The sampler R package is designed to enable data scientists to design, draw, and analyze simple or complex samples using data frames. It enables you to load machine-readable files (e.g. .csv, .tsv, etc.) in R containing a sampling frame or collected data, store them as objects, and perform sampling techniques and analysis using clear and concise methods.
Specifically, a data scientist can use the sampler R package to:
The sampler R package builds a bridge for survey administrators between the free and open-source R environment and no-to-low cost Open Data Kit (ODK)-based toolkits such as Ona and ELMO. The sampler package is available via CRAN or GitHub for use in R and R Studio.
install.packages("sampler")
library(sampler)
install.packages("devtools"); library(devtools)
devtools::install_github("mbaldassaro/sampler"); library(sampler)
The sampler R package includes the following datasets:
albania
: dataset containing 2017 Albania election results by polling station published by the Central Election Commission and opened by the Coalition of Domestic Observers & Democracy Internationalopening
: dataset 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 analysisFull documentation of datasets and functions can be found on RDocumentation
rsampcalc(N, e, ci=95,p=0.5, over=0)
Where:
N
is population universe (e.g. 10000, nrow(df))
e
is tolerable margin of error (integer or float, e.g. 5, 2.5)
ci
(optional) is 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) is anticipated response distribution (defaults to 0.5; takes value between 0 and 1 as input)
* over
(optional) is desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)
Returns appropriate sample size (rounded up to nearest integer)
Example:
rsampcalc(N=5361, e=3, ci=95, p=0.5, over=0.1)
Source: Sampling Design & Analysis, S. Lohr, 1999, equation 2.17
rsamp(df, n, over=0, rep=FALSE)
Where:
df
is object containing full sampling data frame
n
is sample size (integer or object containing sample size)
over
(optional) is desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)
rep
(optional) is boolean for a sample with repalcement (TRUE) or without replacement (defaults to FALSE)
Returns a simple random sample of size n
Example:
rsamp(albania, n=360, over=0.1, rep=FALSE)
or
size <- rsampcalc(nrow(albania), 3, 95, 0.5)
rsamp(albania, size)
ssampcalc(df, n, strata, over=0)
Where:
df
is object containing sampling data frame
n
is sample size (integer) or object containing sample size
strata
is variable in sampling data frame by which to stratify
over
(optional) is desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)
Returns proportional sample size per strata (rounded up to nearest integer)
Example:
ssampcalc(df=albania, n=544, strata=qarku, over=0.05)
or
size <- rsampcalc(nrow(albania), 3, 95, 0.5)
ssampcalc(albania, size, qarku)
Source: Sampling Design & Analysis, S. Lohr, 1999, 4.4
ssamp(df, n, strata, over=1)
Where:
df
is object containing full sampling data frame
n
is sample size (integer, or object containing sample size)
strata
is variable in sampling data frame by which to stratify (e.g. region)
over
(optional) is desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)
Returns stratified sample using proportional allocation without replacement
Example:
ssamp(df=albania, n=360, strata=qarku, over=0.1)
or
size <- rsampcalc(nrow(albania), 3, 95, 0.5)
ssamp(albania, size, qarku)
psampcalc(df, n, strata, unit, over=0)
Where:
df
is object containing full sampling data frame
n
is sample size (integer) or object containing sample size
strata
is variable in sampling data frame by which to stratify
unit
is variable in sampling data frame containing sub-units (e.g. population)
* over
(optional) is desired oversampling proportion (defaults to 0; takes value between 0 and 1 as input)
Returns sample size per strata based on sub-units (rounded up to nearest integer)
Example
psampcalc(df=albania, n=544, strata=qarku, unit=zgjedhes, over=0.1)
Source: Sampling Design & Analysis, S. Lohr, 1999, 4.4
rpro(df, col_name, ci=95, na="", N=0)
Where:
df
is object containing data frame on which to perform analysis (e.g. data)
col_name
is variable in data frame for which you want to calculate proportion and margin of error
ci
(optional) is 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) is value that you want to filter and exclude (defaults to include everything)
* N
(optional) is population universe (e.g. 10000, nrow(df)); if N value is passed as an argument, margin of error will be calculated using fpc
Returns table of responses (n), proportions (midpoint), margins of error, lower and upper bounds by factor for a given variable
Example:
rpro(df=opening, col_name=openTime, ci=95, na="n/a", N=5361)
Source: Sampling Design & Analysis, S. Lohr, 1999, Equation 2.15
spro(fulldf, sampdf, strata, col_name, ci=95, na="")
Where:
fulldf
is object containing original data frame used to draw sample
sampdf
is object containing data frame on which to perform analysis
strata
is variable in both data frames by which to stratify
col_name
is variable in data frame for which you want to calculate proportion and margin of error
ci
(optional) is 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) is value that you want to filter and exclude (defaults to include everything)
Returns table of responses (n), proportions (midpoint), margins of error, lower and upper bounds by factor for a given variable in a stratified sample
Example:
spro(fulldf=albania, sampdf=opening, strata=qarku, col_name=openTime, ci=95, na="n/a")
Source: Sampling Design & Analysis, S. Lohr, 1999, 4.6 & 4.7
cpro(df, numerator, denominator, ci=95, na="", N=0)
Where:
df
is object containing data frame on which to perform analysis
numerator
is variable in data frame for which you want to calculate proportion and margin of error
denominator
is variable in data frame containing population of unequal cluster sizes
ci
(optional) is 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) is value that you want to filter and exclude (defaults to include everything)
N
(optional) is population universe (e.g. 10000, nrow(df)); if N value is passed as an argument, margin of error will be calculated using fpc
Returns table of responses (n), proportions (midpoint), margins of error, lower and upper bounds by factor for a given variable in an unequal-sized cluster sample
Example:
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)
Source 1: Survey Sampling, L. Kish, 1965, Equation 6.3.4
Source 2: Sampling Techniques, W.G. Cochran, 1977, Equation 3.34
rmissing(sampdf, colldf, col_name)
Where:
sampdf
is object containing data frame of sample points
colldf
is object containing data frame of collected data
* col_name
is common variable (i.e. key) in data frames by which to check for missing points
Returns table of sample points missing from collected data
Example:
alsample <- rsamp(df=albania, 544)
alreceived <- rsamp(df=alsample, 390)
rmissing(sampdf=alsample, colldf=alreceived, col_name=qvKod)
smissing(sampdf, colldf, strata, col_name)
Where:
sampdf
is object containing data frame of sample points
colldf
is object containing data frame of collected data
strata
is variable in both data frames by which to stratify
col_name
is common variable (i.e. key) in data frames by which to check for missing points
Returns table of number of sample points by strata missing from collected data
Example:
alsample <- rsamp(df=albania, 544)
alreceived <- rsamp(df=alsample, 390)
smissing(sampdf=alsample, colldf=alreceived, strata=qarku, col_name=qvKod)
dupe(df, col_name)
Where:
df
is object containing data frame of collected data
col_name
is variable within data frame by which to filter for duplicate values
Returns table of duplicate values within collected data
Example:
aldupe <- rsamp(df=albania, n=390, rep=TRUE)
dupe(df=aldupe, col_name=qvKod)
dedupe(df, col_name)
Where:
df
is object containing data frame of collected data
col_name
is variable within data frame by which to filter for duplicate values
Returns table of observations based on unique values within collected data
Example:
aldupe <- rsamp(df=albania, n=390, rep=TRUE)
dedupe(df=aldupe, col_name=qvKod)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.