| Type: | Package |
| Title: | Sample Design, Drawing & Data Analysis Using Data Frames |
| Version: | 0.2.4 |
| Author: | Michael Baldassaro |
| Maintainer: | Michael Baldassaro <mbaldassaro@gmail.com> |
| Description: | Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/mbaldassaro/sampler |
| BugReports: | https://github.com/mbaldassaro/sampler/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | dplyr, tidyr, reshape, purrr |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2019-09-15 15:28:31 UTC; mbaldassaro |
| Repository: | CRAN |
| Date/Publication: | 2019-09-15 15:40:02 UTC |
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/
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)
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)
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
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
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)
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)
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)
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)
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)
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")
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)
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)