Merge pull request #98 from istallworthy/feedback

adding packages and incorporating noah's push

DESCRIPTION

@@ -22,23 +22,24 @@ License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.2
+RoxygenNote: 7.2.3
 Imports:
     WeightIt,
     marginaleffects,
-    tidyr,
     stats,
     ggplot2,
     matrixStats,
-    plyr,
     knitr,
     survey,
     jtools,
     mice,
     stargazer,
     gtools,
     cobalt,
-    stringr
+    stringr,
+    huxtable,
+    officer,
+    flextable
 Suggests: 
     testthat (>= 3.0.0)
 Config/testthat/edition: 3

NAMESPACE

@@ -17,36 +17,4 @@ export(get_reference_values)
 export(make_love_plot)
 export(perform_multiple_comparison_correction)
 export(trimWeights)
-importFrom(WeightIt,weightitMSM)
-importFrom(dplyr,"%>%")
-importFrom(dplyr,bind_rows)
-importFrom(dplyr,filter)
-importFrom(dplyr,mutate)
-importFrom(dplyr,select)
-importFrom(ggplot2,aes)
-importFrom(ggplot2,element_blank)
-importFrom(ggplot2,element_rect)
-importFrom(ggplot2,element_text)
-importFrom(ggplot2,geom_histogram)
-importFrom(ggplot2,geom_point)
-importFrom(ggplot2,geom_text)
-importFrom(ggplot2,geom_vline)
-importFrom(ggplot2,ggplot)
-importFrom(ggplot2,ggsave)
-importFrom(ggplot2,ggtitle)
-importFrom(ggplot2,guide_axis)
-importFrom(ggplot2,scale_y_discrete)
-importFrom(ggplot2,theme)
-importFrom(ggplot2,xlab)
-importFrom(ggplot2,xlim)
-importFrom(gtools,permutations)
-importFrom(jtools,export_summs)
-importFrom(knitr,kable)
-importFrom(marginaleffects,avg_predictions)
-importFrom(marginaleffects,hypotheses)
-importFrom(mice,pool)
-importFrom(stargazer,stargazer)
-importFrom(stats,p.adjust)
-importFrom(stringr,str_count)
-importFrom(survey,svydesign)
 importFrom(survey,svyglm)

R/compareHistories.R

@@ -618,18 +618,18 @@ compareHistories <- function(home_dir, exposure, exposure_time_pts, outcome, mod
   comparisons <- comparisons[order(comparisons$dose), ]
 
   if (length(colors) > 1) { # If user input a list of colors
-    p <- ggplot2::ggplot(data = comparisons, aes(x = estimate, y = history, color = dose)) +
+    p <- ggplot2::ggplot(data = comparisons, ggplot2::aes(x = estimate, y = history, color = dose)) +
       ggplot2::geom_point(size = 5) +
       ggplot2::scale_color_manual(values = colors) +
       ggplot2::scale_y_discrete(limits = c(as.character(comparisons$history)), expand = c(0, 0.2)) +
-      ggplot2::geom_errorbarh(aes(xmin = low_ci, xmax = high_ci), height = 0.6) +
+      ggplot2::geom_errorbarh(ggplot2::aes(xmin = low_ci, xmax = high_ci), height = 0.6) +
       ggplot2::xlab(paste0("Predicted ", out_lab, " Value")) +
       ggplot2::ylab(paste0(exp_lab, " Exposure History")) +
       ggplot2::xlim(min(comparisons$low_ci) - 1 * sd(comparisons$low_ci),
                     max(comparisons$high_ci) + 1 * sd(comparisons$high_ci)) +
       ggplot2::theme(text = ggplot2::element_text(size = 14)) +
-      ggplot2::theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
-                     panel.background = element_blank(), axis.line = ggplot2::element_line(colour = "black"))
+      ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = element_blank(),
+                     panel.background = ggplot2::element_blank(), axis.line = ggplot2::element_line(colour = "black"))
 
     if (save.out){
       ggplot2::ggsave(paste0(home_dir, "/plots/", exposure, "-", outcome, ".jpeg"), plot = p)
@@ -639,18 +639,18 @@ compareHistories <- function(home_dir, exposure, exposure_time_pts, outcome, mod
     }
 
   } else { # If user lists a palette (default)
-    p <- ggplot2::ggplot(data = comparisons, aes(x = estimate, y = history, color = dose)) +
+    p <- ggplot2::ggplot(data = comparisons, ggplot2::aes(x = estimate, y = history, color = dose)) +
       ggplot2::geom_point(size = 5) +
       ggplot2::scale_colour_brewer(palette = colors) +
       ggplot2::scale_y_discrete(limits = c(as.character(comparisons$history)), expand = c(0, 0.2)) +
-      ggplot2::geom_errorbarh(aes(xmin = low_ci, xmax = high_ci), height = 0.6) +
+      ggplot2::geom_errorbarh(ggplot2::aes(xmin = low_ci, xmax = high_ci), height = 0.6) +
       ggplot2::xlab(paste0("Predicted ", out_lab, " Value")) +
       ggplot2::ylab(paste0(exp_lab, " Exposure History")) +
       ggplot2::xlim(min(comparisons$low_ci) - 1 * sd(comparisons$low_ci),
                     max(comparisons$high_ci) + 1 * sd(comparisons$high_ci)) +
       ggplot2::theme(text = ggplot2::element_text(size = 14)) +
-      ggplot2::theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
-                     panel.background = element_blank(), axis.line = ggplot2::element_line(colour = "black")) +
+      ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(),
+                     panel.background = ggplot2::element_blank(), axis.line = ggplot2::element_line(colour = "black")) +
       ggplot2::guides(fill = ggplot2::guide_legend(title="Dosage"))
 
     if (save.out){

R/createFormulas.R

@@ -247,15 +247,16 @@ createFormulas <- function(home_dir, exposure, exposure_time_pts, outcome, type,
           #                 as.numeric(covar_time) > 0) %>% # Finds any lagged imbalanced covars
           #   dplyr::select(covariate)
 
-          new <- bal_stats[bal_stats$balanced ==0 &
+          new <- bal_stats[bal_stats$balanced == 0 &
                              bal_stats$exp_time == time &
                              as.numeric(bal_stats$covar_time) < exposure_time_pts[x-1] &
                              as.numeric(bal_stats$covar_time) > 0, ]
-          new <- new[, "covariate"]
 
 
           if (nrow(new) > 0) {
 
+            new <- new[, "covariate"]
+
             new <- as.character(unlist(new))
 
             time_var_include <- c(time_var_include, new)

R/createWeights.R

@@ -172,7 +172,7 @@ createWeights <- function(home_dir, data, exposure, outcome, formulas, method =
     calculate_weights <- function(data, form, weights_method, SL.library) {
 
       if(weights_method == "super"){
-        fit <- weightitMSM(form,
+        fit <- WeightIt::weightitMSM(form,
                            data = data,
                            method = weights_method,
                            stabilize = TRUE,
@@ -183,7 +183,7 @@ createWeights <- function(home_dir, data, exposure, outcome, formulas, method =
                            over = FALSE)
       }
       else{
-        fit <- weightitMSM(form,
+        fit <- WeightIt::weightitMSM(form,
                            data = data,
                            method = weights_method,
                            stabilize = TRUE,
@@ -227,7 +227,7 @@ createWeights <- function(home_dir, data, exposure, outcome, formulas, method =
         }
 
         # Writes image of the histogram of weights to assess heavy tails
-        p <- ggplot2::ggplot(data = as.data.frame(fit$weight), aes(x = fit$weight)) +
+        p <- ggplot2::ggplot(data = as.data.frame(fit$weight), ggplot2::aes(x = fit$weight)) +
           ggplot2::geom_histogram(color = 'black', bins = 15) +
           ggplot2::xlab("Weights") +
           ggplot2::ggtitle(paste0("Distribution of ", weights_method, " weights"))
@@ -283,7 +283,7 @@ createWeights <- function(home_dir, data, exposure, outcome, formulas, method =
         }
 
         # Writes image of the histogram of weights to assess heavy tails
-        p <- ggplot2::ggplot(data = as.data.frame(fit$weight), aes(x = fit$weight)) +
+        p <- ggplot2::ggplot(data = as.data.frame(fit$weight), ggplot2::aes(x = fit$weight)) +
           ggplot2::geom_histogram(color = 'black', bins = 15) +
           ggplot2::xlab("Weights") +
           ggplot2::ggtitle(paste0("Distribution of ", weights_method, " weights"))
@@ -340,7 +340,7 @@ createWeights <- function(home_dir, data, exposure, outcome, formulas, method =
       }
 
       # Writes image of the histogram of weights to assess heavy tails
-      p <- ggplot2::ggplot(data = as.data.frame(weights[[1]]$weights), aes(x = weights[[1]]$weights)) +
+      p <- ggplot2::ggplot(data = as.data.frame(weights[[1]]$weights), ggplot2::aes(x = weights[[1]]$weights)) +
         ggplot2::geom_histogram(color = 'black', bins = 15) +
         ggplot2::xlab("Weights") +
         ggplot2::ggtitle(paste0("Distribution of ", weights_method, " weights"))

R/fitModel.R

@@ -5,6 +5,7 @@
 #' weights.
 #' @seealso {[survey::svyglm()] for more on family/link specifications,
 #'   <https://www.rdocumentation.org/packages/survey/versions/4.2-1/topics/svyglm>}
+#' @importFrom survey svyglm
 #' @param home_dir path to home directory (required if 'save.out' = TRUE)
 #' @param data data in wide format as: a data frame, list of imputed data
 #'   frames, or mids object

R/make_love_plot.R

@@ -105,9 +105,9 @@ make_love_plot <- function(home_dir, folder, exposure, exposure_time_pt, exposur
   }
 
   # Make love plot per exposure time point
-  lp <- ggplot2::ggplot(aes(x = avg_bal, y = reorder(as.factor(covariate), avg_bal)), data = balance_stats) +
-    ggplot2::geom_point(aes(y = reorder(as.factor(covariate), avg_bal), x = avg_bal, fill = "white", alpha = 1)) +
-    ggplot2::geom_text(aes(label = labels, hjust = -0.2, vjust = 0.2), size = 1.5, color = "red") +
+  lp <- ggplot2::ggplot(ggplot2::aes(x = avg_bal, y = reorder(as.factor(covariate), avg_bal)), data = balance_stats) +
+    ggplot2::geom_point(ggplot2::aes(y = reorder(as.factor(covariate), avg_bal), x = avg_bal, fill = "white", alpha = 1)) +
+    ggplot2::geom_text(ggplot2::aes(label = labels, hjust = -0.2, vjust = 0.2), size = 1.5, color = "red") +
     ggplot2::xlab(x_lab) +
     ggplot2::ylab("Covariate") +
     ggplot2::xlim(min_val, max_val) +

R/trimWeights.R

@@ -145,7 +145,7 @@ trimWeights <- function(home_dir, exposure, outcome, weights, quantile = 0.95, v
       }
 
       # Save histogram of new weights
-      p <- ggplot2::ggplot(as.data.frame(t), aes(x = t)) +
+      p <- ggplot2::ggplot(as.data.frame(t), ggplot2::aes(x = t)) +
         ggplot2::geom_histogram(color = 'black', bins = 15)
 
       if(verbose){
@@ -181,9 +181,9 @@ trimWeights <- function(home_dir, exposure, outcome, weights, quantile = 0.95, v
       }
 
       # Save histogram of new weights
-      p <- ggplot2::ggplot(as.data.frame(t), aes(x = t)) +
+      p <- ggplot2::ggplot(as.data.frame(t), ggplot2::aes(x = t)) +
         ggplot2::geom_histogram(color = 'black', bins = 15) +
-        ggtitle(paste0("Weights trimmed at the ", quantile, "th value"))
+        ggplot2::ggtitle(paste0("Weights trimmed at the ", quantile, "th value"))
 
       if(verbose){
         print(p)

examplePipelineRevised.Rmd

@@ -56,7 +56,7 @@ library(devMSMsHelpers)
 #prior to re-installing using the code above. Sorry, this is annoying!
 
 #conducting package checks & tests from documentation --just for IS to run; comment out when testing the workflow
-# devtools::check()
+devtools::check()
 ```
 
 
@@ -198,9 +198,9 @@ data <- mice::complete(imputed_data, 1) #just for testing purposes
 #IS testing multi-level factors
 data$"PmMrSt2" = as.factor(data$"PmMrSt2")
 data$HomeOwnd = as.factor(data$HomeOwnd)
-# library(dplyr)
-# data <- data %>%
-#   dplyr::select(-c(contains(c(":", "Childhood", "Infancy", "Toddlerhood", "pcx")))) #removing these --came from old dataset
+library(dplyr)
+data <- data %>%
+  dplyr::select(-c(contains(c(":", "Childhood", "Infancy", "Toddlerhood", "pcx")))) #removing these --came from old dataset
 
 ```