Description Usage Arguments Details Value Examples
Some counting methods in this package only accept vote
object created by
this function. So the first step should always be using this function. The function
will return the modified ballots and some other helpful information. See Details
and Values.
1  create_vote(x, xtype = 2, candidate = NULL, isna = NULL)

x 
a data.frame, matrix or list of raw ballots. See Details. 
xtype 
should be 1, 2 (default) or 3, designating the
type of 
candidate 
if 
isna 
entries which should be taken as NAs.

The function accepts the following input:
(1) when xtype
is 1, x must be a matrix. Column names are candidate names (if
column names are NULL, they will be created: x1, x2, x3...). Candidate number is the number
of columns of the matrix. Entry ij is the numeric score assigned by
the ith voter to the jth candidate.
(2) when xtype
is 2, x can be a matrix or data.frame.
Candidate number is the length of candidate
.
Entries are names (character or numeric) of candidates.
The i1, i2, i3... entries are the 1st, 2nd,
3rd... preferences of voter i.
(3) when xtype
is 3, x should be a list.
Each element of the list is a ballot, a vector
contains the names (character or numeric) of candidates.
The 1st preference is in the 1st position of
the vector, the 2nd preference is in the 2nd position... The number of candidates is the length
of candidate
; as a result, a ballot with number of names
larger than candidate number is
labelled as wrong.
The function also returns Condorcet matrix. Suppose candidates are i, j, k. The voter likes i best, so he assigns 1 to i. The 2nd choice is j, so he assigns 2 to j, leaving k as NA. Now computing the Condorcet matrix: since i's score is smaller than j' score, we add 1 to the ij cell of the matrix, and add 0 to the ji cell. Candidate k's NA is automatically set to the highest (that is, the worst) score: 3 (since there are 3 candidates); i < k, so we add 1 to the ik cell and add 0 to ki cell. Besides, there is also a score difference matrix: we add 2  1 = 1 to the ij cell of score difference matrix, and add 3  1 = 2 to the ik cell. If tie appears, both sides acquire 0.
Note the ways we calculate the Condorcet matrix. (1) It allow ties, that is,
duplicated score values. (2) NA is deems as the worst, which means: if a
voter does not mention a candidate, the candidate will be given the
highest (worst) score. (3) Ballots mention only one name are assumed
to express preference, since unmentioned candidates are assumed to
be equally hated. (4) The Condorcet matrix returned
by create_vote
uses ballots that may have duplicated values
and have only one valid entry. However, Condorcet family
methods in this package provide possibility to recalculate the matrix.
And, the simplest way to get rid of duplicated values and NAs is
to delete some ballots.
an object of class vote
is returned, which
is essentially a list. It has the following elements.
(1) call
the call.
(2) ballot
the returned ballot. It is always a score matrix.
The column names are candidate names; entries are numeric scores
assigned by voters. Missing values are all set to NA.
(3) nas
those which are taken as NA in data cleaning.
(4) candidate
candidate names.
(5) candidate_num
number of candidates.
(6) ballot_num
number of ballots.
(7) ballot_at_least_one
number of ballots that mention
at least one candidate.
(8) cdc
the Condorcet matrix calculated with ballots
that have no NA entries.
(9) cdc_with_na
the Condorcet matrix calculated with
ballots that have at least one valid entry.
(10) dif
the score difference matrix calculated with
ballots that have no NA entries.
(11) dif_with_na
the score difference matrix calculated
with ballots that have at least one valid entry.
(12) row_with_na
rows of ballot
with NAs.
(13) row_non_na
for rows with NAs, the number of
nonNA entries of them.
(14) row_with_dup
rows of ballot
with
duplicated score values.
(15) approval_able
if length of row_non_dup
is 0, then it is TRUE, else, FALSE. It indicates whether approval
method can be used. When xtype
is 2 or 3, it is always TRUE.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  # xtype is 2
raw < c(
rep(c('m', 'n', 'c', 'k'), 42),
rep(c('n', 'c', 'k', 'm'), 26),
rep(c('c', 'k', 'n', 'm'), 15),
rep(c('k', 'c', 'n', 'm'), 17)
)
raw < matrix(raw, ncol = 4, byrow = TRUE)
vote < create_vote(raw, xtype = 2, candidate = c('m', 'n', 'k', 'c'))
# xtype is 3
raw < list(
c('a', 'e', 'c', 'd', 'b'),
c('b', 'a', 'e'),
c('c', 'd', 'b'),
c('d', 'a', 'b'),
c('a', 'a', 'b', 'b', 'b'),
c(NA, NA, NA, NA),
v7 = NULL,
v8 = c('a', NA, NA, NA, NA, NA, NA),
v9 = rep(" ", 3)
)
y < check_dup_wrong(raw, xtype = 3, candidate = letters[1: 4])
raw2 < raw[y$row_with_wrong]
vote < create_vote(raw2, xtype = 3, candidate = letters[1: 4])
# xtype is 1
raw < rbind(
c(1, 2, 5, 3, 3),
c(2, 1, 1, 3, 5),
c(1, 2, 5, 3, 4),
c(1, 2, 5, 3, 4),
c(NA, NA, NA, NA, NA),
c(NA, 3, 5, 1, 2),
c(NA, 999, NA, 1, 5)
)
vote < create_vote(raw, xtype = 1, isna = 999)

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