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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.
Examples
Compare Probability of an Event with Benchmark
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 columnbenchmark_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## pipeabledata|>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 valuesbenchmark_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)Compare Score with a Benchmark
scores<-80+23*scale(rnorm(172))# 80 = mean, 23 = sddata<-data.frame(scores=scores)# with dataframe columnbenchmark_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# pipeabledata|>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 valuesbenchmark_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 93Compare Time with a Benchmark
data<-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 0.314Compare Means Between Groups
# Wide data - defaultdata_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 datadata_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 5.77Compare Means Within Groups
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 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 -3.46Compare Rates Between Groups
design=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 0.776Compare Rates Within Groups
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")#>#> ── Compare Rates Within Groups ─────────────────────────────────────────────────#> → McNemar's Test#> p_value lower_ci upper_ci#> 0.125 0.000569 0.59