Package 'uxr' reference manual

Title:	User Experience Research
Description:	Provides convenience functions for user experience research with an emphasis on quantitative user experience testing and reporting. The functions are designed to translate statistical approaches to applied user experience research.
Authors:	Joe Chelladurai [aut, cre] (ORCID: <https://orcid.org/0000-0001-8477-3753>)
Maintainer:	Joe Chelladurai <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.0
Built:	2026-05-16 05:57:29 UTC
Source:	https://github.com/joe-chelladurai/uxr

Compare Probability of an Event with Benchmark

Description

Compare Probability of an Event with Benchmark

Usage

benchmark_event(
  data,
  column,
  benchmark,
  event,
  count,
  total,
  event_type = "",
  remove_missing = TRUE,
  notes = "minimal",
  input = "long",
  output = "console"
)
benchmark_event(
  data,
  column,
  benchmark,
  event,
  count,
  total,
  event_type = "",
  remove_missing = TRUE,
  notes = "minimal",
  input = "long",
  output = "console"
)

Arguments

data

dataset

column

name of column

benchmark

benchmark

event

specify event as given in column (example: 0, "pass", "success")

count

number of times event has occurred. Use only when using input = "values"

total

total number of all events. Use only when using input = "values"

event_type

Optional: a string describing the type of event. For example, success, failure, etc.

remove_missing

TRUE/FALSE (Default is TRUE)

notes

whether output should contain minimal or technical type of notes. Defaults to "minimal". Use "none" to turn off.

input

Default: "long" - long form of data, "values" to pass values directly. If using this option, must specify count and total.

output

Default: "console" - prints output in console and returns tibble invisibly.

Value

Dataframe of results when saved to an object. Show console output by default

Examples

data <- data.frame(task_1 = c("y", "y", "y", "y", "n", "n", "n", NA, NA, NA, NA, NA, NA, NA),
                   task_2 = c(0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1))
# With dataframe columns

benchmark_event(data, column = task_1, benchmark = 0.8, event = "y")
benchmark_event(data, column = task_2, benchmark = 0.3, event = 1, event_type = "success")

# Also pipeable
data |>
  benchmark_event(column = task_2, benchmark = 0.3, event = 1, event_type = "success")

# With direct values
benchmark_event(benchmark = 0.8, count = 4, total = 7, input = "values")
data <- data.frame(task_1 = c("y", "y", "y", "y", "n", "n", "n", NA, NA, NA, NA, NA, NA, NA),
                   task_2 = c(0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1))
# With dataframe columns

benchmark_event(data, column = task_1, benchmark = 0.8, event = "y")
benchmark_event(data, column = task_2, benchmark = 0.3, event = 1, event_type = "success")

# Also pipeable
data |>
  benchmark_event(column = task_2, benchmark = 0.3, event = 1, event_type = "success")

# With direct values
benchmark_event(benchmark = 0.8, count = 4, total = 7, input = "values")

Compare Score with a Benchmark

Description

Compare Score with a Benchmark

Usage

benchmark_score(
  data,
  column,
  benchmark,
  mean,
  sd,
  n,
  tail = "one",
  remove_missing = TRUE,
  input = "long",
  output = "console"
)
benchmark_score(
  data,
  column,
  benchmark,
  mean,
  sd,
  n,
  tail = "one",
  remove_missing = TRUE,
  input = "long",
  output = "console"
)

Arguments

data

dataframe

column

a column of scores from the dataframe

benchmark

benchmark

mean

if input = "values", enter mean value

sd

if input = "values", enter standard deviation value

n

if input = "values", enter total number of scores

tail

one-tailed or two-tailed test

remove_missing

TRUE/FALSE (Default is TRUE)

input

Default: "long" - long form of data, "values" to pass values directly. If using this option, must specify mean, sd, and n.

output

Default: "console" - prints output in console and returns tibble invisibly.

Value

dataframe of results when saved to an object. show console output by default

Examples

scores <- 80 + 23 * scale(rnorm(172)) # 80 = mean, 23 = sd
data <- data.frame(scores = scores)
benchmark_score(data, scores, 67)
data |> benchmark_score(scores, 67)
benchmark_score(mean = 80, sd = 23, n = 172, benchmark = 67, input = "values")
scores <- 80 + 23 * scale(rnorm(172)) # 80 = mean, 23 = sd
data <- data.frame(scores = scores)
benchmark_score(data, scores, 67)
data |> benchmark_score(scores, 67)
benchmark_score(mean = 80, sd = 23, n = 172, benchmark = 67, input = "values")

Compare Time with a Benchmark

Description

Compare Time with a Benchmark

Usage

benchmark_time(
  data,
  column,
  benchmark,
  alpha,
  remove_missing = FALSE,
  input = "long",
  output = "console"
)
benchmark_time(
  data,
  column,
  benchmark,
  alpha,
  remove_missing = FALSE,
  input = "long",
  output = "console"
)

Arguments

data

dataframe

column

a column or vector of time values

benchmark

benchmark

alpha

alpha

remove_missing

TRUE/FALSE (Default is TRUE)

input

Default: "long" - long form of data, "values" to pass values directly. If using this option, must specify count and total.

output

Default: "console" - prints output in console and returns tibble invisibly.

Value

lower_ci, upper_ci, t, probability

Examples

data <- data.frame(time = c(60, 53, 70, 42, 62, 43, 81))
benchmark_time(data, column = time, benchmark = 60, alpha = 0.05)
data <- data.frame(time = c(60, 53, 70, 42, 62, 43, 81))
benchmark_time(data, column = time, benchmark = 60, alpha = 0.05)

Compare Means Between Groups

Description

Compare Means Between Groups

Usage

compare_means_between_groups(
  data,
  var1,
  var2,
  variable,
  grouping_variable,
  groups,
  test = "Welch",
  input = "wide",
  output = "console"
)
compare_means_between_groups(
  data,
  var1,
  var2,
  variable,
  grouping_variable,
  groups,
  test = "Welch",
  input = "wide",
  output = "console"
)

Arguments

data

data

var1

variable 1

var2

variable 2

variable

variable

grouping_variable

Group

groups

Specify groups from grouping variable

test

Default: "Welch", choose between "student" and "Welch"

input

Default: "wide", choose between "long" and "wide". "wide" requires data var1 var2. "long" requires data, variable, grouping_variable groups

output

Default: "console" - prints output in console and returns tibble invisibly.

Value

results

Examples


# Wide data - default
data_wide <- data.frame(A = c(4, 2, 5, 3, 6, 2, 5),
                        B = c(5, 2, 1, 2, 1, 3, 2))

compare_means_between_groups(data_wide, var1 = A, var2 = B)

# Long data
data_long <- data_wide |> tibble::rowid_to_column("id") |>
  tidyr::pivot_longer(cols = -id, names_to = "group", values_to = "variable")

compare_means_between_groups(data_long, variable = variable,
                             grouping_variable = group, groups = c("A", "B"), input = "long")
# Wide data - default
data_wide <- data.frame(A = c(4, 2, 5, 3, 6, 2, 5),
                        B = c(5, 2, 1, 2, 1, 3, 2))

compare_means_between_groups(data_wide, var1 = A, var2 = B)

# Long data
data_long <- data_wide |> tibble::rowid_to_column("id") |>
  tidyr::pivot_longer(cols = -id, names_to = "group", values_to = "variable")

compare_means_between_groups(data_long, variable = variable,
                             grouping_variable = group, groups = c("A", "B"), input = "long")

Compare Means Within Groups

Description

Compare Means Within Groups

Usage

compare_means_within_groups(
  data,
  var1,
  var2,
  input = "wide",
  output = "console"
)
compare_means_within_groups(
  data,
  var1,
  var2,
  input = "wide",
  output = "console"
)

Arguments

data

dataframe

var1

variable 1

var2

variable 2

input

Default: "long" - long form of data, "values" to pass values directly. If using this option, must specify mean, sd, and n.

output

Default: "console" - prints output in console and returns tibble invisibly.

Value

results

Examples

data <- data.frame(id = c(1:7),
  task1 = c(4, 1, 2, 3, 8, 4, 4),
  task2 = c(7, 13, 9, 7, 18, 8, 10))
compare_means_within_groups(data, task1, task2)
data <- data.frame(id = c(1:7),
  task1 = c(4, 1, 2, 3, 8, 4, 4),
  task2 = c(7, 13, 9, 7, 18, 8, 10))
compare_means_within_groups(data, task1, task2)

Compare Rates Between Groups

Description

Compare Rates Between Groups

Usage

compare_rates_between_groups(
  data,
  group,
  event,
  test,
  input = "long",
  output = "console"
)
compare_rates_between_groups(
  data,
  group,
  event,
  test,
  input = "long",
  output = "console"
)

Arguments

data

data

group

column in dataframe : group

event

column in dataframe : event

test

Type of test (fisher, n-1 two prop)

input

Defaults to "long"

output

"console" prints output to console; "tibble" returns tibble

Value

results

Examples

design = c("A","B")
complete = c(34, 24)
incomplete = c(317, 301)
data <- data.frame(design, complete, incomplete)
data <- data |> tidyr::pivot_longer(!design, names_to = "rate", values_to = "n") |>
  tidyr::uncount(n)
compare_rates_between_groups(data, group = design, event = rate)
design = c("A","B")
complete = c(34, 24)
incomplete = c(317, 301)
data <- data.frame(design, complete, incomplete)
data <- data |> tidyr::pivot_longer(!design, names_to = "rate", values_to = "n") |>
  tidyr::uncount(n)
compare_rates_between_groups(data, group = design, event = rate)

Compare Rates Within Groups

Description

Compare Rates Within Groups

Usage

compare_rates_within_groups(
  data,
  x,
  y,
  conf_level = 0.95,
  input,
  output = "console"
)
compare_rates_within_groups(
  data,
  x,
  y,
  conf_level = 0.95,
  input,
  output = "console"
)

Arguments

data

data

x

var 1

y

var 2

conf_level

Confidence level

input

input type currently only accepts "wide"

output

Default is "console", also accepts "tibble"

Value

results

Examples

A <- c(1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
B <- c(0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0)
data <- data.frame(A, B)
compare_rates_within_groups(data, A, B, input = "wide")
A <- c(1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
B <- c(0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0)
data <- data.frame(A, B)
compare_rates_within_groups(data, A, B, input = "wide")

T Distribution

Description

T Distribution

Usage

dist_t(t, df, tail)
dist_t(t, df, tail)

Arguments

t

df

degrees of freedom

tail

'one' or 'two'

Value

value

Examples

dist_t(1.4, 2, "one")
dist_t(1.4, 2, "two")
dist_t(1.4, 2, "one")
dist_t(1.4, 2, "two")

Get concordant and discordant pairs for two variables

Description

Get concordant and discordant pairs for two variables

Usage

get_concordant_discordant_pairs(data, x, y)
get_concordant_discordant_pairs(data, x, y)

Arguments

data

= data

x

variable 1

y

variable 2

Value

a data frame

Examples

mtcars$id <- seq.int(nrow(mtcars))
get_concordant_discordant_pairs(mtcars, x = vs, y = am)
mtcars$id <- seq.int(nrow(mtcars))
get_concordant_discordant_pairs(mtcars, x = vs, y = am)

Get Confidence Intervals for Event Rate

Description

Get Confidence Intervals for Event Rate

Usage

get_confidence_intervals_event(event, total, confidence_level)
get_confidence_intervals_event(event, total, confidence_level)

Arguments

event

event

total

total

confidence_level

confidence level as z value

Value

lower_ci, upper_ci

Examples

get_confidence_intervals_event(event = 80,
                               total = 100,
                               confidence_level = 1.96)
get_confidence_intervals_event(event = 80,
                               total = 100,
                               confidence_level = 1.96)

Get Confidence Intervals Within Groups

Description

Get Confidence Intervals Within Groups

Usage

get_confidence_intervals_within_groups(data, x, y, conf_level = 0.95)
get_confidence_intervals_within_groups(data, x, y, conf_level = 0.95)

Arguments

data

data

x

var 1

y

var 2

conf_level

Confidence level

Value

results

Examples

A <- c(1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
B <- c(0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0)
data <- data.frame(A, B)
get_confidence_intervals_within_groups(data, A, B)
A <- c(1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
B <- c(0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0)
data <- data.frame(A, B)
get_confidence_intervals_within_groups(data, A, B)

Mean Confidence Intervals

Description

Mean Confidence Intervals

Usage

stat_mean_ci(x, alpha)
stat_mean_ci(x, alpha)

Arguments

x

values

alpha

alpha

Value

lower_ci, upper_ci

Examples

stat_mean_ci(c(1, 2, 3, 4, 5, 6, 7), 1.96)
stat_mean_ci(c(2, 4, 6, 8), 1.96)
stat_mean_ci(c(1, 2, 3, 4, 5, 6, 7), 1.96)
stat_mean_ci(c(2, 4, 6, 8), 1.96)

Mean Confidence Intervals (Large Samples)

Description

Mean Confidence Intervals (Large Samples)

Usage

stat_mean_ci_2(x, z)
stat_mean_ci_2(x, z)

Arguments

x

values

z

z value

Value

lower_ci, upper_ci

Examples

stat_mean_ci_2(c(1, 2, 3, 4, 5, 6, 7), 1.96)
stat_mean_ci_2(c(2, 4, 6, 8), 1.96)
stat_mean_ci_2(c(1, 2, 3, 4, 5, 6, 7), 1.96)
stat_mean_ci_2(c(2, 4, 6, 8), 1.96)

Observed Expected Table

Description

Observed Expected Table

Usage

table_observed_expected(data, x, y)
table_observed_expected(data, x, y)

Arguments

data

data

x

y

Value

results

Chi-squared One Sample

Description

Chi-squared One Sample

Usage

test_chisq_one(data, x)
test_chisq_one(data, x)

Arguments

data

data

x

Value

results

Examples

data <- tibble::tribble(~fruit, ~count,
                        "Apple"          , 29,
                        "Banana"         , 24,
                        "Cucumber"       , 22,
                        "Dragon Fruit"   , 19
                        )

data <- data |>
  tidyr::uncount(weights = count) |>
  tibble::rowid_to_column("id")

test_chisq_one(data, fruit)
data <- tibble::tribble(~fruit, ~count,
                        "Apple"          , 29,
                        "Banana"         , 24,
                        "Cucumber"       , 22,
                        "Dragon Fruit"   , 19
                        )

data <- data |>
  tidyr::uncount(weights = count) |>
  tibble::rowid_to_column("id")

test_chisq_one(data, fruit)

Chi-squared Two Sample

Description

Chi-squared Two Sample

Usage

test_chisq_two(data, x, y)
test_chisq_two(data, x, y)

Arguments

data

data

x

y

Value

results

Examples

data <- tibble::tribble(~fruit, ~count,
                        "Apple"          , 29,
                        "Banana"         , 24,
                        "Cucumber"       , 22,
                        "Dragon Fruit"   , 19
                        )

data <- data |>
  tidyr::uncount(weights = count) |>
  tibble::rowid_to_column("id")

test_chisq_one(data, fruit)
data <- tibble::tribble(~fruit, ~count,
                        "Apple"          , 29,
                        "Banana"         , 24,
                        "Cucumber"       , 22,
                        "Dragon Fruit"   , 19
                        )

data <- data |>
  tidyr::uncount(weights = count) |>
  tibble::rowid_to_column("id")

test_chisq_one(data, fruit)

Fisher's Test

Description

Fisher's Test

Usage

test_fisher(data, x, y)
test_fisher(data, x, y)

Arguments

data

data

x

y

Value

results

Examples

design = c("A","B")
complete = c(11, 5)
incomplete = c(1, 5)
data <- data.frame(design, complete, incomplete)
data <- data |> tidyr::pivot_longer(!design, names_to = "rate", values_to = "n") |>
  tidyr::uncount(n)
test_fisher(data, design, rate)
design = c("A","B")
complete = c(11, 5)
incomplete = c(1, 5)
data <- data.frame(design, complete, incomplete)
data <- data |> tidyr::pivot_longer(!design, names_to = "rate", values_to = "n") |>
  tidyr::uncount(n)
test_fisher(data, design, rate)

McNemar Test

Description

McNemar Test

Usage

test_mcnemar(data, x, y)
test_mcnemar(data, x, y)

Arguments

data

data

x

var 1

y

var 2

Value

results

Examples

A <- c(1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
B <- c(0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0)
data <- data.frame(A, B)
test_mcnemar(data, A, B)
A <- c(1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
B <- c(0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0)
data <- data.frame(A, B)
test_mcnemar(data, A, B)

N-1 Two Proportions Test

Description

N-1 Two Proportions Test

Usage

test_n_1_prop(data, x, y, conf_level = 0.95)
test_n_1_prop(data, x, y, conf_level = 0.95)

Arguments

data

data

x

y

conf_level

Confidence Level (default = 0.95)

Value

results

Examples

design = c("A","B")
complete = c(37, 22)
incomplete = c(418, 416)
data <- data.frame(design, complete, incomplete)
data <- data |> tidyr::pivot_longer(!design, names_to = "rate", values_to = "n") |>
  tidyr::uncount(n)
test_n_1_prop(data, design, rate, conf_level = 0.95)
design = c("A","B")
complete = c(37, 22)
incomplete = c(418, 416)
data <- data.frame(design, complete, incomplete)
data <- data |> tidyr::pivot_longer(!design, names_to = "rate", values_to = "n") |>
  tidyr::uncount(n)
test_n_1_prop(data, design, rate, conf_level = 0.95)

T-test

Description

T-test

Usage

test_t(x, y, ...)
test_t(x, y, ...)

Arguments

x

y

...

other arguments passed to t-test

Value

results

Examples


test_t(mtcars$mpg, mtcars$am)
test_t(mtcars$mpg, mtcars$am)

Paired t-test

Description

Paired t-test

Usage

test_t_paired(x, y, ...)
test_t_paired(x, y, ...)

Arguments

x

y

...

other arguments passed to paired t-test

Value

results

Wald Confidence Intervals

Description

Wald Confidence Intervals

Usage

test_wald(success, total, conf_level = 0.95)
test_wald(success, total, conf_level = 0.95)

Arguments

success

success

total

total

conf_level

conf_level (default: 0.95)

Value

lower_ci, upper_ci

Examples

test_wald(10, 12, 0.95)
test_wald(5, 7, 0.95)
test_wald(10, 12, 0.95)
test_wald(5, 7, 0.95)

Adjusted Wald Confidence Intervals

Description

Adjusted Wald Confidence Intervals

Usage

test_wald_adj(success, total, conf_level = 0.95)
test_wald_adj(success, total, conf_level = 0.95)

Arguments

success

success

total

total

conf_level

conf_level (default: 0.95)

Value

lower_ci, upper_ci

Examples

test_wald_adj(10, 12, 0.95)
test_wald_adj(5, 7, 0.95)
test_wald_adj(10, 12, 0.95)
test_wald_adj(5, 7, 0.95)

Package 'uxr'

Help Index

Compare Probability of an Event with Benchmark

Description

Usage

Arguments

Value

Examples

Compare Score with a Benchmark

Description

Usage

Arguments

Value

Examples

Compare Time with a Benchmark

Description

Usage

Arguments

Value

Examples

Compare Means Between Groups

Description

Usage

Arguments

Value

Examples

Compare Means Within Groups

Description

Usage

Arguments

Value

Examples

Compare Rates Between Groups

Description

Usage

Arguments

Value

Examples

Compare Rates Within Groups

Description

Usage

Arguments

Value

Examples

T Distribution

Description

Usage

Arguments

Value

Examples

Get concordant and discordant pairs for two variables

Description

Usage

Arguments

Value

Examples

Get Confidence Intervals for Event Rate

Description

Usage

Arguments

Value

Examples

Get Confidence Intervals Within Groups

Description

Usage

Arguments

Value

Examples

Mean Confidence Intervals

Description

Usage

Arguments

Value

Examples

Mean Confidence Intervals (Large Samples)

Description

Usage

Arguments

Value

Examples