5 2.1 virginica
30: 7.1 3.0 5.9 2.1 virginica
31: 7.2 3.0 5.8 1.6 virginica
32: 7.6 3.0 6.6 2.1 virginica
33: 7.7 3.0 6.1 2.3 virginica
34: 6.4 3.1 5.5 1.8 virginica
35: 6.7 3.1 5.6 2.4 virginica
36: 6.9 3.1 5.4 2.1 virginica
37: 6.9 3.1 5.1 2.3 virginica
38: 6.4 3.2 5.3 2.3 virginica
39: 6.5 3.2 5.1 2.0 virginica
40: 6.8 3.2 5.9 2.3 virginica
41: 6.9 3.2 5.7 2.3 virginica
42: 7.2 3.2 6.0 1.8 virginica
43: 6.3 3.3 6.0 2.5 virginica
44: 6.7 3.3 5.7 2.1 virginica
45: 6.7 3.3 5.7 2.5 virginica
46: 6.2 3.4 5.4 2.3 virginica
47: 6.3 3.4 5.6 2.4 virginica
48: 7.2 3.6 6.1 2.5 virginica
49: 7.7 3.8 6.7 2.2 virginica
50: 7.9 3.8 6.4 2.0 virginica
Sepal.Length Sepal.Width Petal.Length Petal.Width SpeciesirisDT[Species %like% "^v"] # using %like% helper function
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
6: 6.1 2.6 5.6 1.4 virginica
7: 7.7 2.6 6.9 2.3 virginica
8: 5.8 2.7 5.1 1.9 virginica
9: 5.8 2.7 5.1 1.9 virginica
10: 6.3 2.7 4.9 1.8 virginica
11: 6.4 2.7 5.3 1.9 virginica
12: 5.6 2.8 4.9 2.0 virginica
13: 5.8 2.8 5.1 2.4 virginica
14: 6.2 2.8 4.8 1.8 virginica
15: 6.3 2.8 5.1 1.5 virginica
16: 6.4 2.8 5.6 2.1 virginica
17: 6.4 2.8 5.6 2.2 virginica
18: 7.4 2.8 6.1 1.9 virginica
19: 7.7 2.8 6.7 2.0 virginica
20: 6.3 2.9 5.6 1.8 virginica
21: 7.3 2.9 6.3 1.8 virginica
22: 5.9 3.0 5.1 1.8 virginica
23: 6.0 3.0 4.8 1.8 virginica
24: 6.1 3.0 4.9 1.8 virginica
25: 6.5 3.0 5.8 2.2 virginica
26: 6.5 3.0 5.5 1.8 virginica
27: 6.5 3.0 5.2 2.0 virginica
28: 6.7 3.0 5.2 2.3 virginica
29: 6.8 3.0 5.5 2.1 virginica
30: 7.1 3.0 5.9 2.1 virginica
31: 7.2 3.0 5.8 1.6 virginica
32: 7.6 3.0 6.6 2.1 virginica
33: 7.7 3.0 6.1 2.3 virginica
34: 6.4 3.1 5.5 1.8 virginica
35: 6.7 3.1 5.6 2.4 virginica
36: 6.9 3.1 5.4 2.1 virginica
37: 6.9 3.1 5.1 2.3 virginica
38: 6.4 3.2 5.3 2.3 virginica
39: 6.5 3.2 5.1 2.0 virginica
40: 6.8 3.2 5.9 2.3 virginica
41: 6.9 3.2 5.7 2.3 virginica
42: 7.2 3.2 6.0 1.8 virginica
43: 6.3 3.3 6.0 2.5 virginica
44: 6.7 3.3 5.7 2.1 virginica
45: 6.7 3.3 5.7 2.5 virginica
46: 6.2 3.4 5.4 2.3 virginica
47: 6.3 3.4 5.6 2.4 virginica
48: 7.2 3.6 6.1 2.5 virginica
49: 7.7 3.8 6.7 2.2 virginica
50: 7.9 3.8 6.4 2.0 virginica
51: 5.0 2.0 3.5 1.0 versicolor
52: 6.0 2.2 4.0 1.0 versicolor
53: 6.2 2.2 4.5 1.5 versicolor
54: 5.0 2.3 3.3 1.0 versicolor
55: 5.5 2.3 4.0 1.3 versicolor
56: 6.3 2.3 4.4 1.3 versicolor
57: 4.9 2.4 3.3 1.0 versicolor
58: 5.5 2.4 3.8 1.1 versicolor
59: 5.5 2.4 3.7 1.0 versicolor
60: 5.1 2.5 3.0 1.1 versicolor
61: 5.5 2.5 4.0 1.3 versicolor
62: 5.6 2.5 3.9 1.1 versicolor
63: 6.3 2.5 4.9 1.5 versicolor
64: 5.5 2.6 4.4 1.2 versicolor
65: 5.7 2.6 3.5 1.0 versicolor
66: 5.8 2.6 4.0 1.2 versicolor
67: 5.2 2.7 3.9 1.4 versicolor
68: 5.6 2.7 4.2 1.3 versicolor
69: 5.8 2.7 4.1 1.0 versicolor
70: 5.8 2.7 3.9 1.2 versicolor
71: 6.0 2.7 5.1 1.6 versicolor
72: 5.7 2.8 4.5 1.3 versicolor
73: 5.7 2.8 4.1 1.3 versicolor
74: 6.1 2.8 4.0 1.3 versicolor
75: 6.1 2.8 4.7 1.2 versicolor
76: 6.5 2.8 4.6 1.5 versicolor
77: 6.8 2.8 4.8 1.4 versicolor
78: 5.6 2.9 3.6 1.3 versicolor
79: 5.7 2.9 4.2 1.3 versicolor
80: 6.0 2.9 4.5 1.5 versicolor
81: 6.1 2.9 4.7 1.4 versicolor
82: 6.2 2.9 4.3 1.3 versicolor
83: 6.4 2.9 4.3 1.3 versicolor
84: 6.6 2.9 4.6 1.3 versicolor
85: 5.4 3.0 4.5 1.5 versicolor
86: 5.6 3.0 4.5 1.5 versicolor
87: 5.6 3.0 4.1 1.3 versicolor
88: 5.7 3.0 4.2 1.2 versicolor
89: 5.9 3.0 4.2 1.5 versicolor
90: 6.1 3.0 4.6 1.4 versicolor
91: 6.6 3.0 4.4 1.4 versicolor
92: 6.7 3.0 5.0 1.7 versicolor
93: 6.7 3.1 4.4 1.4 versicolor
94: 6.7 3.1 4.7 1.5 versicolor
95: 6.9 3.1 4.9 1.5 versicolor
96: 5.9 3.2 4.8 1.8 versicolor
97: 6.4 3.2 4.5 1.5 versicolor
98: 7.0 3.2 4.7 1.4 versicolor
99: 6.3 3.3 4.7 1.6 versicolor
100: 6.0 3.4 4.5 1.6 versicolor
Sepal.Length Sepal.Width Petal.Length Petal.Width SpeciesirisDT[Petal.Width %between% c(0.3, 0.4)] # values in an interval
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 4.5 2.3 1.3 0.3 setosa
2: 4.8 3.0 1.4 0.3 setosa
3: 4.6 3.4 1.4 0.3 setosa
4: 5.0 3.4 1.6 0.4 setosa
5: 5.4 3.4 1.5 0.4 setosa
6: 5.0 3.5 1.3 0.3 setosa
7: 5.1 3.5 1.4 0.3 setosa
8: 5.1 3.7 1.5 0.4 setosa
9: 5.1 3.8 1.5 0.3 setosa
10: 5.1 3.8 1.9 0.4 setosa
11: 5.7 3.8 1.7 0.3 setosa
12: 5.4 3.9 1.7 0.4 setosa
13: 5.4 3.9 1.3 0.4 setosa
14: 5.7 4.4 1.5 0.4 setosaDT[i, j, by]
j: what to do? -> select columnsirisDT[, Species] # returns a vector
[1] virginica virginica virginica virginica virginica
[6] virginica virginica virginica virginica virginica
[11] virginica virginica virginica virginica virginica
[16] virginica virginica virginica virginica virginica
[21] virginica virginica virginica virginica virginica
[26] virginica virginica virginica virginica virginica
[31] virginica virginica virginica virginica virginica
[36] virginica virginica virginica virginica virginica
[41] virginica virginica virginica virginica virginica
[46] virginica virginica virginica virginica virginica
[51] versicolor versicolor versicolor versicolor versicolor
[56] versicolor versicolor versicolor versicolor versicolor
[61] versicolor versicolor versicolor versicolor versicolor
[66] versicolor versicolor versicolor versicolor versicolor
[71] versicolor versicolor versicolor versicolor versicolor
[76] versicolor versicolor versicolor versicolor versicolor
[81] versicolor versicolor versicolor versicolor versicolor
[86] versicolor versicolor versicolor versicolor versicolor
[91] versicolor versicolor versicolor versicolor versicolor
[96] versicolor versicolor versicolor versicolor versicolor
[101] setosa setosa setosa setosa setosa
[106] setosa setosa setosa setosa setosa
[111] setosa setosa setosa setosa setosa
[116] setosa setosa setosa setosa setosa
[121] setosa setosa setosa setosa setosa
[126] setosa setosa setosa setosa setosa
[131] setosa setosa setosa setosa setosa
[136] setosa setosa setosa setosa setosa
[141] setosa setosa setosa setosa setosa
[146] setosa setosa setosa setosa setosa
Levels: setosa versicolor virginicairisDT[, "Species"] # returns a dataframe/data.table
Species
1: virginica
2: virginica
3: virginica
4: virginica
5: virginica
---
146: setosa
147: setosa
148: setosa
149: setosa
150: setosairisDT[, -c("Species")]
Sepal.Length Sepal.Width Petal.Length Petal.Width
1: 6.0 2.2 5.0 1.5
2: 4.9 2.5 4.5 1.7
3: 5.7 2.5 5.0 2.0
4: 6.3 2.5 5.0 1.9
5: 6.7 2.5 5.8 1.8
---
146: 5.4 3.9 1.3 0.4
147: 5.8 4.0 1.2 0.2
148: 5.2 4.1 1.5 0.1
149: 5.5 4.2 1.4 0.2
150: 5.7 4.4 1.5 0.4irisDT[, !c("Species")]
Sepal.Length Sepal.Width Petal.Length Petal.Width
1: 6.0 2.2 5.0 1.5
2: 4.9 2.5 4.5 1.7
3: 5.7 2.5 5.0 2.0
4: 6.3 2.5 5.0 1.9
5: 6.7 2.5 5.8 1.8
---
146: 5.4 3.9 1.3 0.4
147: 5.8 4.0 1.2 0.2
148: 5.2 4.1 1.5 0.1
149: 5.5 4.2 1.4 0.2
150: 5.7 4.4 1.5 0.4irisDT[, list(Species, LS = Sepal.Length)] # select and rename
Species LS
1: virginica 6.0
2: virginica 4.9
3: virginica 5.7
4: virginica 6.3
5: virginica 6.7
---
146: setosa 5.4
147: setosa 5.8
148: setosa 5.2
149: setosa 5.5
150: setosa 5.7# .() is an alias for list
irisDT[, .(Species, SL = Sepal.Length)]
Species SL
1: virginica 6.0
2: virginica 4.9
3: virginica 5.7
4: virginica 6.3
5: virginica 6.7
---
146: setosa 5.4
147: setosa 5.8
148: setosa 5.2
149: setosa 5.5
150: setosa 5.7irisDT[Species == "virginica" & Sepal.Length > 7 & Sepal.Width < 3, !c("Species")]
Sepal.Length Sepal.Width Petal.Length Petal.Width
1: 7.7 2.6 6.9 2.3
2: 7.4 2.8 6.1 1.9
3: 7.7 2.8 6.7 2.0
4: 7.3 2.9 6.3 1.8irisDT[Species == "virginica" &
Sepal.Length > 7 &Sepal.Width < 3,.(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)]
Sepal.Length Sepal.Width Petal.Length Petal.Width
1: 7.7 2.6 6.9 2.3
2: 7.4 2.8 6.1 1.9
3: 7.7 2.8 6.7 2.0
4: 7.3 2.9 6.3 1.8DT[i, j, by]
j: what to do? -> compute new columns preserving existing onesdata.table uses a new operator := to add/update/delete columns (by reference)
irisDT[, maxLength := max(Sepal.Length, Petal.Length)][,
minWidth := min(Sepal.Length, Petal.Width)]
irisDT
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
---
146: 5.4 3.9 1.3 0.4 setosa
147: 5.8 4.0 1.2 0.2 setosa
148: 5.2 4.1 1.5 0.1 setosa
149: 5.5 4.2 1.4 0.2 setosa
150: 5.7 4.4 1.5 0.4 setosa
maxLength minWidth
1: 7.9 0.1
2: 7.9 0.1
3: 7.9 0.1
4: 7.9 0.1
5: 7.9 0.1
---
146: 7.9 0.1
147: 7.9 0.1
148: 7.9 0.1
149: 7.9 0.1
150: 7.9 0.1LHS := RHS form
irisDT[, c("maxLength", "minWidth") :=
list(
max(Sepal.Length, Petal.Length),
min(Sepal.Width, Petal.Width)
)]
irisDT
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
---
146: 5.4 3.9 1.3 0.4 setosa
147: 5.8 4.0 1.2 0.2 setosa
148: 5.2 4.1 1.5 0.1 setosa
149: 5.5 4.2 1.4 0.2 setosa
150: 5.7 4.4 1.5 0.4 setosa
maxLength minWidth
1: 7.9 0.1
2: 7.9 0.1
3: 7.9 0.1
4: 7.9 0.1
5: 7.9 0.1
---
146: 7.9 0.1
147: 7.9 0.1
148: 7.9 0.1
149: 7.9 0.1
150: 7.9 0.1Functional form
irisDT[, `:=` (
maxLength = max(Sepal.Length, Petal.Length),
minWidth = min(Sepal.Width, Petal.Width)
)]
irisDT
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
---
146: 5.4 3.9 1.3 0.4 setosa
147: 5.8 4.0 1.2 0.2 setosa
148: 5.2 4.1 1.5 0.1 setosa
149: 5.5 4.2 1.4 0.2 setosa
150: 5.7 4.4 1.5 0.4 setosa
maxLength minWidth
1: 7.9 0.1
2: 7.9 0.1
3: 7.9 0.1
4: 7.9 0.1
5: 7.9 0.1
---
146: 7.9 0.1
147: 7.9 0.1
148: 7.9 0.1
149: 7.9 0.1
150: 7.9 0.1DT[i, j, by]
j: what to do? -> compute new columns dropping existing onesirisDT[, .(maxLength = pmax(Sepal.Length, Petal.Length),
minWidth = pmin(Sepal.Width, Petal.Width))]
maxLength minWidth
1: 6.0 1.5
2: 4.9 1.7
3: 5.7 2.0
4: 6.3 1.9
5: 6.7 1.8
---
146: 5.4 0.4
147: 5.8 0.2
148: 5.2 0.1
149: 5.5 0.2
150: 5.7 0.4newIris <- irisDT[, .(Sepal.Area = Sepal.Width * Sepal.Length,
Petal.Area = Petal.Width * Petal.Length)]
newIris[, Area.Ratio := Petal.Area / Sepal.Area]
newIris
Sepal.Area Petal.Area Area.Ratio
1: 13.20 7.50 0.568181818
2: 12.25 7.65 0.624489796
3: 14.25 10.00 0.701754386
4: 15.75 9.50 0.603174603
5: 16.75 10.44 0.623283582
---
146: 21.06 0.52 0.024691358
147: 23.20 0.24 0.010344828
148: 21.32 0.15 0.007035647
149: 23.10 0.28 0.012121212
150: 25.08 0.60 0.023923445irisDT[, .(meanSepalLength = mean(Sepal.Length),
meanSepalWidth = mean(Sepal.Width),
meanPetalLength = mean(Petal.Length),
meanPetalWidth = mean(Petal.Width))]
meanSepalLength meanSepalWidth meanPetalLength meanPetalWidth
1: 5.843333 3.057333 3.758 1.199333irisDT[, .(nSamples = .N, nSpecies = uniqueN(Species))
]
nSamples nSpecies
1: 150 3irisDT[Species == "versicolor",.(
meanSepalLength = mean(Sepal.Length),
meanSepalWidth = mean(Sepal.Width),
meanPetalLength = mean(Petal.Length),
meanPetalWidth = mean(Petal.Width)
)]
meanSepalLength meanSepalWidth meanPetalLength meanPetalWidth
1: 5.936 2.77 4.26 1.326irisDT[, .(meanSepalLength = mean(Sepal.Length),
meanSepalWidth = mean(Sepal.Width),
meanPetalLength = mean(Petal.Length),
meanPetalWidth = mean(Petal.Width)), by = "Species"]
Species meanSepalLength meanSepalWidth meanPetalLength
1: virginica 6.588 2.974 5.552
2: versicolor 5.936 2.770 4.260
3: setosa 5.006 3.428 1.462
meanPetalWidth
1: 2.026
2: 1.326
3: 0.246irisDT[, .(nSamples = .N, nSpecies = uniqueN(Species)), by = .(Species)]
Species nSamples nSpecies
1: virginica 50 1
2: versicolor 50 1
3: setosa 50 1Inner join: return all rows from x where there are matching values in y and all columns from x and y. In case of multiple matches between x and y, all combinations of the matches are returned.
Full join: return all rows and columns from both x and y.
Left join: return all rows from x and all columns from x and y. In case of multiple matches between x and y, all combinations of the matches are returned.
Right join: return all rows from y and all columns from x and y. In case of multiple matches between x and y, all combinations of the matches are returned.
DT[i, on]
i: join to which data.table?on: join key columns? id x
1: 1 9
2: 2 12
3: 4 14
4: 5 21
5: 6 8 id y
1: 1 8
2: 3 14
3: 4 19
4: 6 2
5: 6 4y[x, on = .(id), nomatch = 0]
id y x
1: 1 8 9
2: 4 19 14
3: 6 2 8
4: 6 4 8merge.data.table(x = x, y = y, by = "id", all = TRUE)
id x y
1: 1 9 8
2: 2 12 NA
3: 3 NA 14
4: 4 14 19
5: 5 21 NA
6: 6 8 2
7: 6 8 4merge.data.table(x = x, y = y, by = "id", all.x = TRUE)
id x y
1: 1 9 8
2: 2 12 NA
3: 4 14 19
4: 5 21 NA
5: 6 8 2
6: 6 8 4y[x, on = .(id)]
id y x
1: 1 8 9
2: 2 NA 12
3: 4 19 14
4: 5 NA 21
5: 6 2 8
6: 6 4 8data.table::merge.data.table(x = x, y = y, by = "id", all.y = T)
id x y
1: 1 9 8
2: 3 NA 14
3: 4 14 19
4: 6 8 2
5: 6 8 4x[y, on = .(id)]
id x y
1: 1 9 8
2: 3 NA 14
3: 4 14 19
4: 6 8 2
5: 6 8 4x[!y, on = .(id)]
id x
1: 2 12
2: 5 21No need of the on argument when performing a join
setkey(x, id)
setkey(y, "id")
haskey(x); haskey(y)
[1] TRUE[1] TRUEkey(x); key(y)
[1] "id"[1] "id"# Inner join x and y
x[y, nomatch = 0]
id x y
1: 1 9 8
2: 4 14 19
3: 6 8 2
4: 6 8 4# Right join x and y
x[y]
id x y
1: 1 9 8
2: 3 NA 14
3: 4 14 19
4: 6 8 2
5: 6 8 4# Left join x and y
y[x]
id y x
1: 1 8 9
2: 2 NA 12
3: 4 19 14
4: 5 NA 21
5: 6 2 8
6: 6 4 8library(dplyr)
library(data.table)
data(starwars)
starwarsDT <- as.data.table(starwars)
starwarsDT[1]
name height mass hair_color skin_color eye_color
1: Luke Skywalker 172 77 blond fair blue
birth_year sex gender homeworld species
1: 19 male masculine Tatooine Human
films
1: The Empire Strikes Back,Revenge of the Sith,Return of the Jedi,A New Hope,The Force Awakens
vehicles starships
1: Snowspeeder,Imperial Speeder Bike X-wing,Imperial shuttlestarwarsDT[, .(min = min(mass, na.rm = T),
max = max(mass, na.rm = T),
nobs = .N), by = homeworld][nobs >1]
homeworld min max nobs
1: Tatooine 32.0 136.0 10
2: Naboo 32.0 85.0 11
3: Alderaan 49.0 79.0 3
4: Kashyyyk 112.0 136.0 2
5: Corellia 77.0 80.0 2
6: <NA> 17.0 140.0 10
7: Kamino 78.2 88.0 3
8: Coruscant 50.0 50.0 3
9: Ryloth 55.0 55.0 2
10: Mirial 50.0 56.2 2Tidy data
Two fundamental verbs
melt(): ‘Wide’ -> ‘long’ datadcast(): ‘long’ -> ‘wide’ dataWide format
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
melt(wide_df, id.vars = 'grouping variable', variable.name = 'measures_across_grouping_var',value.name = 'value_name_as_desired')
Long to wide
dcast(long_df, group ~ atttribute, value.var = "value")
irisUnited <- irisDT[ , Sepal.Length.Width := paste0(Sepal.Length, "/", Sepal.Width)]
irisUnited[1:5]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6.0 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
maxLength minWidth Sepal.Length.Width
1: 7.9 0.1 6/2.2
2: 7.9 0.1 4.9/2.5
3: 7.9 0.1 5.7/2.5
4: 7.9 0.1 6.3/2.5
5: 7.9 0.1 6.7/2.5irisSeparated = irisUnited[, c('Sepal.Length', 'Sepal.Width') := tstrsplit(Sepal.Length.Width, "/")]
irisSeparated[1:5]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6 2.2 5.0 1.5 virginica
2: 4.9 2.5 4.5 1.7 virginica
3: 5.7 2.5 5.0 2.0 virginica
4: 6.3 2.5 5.0 1.9 virginica
5: 6.7 2.5 5.8 1.8 virginica
maxLength minWidth Sepal.Length.Width
1: 7.9 0.1 6/2.2
2: 7.9 0.1 4.9/2.5
3: 7.9 0.1 5.7/2.5
4: 7.9 0.1 6.3/2.5
5: 7.9 0.1 6.7/2.5Exercise 1
data("relig_income")
a = as.data.table(relig_income)
a[1:10]
religion <$10k $10-20k $20-30k $30-40k $40-50k
1: Agnostic 27 34 60 81 76
2: Atheist 12 27 37 52 35
3: Buddhist 27 21 30 34 33
4: Catholic 418 617 732 670 638
5: Don’t know/refused 15 14 15 11 10
6: Evangelical Prot 575 869 1064 982 881
7: Hindu 1 9 7 9 11
8: Historically Black Prot 228 244 236 238 197
9: Jehovah's Witness 20 27 24 24 21
10: Jewish 19 19 25 25 30
$50-75k $75-100k $100-150k >150k Don't know/refused
1: 137 122 109 84 96
2: 70 73 59 74 76
3: 58 62 39 53 54
4: 1116 949 792 633 1489
5: 35 21 17 18 116
6: 1486 949 723 414 1529
7: 34 47 48 54 37
8: 223 131 81 78 339
9: 30 15 11 6 37
10: 95 69 87 151 162b = melt(a, id.vars = "religion", variable.name = "income_category", value.name = "n")
Exercise 2
data("storms")
aa = as.data.table(storms)
aa[, date:= paste0(year, "-", month,"-", day)]
.SD: Subset of Data for each group
.SDcols: Columns of the data.table that are included in .SDcols = sapply(irisDT, is.numeric)
cols = names(cols)[cols]
irisDT[, lapply(.SD, mean), by = Species, .SDcols = cols]
Species Petal.Length Petal.Width maxLength minWidth
1: virginica 5.552 2.026 7.9 0.1
2: versicolor 4.260 1.326 7.9 0.1
3: setosa 1.462 0.246 7.9 0.1 Species Petal.Length Petal.Width
1: virginica 5.552 2.026
2: versicolor 4.260 1.326
3: setosa 1.462 0.246irisDT[ , unlist(recursive = FALSE, lapply(.(mean = mean, sd = sd, min = min, max = max),
function(f) lapply(.SD, f))), by = Species]
Species mean.Sepal.Length mean.Sepal.Width mean.Petal.Length
1: virginica NA NA 5.552
2: versicolor NA NA 4.260
3: setosa NA NA 1.462
mean.Petal.Width mean.maxLength mean.minWidth
1: 2.026 7.9 0.1
2: 1.326 7.9 0.1
3: 0.246 7.9 0.1
mean.Sepal.Length.Width sd.Sepal.Length sd.Sepal.Width
1: NA 0.6358796 0.3224966
2: NA 0.5161711 0.3137983
3: NA 0.3524897 0.3790644
sd.Petal.Length sd.Petal.Width sd.maxLength sd.minWidth
1: 0.5518947 0.2746501 0 0
2: 0.4699110 0.1977527 0 0
3: 0.1736640 0.1053856 0 0
sd.Sepal.Length.Width min.Sepal.Length min.Sepal.Width
1: NA 4.9 2.2
2: NA 4.9 2
3: NA 4.3 2.3
min.Petal.Length min.Petal.Width min.maxLength min.minWidth
1: 4.5 1.4 7.9 0.1
2: 3.0 1.0 7.9 0.1
3: 1.0 0.1 7.9 0.1
min.Sepal.Length.Width max.Sepal.Length max.Sepal.Width
1: 4.9/2.5 7.9 3.8
2: 4.9/2.4 7 3.4
3: 4.3/3 5.8 4.4
max.Petal.Length max.Petal.Width max.maxLength max.minWidth
1: 6.9 2.5 7.9 0.1
2: 5.1 1.8 7.9 0.1
3: 1.9 0.6 7.9 0.1
max.Sepal.Length.Width
1: 7.9/3.8
2: 7/3.2
3: 5/3.6Exercise 1 - Compute the minimum, maximum, median and mean for all numeric columns
data("starwars")
starwarsDT = as.data.table(starwars)
cols = sapply(starwarsDT, is.numeric)
cols = names(cols)[cols]
starwarsDT[, height:= as.numeric(height)]
starwarsDT[, unlist(recursive = F,
lapply(.(min = min, max = max, median = median, mean = mean), function(f) lapply(.SD, f))), by = .(homeworld, species), .SDcols = cols]
homeworld species min.height min.mass min.birth_year
1: Tatooine Human 163 NA 19.0
2: Tatooine Droid 97 32.0 NA
3: Naboo Droid 96 32.0 33.0
4: Alderaan Human 150 NA NA
5: Stewjon Human 182 77.0 57.0
6: Eriadu Human 180 NA 64.0
7: Kashyyyk Wookiee 228 112.0 NA
8: Corellia Human 170 77.0 21.0
9: Rodia Rodian 173 74.0 44.0
10: Nal Hutta Hutt 175 1358.0 600.0
11: Bestine IV Human 180 110.0 NA
12: <NA> Yoda's species 66 17.0 896.0
13: Naboo Human 157 NA NA
14: Kamino Human 183 78.2 31.5
15: <NA> Droid NA NA NA
16: Trandosha Trandoshan 190 113.0 53.0
17: Socorro Human 177 79.0 31.0
18: Bespin Human 175 79.0 37.0
19: Mon Cala Mon Calamari 180 83.0 41.0
20: Chandrila Human 150 NA 48.0
21: <NA> Human NA NA NA
22: Endor Ewok 88 20.0 8.0
23: Sullust Sullustan 160 68.0 NA
24: Cato Neimoidia Neimodian 191 90.0 NA
25: Coruscant Human 167 NA NA
26: Naboo Gungan 196 NA NA
27: Naboo <NA> 183 NA NA
28: Toydaria Toydarian 137 NA NA
29: Malastare Dug 112 40.0 NA
30: Dathomir Zabrak 175 80.0 54.0
31: Ryloth Twi'lek 178 NA NA
32: Vulpter Vulptereen 94 45.0 NA
33: Troiken Xexto 122 NA NA
34: Tund Toong 163 65.0 NA
35: Haruun Kal Human 188 84.0 72.0
36: Cerea Cerean 198 82.0 92.0
37: Glee Anselm Nautolan 196 87.0 NA
38: Iridonia Zabrak 171 NA NA
39: Coruscant Tholothian 184 50.0 NA
40: Iktotch Iktotchi 188 NA NA
41: Quermia Quermian 264 NA NA
42: Dorin Kel Dor 188 80.0 22.0
43: Champala Chagrian 196 NA NA
44: Geonosis Geonosian 183 80.0 NA
45: Mirial Mirialan 166 50.0 40.0
46: Serenno Human 193 80.0 102.0
47: Concord Dawn Human 183 79.0 66.0
48: Zolan Clawdite 168 55.0 NA
49: Ojom Besalisk 198 102.0 NA
50: Kamino Kaminoan 213 NA NA
51: Aleen Minor Aleena 79 15.0 NA
52: Skako Skakoan 193 48.0 NA
53: Muunilinst Muun 191 NA NA
54: Shili Togruta 178 57.0 NA
55: Kalee Kaleesh 216 159.0 NA
56: Umbara <NA> 178 48.0 NA
57: Utapau Pau'an 206 80.0 NA
58: <NA> <NA> NA NA NA
homeworld species min.height min.mass min.birth_year
max.height max.mass max.birth_year median.height median.mass
1: 202 NA 82.0 180.5 NA
2: 167 75.0 NA 132.0 53.5
3: 96 32.0 33.0 96.0 32.0
4: 191 NA NA 188.0 NA
5: 182 77.0 57.0 182.0 77.0
6: 180 NA 64.0 180.0 NA
7: 234 136.0 NA 231.0 124.0
8: 180 80.0 29.0 175.0 78.5
9: 173 74.0 44.0 173.0 74.0
10: 175 1358.0 600.0 175.0 1358.0
11: 180 110.0 NA 180.0 110.0
12: 66 17.0 896.0 66.0 17.0
13: 185 NA NA 165.0 NA
14: 183 78.2 31.5 183.0 78.2
15: NA NA NA NA NA
16: 190 113.0 53.0 190.0 113.0
17: 177 79.0 31.0 177.0 79.0
18: 175 79.0 37.0 175.0 79.0
19: 180 83.0 41.0 180.0 83.0
20: 150 NA 48.0 150.0 NA
21: NA NA NA NA NA
22: 88 20.0 8.0 88.0 20.0
23: 160 68.0 NA 160.0 68.0
24: 191 90.0 NA 191.0 90.0
25: 170 NA NA 168.5 NA
26: 224 NA NA 206.0 NA
27: 183 NA NA 183.0 NA
28: 137 NA NA 137.0 NA
29: 112 40.0 NA 112.0 40.0
30: 175 80.0 54.0 175.0 80.0
31: 180 NA NA 179.0 NA
32: 94 45.0 NA 94.0 45.0
33: 122 NA NA 122.0 NA
34: 163 65.0 NA 163.0 65.0
35: 188 84.0 72.0 188.0 84.0
36: 198 82.0 92.0 198.0 82.0
37: 196 87.0 NA 196.0 87.0
38: 171 NA NA 171.0 NA
39: 184 50.0 NA 184.0 50.0
40: 188 NA NA 188.0 NA
41: 264 NA NA 264.0 NA
42: 188 80.0 22.0 188.0 80.0
43: 196 NA NA 196.0 NA
44: 183 80.0 NA 183.0 80.0
45: 170 56.2 58.0 168.0 53.1
46: 193 80.0 102.0 193.0 80.0
47: 183 79.0 66.0 183.0 79.0
48: 168 55.0 NA 168.0 55.0
49: 198 102.0 NA 198.0 102.0
50: 229 NA NA 221.0 NA
51: 79 15.0 NA 79.0 15.0
52: 193 48.0 NA 193.0 48.0
53: 191 NA NA 191.0 NA
54: 178 57.0 NA 178.0 57.0
55: 216 159.0 NA 216.0 159.0
56: 178 48.0 NA 178.0 48.0
57: 206 80.0 NA 206.0 80.0
58: NA NA NA NA NA
max.height max.mass max.birth_year median.height median.mass
median.birth_year mean.height mean.mass mean.birth_year
1: 44.45 179.2500 NA 47.475
2: NA 132.0000 53.5 NA
3: 33.00 96.0000 32.0 33.000
4: NA 176.3333 NA NA
5: 57.00 182.0000 77.0 57.000
6: 64.00 180.0000 NA 64.000
7: NA 231.0000 124.0 NA
8: 25.00 175.0000 78.5 25.000
9: 44.00 173.0000 74.0 44.000
10: 600.00 175.0000 1358.0 600.000
11: NA 180.0000 110.0 NA
12: 896.00 66.0000 17.0 896.000
13: NA 168.4000 NA NA
14: 31.50 183.0000 78.2 31.500
15: NA NA NA NA
16: 53.00 190.0000 113.0 53.000
17: 31.00 177.0000 79.0 31.000
18: 37.00 175.0000 79.0 37.000
19: 41.00 180.0000 83.0 41.000
20: 48.00 150.0000 NA 48.000
21: NA NA NA NA
22: 8.00 88.0000 20.0 8.000
23: NA 160.0000 68.0 NA
24: NA 191.0000 90.0 NA
25: NA 168.5000 NA NA
26: NA 208.6667 NA NA
27: NA 183.0000 NA NA
28: NA 137.0000 NA NA
29: NA 112.0000 40.0 NA
30: 54.00 175.0000 80.0 54.000
31: NA 179.0000 NA NA
32: NA 94.0000 45.0 NA
33: NA 122.0000 NA NA
34: NA 163.0000 65.0 NA
35: 72.00 188.0000 84.0 72.000
36: 92.00 198.0000 82.0 92.000
37: NA 196.0000 87.0 NA
38: NA 171.0000 NA NA
39: NA 184.0000 50.0 NA
40: NA 188.0000 NA NA
41: NA 264.0000 NA NA
42: 22.00 188.0000 80.0 22.000
43: NA 196.0000 NA NA
44: NA 183.0000 80.0 NA
45: 49.00 168.0000 53.1 49.000
46: 102.00 193.0000 80.0 102.000
47: 66.00 183.0000 79.0 66.000
48: NA 168.0000 55.0 NA
49: NA 198.0000 102.0 NA
50: NA 221.0000 NA NA
51: NA 79.0000 15.0 NA
52: NA 193.0000 48.0 NA
53: NA 191.0000 NA NA
54: NA 178.0000 57.0 NA
55: NA 216.0000 159.0 NA
56: NA 178.0000 48.0 NA
57: NA 206.0000 80.0 NA
58: NA NA NA NA
median.birth_year mean.height mean.mass mean.birth_yearExercise 2 - all colnames to upper case
starwarsDT = as.data.table(starwars)
setnames(starwarsDT, toupper(names(starwarsDT)))
Convert all character columns to upper case
NAME HEIGHT MASS HAIR_COLOR
1: LUKE SKYWALKER 172 77 BLOND
2: C-3PO 167 75 <NA>
3: R2-D2 96 32 <NA>
4: DARTH VADER 202 136 NONE
5: LEIA ORGANA 150 49 BROWN
6: OWEN LARS 178 120 BROWN, GREY
7: BERU WHITESUN LARS 165 75 BROWN
8: R5-D4 97 32 <NA>
9: BIGGS DARKLIGHTER 183 84 BLACK
10: OBI-WAN KENOBI 182 77 AUBURN, WHITE
11: ANAKIN SKYWALKER 188 84 BLOND
12: WILHUFF TARKIN 180 <NA> AUBURN, GREY
13: CHEWBACCA 228 112 BROWN
14: HAN SOLO 180 80 BROWN
15: GREEDO 173 74 <NA>
16: JABBA DESILIJIC TIURE 175 1358 <NA>
17: WEDGE ANTILLES 170 77 BROWN
18: JEK TONO PORKINS 180 110 BROWN
19: YODA 66 17 WHITE
20: PALPATINE 170 75 GREY
21: BOBA FETT 183 78.2 BLACK
22: IG-88 200 140 NONE
23: BOSSK 190 113 NONE
24: LANDO CALRISSIAN 177 79 BLACK
25: LOBOT 175 79 NONE
26: ACKBAR 180 83 NONE
27: MON MOTHMA 150 <NA> AUBURN
28: ARVEL CRYNYD <NA> <NA> BROWN
29: WICKET SYSTRI WARRICK 88 20 BROWN
30: NIEN NUNB 160 68 NONE
31: QUI-GON JINN 193 89 BROWN
32: NUTE GUNRAY 191 90 NONE
33: FINIS VALORUM 170 <NA> BLOND
34: JAR JAR BINKS 196 66 NONE
35: ROOS TARPALS 224 82 NONE
36: RUGOR NASS 206 <NA> NONE
37: RIC OLIÉ 183 <NA> BROWN
38: WATTO 137 <NA> BLACK
39: SEBULBA 112 40 NONE
40: QUARSH PANAKA 183 <NA> BLACK
41: SHMI SKYWALKER 163 <NA> BLACK
42: DARTH MAUL 175 80 NONE
43: BIB FORTUNA 180 <NA> NONE
44: AYLA SECURA 178 55 NONE
45: DUD BOLT 94 45 NONE
46: GASGANO 122 <NA> NONE
47: BEN QUADINAROS 163 65 NONE
48: MACE WINDU 188 84 NONE
49: KI-ADI-MUNDI 198 82 WHITE
50: KIT FISTO 196 87 NONE
51: EETH KOTH 171 <NA> BLACK
52: ADI GALLIA 184 50 NONE
53: SAESEE TIIN 188 <NA> NONE
54: YARAEL POOF 264 <NA> NONE
55: PLO KOON 188 80 NONE
56: MAS AMEDDA 196 <NA> NONE
57: GREGAR TYPHO 185 85 BLACK
58: CORDÉ 157 <NA> BROWN
59: CLIEGG LARS 183 <NA> BROWN
60: POGGLE THE LESSER 183 80 NONE
61: LUMINARA UNDULI 170 56.2 BLACK
62: BARRISS OFFEE 166 50 BLACK
63: DORMÉ 165 <NA> BROWN
64: DOOKU 193 80 WHITE
65: BAIL PRESTOR ORGANA 191 <NA> BLACK
66: JANGO FETT 183 79 BLACK
67: ZAM WESELL 168 55 BLONDE
68: DEXTER JETTSTER 198 102 NONE
69: LAMA SU 229 88 NONE
70: TAUN WE 213 <NA> NONE
71: JOCASTA NU 167 <NA> WHITE
72: RATTS TYERELL 79 15 NONE
73: R4-P17 96 <NA> NONE
74: WAT TAMBOR 193 48 NONE
75: SAN HILL 191 <NA> NONE
76: SHAAK TI 178 57 NONE
77: GRIEVOUS 216 159 NONE
78: TARFFUL 234 136 BROWN
79: RAYMUS ANTILLES 188 79 BROWN
80: SLY MOORE 178 48 NONE
81: TION MEDON 206 80 NONE
82: FINN <NA> <NA> BLACK
83: REY <NA> <NA> BROWN
84: POE DAMERON <NA> <NA> BROWN
85: BB8 <NA> <NA> NONE
86: CAPTAIN PHASMA <NA> <NA> UNKNOWN
87: PADMÉ AMIDALA 165 45 BROWN
NAME HEIGHT MASS HAIR_COLOR
SKIN_COLOR EYE_COLOR BIRTH_YEAR SEX
1: FAIR BLUE 19 MALE
2: GOLD YELLOW 112 NONE
3: WHITE, BLUE RED 33 NONE
4: WHITE YELLOW 41.9 MALE
5: LIGHT BROWN 19 FEMALE
6: LIGHT BLUE 52 MALE
7: LIGHT BLUE 47 FEMALE
8: WHITE, RED RED <NA> NONE
9: LIGHT BROWN 24 MALE
10: FAIR BLUE-GRAY 57 MALE
11: FAIR BLUE 41.9 MALE
12: FAIR BLUE 64 MALE
13: UNKNOWN BLUE 200 MALE
14: FAIR BROWN 29 MALE
15: GREEN BLACK 44 MALE
16: GREEN-TAN, BROWN ORANGE 600 HERMAPHRODITIC
17: FAIR HAZEL 21 MALE
18: FAIR BLUE <NA> MALE
19: GREEN BROWN 896 MALE
20: PALE YELLOW 82 MALE
21: FAIR BROWN 31.5 MALE
22: METAL RED 15 NONE
23: GREEN RED 53 MALE
24: DARK BROWN 31 MALE
25: LIGHT BLUE 37 MALE
26: BROWN MOTTLE ORANGE 41 MALE
27: FAIR BLUE 48 FEMALE
28: FAIR BROWN <NA> MALE
29: BROWN BROWN 8 MALE
30: GREY BLACK <NA> MALE
31: FAIR BLUE 92 MALE
32: MOTTLED GREEN RED <NA> MALE
33: FAIR BLUE 91 MALE
34: ORANGE ORANGE 52 MALE
35: GREY ORANGE <NA> MALE
36: GREEN ORANGE <NA> MALE
37: FAIR BLUE <NA> <NA>
38: BLUE, GREY YELLOW <NA> MALE
39: GREY, RED ORANGE <NA> MALE
40: DARK BROWN 62 <NA>
41: FAIR BROWN 72 FEMALE
42: RED YELLOW 54 MALE
43: PALE PINK <NA> MALE
44: BLUE HAZEL 48 FEMALE
45: BLUE, GREY YELLOW <NA> MALE
46: WHITE, BLUE BLACK <NA> MALE
47: GREY, GREEN, YELLOW ORANGE <NA> MALE
48: DARK BROWN 72 MALE
49: PALE YELLOW 92 MALE
50: GREEN BLACK <NA> MALE
51: BROWN BROWN <NA> MALE
52: DARK BLUE <NA> FEMALE
53: PALE ORANGE <NA> MALE
54: WHITE YELLOW <NA> MALE
55: ORANGE BLACK 22 MALE
56: BLUE BLUE <NA> MALE
57: DARK BROWN <NA> MALE
58: LIGHT BROWN <NA> FEMALE
59: FAIR BLUE 82 MALE
60: GREEN YELLOW <NA> MALE
61: YELLOW BLUE 58 FEMALE
62: YELLOW BLUE 40 FEMALE
63: LIGHT BROWN <NA> FEMALE
64: FAIR BROWN 102 MALE
65: TAN BROWN 67 MALE
66: TAN BROWN 66 MALE
67: FAIR, GREEN, YELLOW YELLOW <NA> FEMALE
68: BROWN YELLOW <NA> MALE
69: GREY BLACK <NA> MALE
70: GREY BLACK <NA> FEMALE
71: FAIR BLUE <NA> FEMALE
72: GREY, BLUE UNKNOWN <NA> MALE
73: SILVER, RED RED, BLUE <NA> NONE
74: GREEN, GREY UNKNOWN <NA> MALE
75: GREY GOLD <NA> MALE
76: RED, BLUE, WHITE BLACK <NA> FEMALE
77: BROWN, WHITE GREEN, YELLOW <NA> MALE
78: BROWN BLUE <NA> MALE
79: LIGHT BROWN <NA> MALE
80: PALE WHITE <NA> <NA>
81: GREY BLACK <NA> MALE
82: DARK DARK <NA> MALE
83: LIGHT HAZEL <NA> FEMALE
84: LIGHT BROWN <NA> MALE
85: NONE BLACK <NA> NONE
86: UNKNOWN UNKNOWN <NA> <NA>
87: LIGHT BROWN 46 FEMALE
SKIN_COLOR EYE_COLOR BIRTH_YEAR SEX
GENDER HOMEWORLD SPECIES
1: MASCULINE TATOOINE HUMAN
2: MASCULINE TATOOINE DROID
3: MASCULINE NABOO DROID
4: MASCULINE TATOOINE HUMAN
5: FEMININE ALDERAAN HUMAN
6: MASCULINE TATOOINE HUMAN
7: FEMININE TATOOINE HUMAN
8: MASCULINE TATOOINE DROID
9: MASCULINE TATOOINE HUMAN
10: MASCULINE STEWJON HUMAN
11: MASCULINE TATOOINE HUMAN
12: MASCULINE ERIADU HUMAN
13: MASCULINE KASHYYYK WOOKIEE
14: MASCULINE CORELLIA HUMAN
15: MASCULINE RODIA RODIAN
16: MASCULINE NAL HUTTA HUTT
17: MASCULINE CORELLIA HUMAN
18: MASCULINE BESTINE IV HUMAN
19: MASCULINE <NA> YODA'S SPECIES
20: MASCULINE NABOO HUMAN
21: MASCULINE KAMINO HUMAN
22: MASCULINE <NA> DROID
23: MASCULINE TRANDOSHA TRANDOSHAN
24: MASCULINE SOCORRO HUMAN
25: MASCULINE BESPIN HUMAN
26: MASCULINE MON CALA MON CALAMARI
27: FEMININE CHANDRILA HUMAN
28: MASCULINE <NA> HUMAN
29: MASCULINE ENDOR EWOK
30: MASCULINE SULLUST SULLUSTAN
31: MASCULINE <NA> HUMAN
32: MASCULINE CATO NEIMOIDIA NEIMODIAN
33: MASCULINE CORUSCANT HUMAN
34: MASCULINE NABOO GUNGAN
35: MASCULINE NABOO GUNGAN
36: MASCULINE NABOO GUNGAN
37: <NA> NABOO <NA>
38: MASCULINE TOYDARIA TOYDARIAN
39: MASCULINE MALASTARE DUG
40: <NA> NABOO <NA>
41: FEMININE TATOOINE HUMAN
42: MASCULINE DATHOMIR ZABRAK
43: MASCULINE RYLOTH TWI'LEK
44: FEMININE RYLOTH TWI'LEK
45: MASCULINE VULPTER VULPTEREEN
46: MASCULINE TROIKEN XEXTO
47: MASCULINE TUND TOONG
48: MASCULINE HARUUN KAL HUMAN
49: MASCULINE CEREA CEREAN
50: MASCULINE GLEE ANSELM NAUTOLAN
51: MASCULINE IRIDONIA ZABRAK
52: FEMININE CORUSCANT THOLOTHIAN
53: MASCULINE IKTOTCH IKTOTCHI
54: MASCULINE QUERMIA QUERMIAN
55: MASCULINE DORIN KEL DOR
56: MASCULINE CHAMPALA CHAGRIAN
57: MASCULINE NABOO HUMAN
58: FEMININE NABOO HUMAN
59: MASCULINE TATOOINE HUMAN
60: MASCULINE GEONOSIS GEONOSIAN
61: FEMININE MIRIAL MIRIALAN
62: FEMININE MIRIAL MIRIALAN
63: FEMININE NABOO HUMAN
64: MASCULINE SERENNO HUMAN
65: MASCULINE ALDERAAN HUMAN
66: MASCULINE CONCORD DAWN HUMAN
67: FEMININE ZOLAN CLAWDITE
68: MASCULINE OJOM BESALISK
69: MASCULINE KAMINO KAMINOAN
70: FEMININE KAMINO KAMINOAN
71: FEMININE CORUSCANT HUMAN
72: MASCULINE ALEEN MINOR ALEENA
73: FEMININE <NA> DROID
74: MASCULINE SKAKO SKAKOAN
75: MASCULINE MUUNILINST MUUN
76: FEMININE SHILI TOGRUTA
77: MASCULINE KALEE KALEESH
78: MASCULINE KASHYYYK WOOKIEE
79: MASCULINE ALDERAAN HUMAN
80: <NA> UMBARA <NA>
81: MASCULINE UTAPAU PAU'AN
82: MASCULINE <NA> HUMAN
83: FEMININE <NA> HUMAN
84: MASCULINE <NA> HUMAN
85: MASCULINE <NA> DROID
86: <NA> <NA> <NA>
87: FEMININE NABOO HUMAN
GENDER HOMEWORLD SPECIES
FILMS
1: C("THE EMPIRE STRIKES BACK", "REVENGE OF THE SITH", "RETURN OF THE JEDI", "A NEW HOPE", "THE FORCE AWAKENS")
2: C("THE EMPIRE STRIKES BACK", "ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH", "RETURN OF THE JEDI", "A NEW HOPE")
3: C("THE EMPIRE STRIKES BACK", "ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH", "RETURN OF THE JEDI", "A NEW HOPE", "THE FORCE AWAKENS")
4: C("THE EMPIRE STRIKES BACK", "REVENGE OF THE SITH", "RETURN OF THE JEDI", "A NEW HOPE")
5: C("THE EMPIRE STRIKES BACK", "REVENGE OF THE SITH", "RETURN OF THE JEDI", "A NEW HOPE", "THE FORCE AWAKENS")
6: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH", "A NEW HOPE")
7: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH", "A NEW HOPE")
8: A NEW HOPE
9: A NEW HOPE
10: C("THE EMPIRE STRIKES BACK", "ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH", "RETURN OF THE JEDI", "A NEW HOPE")
11: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
12: C("REVENGE OF THE SITH", "A NEW HOPE")
13: C("THE EMPIRE STRIKES BACK", "REVENGE OF THE SITH", "RETURN OF THE JEDI", "A NEW HOPE", "THE FORCE AWAKENS")
14: C("THE EMPIRE STRIKES BACK", "RETURN OF THE JEDI", "A NEW HOPE", "THE FORCE AWAKENS")
15: A NEW HOPE
16: C("THE PHANTOM MENACE", "RETURN OF THE JEDI", "A NEW HOPE")
17: C("THE EMPIRE STRIKES BACK", "RETURN OF THE JEDI", "A NEW HOPE")
18: A NEW HOPE
19: C("THE EMPIRE STRIKES BACK", "ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH", "RETURN OF THE JEDI")
20: C("THE EMPIRE STRIKES BACK", "ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH", "RETURN OF THE JEDI")
21: C("THE EMPIRE STRIKES BACK", "ATTACK OF THE CLONES", "RETURN OF THE JEDI")
22: THE EMPIRE STRIKES BACK
23: THE EMPIRE STRIKES BACK
24: C("THE EMPIRE STRIKES BACK", "RETURN OF THE JEDI")
25: THE EMPIRE STRIKES BACK
26: C("RETURN OF THE JEDI", "THE FORCE AWAKENS")
27: RETURN OF THE JEDI
28: RETURN OF THE JEDI
29: RETURN OF THE JEDI
30: RETURN OF THE JEDI
31: THE PHANTOM MENACE
32: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
33: THE PHANTOM MENACE
34: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE")
35: THE PHANTOM MENACE
36: THE PHANTOM MENACE
37: THE PHANTOM MENACE
38: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE")
39: THE PHANTOM MENACE
40: THE PHANTOM MENACE
41: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE")
42: THE PHANTOM MENACE
43: RETURN OF THE JEDI
44: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
45: THE PHANTOM MENACE
46: THE PHANTOM MENACE
47: THE PHANTOM MENACE
48: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
49: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
50: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
51: C("THE PHANTOM MENACE", "REVENGE OF THE SITH")
52: C("THE PHANTOM MENACE", "REVENGE OF THE SITH")
53: C("THE PHANTOM MENACE", "REVENGE OF THE SITH")
54: THE PHANTOM MENACE
55: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
56: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE")
57: ATTACK OF THE CLONES
58: ATTACK OF THE CLONES
59: ATTACK OF THE CLONES
60: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH")
61: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH")
62: ATTACK OF THE CLONES
63: ATTACK OF THE CLONES
64: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH")
65: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH")
66: ATTACK OF THE CLONES
67: ATTACK OF THE CLONES
68: ATTACK OF THE CLONES
69: ATTACK OF THE CLONES
70: ATTACK OF THE CLONES
71: ATTACK OF THE CLONES
72: THE PHANTOM MENACE
73: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH")
74: ATTACK OF THE CLONES
75: ATTACK OF THE CLONES
76: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH")
77: REVENGE OF THE SITH
78: REVENGE OF THE SITH
79: C("REVENGE OF THE SITH", "A NEW HOPE")
80: C("ATTACK OF THE CLONES", "REVENGE OF THE SITH")
81: REVENGE OF THE SITH
82: THE FORCE AWAKENS
83: THE FORCE AWAKENS
84: THE FORCE AWAKENS
85: THE FORCE AWAKENS
86: THE FORCE AWAKENS
87: C("ATTACK OF THE CLONES", "THE PHANTOM MENACE", "REVENGE OF THE SITH")
FILMS
VEHICLES
1: C("SNOWSPEEDER", "IMPERIAL SPEEDER BIKE")
2: CHARACTER(0)
3: CHARACTER(0)
4: CHARACTER(0)
5: IMPERIAL SPEEDER BIKE
6: CHARACTER(0)
7: CHARACTER(0)
8: CHARACTER(0)
9: CHARACTER(0)
10: TRIBUBBLE BONGO
11: C("ZEPHYR-G SWOOP BIKE", "XJ-6 AIRSPEEDER")
12: CHARACTER(0)
13: AT-ST
14: CHARACTER(0)
15: CHARACTER(0)
16: CHARACTER(0)
17: SNOWSPEEDER
18: CHARACTER(0)
19: CHARACTER(0)
20: CHARACTER(0)
21: CHARACTER(0)
22: CHARACTER(0)
23: CHARACTER(0)
24: CHARACTER(0)
25: CHARACTER(0)
26: CHARACTER(0)
27: CHARACTER(0)
28: CHARACTER(0)
29: CHARACTER(0)
30: CHARACTER(0)
31: TRIBUBBLE BONGO
32: CHARACTER(0)
33: CHARACTER(0)
34: CHARACTER(0)
35: CHARACTER(0)
36: CHARACTER(0)
37: CHARACTER(0)
38: CHARACTER(0)
39: CHARACTER(0)
40: CHARACTER(0)
41: CHARACTER(0)
42: SITH SPEEDER
43: CHARACTER(0)
44: CHARACTER(0)
45: CHARACTER(0)
46: CHARACTER(0)
47: CHARACTER(0)
48: CHARACTER(0)
49: CHARACTER(0)
50: CHARACTER(0)
51: CHARACTER(0)
52: CHARACTER(0)
53: CHARACTER(0)
54: CHARACTER(0)
55: CHARACTER(0)
56: CHARACTER(0)
57: CHARACTER(0)
58: CHARACTER(0)
59: CHARACTER(0)
60: CHARACTER(0)
61: CHARACTER(0)
62: CHARACTER(0)
63: CHARACTER(0)
64: FLITKNOT SPEEDER
65: CHARACTER(0)
66: CHARACTER(0)
67: KORO-2 EXODRIVE AIRSPEEDER
68: CHARACTER(0)
69: CHARACTER(0)
70: CHARACTER(0)
71: CHARACTER(0)
72: CHARACTER(0)
73: CHARACTER(0)
74: CHARACTER(0)
75: CHARACTER(0)
76: CHARACTER(0)
77: TSMEU-6 PERSONAL WHEEL BIKE
78: CHARACTER(0)
79: CHARACTER(0)
80: CHARACTER(0)
81: CHARACTER(0)
82: CHARACTER(0)
83: CHARACTER(0)
84: CHARACTER(0)
85: CHARACTER(0)
86: CHARACTER(0)
87: CHARACTER(0)
VEHICLES
STARSHIPS
1: C("X-WING", "IMPERIAL SHUTTLE")
2: CHARACTER(0)
3: CHARACTER(0)
4: TIE ADVANCED X1
5: CHARACTER(0)
6: CHARACTER(0)
7: CHARACTER(0)
8: CHARACTER(0)
9: X-WING
10: C("JEDI STARFIGHTER", "TRADE FEDERATION CRUISER", "NABOO STAR SKIFF", "JEDI INTERCEPTOR", "BELBULLAB-22 STARFIGHTER")
11: C("TRADE FEDERATION CRUISER", "JEDI INTERCEPTOR", "NABOO FIGHTER")
12: CHARACTER(0)
13: C("MILLENNIUM FALCON", "IMPERIAL SHUTTLE")
14: C("MILLENNIUM FALCON", "IMPERIAL SHUTTLE")
15: CHARACTER(0)
16: CHARACTER(0)
17: X-WING
18: X-WING
19: CHARACTER(0)
20: CHARACTER(0)
21: SLAVE 1
22: CHARACTER(0)
23: CHARACTER(0)
24: MILLENNIUM FALCON
25: CHARACTER(0)
26: CHARACTER(0)
27: CHARACTER(0)
28: A-WING
29: CHARACTER(0)
30: MILLENNIUM FALCON
31: CHARACTER(0)
32: CHARACTER(0)
33: CHARACTER(0)
34: CHARACTER(0)
35: CHARACTER(0)
36: CHARACTER(0)
37: NABOO ROYAL STARSHIP
38: CHARACTER(0)
39: CHARACTER(0)
40: CHARACTER(0)
41: CHARACTER(0)
42: SCIMITAR
43: CHARACTER(0)
44: CHARACTER(0)
45: CHARACTER(0)
46: CHARACTER(0)
47: CHARACTER(0)
48: CHARACTER(0)
49: CHARACTER(0)
50: CHARACTER(0)
51: CHARACTER(0)
52: CHARACTER(0)
53: CHARACTER(0)
54: CHARACTER(0)
55: JEDI STARFIGHTER
56: CHARACTER(0)
57: NABOO FIGHTER
58: CHARACTER(0)
59: CHARACTER(0)
60: CHARACTER(0)
61: CHARACTER(0)
62: CHARACTER(0)
63: CHARACTER(0)
64: CHARACTER(0)
65: CHARACTER(0)
66: CHARACTER(0)
67: CHARACTER(0)
68: CHARACTER(0)
69: CHARACTER(0)
70: CHARACTER(0)
71: CHARACTER(0)
72: CHARACTER(0)
73: CHARACTER(0)
74: CHARACTER(0)
75: CHARACTER(0)
76: CHARACTER(0)
77: BELBULLAB-22 STARFIGHTER
78: CHARACTER(0)
79: CHARACTER(0)
80: CHARACTER(0)
81: CHARACTER(0)
82: CHARACTER(0)
83: CHARACTER(0)
84: T-70 X-WING FIGHTER
85: CHARACTER(0)
86: CHARACTER(0)
87: C("H-TYPE NUBIAN YACHT", "NABOO STAR SKIFF", "NABOO FIGHTER")
STARSHIPSExercise 3
ts_diameter and hu_diameterstormsDT <- as.data.table(storms)
numericCols = sapply(stormsDT, is.numeric)
numericCols = names(numericCols)[numericCols]
stormsDTNew <- na.omit(stormsDT, cols = numericCols)
characterCols <- sapply(stormsDTNew, is.character)
characterCols <- names(characterCols)[characterCols]
colsOfInterest <- c("ts_diameter", "hu_diameter")
stormsDTNew[, lapply(.SD, mean), .SDcols = colsOfInterest, by = characterCols]
name status ts_diameter hu_diameter
1: Alex tropical depression 0.00000 0.00000
2: Alex tropical storm 150.80013 0.00000
3: Alex hurricane 304.40871 63.56690
4: Bonnie tropical depression 0.00000 0.00000
5: Bonnie tropical storm 75.40637 0.00000
---
273: Joaquin tropical storm 198.92054 0.00000
274: Joaquin hurricane 332.98376 85.00923
275: Kate tropical depression 0.00000 0.00000
276: Kate tropical storm 85.48651 0.00000
277: Kate hurricane 273.31025 31.64645.SD: Subset of data - choose row by ordinal position
irisDT[, .SD[1L]]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 6 2.2 5 1.5 virginica
maxLength minWidth Sepal.Length.Width
1: 7.9 0.1 6/2.2irisDT[, .SD[1L], by = Species]
Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1: virginica 6 2.2 5.0 1.5
2: versicolor 5 2 3.5 1.0
3: setosa 4.5 2.3 1.3 0.3
maxLength minWidth Sepal.Length.Width
1: 7.9 0.1 6/2.2
2: 7.9 0.1 5/2
3: 7.9 0.1 4.5/2.3irisDT[, .SD[1L], by = Species, .SDcols = c("Petal.Length", "Petal.Width")]
Species Petal.Length Petal.Width
1: virginica 5.0 1.5
2: versicolor 3.5 1.0
3: setosa 1.3 0.3.SD: Subset of Data - select top entries (in each group)
irisDT[order(-Sepal.Length), .SD[1:2]]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 7.9 3.8 6.4 2.0 virginica
2: 7.7 2.6 6.9 2.3 virginica
maxLength minWidth Sepal.Length.Width
1: 7.9 0.1 7.9/3.8
2: 7.9 0.1 7.7/2.6 Species Sepal.Length Sepal.Width
1: virginica 7.9 3.8
2: versicolor 7 3.2
3: setosa 5.8 4irisDT[, text := {
if(Sepal.Length > 4 & Sepal.Width > 4) "sepal length and width is larger than 4"
else if(Petal.Length < 2 & Petal.Width < 2) "petal length and width is smaller than 2"
else "other"
}, by = .(Species, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)][, .SD[1], by = text]
text Sepal.Length Sepal.Width
1: other 6 2.2
2: petal length and width is smaller than 2 4.5 2.3
3: sepal length and width is larger than 4 5.2 4.1
Petal.Length Petal.Width Species maxLength minWidth
1: 5.0 1.5 virginica 7.9 0.1
2: 1.3 0.3 setosa 7.9 0.1
3: 1.5 0.1 setosa 7.9 0.1
Sepal.Length.Width
1: 6/2.2
2: 4.5/2.3
3: 5.2/4.1bin_iris <- function(sl, sw, pl, pw){
if(sl > 4 & sw > 4) "sepal length and width is larger than 4"
else if(pl < 2 & pw < 2) "petal length and width is smaller than 2"
else "other"
}
irisDT[, text := bin_iris(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
by = .(Species, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)][, .SD[1], by = text]
text Sepal.Length Sepal.Width
1: other 6 2.2
2: petal length and width is smaller than 2 4.5 2.3
3: sepal length and width is larger than 4 5.2 4.1
Petal.Length Petal.Width Species maxLength minWidth
1: 5.0 1.5 virginica 7.9 0.1
2: 1.3 0.3 setosa 7.9 0.1
3: 1.5 0.1 setosa 7.9 0.1
Sepal.Length.Width
1: 6/2.2
2: 4.5/2.3
3: 5.2/4.1Exercise 1
Print the fifth row for each species.
starwarsDT <- as.data.table(starwars)
starwarsDT[, if(.N >= 5) .SD[5L], by = species]
species name height mass hair_color skin_color
1: Human Beru Whitesun lars 165 75 brown light
2: Droid R4-P17 96 NA none silver, red
eye_color birth_year sex gender homeworld
1: blue 47 female feminine Tatooine
2: red, blue NA none feminine <NA>
films vehicles
1: Attack of the Clones,Revenge of the Sith,A New Hope
2: Attack of the Clones,Revenge of the Sith
starships
1:
2: Exercise 2
stormsDT <- as.data.table(storms)
stormsDT[, .(name, status, pressure)][order(name, pressure)][, .SD[1:min(5, .N)], by = name]
name status pressure
1: AL011993 tropical depression 999
2: AL011993 tropical depression 999
3: AL011993 tropical depression 999
4: AL011993 tropical depression 999
5: AL011993 tropical depression 1000
---
978: Zeta tropical storm 994
979: Zeta tropical storm 994
980: Zeta tropical storm 994
981: Zeta tropical storm 994
982: Zeta tropical storm 994For attribution, please cite this work as
Okola (2020, Dec. 4). Basil Okola: A deeper look into data.table functionalities. Retrieved from https://bokola214.netlify.app/posts/welcome/
BibTeX citation
@misc{okola2020a,
author = {Okola, Basil},
title = {Basil Okola: A deeper look into data.table functionalities},
url = {https://bokola214.netlify.app/posts/welcome/},
year = {2020}
}