split_word: Split Words
In textshape: Tools for Reshaping Text
Description
Usage
Arguments
Value
Examples
Description
Split words.
Usage
1
2
3
4
5
6
7
split_word(x, ...)
## Default S3 method:
split_word(x, lower = TRUE, ...)
## S3 method for class 'data.frame'
split_word(x, text.var = TRUE, lower = TRUE, ...)
Arguments
x
A data.frame or character vector with words.
lower
logical. If TRUE the words are converted to lower case.
text.var
The name of the text variable. If TRUE
split_word tries to detect the text column with words.
...
Ignored.
Value
Returns a list of vectors of words or an expanded
data.table with words split apart.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
(x <- c(
"Mr. Brown comes! He says hello. i give him coffee.",
"I'll go at 5 p. m. eastern time. Or somewhere in between!",
"go there"
))
split_word(x)
split_word(x, lower=FALSE)
data(DATA)
split_word(DATA)
split_word(DATA, lower=FALSE)
## Larger data set
split_word(hamlet)
Example output
[1] "Mr. Brown comes! He says hello. i give him coffee."
[2] "I'll go at 5 p. m. eastern time. Or somewhere in between!"
[3] "go there"
[[1]]
[1] "mr" "brown" "comes" "he" "says" "hello" "i" "give"
[9] "him" "coffee"
[[2]]
[1] "i'll" "go" "at" "5" "p" "m"
[7] "eastern" "time" "or" "somewhere" "in" "between"
[[3]]
[1] "go" "there"
[[1]]
[1] "Mr" "Brown" "comes" "He" "says" "hello" "i" "give"
[9] "him" "coffee"
[[2]]
[1] "I'll" "go" "at" "5" "p" "m"
[7] "eastern" "time" "Or" "somewhere" "in" "between"
[[3]]
[1] "go" "there"
person sex adult state code element_id word_id
1: sam m 0 computer K1 1 1
2: sam m 0 is K1 1 2
3: sam m 0 fun K1 1 3
4: sam m 0 not K1 1 4
5: sam m 0 too K1 1 5
6: sam m 0 fun K1 1 6
7: greg m 0 no K2 2 1
8: greg m 0 it's K2 2 2
9: greg m 0 not K2 2 3
10: greg m 0 it's K2 2 4
11: greg m 0 dumb K2 2 5
12: teacher m 1 what K3 3 1
13: teacher m 1 should K3 3 2
14: teacher m 1 we K3 3 3
15: teacher m 1 do K3 3 4
16: sam m 0 you K4 4 1
17: sam m 0 liar K4 4 2
18: sam m 0 it K4 4 3
19: sam m 0 stinks K4 4 4
20: greg m 0 i K5 5 1
21: greg m 0 am K5 5 2
22: greg m 0 telling K5 5 3
23: greg m 0 the K5 5 4
24: greg m 0 truth K5 5 5
25: sally f 0 how K6 6 1
26: sally f 0 can K6 6 2
27: sally f 0 we K6 6 3
28: sally f 0 be K6 6 4
29: sally f 0 certain K6 6 5
30: greg m 0 there K7 7 1
31: greg m 0 is K7 7 2
32: greg m 0 no K7 7 3
33: greg m 0 way K7 7 4
34: sam m 0 i K8 8 1
35: sam m 0 distrust K8 8 2
36: sam m 0 you K8 8 3
37: sally f 0 what K9 9 1
38: sally f 0 are K9 9 2
39: sally f 0 you K9 9 3
40: sally f 0 talking K9 9 4
41: sally f 0 about K9 9 5
42: researcher f 1 shall K10 10 1
43: researcher f 1 we K10 10 2
44: researcher f 1 move K10 10 3
45: researcher f 1 on K10 10 4
46: researcher f 1 good K10 10 5
47: researcher f 1 then K10 10 6
48: greg m 0 i'm K11 11 1
49: greg m 0 hungry K11 11 2
50: greg m 0 let's K11 11 3
51: greg m 0 eat K11 11 4
52: greg m 0 you K11 11 5
53: greg m 0 already K11 11 6
person sex adult state code element_id word_id
person sex adult state code element_id word_id
1: sam m 0 Computer K1 1 1
2: sam m 0 is K1 1 2
3: sam m 0 fun K1 1 3
4: sam m 0 Not K1 1 4
5: sam m 0 too K1 1 5
6: sam m 0 fun K1 1 6
7: greg m 0 No K2 2 1
8: greg m 0 it's K2 2 2
9: greg m 0 not K2 2 3
10: greg m 0 it's K2 2 4
11: greg m 0 dumb K2 2 5
12: teacher m 1 What K3 3 1
13: teacher m 1 should K3 3 2
14: teacher m 1 we K3 3 3
15: teacher m 1 do K3 3 4
16: sam m 0 You K4 4 1
17: sam m 0 liar K4 4 2
18: sam m 0 it K4 4 3
19: sam m 0 stinks K4 4 4
20: greg m 0 I K5 5 1
21: greg m 0 am K5 5 2
22: greg m 0 telling K5 5 3
23: greg m 0 the K5 5 4
24: greg m 0 truth K5 5 5
25: sally f 0 How K6 6 1
26: sally f 0 can K6 6 2
27: sally f 0 we K6 6 3
28: sally f 0 be K6 6 4
29: sally f 0 certain K6 6 5
30: greg m 0 There K7 7 1
31: greg m 0 is K7 7 2
32: greg m 0 no K7 7 3
33: greg m 0 way K7 7 4
34: sam m 0 I K8 8 1
35: sam m 0 distrust K8 8 2
36: sam m 0 you K8 8 3
37: sally f 0 What K9 9 1
38: sally f 0 are K9 9 2
39: sally f 0 you K9 9 3
40: sally f 0 talking K9 9 4
41: sally f 0 about K9 9 5
42: researcher f 1 Shall K10 10 1
43: researcher f 1 we K10 10 2
44: researcher f 1 move K10 10 3
45: researcher f 1 on K10 10 4
46: researcher f 1 Good K10 10 5
47: researcher f 1 then K10 10 6
48: greg m 0 I'm K11 11 1
49: greg m 0 hungry K11 11 2
50: greg m 0 Let's K11 11 3
51: greg m 0 eat K11 11 4
52: greg m 0 You K11 11 5
53: greg m 0 already K11 11 6
person sex adult state code element_id word_id
act tot scene location
1: Act1 1.1 Scene I Elsinore. A platform before the castle.
2: Act1 1.1 Scene I Elsinore. A platform before the castle.
3: Act1 2.1 Scene I Elsinore. A platform before the castle.
4: Act1 2.1 Scene I Elsinore. A platform before the castle.
5: Act1 2.1 Scene I Elsinore. A platform before the castle.
---
29375: Act5 1150.3 Scene II A hall in the castle.
29376: Act5 1150.3 Scene II A hall in the castle.
29377: Act5 1150.3 Scene II A hall in the castle.
29378: Act5 1150.3 Scene II A hall in the castle.
29379: Act5 1150.3 Scene II A hall in the castle.
person died dialogue element_id word_id
1: Bernardo FALSE who's 1 1
2: Bernardo FALSE there 1 2
3: Francisco FALSE nay 2 1
4: Francisco FALSE answer 2 2
5: Francisco FALSE me 2 3
---
29375: Prince Fortinbras FALSE go 2007 1
29376: Prince Fortinbras FALSE bid 2007 2
29377: Prince Fortinbras FALSE the 2007 3
29378: Prince Fortinbras FALSE soldiers 2007 4
29379: Prince Fortinbras FALSE shoot 2007 5
textshape documentation built on May 29, 2021, 1:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
Split words.
Usage
1 2 3 4 5 6 7 | split_word(x, ...)
## Default S3 method:
split_word(x, lower = TRUE, ...)
## S3 method for class 'data.frame'
split_word(x, text.var = TRUE, lower = TRUE, ...)
|
Arguments
x |
A |
lower |
logical. If |
text.var |
The name of the text variable. If |
... |
Ignored. |
Value
Returns a list of vectors of words or an expanded
data.table with words split apart.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | (x <- c(
"Mr. Brown comes! He says hello. i give him coffee.",
"I'll go at 5 p. m. eastern time. Or somewhere in between!",
"go there"
))
split_word(x)
split_word(x, lower=FALSE)
data(DATA)
split_word(DATA)
split_word(DATA, lower=FALSE)
## Larger data set
split_word(hamlet)
|
Example output
[1] "Mr. Brown comes! He says hello. i give him coffee."
[2] "I'll go at 5 p. m. eastern time. Or somewhere in between!"
[3] "go there"
[[1]]
[1] "mr" "brown" "comes" "he" "says" "hello" "i" "give"
[9] "him" "coffee"
[[2]]
[1] "i'll" "go" "at" "5" "p" "m"
[7] "eastern" "time" "or" "somewhere" "in" "between"
[[3]]
[1] "go" "there"
[[1]]
[1] "Mr" "Brown" "comes" "He" "says" "hello" "i" "give"
[9] "him" "coffee"
[[2]]
[1] "I'll" "go" "at" "5" "p" "m"
[7] "eastern" "time" "Or" "somewhere" "in" "between"
[[3]]
[1] "go" "there"
person sex adult state code element_id word_id
1: sam m 0 computer K1 1 1
2: sam m 0 is K1 1 2
3: sam m 0 fun K1 1 3
4: sam m 0 not K1 1 4
5: sam m 0 too K1 1 5
6: sam m 0 fun K1 1 6
7: greg m 0 no K2 2 1
8: greg m 0 it's K2 2 2
9: greg m 0 not K2 2 3
10: greg m 0 it's K2 2 4
11: greg m 0 dumb K2 2 5
12: teacher m 1 what K3 3 1
13: teacher m 1 should K3 3 2
14: teacher m 1 we K3 3 3
15: teacher m 1 do K3 3 4
16: sam m 0 you K4 4 1
17: sam m 0 liar K4 4 2
18: sam m 0 it K4 4 3
19: sam m 0 stinks K4 4 4
20: greg m 0 i K5 5 1
21: greg m 0 am K5 5 2
22: greg m 0 telling K5 5 3
23: greg m 0 the K5 5 4
24: greg m 0 truth K5 5 5
25: sally f 0 how K6 6 1
26: sally f 0 can K6 6 2
27: sally f 0 we K6 6 3
28: sally f 0 be K6 6 4
29: sally f 0 certain K6 6 5
30: greg m 0 there K7 7 1
31: greg m 0 is K7 7 2
32: greg m 0 no K7 7 3
33: greg m 0 way K7 7 4
34: sam m 0 i K8 8 1
35: sam m 0 distrust K8 8 2
36: sam m 0 you K8 8 3
37: sally f 0 what K9 9 1
38: sally f 0 are K9 9 2
39: sally f 0 you K9 9 3
40: sally f 0 talking K9 9 4
41: sally f 0 about K9 9 5
42: researcher f 1 shall K10 10 1
43: researcher f 1 we K10 10 2
44: researcher f 1 move K10 10 3
45: researcher f 1 on K10 10 4
46: researcher f 1 good K10 10 5
47: researcher f 1 then K10 10 6
48: greg m 0 i'm K11 11 1
49: greg m 0 hungry K11 11 2
50: greg m 0 let's K11 11 3
51: greg m 0 eat K11 11 4
52: greg m 0 you K11 11 5
53: greg m 0 already K11 11 6
person sex adult state code element_id word_id
person sex adult state code element_id word_id
1: sam m 0 Computer K1 1 1
2: sam m 0 is K1 1 2
3: sam m 0 fun K1 1 3
4: sam m 0 Not K1 1 4
5: sam m 0 too K1 1 5
6: sam m 0 fun K1 1 6
7: greg m 0 No K2 2 1
8: greg m 0 it's K2 2 2
9: greg m 0 not K2 2 3
10: greg m 0 it's K2 2 4
11: greg m 0 dumb K2 2 5
12: teacher m 1 What K3 3 1
13: teacher m 1 should K3 3 2
14: teacher m 1 we K3 3 3
15: teacher m 1 do K3 3 4
16: sam m 0 You K4 4 1
17: sam m 0 liar K4 4 2
18: sam m 0 it K4 4 3
19: sam m 0 stinks K4 4 4
20: greg m 0 I K5 5 1
21: greg m 0 am K5 5 2
22: greg m 0 telling K5 5 3
23: greg m 0 the K5 5 4
24: greg m 0 truth K5 5 5
25: sally f 0 How K6 6 1
26: sally f 0 can K6 6 2
27: sally f 0 we K6 6 3
28: sally f 0 be K6 6 4
29: sally f 0 certain K6 6 5
30: greg m 0 There K7 7 1
31: greg m 0 is K7 7 2
32: greg m 0 no K7 7 3
33: greg m 0 way K7 7 4
34: sam m 0 I K8 8 1
35: sam m 0 distrust K8 8 2
36: sam m 0 you K8 8 3
37: sally f 0 What K9 9 1
38: sally f 0 are K9 9 2
39: sally f 0 you K9 9 3
40: sally f 0 talking K9 9 4
41: sally f 0 about K9 9 5
42: researcher f 1 Shall K10 10 1
43: researcher f 1 we K10 10 2
44: researcher f 1 move K10 10 3
45: researcher f 1 on K10 10 4
46: researcher f 1 Good K10 10 5
47: researcher f 1 then K10 10 6
48: greg m 0 I'm K11 11 1
49: greg m 0 hungry K11 11 2
50: greg m 0 Let's K11 11 3
51: greg m 0 eat K11 11 4
52: greg m 0 You K11 11 5
53: greg m 0 already K11 11 6
person sex adult state code element_id word_id
act tot scene location
1: Act1 1.1 Scene I Elsinore. A platform before the castle.
2: Act1 1.1 Scene I Elsinore. A platform before the castle.
3: Act1 2.1 Scene I Elsinore. A platform before the castle.
4: Act1 2.1 Scene I Elsinore. A platform before the castle.
5: Act1 2.1 Scene I Elsinore. A platform before the castle.
---
29375: Act5 1150.3 Scene II A hall in the castle.
29376: Act5 1150.3 Scene II A hall in the castle.
29377: Act5 1150.3 Scene II A hall in the castle.
29378: Act5 1150.3 Scene II A hall in the castle.
29379: Act5 1150.3 Scene II A hall in the castle.
person died dialogue element_id word_id
1: Bernardo FALSE who's 1 1
2: Bernardo FALSE there 1 2
3: Francisco FALSE nay 2 1
4: Francisco FALSE answer 2 2
5: Francisco FALSE me 2 3
---
29375: Prince Fortinbras FALSE go 2007 1
29376: Prince Fortinbras FALSE bid 2007 2
29377: Prince Fortinbras FALSE the 2007 3
29378: Prince Fortinbras FALSE soldiers 2007 4
29379: Prince Fortinbras FALSE shoot 2007 5
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.