Merge pull request #36 from arcaldwell49/shiny-updates

Shiny updates

Author: arcaldwell49

.gitignore

@@ -13,3 +13,4 @@ inst/doc
 .DS_Store
 NUL
 tests/testthat/Rplots.pdf
+.DS_Store

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Superpower
 Title: Simulation-Based Power Analysis for Factorial Designs
-Version: 0.2.0
+Version: 0.2.1
 Authors@R: c(person(given = "Aaron",
            family = "Caldwell",
            role = c("aut", "cre"),

NEWS.Rmd

@@ -5,6 +5,11 @@ output: github_document
 
 Updated `r format(Sys.time(), "%a %b %d %Y")`
 
+# Superpower 0.2.1 -- "Jade"
+- Minor fix to the plot_power function
+- Major updates to the Shiny apps
+  - Changes design input to a more friendly UI
+
 # Superpower 0.2.0 -- "Green Lantern"
 - Added power_oneway_ancova & power_con_ancova to allow for a basic power analysis of an analysis of covariance (ANCOVA) for one-way, between group designs.
 - Added ANCOVA_analytic and ANCOVA_contrast which allow for power analyses for factorial designs and user specified contrasts.

NEWS.md

@@ -1,93 +1,99 @@
 NEWS
 ================
 
-Updated Tue Dec 07 2021
+Updated Mon Oct 10 2022
+
+# Superpower 0.2.1 – “Jade”
+
+-   Minor fix to the plot_power function
+-   Major updates to the Shiny apps
+    -   Changes design input to a more friendly UI
 
 # Superpower 0.2.0 – “Green Lantern”
 
--   Added power\_oneway\_ancova & power\_con\_ancova to allow for a
-    basic power analysis of an analysis of covariance (ANCOVA) for
-    one-way, between group designs.
--   Added ANCOVA\_analytic and ANCOVA\_contrast which allow for power
+-   Added power_oneway_ancova & power_con_ancova to allow for a basic
+    power analysis of an analysis of covariance (ANCOVA) for one-way,
+    between group designs.
+-   Added ANCOVA_analytic and ANCOVA_contrast which allow for power
     analyses for factorial designs and user specified contrasts.
--   Added the label\_list argument to ANOVA\_design and ANCOVA\_analytic
+-   Added the label_list argument to ANOVA_design and ANCOVA_analytic
     functions. Now labels can be assigned to factors and levels in a
     more sane fashion using named lists.
 
 # Superpower 0.1.2 – “Rex Splode”
 
--   Minor fixes to power\_standardized\_alpha to keep Superpower on CRAN
+-   Minor fixes to power_standardized_alpha to keep Superpower on CRAN
 
 # Superpower 0.1.1 – “Black Canary”
 
--   Added morey\_plot functions.
+-   Added morey_plot functions.
     -   Plot the effect size (x-axis) at different sample sizes (facets)
         and at different alpha levels (color).
     -   These plots are helpful in determining the sensitivity of
         statistical tests (t-test and F-test) across a range of effect
         sizes.
--   Added confint method for ANOVA\_power produced objects
-    -   Calculates confidence level for binomial proportion (\# of
+-   Added confint method for ANOVA_power produced objects
+    -   Calculates confidence level for binomial proportion (# of
         results that are below alpha level) confidence intervals
-        (Wilson, 1927)).
+        (Wilson, 1927).
 -   Minor changes to Shiny apps to fix glitches.
 
 # Superpower 0.1.0
 
--   Added ANOVA\_exact2 function as an extension of ANOVA\_exact
+-   Added ANOVA_exact2 function as an extension of ANOVA_exact
     -   Now functional across all sample sizes but does not return a
         dataframe of afex aov object
--   liberal\_lambda argument added: allows users to specify the type of
+-   liberal_lambda argument added: allows users to specify the type of
     lambda calculations
-    -   When liberal\_lambda = TRUE; lambda = cohen\_f^2 \* (num\_df +
-        den\_df + 1)
-    -   When liberal\_lambda = FALSE; lambda = cohen\_f^2 \* den\_df
+    -   When liberal_lambda = TRUE; lambda = cohen_f^2 \* (num_df +
+        den_df + 1)
+    -   When liberal_lambda = FALSE; lambda = cohen_f^2 \* den_df
 -   Optimal alpha functions from JustifieR package added
--   ANOVA\_compromise function added which allows a compromise power
+-   ANOVA_compromise function added which allows a compromise power
     analysis to be performed for all comparisons in a design
--   ANOVA\_design now returns as a class “design\_aov” with specific
-    print and plot methods see ?`design_aov-methods`
-    -   generate\_cor\_matrix function is now a non-exported function
-        within the package (no longer contained within ANOVA\_design)
--   All simulation functions ANOVA\_power, ANOVA\_exact, and
-    ANOVA\_exact2 now returns as a class “sim\_result” with specific
-    print and plot methods see ?`sim_result-methods`
--   plot\_power now has reduced sample size limitations -Option to use
-    ANOVA\_exact2 (exact2 argument) improves functionality (not limited
+-   ANOVA_design now returns as a class “design_aov” with specific print
+    and plot methods see ?`design_aov-methods`
+    -   generate_cor_matrix function is now a non-exported function
+        within the package (no longer contained within ANOVA_design)
+-   All simulation functions ANOVA_power, ANOVA_exact, and ANOVA_exact2
+    now returns as a class “sim_result” with specific print and plot
+    methods see ?`sim_result-methods`
+-   plot_power now has reduced sample size limitations -Option to use
+    ANOVA_exact2 (exact2 argument) improves functionality (not limited
     to product of factors)
 -   Updated vignettes to include updated information on functions
     -   New vignette “Introduction to Justifying Alpha Levels”
 -   New Shiny App: justify
-    -   Creates a UI for utilizing the ANOVA\_compromise function via
+    -   Creates a UI for utilizing the ANOVA_compromise function via
         Shiny
 
 # Superpower 0.0.5
 
--   Superpower\_options(“plot”) is now set to TRUE. Plots will, by
-    default, be printed -Easily reset with Superpower\_options(plot =
+-   Superpower_options(“plot”) is now set to TRUE. Plots will, by
+    default, be printed -Easily reset with Superpower_options(plot =
     FALSE)
--   plot\_power has new features -Plots now show desired power -min\_n
-    is now limited; smallest min\_n allowed is equal to the product of
-    the design (e.g., ’2b\*2b’ has a smallest min\_n of 4)
--   Small update to plot\_power to fix minor error in original code
+-   plot_power has new features -Plots now show desired power -min_n is
+    now limited; smallest min_n allowed is equal to the product of the
+    design (e.g., ’2b\*2b’ has a smallest min_n of 4)
+-   Small update to plot_power to fix minor error in original code
     -Error resulted in power estimates being \~0.1-0.5% off actual power
     estimate
 
 # Superpower 0.0.4
 
--   Added emmeans\_power function
+-   Added emmeans_power function
     -   Documentation added to the vignette
 -   Small updates to the Shiny apps to fix typos
 
 # Superpower 0.0.3
 
 -   Unequal sample size in the design is now permitted -Limited to the
-    ANOVA\_design and ANOVA\_power functions
+    ANOVA_design and ANOVA_power functions
 
 -   Added estimated marginal means comparisons using `emmeans` R
     package.
 
-    -   `emm = TRUE` in the ANOVA\_power, ANOVA\_exact, and plot\_power
+    -   `emm = TRUE` in the ANOVA_power, ANOVA_exact, and plot_power
         will result in emmeans being calculated
     -   Default is all pairwise comparisons but this can be modified
         with `contrast_type` and `emm_comp` options
@@ -96,13 +102,13 @@ Updated Tue Dec 07 2021
 
     -   Options that have crossover between functions can now be set
         globally for the package
-    -   Includes: verbose, emm, emm\_model, contrast\_type,
-        alpha\_level, and plot
-    -   These global options can be seen with Superpower\_options()
+    -   Includes: verbose, emm, emm_model, contrast_type, alpha_level,
+        and plot
+    -   These global options can be seen with Superpower_options()
 
 -   Updated Shiny Apps
 
-    -   Unequal n allowed for ANOVA\_power
+    -   Unequal n allowed for ANOVA_power
     -   Added numeric input for alpha level (no longer slider)
     -   Now includes emmeans options
     -   kableExtra, emmeans, magrittr, and dplyr packages now needed to

R/ANOVA_power.R

@@ -161,9 +161,9 @@ ANOVA_power <- function(design_result,
     stop("alpha_level must be less than 1 and greater than zero")
   }
 
-  ###############
-  # 2. Read in Environment Data ----
-  ###############
+
+  # Read in Environment Data ----
+
 
   design <- design_result$design #String used to specify the design
 
@@ -198,9 +198,9 @@ ANOVA_power <- function(design_result,
   n_vec <- n # store vector n as n - this is because the code below uses n as a single number, so quick fix for legacy reasons
   n <- max(n) # now set n to max n for ANOVA_design function
 
-  ###############
-  # 3. Specify factors for formula ----
-  ###############
+
+  # Specify factors for formula ----
+
 
   frml1 <- design_result$frml1
   frml2 <- design_result$frml2
@@ -247,9 +247,9 @@ ANOVA_power <- function(design_result,
   if (run_manova == TRUE) {
     manova_result <- Anova_mlm_table(aov_result$Anova)
   }
-  ###############
+
   # 5. Set up dataframe for simulation results
-  ###############
+
 
   #How many possible planned comparisons are there (to store p and es)
   possible_pc <- (((prod(
@@ -315,9 +315,9 @@ ANOVA_power <- function(design_result,
   }
 
 
-  ###############
-  # 7. Start Simulation ----
-  ###############
+
+  # Start Simulation ----
+
   #withProgress(message = 'Running simulations', value = 0, { #block outside of Shiny
   for (i in 1:nsims) { #for each simulated experiment
     #incProgress(1/nsims, detail = paste("Now running simulation", i, "out of",nsims,"simulations")) #Block outside of Shiny
@@ -420,13 +420,13 @@ ANOVA_power <- function(design_result,
   }
   #}) #close withProgress Block outside of Shiny
 
-  ############################################
+
   #End Simulation              ###############
 
 
-  ###############
-  # 8. Plot Results ----
-  ###############
+
+  # Plot Results ----
+
 
   # melt the data into a long format for plots in ggplot2
 
@@ -479,9 +479,9 @@ ANOVA_power <- function(design_result,
     facet_grid(variable ~ .) +
     labs(x = expression(p)) +
     theme_bw()
-  ###############
-  # 9. Sumary of power and effect sizes of main effects and contrasts ----
-  ###############
+
+  # Sumary of power and effect sizes of main effects and contrasts ----
+
 
   #Main effects and interactions from the ANOVA
   power = as.data.frame(apply(as.matrix(sim_data[(1:(2 ^ factors - 1))]), 2,
@@ -525,9 +525,9 @@ ANOVA_power <- function(design_result,
     names(manova_result) = c("power")
   }
 
-  #######################
+
   # Return Results ----
-  #######################
+
   if (verbose == TRUE) {
     # The section below should be blocked out when in Shiny
     cat("Power and Effect sizes for ANOVA tests")

R/plot_power.R

@@ -337,9 +337,9 @@ plot_power <- function(design_result,
     plot_data_emm <- suppressMessages(melt(power_df_emm, id = c('n')))
 
     #create data frame for annotation for desired power for emmeans
-    annotate_df_emm <- as.data.frame(matrix(0, ncol = 4, nrow = length(levels(exact_result$emm_results$contrast)))) #three rows, for N, power, and variable label
+    annotate_df_emm <- as.data.frame(matrix(0, ncol = 4, nrow = length((exact_result$emm_results$contrast)))) #three rows, for N, power, and variable label
     colnames(annotate_df_emm) <- c("n", "power", "variable", "label") # add columns names
-    annotate_df_emm$variable <- as.factor(levels(exact_result$emm_results$contrast)) #add variable label names
+    annotate_df_emm$variable <- as.factor((exact_result$emm_results$contrast)) #add variable label names
     emm_n = annotate_df_emm
 
     i<-1