Package 'sampler'

Albania 2017 Election Results by Polling Station

Description

Data set containing 2017 Albania election results by polling station published by the Central Election Commission and opened by the Coalition of Domestic Observers & Democracy International.

Usage

albania

Format

A data frame with 5362 rows and 45 variables

qarku

district, 12 in total

Q_ID

geocode for district

bashkia

municipality, 61 in total

BAS_ID

geocode for municipality

zaz

election area zone, 90 in total

njesiaAdministrative

village, 373 in total

COM_ID

geocode for village

qvKod

polling station identifier

zgjedhes

number of total registered voters

meshkuj

number of male registered voters

femra

number of female registered voters

totalSeats

number of seats contested by district

vendndodhja

name of polling center containing polling stations

ambienti

type of polling center, 5 in total

totalVoters

number of total registered voters that cast ballots

femVoters

number of female registered voters that cast ballots

maleVoters

number of male registered voters that cast ballots

unusedBallots

number of ballots not used

damagedBallots

number of ballots damaged

ballotsCast

number of total ballots cast

invalidVotes

number of ballots cast that were invalidated

validVotes

number of valid ballots cast

lsi

number of ballots cast for LSI

ps

number of ballots cast for PS

pkd

number of ballots cast for PKD

sfida

number of ballots cast for SFIDA

pr

number of ballots cast for PR

pd

number of ballots cast for PD

pbdksh

number of ballots cast for PBDKSH

adk

number of ballots cast for ADK

psd

number of ballots cast for PSD

ad

number of ballots cast for AD

frd

number of ballots cast for FRD

pds

number of ballots cast for PDS

pdiu

number of ballots cast for PDIU

aak

number of ballots cast for AAK

mega

number of ballots cast for MEGA

pksh

number of ballots cast for PKSH

apd

number of ballots cast for APD

libra

number of ballots cast for LIBRA

psSeats

number of seats won by PS

pdSeats

number of seats won by PD

lsiSeats

number of seats won by LSI

pdiuSeats

number of seats won by PDIU

psdSeats

number of seats won by PSD

Source

https://albaniaelectiondata.herokuapp.com/

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Calculate proportion and margin of error (unequal-sized cluster sample)

Description

Calculate proportion and margin of error (unequal-sized cluster sample)

Usage

cpro(df, numerator, denominator, ci = 95, na = "", N = 0)

Arguments

df	object containing data frame on which to perform analysis
numerator	variable in data frame for which you want to calculate proportion and margin of error
denominator	variable in data frame containing population sizes of unequal clusters
ci	(optional) confidence level for establishing a confidence interval using z-score (defaults to 95; restricted to 80, 85, 90, 95 or 99 as input)
na	(optional) value that you want to filter and exclude (defaults to include everything)
N	(optional) population universe (e.g. 10000, nrow(df)); if N value is passed as an argument, margin of error will be calculated using fpc

Value

Returns table of responses (n), proportions, margins of error, lower and upper bounds by factor for a given variable in a stratified sample

References

[1] Survey Sampling, L. Kish, 1965, Equation 6.3.4 [2] Sampling Techniques, W.G. Cochran, 1977, Equation 3.34

Examples

alresults <- ssamp(albania, 890, qarku)
cpro(df=alresults, numerator=totalVoters, denominator=zgjedhes, ci=95)
cpro(df=alresults, numerator=pd, denominator=validVotes, ci=95, N=5361)

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Removes duplicate observations within collected data

Description

Removes duplicate observations within collected data

Usage

dedupe(df, col_name)

Arguments

df	object containing data frame of collected data
col_name	variable within data frame by which to filter for duplicate values

Value

Returns table of all data based on unique values within collected data

Examples

aldupe <- rsamp(df=albania, n=390, rep=TRUE)
dedupe(df=aldupe, col_name=qvKod)

---

Identifies duplicate values within collected data

Description

Identifies duplicate values within collected data

Usage

dupe(df, col_name)

Arguments

df	object containing data frame of collected data
col_name	variable within data frame by which to filter for duplicate values

Value

Returns table of duplicate values within collected data

Examples

aldupe <- rsamp(df=albania, n=390, rep=TRUE)
dupe(df=aldupe, col_name=qvKod)

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Albania 2017 CDO Election Observation Data Findings on Opening Process

Description

Data set containing 2017 Albania election observation findings on polling station opening process by the Coalition of Domestic Observers (CDO) CDO conducted a statistically-based observation (SBO) exercise, deploying observers to a random sample of polling stations for the 25 June 2017 Albanian elections. This is a subset of observation data collected by CDO observers that includes data that was used to perform statistical analysis.

Usage

opening

Format

A data frame with 524 rows and 19 variables

qarku

district, 12 in total

psID

polling station identifier

votersList

number of registered voters at the polling station

ballotPapers

number of ballot papers at the polling station

pubPriv

type of polling station, public or private

openTime

time when polling station opening, in 30 minute ranges

numKommish

number of commissioners present at polling station

secrecyOpen

yes-no if polling station enabled voters to cast ballots in secrecy, po or jo

movementOpen

yes-no if polling station provided sufficient space to vote, po or jo

removeMatInside

yes-no if campaign materials were removed from inside polling station, po or jo

removeMatOutside

yes-no if campaign materials were removed from outside polling station, po or jo

pvComplete

yes-no if commissioners completed the opening record checklist sheet, po or jo

boxChecked

yes-no if commissioners checked to ensure the ballot box was empty before opening, po or jo

boxSealed

yes-no if commissioners sealed the ballot box to prevent ballot tampering, po or jo

recordBox

yes-no if commissioners recorded the seal number on the ballot box, po or jo

centerMat

yes-no if there were all election materials were available at the polling station, po or jo

blindTools

yes-no if the polling station was equipped for blind voters, po or jo

disabledTools

yes-no-partially if the polling station was equipped for disabled voters, po or jo or pjeserisht

overallOpen

very good-good-problematic-very problematic an overall assessment of the opening process, shummir,mir,meprob,shumprob

Source

https://ona.io/cdo/35080/216662

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Determines sample size by strata using sub-units

Description

Determines sample size by strata using sub-units

Usage

psampcalc(df, n, strata, unit, over = 0)

Arguments

df	object containing full sampling data frame (e.g. data)
n	sample size (integer) or object containing sample size
strata	variable in sampling data frame by which to stratify (e.g. region)
unit	variable in sampling data frame containing sub-units (e.g. population)
over	(optional) desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)

Value

Returns sample size per strata based on sub-units (rounded up to nearest integer)

References

[1] Sampling Design & Analysis, S. Lohr, 1999, 4.4

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Identifies missing points between sample and collected data

Description

Identifies missing points between sample and collected data

Usage

rmissing(sampdf, colldf, col_name)

Arguments

sampdf	object containing data frame of sample points
colldf	object containing data frame of collected data
col_name	common variable (i.e. key) in data frames by which to check for missing points

Value

Returns table of sample points missing from collected data

References

Simplified wrapper around dplyr::anti_join()

Examples

alsample <- rsamp(df=albania, 544)
alreceived <- rsamp(df=alsample, 390)
rmissing(sampdf=alsample, colldf=alreceived, col_name=qvKod)

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Calculate proportion and margin of error (simple random sample)

Description

Calculate proportion and margin of error (simple random sample)

Usage

rpro(df, col_name, ci = 95, na = "", N = 0)

Arguments

df	object containing data frame on which to perform analysis (e.g. data)
col_name	variable in data frame for which you want to calculate proportion and margin of error
ci	(optional) confidence level for establishing a confidence interval using z-score (defaults to 95; restricted to 80, 85, 90, 95 or 99 as input)
na	(optional) value that you want to filter and exclude (defaults to include everything)
N	(optional) population universe (e.g. 10000, nrow(df)); if N value is passed as an argument, margin of error will be calculated using fpc

Value

Returns table of responses (n), proportions, margins of error, lower and upper bounds by factor for a given variable

References

[1] Sampling Design & Analysis, S. Lohr, 1999, Equation 2.15

Examples

rpro(df=opening, col_name=openTime, ci=95, na="n/a", N=5361)

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Draws simple random sample without replacement

Description

Draws simple random sample without replacement

Usage

rsamp(df, n, over = 0, rep = FALSE)

Arguments

df	object containing full sampling data frame (e.g. data)
n	sample size (integer) or object containing sample size
over	(optional) desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)
rep	(optional)

Value

Returns simple random sample without replacement

References

Simplified wrapper around dplyr::sample_n()

Examples

rsamp(albania, n=360, over=0.1, rep=FALSE)

size <- rsampcalc(nrow(albania), 3, 95, 0.5)
randomsample <- rsamp(albania, size)

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Determines random sample size

Description

Determines random sample size

Usage

rsampcalc(N, e, ci = 95, p = 0.5, over = 0)

Arguments

N	population universe (e.g. 10000, nrow(df))
e	tolerable margin of error (integer or float, e.g. 5, 2.5)
ci	(optional) confidence level for establishing a confidence interval using z-score (defaults to 95; restricted to 80, 85, 90, 95 or 99 as input)
p	(optional) anticipated response distribution (defaults to 0.5; takes value between 0 and 1 as input)
over	(optional) desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)

Value

Returns appropriate sample size (rounded up to nearest integer)

References

[1] Sampling Design & Analysis, S. Lohr, 1999, equation 2.17

Examples

rsampcalc(N=5361, e=3, ci=95, p=0.5, over=0.1)

rsampcalc(nrow(data), 3)

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Identifies number of missing points by strata between sample and collected data

Description

Identifies number of missing points by strata between sample and collected data

Usage

smissing(sampdf, colldf, strata, col_name)

Arguments

sampdf	object containing data frame of sample points
colldf	object containing data frame of collected data
strata	variable in both data frames by which to stratify
col_name	common variable (i.e. key) in data frames by which to check for missing points

Value

Returns table of number of sample points by strata missing from collected data

References

Simplified wrapper around dplyr::anti_join()

Examples

alsample <- rsamp(df=albania, 544)
alreceived <- rsamp(df=alsample, 390)
smissing(sampdf=alsample, colldf=alreceived, strata=qarku, col_name=qvKod)

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Calculate proportion and margin of error (stratified sample)

Description

Calculate proportion and margin of error (stratified sample)

Usage

spro(fulldf, sampdf, strata, col_name, ci = 95, na = "")

Arguments

fulldf	object containing original data frame used to draw sample
sampdf	object containing data frame on which to perform analysis
strata	variable in both data frames by which to stratify
col_name	variable in data frame for which you want to calculate proportion and margin of error
ci	(optional) confidence level for establishing a confidence interval using z-score (defaults to 95; restricted to 80, 85, 90, 95 or 99 as input)
na	(optional) value that you want to filter and exclude (defaults to include everything)

Value

Returns table of responses (n), proportions, margins of error, lower and upper bounds by factor for a given variable in a stratified sample

References

[1] Sampling Design & Analysis, S. Lohr, 1999, 4.6 & 4.7

Examples

spro(fulldf=albania, sampdf=opening, strata=qarku, col_name=openTime, ci=95, na="n/a")

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Draws stratifed sample without replacement using proportional allocation

Description

Draws stratifed sample without replacement using proportional allocation

Usage

ssamp(df, n, strata, over = 1)

Arguments

df	object containing full sampling data frame (e.g. data)
n	sample size (integer) or object containing sample size
strata	variable in sampling data frame by which to stratify (e.g. region)
over	(optional) desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)

Value

Returns stratified sample without replacement

Examples

ssamp(df=albania, n=360, strata=qarku, over=0.1)

size <- rsampcalc(nrow(albania), 3, 95, 0.5)
stratifiedsample <- ssamp(albania, size, qarku)

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Determines sample size by strata using proportional allocation

Description

Determines sample size by strata using proportional allocation

Usage

ssampcalc(df, n, strata, over = 0)

Arguments

df	object containing sampling data frame (e.g. data)
n	sample size (integer) or object containing sample size
strata	variable in sampling data frame by which to stratify (e.g. region)
over	(optional) desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)

Value

Returns proportional sample size per strata (rounded up to nearest integer)

References

[1] Sampling Design & Analysis, S. Lohr, 1999, 4.4

Examples

ssampcalc(df=albania, n=544, strata=qarku, over=0.05)

size <- rsampcalc(nrow(albania), 3, 95, 0.5)
ssampcalc(albania, size, qarku)

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