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:

- 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
- perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs

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)
::install_github("mbaldassaro/sampler"); library(sampler) devtools
```

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 International`opening`

: 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 analysis

Full 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

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

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

```
<- rsampcalc(nrow(albania), 3, 95, 0.5)
size 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:

```
<- ssamp(albania, 890, qarku)
alresults 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:

```
<- rsamp(df=albania, 544)
alsample <- rsamp(df=alsample, 390)
alreceived 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:

```
<- rsamp(df=albania, 544)
alsample <- rsamp(df=alsample, 390)
alreceived 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:

```
<- rsamp(df=albania, n=390, rep=TRUE)
aldupe 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:

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