Type: Package
Title: Experience Research
Version: 0.1.1
Description: Provides convenience functions for researching experiences including user, customer, patient, employee, and other human experiences. It provides a suite of tools to simplify data exploration such as benchmarking, comparing groups, and checking for differences. The outputs translate statistical approaches in applied experience research to human readable output.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: cli, dplyr, huxtable, magrittr, scales, stringr, tibble
RoxygenNote: 7.2.1
NeedsCompilation: no
Packaged: 2022-10-29 14:09:25 UTC; Home
Author: Joe Chelladurai ORCID iD [aut, cre]
Maintainer: Joe Chelladurai <joe.chelladurai@outlook.com>
Repository: CRAN
Date/Publication: 2022-10-31 14:10:12 UTC

experiences: Experience Research

Description

Provides convenience functions for researching experiences. The functions are designed to translate statistical approaches to applied experience research.

Author(s)

Maintainer: Joe Chelladurai joe.chelladurai@outlook.com (ORCID)


Compare Probability of an Event with Benchmark

Description

Compare Probability of an Event with Benchmark

Usage

compare_benchmark_event(
  benchmark,
  event,
  total,
  event_type = "",
  notes = c("minimal", "technical")
)

Arguments

benchmark

benchmark

event

event

total

total

event_type

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

notes

whether output should contain minimal, technical, or executive type of notes.

Value

list of event rate, probability, notes

Examples

compare_benchmark_event(benchmark = 0.7,
                     event = 10,
                     total = 12,
                     event_type = "success",
                     notes = "minimal")

Compare Score with a Benchmark

Description

Compare Score with a Benchmark

Usage

compare_benchmark_score(
  data,
  benchmark,
  alpha,
  tail = "one",
  remove_missing = TRUE
)

Arguments

data

a column or vector of scores

benchmark

benchmark

alpha

alpha

tail

one-tailed or two-tailed test

remove_missing

TRUE/FALSE remove missing values? (default is TRUE)

Value

lower_ci, upper_ci, t, probability

Examples

data <- 68 + 17 * scale(rnorm(20)) # 68 = mean, 17 = sd
compare_benchmark_score(data, benchmark = 60, alpha = 0.5)

Compare Time with a Benchmark

Description

Compare Time with a Benchmark

Usage

compare_benchmark_time(benchmark, time, alpha, remove_missing = FALSE)

Arguments

benchmark

benchmark

time

a column or vector of time values

alpha

alpha

remove_missing

TRUE/FALSE remove missing values?

Value

lower_ci, upper_ci, t, probability

Examples

compare_benchmark_time(time = c(60, 53, 70, 42, 62, 43, 81),
                       benchmark = 60,
                       alpha = 0.05)

T distribution - one-tailed

Description

T distribution - one-tailed

Usage

t_dist_one_tailed(t_score, degrees_of_freedom)

Arguments

t_score

t value

degrees_of_freedom

degrees of freedom

Value

value


T distribution - two-tailed

Description

T distribution - two-tailed

Usage

t_dist_two_tailed(t_score, degrees_of_freedom)

Arguments

t_score

t value

degrees_of_freedom

degrees of freedom

Value

value