| Type: | Package |
| Title: | User Experience Research |
| Version: | 0.2.0 |
| 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. |
| URL: | https://joe-chelladurai.github.io/uxr/ |
| BugReports: | https://github.com/joe-chelladurai/uxr/issues |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Imports: | cli, dplyr, huxtable, magrittr, purrr, rlang, scales, stringr, tibble, tidyr |
| Language: | en |
| RoxygenNote: | 7.2.1 |
| Depends: | R (≥ 2.10) |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2022-12-23 20:13:41 UTC; Home |
| Author: | Joe Chelladurai 
[aut, cre] |
| Maintainer: | Joe Chelladurai <joe.chelladurai@yahoo.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-12-25 07:20:02 UTC |
uxr: 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.
Author(s)
Maintainer: Joe Chelladurai joe.chelladurai@yahoo.com (ORCID)
See Also
Useful links:
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling rhs(lhs).
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"
)
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")
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"
)
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")
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"
)
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)
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"
)
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")
Compare Means Within Groups
Description
Compare Means Within Groups
Usage
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)
Compare Rates Between Groups
Description
Compare Rates Between Groups
Usage
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)
Compare Rates Within Groups
Description
Compare Rates Within Groups
Usage
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")
T Distribution
Description
T Distribution
Usage
dist_t(t, df, tail)
Arguments
t |
t |
df |
degrees of freedom |
tail |
'one' or 'two' |
Value
value
Examples
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)
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)
Get Confidence Intervals for Event Rate
Description
Get Confidence Intervals for Event Rate
Usage
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 Within Groups
Description
Get Confidence Intervals Within Groups
Usage
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)
Mean Confidence Intervals
Description
Mean Confidence Intervals
Usage
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)
Mean Confidence Intervals (Large Samples)
Description
Mean Confidence Intervals (Large Samples)
Usage
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)
Observed Expected Table
Description
Observed Expected Table
Usage
table_observed_expected(data, x, y)
Arguments
data |
data |
x |
x |
y |
y |
Value
results
Chi-squared One Sample
Description
Chi-squared One Sample
Usage
test_chisq_one(data, x)
Arguments
data |
data |
x |
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)
Chi-squared Two Sample
Description
Chi-squared Two Sample
Usage
test_chisq_two(data, x, y)
Arguments
data |
data |
x |
x |
y |
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)
Fisher's Test
Description
Fisher's Test
Usage
test_fisher(data, x, y)
Arguments
data |
data |
x |
x |
y |
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)
McNemar Test
Description
McNemar Test
Usage
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)
N-1 Two Proportions Test
Description
N-1 Two Proportions Test
Usage
test_n_1_prop(data, x, y, conf_level = 0.95)
Arguments
data |
data |
x |
x |
y |
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)
T-test
Description
T-test
Usage
test_t(x, y, ...)
Arguments
x |
x |
y |
y |
... |
other arguments passed to t-test |
Value
results
Examples
test_t(mtcars$mpg, mtcars$am)
Paired t-test
Description
Paired t-test
Usage
test_t_paired(x, y, ...)
Arguments
x |
x |
y |
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)
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)
Adjusted Wald Confidence Intervals
Description
Adjusted Wald Confidence Intervals
Usage
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)