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The purpose of this package is to provide convenience functions for user experience research. Particularly, this package focuses on quantitative user experience testing and reporting.
You can install the uxr package with:
install.packages("uxr")library(uxr)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 column
benchmark_event(data,
column = task_1,
benchmark = 0.8,
event = "y")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the event rate of 58%, the probability that this rate exceeds a
#> benchmark of 80% is 3%
#> term result
#> count 4
#> total 7
#> benchmark 0.8
#> result 0.033344
#> probability 0.033benchmark_event(data,
column = task_2,
benchmark = 0.3,
event = 1,
event_type = "success")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the success rate of 42%, the probability that this rate exceeds a
#> benchmark of 30% is 78%
#> term result
#> count 6
#> total 14
#> benchmark 0.3
#> result 0.7805158
#> probability 0.781## pipeable
data |>
benchmark_event(column = task_2,
benchmark = 0.3,
event = 1,
event_type = "success")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the success rate of 42%, the probability that this rate exceeds a
#> benchmark of 30% is 78%
#> term result
#> count 6
#> total 14
#> benchmark 0.3
#> result 0.7805158
#> probability 0.781# specify `input = "values` to use with direct values
benchmark_event(benchmark = 0.8,
count = 9,
total = 11,
input = "values")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the event rate of 82%, the probability that this rate exceeds a
#> benchmark of 80% is 38%
#> term result
#> count 9
#> total 11
#> benchmark 0.8
#> result 0.3825985
#> probability 0.383# get confidence intervals
# test_wald_adj(10, 12)scores <- 80 + 23 * scale(rnorm(172)) # 80 = mean, 23 = sd
data <- data.frame(scores = scores)# with dataframe column
benchmark_score(data, scores, 67)
#>
#> ── Compare Score with a Benchmark ──────────────────────────────────────────────
#> We can be 100% confident that the true score is between 67 and 93
#> term result
#> mean 80
#> sd 23
#> se 1.753734
#> n 172
#> df 171
#> probability 2.732705e-12
#> tail one
#> confidence 1
#> margin_of_error 13
#> t 7.412757
#> lower_ci 67
#> upper_ci 93# pipeable
data |> benchmark_score(scores, 67)
#>
#> ── Compare Score with a Benchmark ──────────────────────────────────────────────
#> We can be 100% confident that the true score is between 67 and 93
#> term result
#> mean 80
#> sd 23
#> se 1.753734
#> n 172
#> df 171
#> probability 2.732705e-12
#> tail one
#> confidence 1
#> margin_of_error 13
#> t 7.412757
#> lower_ci 67
#> upper_ci 93# specify `input = "values` to use with direct values
benchmark_score(mean = 80,
sd = 23,
n = 172,
benchmark = 67,
input = "values")
#>
#> ── Compare Score with a Benchmark ──────────────────────────────────────────────
#> We can be 100% confident that the true score is between 67 and 93
#> term result
#> mean 80
#> sd 23
#> se 1.753734
#> n 172
#> df 171
#> probability 2.732705e-12
#> tail one
#> confidence 1
#> margin_of_error 13
#> t 7.412757
#> lower_ci 67
#> upper_ci 93data <- data.frame(time = c(60, 53, 70, 42, 62, 43, 81))
benchmark_time(data, column = time, benchmark = 60, alpha = 0.05)
#>
#> ── Compare Time with a Benchmark ───────────────────────────────────────────────
#> term t.result_table.
#> lower_ci 45.8
#> upper_ci 71.7
#> t 0.509
#> probability 0.314# 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)
#>
#> ── Welch Two Sample t-test ─────────────────────────────────────────────────────
#> term value
#> mean_of_A 3.86
#> mean_of_B 2.29
#> t 1.99
#> df 11.8
#> p_value 0.0707
#> ci_level 0.95
#> lower_ci -0.156
#> upper_ci 3.3# 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")
#>
#> ── Welch Two Sample t-test ─────────────────────────────────────────────────────
#> term value
#> mean_of_A 3.86
#> mean_of_B 2.29
#> t 1.99
#> df 11.8
#> p_value 0.0707
#> ci_level 0.95
#> lower_ci -0.156
#> upper_ci 3.3A <- 51.6 + 4.07 * scale(rnorm(11))
A <- c(A, NA)
B <- 49.6 + 4.63 * scale(rnorm(12))
data <- data.frame(A, B)
compare_means_between_groups(data, A, B)
#>
#> ── Welch Two Sample t-test ─────────────────────────────────────────────────────
#> term value
#> mean_of_A 51.6
#> mean_of_B 49.6
#> t 1.1
#> df 21
#> p_value 0.283
#> ci_level 0.95
#> lower_ci -1.77
#> upper_ci 5.77data <- 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 Means Within Groups ─────────────────────────────────────────────────
#> X. result.estimate
#> mean.difference -6.571429
#> t -5.18
#> p 0.002
#> df 6
#> ci_level 0.95
#> lower_ci -9.68
#> upper_ci -3.46design = c("A","B")
complete = c(10, 4)
incomplete = c(2, 9)
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 Between Groups ────────────────────────────────────────────────
#> → N-1 Two Proportions test
#> term value
#> a 0.526
#> b 0.98
#> c 0.246
#> d 0.16
#> den 0.199
#> num 0.515
#> z 2.59
#> p_value 0.00955
#> n 25
#> lower_ci 0.133
#> upper_ci 0.776A <- 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")
#>
#> ── Compare Rates Within Groups ─────────────────────────────────────────────────
#> → McNemar's Test
#> p_value lower_ci upper_ci
#> 0.125 0.000569 0.59
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