ura provides a set of tools for calculating inter-rater reliability (IRR) statistics by rater, allowing for real-time monitoring of rater reliability. While not the first package to provide users access to IRR diagnostics (e.g., irr), ura aims to provide a simple set of tools for quickly monitoring rater progress and precision. You can use ura to, for instance, find the percentage agreement or Krippendorf’s Alpha of all of the subjects coded by your raters. Another helpful use is to calculate percentage agreement values by rater, providing an efficient way to monitor the relative reliability of your raters.

This package complements a paper, conditionally accepted at PS: Political Science & Politics, entitled “Improving Content Analysis: Tools for Working with Undergraduate Research Assistants.” Please refer to this paper for a more general discussion about training and monitoring student raters. Also, be sure to check out the paper for more information about how to use the tools in ura to monitor progress without compromising reproducibility.

Installation Instructions

ura is available on CRAN and can be installed using:


You can install the most recent development version of ura using the devtools package. First, you have to install devtools using the following code. Note that you only have to do this once:

if(!require(devtools)) install.packages("devtools")

Then, load devtools and use the function install_github() to install ura:

install_github("bengoehring/ura", dependencies = TRUE)

Usage Examples

IRR statistics

ura can be used to calculate key IRR statistics, such as percentage agreement and Krippendorf’s Alpha via the irr_stats() function. This function largely serves as a wrapper around irr::agree() and irr::kripp.alpha() but aims to simplify users’ lives by only requiring the user to provide a dataframe and specify key columns.

For instance, below I calculate the percentage agreement and Krippendorf Alpha of the diagnoses dataset, which notes the psychiatric evaluations of 30 patients from 6 raters. The diagnoses dataset is included with the ura package and is simply a reshaped version of the dataset with the same name in the irr package.


          rater_column = 'rater_id',
          subject_column = 'patient_id',
          coding_column = 'diagnosis')
#> # A tibble: 2 × 3
#>   statistic            value n_subjects
#>   <chr>                <dbl>      <int>
#> 1 Percentage agreement 16.7          30
#> 2 Krippendorf's Alpha   0.43         30

A few things to note here. First, the unit of analysis in diagnoses is rater-subject — that is, each row provides the coding decision of rater i for subject j. All data inputted into a ura function should be long by rater-subject. Second, you will see that the dataframe returned by irr_stats() notes the number of subjects used to calculate the given IRR statistic. In the case of diagnoses, this value is equal to the number of unique subjects in the dataframe:

#> [1] 30

This is not always the case. If your dataframe includes subjects that were coded by more than one rater and subjects coded by a single rater (this is a common approach for balancing efficiency with the need for IRR statistics), ura will automatically only use the subjects coded by more than one rater. The resulting number of subjects will then appear in the n_subjects column.

Percentage Agreement by Rater

The rater_agreement() function is the key method for monitoring rater reliability. While irr_stats() provides pooled IRR statistics across all raters, rater_agreement() provides the percent share of a given raters’ codings that agree with other raters’ codings. In other words, it offers supervisors a method for checking the relative precision of each rater in real time. Since interventions in coding procedures should be used sparingly, I suggest taking a look at the paper linked above for more information about when and why to intervene based on information gleaned from rater_agreement().

In the snippet below, all raters have the same percent agreement: 17%. That is because, as implied by the n_multi_coded column, every rater codes every subject in the diagnoses dataset.

                rater_column = 'rater_id',
                subject_column = 'patient_id',
                coding_column = 'diagnosis')
#> # A tibble: 6 × 3
#>   rater percent_agree n_multi_coded
#>   <int>         <dbl>         <int>
#> 1     1            17            30
#> 2     2            17            30
#> 3     3            17            30
#> 4     4            17            30
#> 5     5            17            30
#> 6     6            17            30

A more helpful use case is when you only have your raters multi-code a subset of subjects. Take this hypothetical dataset, for instance:

example_data <- tibble::tribble(
    ~rater, ~subject,   ~coding,
    1,  1,  1,
    1,  2,  0,
    1,  3,  1,
    1,  4,  0,
    2,  3,  1,
    2,  9,  0,
    2,  10, 1,
    2,  4,  1,
    2,  5,  1,
    2,  6,  1,
    3,  5,  1,
    3,  6,  1,
    3,  7,  1,
    3,  8,  1,

Here, some subjects are coded by multiple raters while others are coded by a single rater. As a result:

                rater_column = 'rater',
                subject_column = 'subject',
                coding_column = 'coding')
#> # A tibble: 3 × 3
#>   rater percent_agree n_multi_coded
#>   <int>         <dbl>         <int>
#> 1     3           100             2
#> 2     2            75             4
#> 3     1            50             2

In terms of interpretation, row 3 shows that of the 2 subjects coded by rater 1 that were also coded by another rater, rater 1 agrees with the other rater(s) 50% of the time. Looking back at example_data, it appears that rater 1 agreed with rater 2 on the coding of subject 3 but not on subject 4.