Combine Columns to Class

Usage

colcomb2class(dataframe, combined.columns, class = "list", percent = TRUE, digits = 2, elim.old = TRUE, zero.replace = 0, override = FALSE)

Arguments

dataframe

A dataframe or qdap class (e.g., termco, question_type, pos_by, character_table).

combined.columns

A list of named vectors of the colnames/indexes of the numeric columns to be combined (summed). If a vector is unnamed a name will be assigned.

class

The class to assign to the output.

percent

logical. If TRUE output given as percent. If FALSE the output is proportion.

digits

Integer; number of decimal places to round when printing.

elim.old

logical. If TRUE eliminates the columns that are combined together by the named match.list. TRUE outputs the table proportionally (see prop).

zero.replace

Value to replace 0 values with.

override

logical. If TRUE the printing options (e.g., percent, digits, etc.) of the dataframe argument are overrode.

Examples

## `termco` example
ml <- list(
    cat1 = c(" the ", " a ", " an "),
    cat2 = c(" I'" ),
    "good",
    the = c("the", " the ", " the", "the")
)
dat1 <- with(raj.act.1,  termco(dialogue, person, ml))
colcomb2class(dat1, list(cats = c("cat1", "cat2")))

           person word.count     good        the      cats
1         Abraham         24        0          0         0
2        Benvolio        621  2(.32%)  44(7.09%) 34(5.48%)
3         Capulet        736  3(.41%)  30(4.08%) 42(5.71%)
4   First Citizen         16        0  3(18.75%) 2(12.50%)
5   First Servant         69 1(1.45%)   5(7.25%) 8(11.59%)
6         Gregory        149        0 16(10.74%)  9(6.04%)
7          Juliet        206  1(.49%)   9(4.37%)  6(2.91%)
8    Lady Capulet        286        0  21(7.34%) 20(6.99%)
9   Lady Montague         28        0          0  2(7.14%)
10       Mercutio        552  2(.36%)  46(8.33%) 49(8.88%)
11       Montague        217  1(.46%)  13(5.99%) 12(5.53%)
12          Nurse        598  2(.33%)  31(5.18%) 45(7.53%)
13          Paris         32        0   1(3.12%)         0
14         Prince        167        0  11(6.59%)  8(4.79%)
15          Romeo       1164  3(.26%)  48(4.12%) 59(5.07%)
16        Sampson        259  1(.39%)  23(8.88%) 19(7.34%)
17 Second Capulet         17        0          0         0
18 Second Servant         41 1(2.44%)   3(7.32%)  2(4.88%)
19        Servant        183  1(.55%)  12(6.56%) 13(7.10%)
20         Tybalt        160        0  10(6.25%) 12(7.50%)


## `question_type` example
dat2 <- question_type(DATA.SPLIT$state, DATA.SPLIT$person)
combs <- list(
    `wh/how` = c("what", "how"),
    oth = c("shall", "implied_do/does/did")
)
colcomb2class(dat2, combs)

      person tot.quest  wh/how     oth
1       greg         1       0 1(100%)
2 researcher         1       0 1(100%)
3      sally         2 2(100%)       0
4    teacher         1 1(100%)       0
5        sam         0       0       0


## `pos_by` example
dat3 <- with(DATA, pos_by(state, list(adult, sex)))
colcomb2class(dat3, qcv(DT, EX, FW))

  adult&sex wrd.cnt       IN       JJ       MD       NN      PRP    PRP$       RB       VB      VBG      VBP     VBZ     WP    WRB       1       2       3
1       0.f      10 1(10.0%) 1(10.0%) 1(10.0%)        0 2(20.0%)       0        0 1(10.0%) 1(10.0%) 1(10.0%)       0 1(10%) 1(10%)       0       0       0
2       0.m      33        0  2(6.1%)        0 6(18.2%) 6(18.2%) 2(6.1%) 4(12.1%)  2(6.1%)  1(3.0%)  2(6.1%) 3(9.1%)      0      0 3(9.1%) 1(3.0%) 1(3.0%)
3       1.f       6 1(16.7%) 1(16.7%) 1(16.7%)        0 1(16.7%)       0 1(16.7%) 1(16.7%)        0        0       0      0      0       0       0       0
4       1.m       4        0        0 1(25.0%)        0 1(25.0%)       0        0 1(25.0%)        0        0       0 1(25%)      0       0       0       0



## data.frame example
dat4 <- data.frame(X=LETTERS[1:5], matrix(sample(0:5, 20, TRUE), ncol = 4))
colcomb2class(dat4, list(new = c("X1", "X4")))

$raw
  X X2 X3 new
1 A  5  2   8
2 B  1  2   6
3 C  2  0   5
4 D  0  4   6
5 E  3  3   2

$prop
  X       X2       X3      new
1 A 33.33333 13.33333 53.33333
2 B 11.11111 22.22222 66.66667
3 C 28.57143  0.00000 71.42857
4 D  0.00000 40.00000 60.00000
5 E 37.50000 37.50000 25.00000

$rnp
  X        X2        X3       new
1 A 5(33.33%) 2(13.33%) 8(53.33%)
2 B 1(11.11%) 2(22.22%) 6(66.67%)
3 C 2(28.57%)         0 5(71.43%)
4 D         0 4(40.00%) 6(60.00%)
5 E 3(37.50%) 3(37.50%) 2(25.00%)

$digits
[1] 2

$percent
[1] TRUE