`outcomerate`

is a lightweight R package that implements the standard
outcome rates for surveys, as defined in the Standard
Definitions
of the American Association of Public Opinion Research (AAPOR).

Although the mathematical formulas are straightforward, it can get
tedious and repetitive calculating all the rates by hand, especially for
sub-groups of your study. The formulas are similar to one another and so
it is also dangerously easy to make a clerical mistake. The
`outcomerate`

package simplifies the analytically workflow by defining
all formulas as a collection of functions.

Install the package from CRAN:

```
install.packages("outcomerate")
```

Alternatively, install the latest development version via github:

```
#install.packages("devtools")
devtools::install_github("ropensci/outcomerate")
```

Letâ€™s say you try to survey 12 people. After finishing the fieldwork, you tabulate all your attempts into a table of disposition outcomes:

| code | disposition | n | | :--- | :-------------------- | -: | | I | Complete interview | 4 | | P | Partial interview | 2 | | R | Refusal and break-off | 1 | | NC | Non-contact | 1 | | O | Other | 1 | | UH | Unknown if household | 1 | | NE | Known ineligible | 1 | | UO | Unknown, other | 1 |

Using this table, you may wish to report some of the common survey outcome rates, such as:

**Response Rate:**The proportion of your sample that results in an interview.**Cooperation Rate:**The proportion of people contacted who participate in your survey.**Refusal Rate:**The proportion of your sample that refused to participate.**Contact Rate:**The proportion of sampled cases where you manage to reach the respondent.**Location Rate:**The proportion of cases (say, in an establishment survey) that you manage to locate.

Most of these rates come under a number of variants, having definitions
that are standardized by AAPOR. The `outcomerate`

function lets your
calculate these rates seamlessly:

```
# load package
library(outcomerate)
# set counts per disposition code (needs to be a named vector)
freq <- c(I = 4, P = 2, R = 1, NC = 1, O = 1, UH = 1, UO = 1, NE = 1)
# calculate rates, assuming 90% of unknown cases are elligble
outcomerate(freq, e = eligibility_rate(freq))
#> RR1 RR2 RR3 RR4 RR5 RR6 COOP1 COOP2 COOP3 COOP4 REF1 REF2 REF3
#> 0.364 0.545 0.370 0.556 0.444 0.667 0.500 0.750 0.571 0.857 0.091 0.093 0.111
#> CON1 CON2 CON3 LOC1 LOC2
#> 0.727 0.741 0.889 0.818 0.833
```

Dispositions do not always come in a tabulated format. Survey analysts
often work with microdata directly, where each row represents an
interview. The `outcomerate`

package allows you to obtain rates using
such a format as well:

```
# define a vector of dispositions
x <- c("I", "P", "I", "UO", "R", "I", "NC", "I", "O", "P", "UH")
# calculate desired rates
outcomerate(x, rate = c("RR2", "CON1"))
#> RR2 CON1
#> 0.55 0.73
# obtain a weighted rate
w <- c(rep(1.3, 6), rep(2.5, 5))
outcomerate(x, weight = w, rate = c("RR2", "CON1"))
#> RR2w CON1w
#> 0.50 0.69
```

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