R-data.table语法解析（二）

library(data.table)
DT = data.table(a=LETTERS[c(1,1:3)],b=4:7,key="a")
DT

	a	b
1	A	4
2	A	5
3	B	6
4	C	7

:= 通过引用的方式增加或者更新列
类似transform方法，但是更容易操作

DT <- data.table(a=rep(1:3, each=2), b=1:6)
DT2 <- transform(DT, c = a^2)
DT[, c:=a^2]
identical(DT,DT2)

	a	b	c
1	1	1	1
2	1	2	1
3	2	3	4
4	2	4	4
5	3	5	9
6	3	6	9

TRUE

DT2 <- within(DT, {
b <- rev(b)
c <- a*2
rm(a)
})
DT[,`:=`(b = rev(b),
c = a*2,
a = NULL)]
identical(DT,DT2)

	b	c
1	6	2
2	5	2
3	4	4
4	3	4
5	2	6
6	1	6

TRUE

DT[,c:=8]
DT[,d:=9L]
DT[,c:=NULL]
DT[2,d:=10L]

	a	b	d	c
1	A	4	9	8
2	A	5	10	8
3	B	6	9	8
4	C	7	9	8

	a	b	d	c
1	A	4	9	8
2	A	5	9	8
3	B	6	9	8
4	C	7	9	8

	a	b	d
1	A	4	9
2	A	5	9
3	B	6	9
4	C	7	9

	a	b	d
1	A	4	9
2	A	5	10
3	B	6	9
4	C	7	9

DT[b>4,b:=d*2L]
DT["A",b:=0L]

	a	b	d
1	A	4	9
2	A	20	10
3	B	18	9
4	C	18	9

	a	b	d
1	A	0	9
2	A	0	10
3	B	18	9
4	C	18	9

DT[,e:=mean(d),by=a]
DT["B",f:=mean(d)]

	a	b	d	e
1	A	0	9	9.5
2	A	0	10	9.5
3	B	18	9	9
4	C	18	9	9

	a	b	d	e	f
1	A	0	9	9.5	NA
2	A	0	10	9.5	NA
3	B	18	9	9	9
4	C	18	9	9	NA

between 选择符合范围判断的项

DT = data.table(a=1:5, b=6:10)
DT[b %between% c(7,9)]

	a	b
1	2	7
2	3	8
3	4	9

chmatch 快速匹配两个字符向量，返回一个匹配成功位置的向量

a<-LETTERS[c(1:5)]
b<-LETTERS[c(4:10)]
chmatch(a,b) 
a %chin% b

1. NA


2. NA


3. NA


4. 1


5. 2

1. FALSE


2. FALSE


3. FALSE


4. TRUE


5. TRUE

dcast.data.table 数据表的整形

melt.data.table用法

require(reshape2)
require(data.table)
DT <- data.table(
           i1 = c(1:5, NA), 
           i2 = c(NA,6,7,8,9,10), 
           f1 = factor(sample(c(letters[1:3], NA), 6, TRUE)), 
           c1 = sample(c(letters[1:3], NA), 6, TRUE), 
           d1 = as.Date(c(1:3,NA,4:5), origin="2013-09-01"), 
           d2 = as.Date(6:1, origin="2012-01-01"))
DT[, l1 := DT[, list(c=list(rep(i1, sample(5,1)))), by = i1]$c] # list cols
DT[, l2 := DT[, list(c=list(rep(c1, sample(5,1)))), by = i1]$c]

melt(DT, id=1:2, measure=3) 
melt(DT, id=c("i1", "i2"), measure="f1", value.factor=TRUE) # same as above, but value is factor

melt(DT, id=c("i1", "f1"), measure=c("d1", "d2")) # date class attribute lost
melt(DT, id=c("i1", "f1"), measure=c("c1", "d1")) # value is char, date attribute lost


melt(DT, id=1, measure=c("l1", "l2")) # value is a list
melt(DT, id=1, measure=c("c1", "l1")) # c1 coerced to list


     melt(DT, id=1, measure=c("c1", "f1")) # value is char
     melt(DT, id=1, measure=c("c1", "i2")) # i2 coerced to char


     melt(DT, id=1, measure=c("c1", "i2"), na.rm=TRUE) # remove NA



Loading required package: reshape2

	i1	i2	f1	c1	d1	d2	l1
1	1	NA	c	a	2013-09-02	2012-01-07	1,1,1
2	2	6	a	b	2013-09-03	2012-01-06	2,2,2,2
3	3	7	c	a	2013-09-04	2012-01-05	3,3,3,3,3
4	4	8	NA	NA	NA	2012-01-04	4
5	5	9	a	b	2013-09-05	2012-01-03	5,5,5,5,5
6	NA	10	c	a	2013-09-06	2012-01-02	NA

	i1	i2	f1	c1	d1	d2	l1	l2
1	1	NA	c	a	2013-09-02	2012-01-07	1,1,1	a,a,a
2	2	6	a	b	2013-09-03	2012-01-06	2,2,2,2	b,b,b,b,b
3	3	7	c	a	2013-09-04	2012-01-05	3,3,3,3,3	a,a
4	4	8	NA	NA	NA	2012-01-04	4	NA
5	5	9	a	b	2013-09-05	2012-01-03	5,5,5,5,5	b,b,b,b,b
6	NA	10	c	a	2013-09-06	2012-01-02	NA	a,a,a

	i1	i2	variable	value
1	1	NA	f1	c
2	2	6	f1	a
3	3	7	f1	c
4	4	8	f1	NA
5	5	9	f1	a
6	NA	10	f1	c

	i1	i2	variable	value
1	1	NA	f1	c
2	2	6	f1	a
3	3	7	f1	c
4	4	8	f1	NA
5	5	9	f1	a
6	NA	10	f1	c

	i1	f1	variable	value
1	1	c	d1	15950
2	2	a	d1	15951
3	3	c	d1	15952
4	4	NA	d1	NA
5	5	a	d1	15953
6	NA	c	d1	15954
7	1	c	d2	15346
8	2	a	d2	15345
9	3	c	d2	15344
10	4	NA	d2	15343
11	5	a	d2	15342
12	NA	c	d2	15341

Warning message:
In melt.data.table(DT, id = c("i1", "f1"), measure = c("c1", "d1")): All 'measure.vars are NOT of the SAME type. By order of hierarchy, the molten data value column will be of type 'character'. Therefore all measure variables that are not of type 'character' will be coerced to. Check the DETAILS section of ?melt.data.table for more on coercion.

	i1	f1	variable	value
1	1	c	c1	a
2	2	a	c1	b
3	3	c	c1	a
4	4	NA	c1	NA
5	5	a	c1	b
6	NA	c	c1	a
7	1	c	d1	15950
8	2	a	d1	15951
9	3	c	d1	15952
10	4	NA	d1	NA
11	5	a	d1	15953
12	NA	c	d1	15954

	i1	variable	value
1	1	l1	1,1,1
2	2	l1	2,2,2,2
3	3	l1	3,3,3,3,3
4	4	l1	4
5	5	l1	5,5,5,5,5
6	NA	l1	NA
7	1	l2	a,a,a
8	2	l2	b,b,b,b,b
9	3	l2	a,a
10	4	l2	NA
11	5	l2	b,b,b,b,b
12	NA	l2	a,a,a

Warning message:
In melt.data.table(DT, id = 1, measure = c("c1", "l1")): All 'measure.vars are NOT of the SAME type. By order of hierarchy, the molten data value column will be of type 'list'. Therefore all measure variables that are not of type 'list' will be coerced to. Check the DETAILS section of ?melt.data.table for more on coercion.

	i1	variable	value
1	1	c1	a
2	2	c1	b
3	3	c1	a
4	4	c1	NA
5	5	c1	b
6	NA	c1	a
7	1	l1	1,1,1
8	2	l1	2,2,2,2
9	3	l1	3,3,3,3,3
10	4	l1	4
11	5	l1	5,5,5,5,5
12	NA	l1	NA

	i1	variable	value
1	1	c1	a
2	2	c1	b
3	3	c1	a
4	4	c1	NA
5	5	c1	b
6	NA	c1	a
7	1	f1	c
8	2	f1	a
9	3	f1	c
10	4	f1	NA
11	5	f1	a
12	NA	f1	c

Warning message:
In melt.data.table(DT, id = 1, measure = c("c1", "i2")): All 'measure.vars are NOT of the SAME type. By order of hierarchy, the molten data value column will be of type 'character'. Therefore all measure variables that are not of type 'character' will be coerced to. Check the DETAILS section of ?melt.data.table for more on coercion.

	i1	variable	value
1	1	c1	a
2	2	c1	b
3	3	c1	a
4	4	c1	NA
5	5	c1	b
6	NA	c1	a
7	1	i2	NA
8	2	i2	6
9	3	i2	7
10	4	i2	8
11	5	i2	9
12	NA	i2	10

Warning message:
In melt.data.table(DT, id = 1, measure = c("c1", "i2"), na.rm = TRUE): All 'measure.vars are NOT of the SAME type. By order of hierarchy, the molten data value column will be of type 'character'. Therefore all measure variables that are not of type 'character' will be coerced to. Check the DETAILS section of ?melt.data.table for more on coercion.

	i1	variable	value
1	1	c1	a
2	2	c1	b
3	3	c1	a
4	5	c1	b
5	NA	c1	a
6	2	i2	6
7	3	i2	7
8	4	i2	8
9	5	i2	9
10	NA	i2	10

melt后，进行cast
reshape2的精髓是把整体数据（比如数据框）打碎（melt，或说熔化），让其回到一个一个数据点的状态，不过，这个数据点是可以根据观测的id名称和变量名称定位找到的（事实上，数据框是二维的，在数据框中，根据观测id和某变量名称，即可交叉定位到某数据点）。然后，再根据观测id和变量名称的组合，随心所欲的锻造需要的数据形态（cast），中间可能有求和、求平均值之类的数据运算。

names(ChickWeight) <- tolower(names(ChickWeight))
DT <- melt(as.data.table(ChickWeight), id=2:4)
dcast(DT, time ~ variable, fun=mean)
dcast(DT, diet ~ variable, fun=mean)
dcast(DT, diet+chick ~ time, drop=FALSE)
dcast(DT, diet+chick ~ time, drop=FALSE, fill=0)
dcast.data.table(DT, chick ~ time, fun=mean, subset=.(time < 10 & chick < 20))

	time	weight
1	0	41.06
2	2	49.22
3	4	59.95918
4	6	74.30612
5	8	91.2449
6	10	107.8367
7	12	129.2449
8	14	143.8125
9	16	168.0851
10	18	190.1915
11	20	209.7174
12	21	218.6889

	diet	weight
1	1	102.6455
2	2	122.6167
3	3	142.95
4	4	135.2627

Warning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generatedWarning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generatedWarning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generatedWarning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generated

	diet	chick	0	2	4	6	8	10	12	14	16	18	20	21
1	1	18	39	35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	1	16	41	45	49	51	57	51	54	NA	NA	NA	NA	NA
3	1	15	41	49	56	64	68	68	67	68	NA	NA	NA	NA
4	1	13	41	48	53	60	65	67	71	70	71	81	91	96
5	1	9	42	51	59	68	85	96	90	92	93	100	100	98
6	1	20	41	47	54	58	65	73	77	89	98	107	115	117
7	1	10	41	44	52	63	74	81	89	96	101	112	120	124
8	1	8	42	50	61	71	84	93	110	116	126	134	125	NA
9	1	17	42	51	61	72	83	89	98	103	113	123	133	142
10	1	19	43	48	55	62	65	71	82	88	106	120	144	157
11	1	4	42	49	56	67	74	87	102	108	136	154	160	157
12	1	6	41	49	59	74	97	124	141	148	155	160	160	157
13	1	11	43	51	63	84	112	139	168	177	182	184	181	175
14	1	3	43	39	55	67	84	99	115	138	163	187	198	202
15	1	1	42	51	59	64	76	93	106	125	149	171	199	205
16	1	12	41	49	56	62	72	88	119	135	162	185	195	205
17	1	2	40	49	58	72	84	103	122	138	162	187	209	215
18	1	5	41	42	48	60	79	106	141	164	197	199	220	223
19	1	14	41	49	62	79	101	128	164	192	227	248	259	266
20	1	7	41	49	57	71	89	112	146	174	218	250	288	305
21	1	24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
22	1	30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
23	1	22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
24	1	23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
25	1	27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
26	1	28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
27	1	26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
28	1	25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
29	1	29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
30	1	21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
31	NA	NA	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
171	4	24	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
172	4	30	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
173	4	22	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
174	4	23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
175	4	27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
176	4	28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
177	4	26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
178	4	25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
179	4	29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
180	4	21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
181	4	33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
182	4	37	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
183	4	36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
184	4	31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
185	4	39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
186	4	38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
187	4	32	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
188	4	40	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
189	4	34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
190	4	35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
191	4	44	42	51	65	86	103	118	127	138	145	146	NA	NA
192	4	45	41	50	61	78	98	117	135	141	147	174	197	196
193	4	43	42	55	69	96	131	157	184	188	197	198	199	200
194	4	41	42	51	66	85	103	124	155	153	175	184	199	204
195	4	47	41	53	66	79	100	123	148	157	168	185	210	205
196	4	49	40	53	64	85	108	128	152	166	184	203	233	237
197	4	46	40	52	62	82	101	120	144	156	173	210	231	238
198	4	50	41	54	67	84	105	122	155	175	205	234	264	264
199	4	42	42	49	63	84	103	126	160	174	204	234	269	281
200	4	48	39	50	62	80	104	125	154	170	222	261	303	322

Warning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generatedWarning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generatedWarning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generatedWarning message:
In `[<-.factor`(`*tmp*`, ri, value = "⋮"): invalid factor level, NA generated

	diet	chick	0	2	4	6	8	10	12	14	16	18	20	21
1	1	18	39	35	0	0	0	0	0	0	0	0	0	0
2	1	16	41	45	49	51	57	51	54	0	0	0	0	0
3	1	15	41	49	56	64	68	68	67	68	0	0	0	0
4	1	13	41	48	53	60	65	67	71	70	71	81	91	96
5	1	9	42	51	59	68	85	96	90	92	93	100	100	98
6	1	20	41	47	54	58	65	73	77	89	98	107	115	117
7	1	10	41	44	52	63	74	81	89	96	101	112	120	124
8	1	8	42	50	61	71	84	93	110	116	126	134	125	0
9	1	17	42	51	61	72	83	89	98	103	113	123	133	142
10	1	19	43	48	55	62	65	71	82	88	106	120	144	157
11	1	4	42	49	56	67	74	87	102	108	136	154	160	157
12	1	6	41	49	59	74	97	124	141	148	155	160	160	157
13	1	11	43	51	63	84	112	139	168	177	182	184	181	175
14	1	3	43	39	55	67	84	99	115	138	163	187	198	202
15	1	1	42	51	59	64	76	93	106	125	149	171	199	205
16	1	12	41	49	56	62	72	88	119	135	162	185	195	205
17	1	2	40	49	58	72	84	103	122	138	162	187	209	215
18	1	5	41	42	48	60	79	106	141	164	197	199	220	223
19	1	14	41	49	62	79	101	128	164	192	227	248	259	266
20	1	7	41	49	57	71	89	112	146	174	218	250	288	305
21	1	24	0	0	0	0	0	0	0	0	0	0	0	0
22	1	30	0	0	0	0	0	0	0	0	0	0	0	0
23	1	22	0	0	0	0	0	0	0	0	0	0	0	0
24	1	23	0	0	0	0	0	0	0	0	0	0	0	0
25	1	27	0	0	0	0	0	0	0	0	0	0	0	0
26	1	28	0	0	0	0	0	0	0	0	0	0	0	0
27	1	26	0	0	0	0	0	0	0	0	0	0	0	0
28	1	25	0	0	0	0	0	0	0	0	0	0	0	0
29	1	29	0	0	0	0	0	0	0	0	0	0	0	0
30	1	21	0	0	0	0	0	0	0	0	0	0	0	0
31	NA	NA	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
171	4	24	0	0	0	0	0	0	0	0	0	0	0	0
172	4	30	0	0	0	0	0	0	0	0	0	0	0	0
173	4	22	0	0	0	0	0	0	0	0	0	0	0	0
174	4	23	0	0	0	0	0	0	0	0	0	0	0	0
175	4	27	0	0	0	0	0	0	0	0	0	0	0	0
176	4	28	0	0	0	0	0	0	0	0	0	0	0	0
177	4	26	0	0	0	0	0	0	0	0	0	0	0	0
178	4	25	0	0	0	0	0	0	0	0	0	0	0	0
179	4	29	0	0	0	0	0	0	0	0	0	0	0	0
180	4	21	0	0	0	0	0	0	0	0	0	0	0	0
181	4	33	0	0	0	0	0	0	0	0	0	0	0	0
182	4	37	0	0	0	0	0	0	0	0	0	0	0	0
183	4	36	0	0	0	0	0	0	0	0	0	0	0	0
184	4	31	0	0	0	0	0	0	0	0	0	0	0	0
185	4	39	0	0	0	0	0	0	0	0	0	0	0	0
186	4	38	0	0	0	0	0	0	0	0	0	0	0	0
187	4	32	0	0	0	0	0	0	0	0	0	0	0	0
188	4	40	0	0	0	0	0	0	0	0	0	0	0	0
189	4	34	0	0	0	0	0	0	0	0	0	0	0	0
190	4	35	0	0	0	0	0	0	0	0	0	0	0	0
191	4	44	42	51	65	86	103	118	127	138	145	146	0	0
192	4	45	41	50	61	78	98	117	135	141	147	174	197	196
193	4	43	42	55	69	96	131	157	184	188	197	198	199	200
194	4	41	42	51	66	85	103	124	155	153	175	184	199	204
195	4	47	41	53	66	79	100	123	148	157	168	185	210	205
196	4	49	40	53	64	85	108	128	152	166	184	203	233	237
197	4	46	40	52	62	82	101	120	144	156	173	210	231	238
198	4	50	41	54	67	84	105	122	155	175	205	234	264	264
199	4	42	42	49	63	84	103	126	160	174	204	234	269	281
200	4	48	39	50	62	80	104	125	154	170	222	261	303	322

	chick	0	2	4	6	8
1	18	39	35	NaN	NaN	NaN
2	16	41	45	49	51	57
3	15	41	49	56	64	68
4	13	41	48	53	60	65
5	9	42	51	59	68	85

duplicated 根据键值判断当前行是否与之前的有重复
unique 返回去掉重复后的值
anyDuplicated 返回第一个重复值的index i 没有就为0
uniqueN 相等于length(unique(x))

DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3), C = rep(1:2, 6), key = "A,B")
DT
duplicated(DT)
unique(DT)

	A	B	C
1	1	1	1
2	1	1	2
3	1	1	1
4	1	2	2
5	2	2	1
6	2	2	2
7	2	3	1
8	2	3	2
9	3	3	1
10	3	4	2
11	3	4	1
12	3	4	2

1. FALSE


2. TRUE


3. TRUE


4. FALSE


5. FALSE


6. TRUE


7. FALSE


8. TRUE


9. FALSE


10. FALSE


11. TRUE


12. TRUE

	A	B	C
1	1	1	1
2	1	2	2
3	2	2	1
4	2	3	1
5	3	3	1
6	3	4	2

DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3), C = rep(1:2, 6), key = "A,B")
DT
duplicated(DT, by="B", fromLast=TRUE)
unique(DT, by="B", fromLast=TRUE)

	A	B	C
1	1	1	1
2	1	1	2
3	1	1	1
4	1	2	2
5	2	2	1
6	2	2	2
7	2	3	1
8	2	3	2
9	3	3	1
10	3	4	2
11	3	4	1
12	3	4	2

1. TRUE


2. TRUE


3. FALSE


4. TRUE


5. TRUE


6. FALSE


7. TRUE


8. TRUE


9. FALSE


10. TRUE


11. TRUE


12. FALSE

	A	B	C
1	1	1	1
2	2	2	2
3	3	3	1
4	3	4	2

anyDuplicated(DT, by=c("A", "B"))
any(duplicated(DT, by=c("A", "B")))

2

TRUE

x[i, j, by, keyby, with = TRUE…..]
根据语法，当数据表的i为data.table时，x必须设置key值，i和x连接
DT[J(…)]就是一种更可读的方法，通过创造一个data.table作为i传入，与DT连接
x[data.table(id)]与x[J(id)]等同 也可以直接写成x[id],结果等与subset(x, key == id)
x[list(id)]与x[.(id)]等同
DT[“b”]

DT = data.table(A=5:1,B=letters[5:1])
DT
setkey(DT,B)
DT[J("b")]
CJ(c(5,NA,1), c(1,3,2))
CJ(c(5,NA,1), c(1,3,2), sorted=FALSE)
x = c(1,1,2)
y = c(4,6,4)
CJ(x, y, unique=TRUE)

	A	B
1	5	e
2	4	d
3	3	c
4	2	b
5	1	a

	A	B
1	2	b

	V1	V2
1	NA	1
2	NA	2
3	NA	3
4	1	1
5	1	2
6	1	3
7	5	1
8	5	2
9	5	3

	V1	V2
1	5	1
2	5	3
3	5	2
4	NA	1
5	NA	3
6	NA	2
7	1	1
8	1	3
9	1	2

	V1	V2	unique
1	1	4	TRUE
2	1	4	TRUE
3	1	6	TRUE
4	1	4	TRUE
5	1	4	TRUE
6	1	6	TRUE
7	2	4	TRUE
8	2	4	TRUE
9	2	6	TRUE

like 通过正则表达式匹配

DT = data.table(Name=c("Mary","George","Martha"), Salary=c(2,3,4))
DT
DT[Name %like% "^Mar"]

	Name	Salary
1	Mary	2
2	George	3
3	Martha	4

	Name	Salary
1	Mary	2
2	Martha	4

merge 合并 data.table的merge方法和data.frame的merge方法，由一个区别就是，在默认情况下，data.table通过共同的key进行合并，而data.frame则是通过共同的列名

dt1 <- data.table(A = letters[1:10], X = 1:10, key = "A")
dt2 <- data.table(A = letters[5:14], Y = 1:10, key = "A")
merge(dt1, dt2)
merge(dt1, dt2, all = TRUE)

	A	X	Y
1	e	5	1
2	f	6	2
3	g	7	3
4	h	8	4
5	i	9	5
6	j	10	6

	A	X	Y
1	a	1	NA
2	b	2	NA
3	c	3	NA
4	d	4	NA
5	e	5	1
6	f	6	2
7	g	7	3
8	h	8	4
9	i	9	5
10	j	10	6
11	k	NA	7
12	l	NA	8
13	m	NA	9
14	n	NA	10

dt1 <- data.table(A = letters[rep(1:3, 2)], X = 1:6, key = "A")
dt2 <- data.table(A = letters[rep(2:4, 2)], Y = 6:1, key = "A")
merge(dt1, dt2, allow.cartesian=TRUE)

	A	X	Y
1	b	2	6
2	b	2	3
3	b	5	6
4	b	5	3
5	c	3	5
6	c	3	2
7	c	6	5
8	c	6	2

na.omit.data.table 根据指定的列去掉行值为空值的行
na.omit(object, cols=seq_along(object), invert=FALSE, …)

DT = data.table(x=c(1,NaN,NA,3), y=c(NAinteger, 1:3), z=c(“a”, NAcharacter, “b”, “c”))
/ default behaviour /
na.omit(DT)
/ omit rows where ‘x’ has a missing value/
na.omit(DT, cols=”x”)
/ omit rows where either ‘x’ or ‘y’ have missing values/
na.omit(DT, cols=c(“x”, “y”))

rbindlist 对一列表的data.table进行行合并，组成一个data.table

DT1 = data.table(A=1:3,B=letters[1:3])
DT2 = data.table(A=4:5,B=letters[4:5])
l = list(DT1,DT2)
rbindlist(l)

	A	B
1	1	a
2	2	b
3	3	c
4	4	d
5	5	e

DT1 = data.table(A=1:3,B=letters[1:3])
DT2 = data.table(B=letters[4:5],A=4:5)
l = list(DT1,DT2)
rbindlist(l, use.names=TRUE)

	A	B
1	1	a
2	2	b
3	3	c
4	4	d
5	5	e

DT1 = data.table(A=1:3,B=letters[1:3])
DT2 = data.table(B=letters[4:5],C=factor(1:2))
l = list(DT1,DT2)
rbindlist(l, use.names=TRUE, fill=TRUE)

	A	B	C
1	1	a	NA
2	2	b	NA
3	3	c	NA
4	NA	d	1
5	NA	e	2

setattr(x,name,value)
setnames(x,old,new)

DT = data.table(a=1:2,b=3:4,c=5:6)
setnames(DT,"b","B")
setnames(DT,3,"C")
DT

	a	B	C
1	1	3	5
2	2	4	6

setcolorder(x, neworder) 对列名进行指定的排序

DT = data.table(A=sample(3, 10, TRUE),B=sample(letters[1:3], 10, TRUE), C=sample(10))
setcolorder(DT, c("C", "A", "B"))
DT

	C	A	B
1	4	2	a
2	2	3	a
3	3	3	a
4	9	2	a
5	7	2	b
6	10	1	c
7	1	3	c
8	5	2	b
9	8	3	b
10	6	2	b

setorder 对行进行排序

DT = data.table(A=sample(3, 10, TRUE),B=sample(letters[1:3], 10, TRUE), C=sample(10))
setorder(DT, A, -B)
setorderv(DT, c("A", "B"), c(1, -1))
DT

	A	B	C
1	1	a	4
2	2	c	9
3	2	c	5
4	2	c	2
5	2	b	10
6	2	a	7
7	3	c	1
8	3	c	8
9	3	a	6
10	3	a	3

setDF 通过引用的方式把数据表变成数据框
setDT 通过引用的方式把数据框变成数据表

X = data.table(x=1:5, y=6:10)
setDF(X)[]
A = list(a=1:5, a=6:10)
setDT(A)[]

	x	y
1	1	6
2	2	7
3	3	8
4	4	9
5	5	10

	a	a
1	1	6
2	2	7
3	3	8
4	4	9
5	5	10

tables 列出所有class为data.table的对象

tables()

     NAME NROW NCOL MB COLS  KEY
[1,] A       5    2  1 a,a      
[2,] DT     10    3  1 A,B,C    
[3,] dt1     6    2  1 A,X   A  
[4,] DT1     3    2  1 A,B      
[5,] dt2     6    2  1 A,Y   A  
[6,] DT2     2    2  1 B,C      
Total: 6MB