update viz function

avallecam · Oct 20, 2020 · 19ba2e0 · 19ba2e0
1 parent 36922bb
commit 19ba2e0
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Showing 5 changed files with 63 additions and 61 deletions.
diff --git a/NAMESPACE b/NAMESPACE
@@ -3,7 +3,6 @@
 export(correct_sero_misclass)
 export(correct_sero_misclass_p)
 export(ggplot_prevalence)
-export(ggplot_prevalence_ii)
 export(rogan_gladen_estimator)
 export(rogan_gladen_stderr_unk)
 export(sample_posterior_r_mcmc_hyperR)

diff --git a/R/serosurvey_viz.R b/R/serosurvey_viz.R
@@ -5,18 +5,17 @@
 #' @describeIn ggplot_prevalence ggplot2 visualization of proportions
 #'
 #' @param data input tibble
-#' @param category denominator level
-#' @param outcome numerator variable
+#' @param denominator_level - denominator values column
+#' @param numerator - numerator variable name column
 #' @param proportion point estimate
 #' @param proportion_upp upper interval
 #' @param proportion_low lower interval
-#' @param breaks_n number of breaks in axis
+#' #param breaks_n number of breaks in axis
 #'
 #' @import skimr
 #' @import tidyverse
 #'
 #' @export ggplot_prevalence
-#' @export ggplot_prevalence_ii
 #'
 #' @examples
 #'
@@ -25,34 +24,14 @@
 #' # https://avallecam.github.io/serosurvey/articles/howto-reprex.html
 #'
 
-ggplot_prevalence <- function(data,category,outcome,
-                              proportion,proportion_upp,proportion_low,
-                              breaks_n=5) {
-  data %>%
-    ggplot(aes(x = {{category}},
-               y = {{proportion}},
-               color={{outcome}},
-               group={{outcome}})) +
-    # geom_point(aes(size=proportion_cv),position = position_dodge(width = 0.5)) +
-    geom_point(position = position_dodge(width = 0.5)) +
-    geom_errorbar(aes(max={{proportion_upp}},min={{proportion_low}}),
-                  position = position_dodge(width = 0.5)) +
-    scale_y_continuous(labels = scales::percent_format(accuracy = 1),
-                       breaks = scales::pretty_breaks(n = {{breaks_n}})) +
-    # scale_size_continuous(labels = scales::percent_format()) +
-    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
-}
-
-#' @describeIn ggplot_prevalence ggplot_prevalence with new arguments
-#' @inheritParams ggplot_prevalence
-#' @param denominator_level - denominator values column
-#' @param numerator - numerator variable name column
-
-ggplot_prevalence_ii <- function(data,
-                                 denominator_level,
-                                 numerator,
-                                 proportion,proportion_upp,proportion_low,
-                              breaks_n=5) {
+ggplot_prevalence <- function(data,
+                              denominator_level,
+                              numerator,
+                              proportion,
+                              proportion_upp,
+                              proportion_low#,
+                              # breaks_n=5
+                              ) {
   data %>%
     ggplot(aes(x = {{denominator_level}},
                y = {{proportion}},
@@ -61,9 +40,36 @@ ggplot_prevalence_ii <- function(data,
     # geom_point(aes(size=proportion_cv),position = position_dodge(width = 0.5)) +
     geom_point(position = position_dodge(width = 0.5)) +
     geom_errorbar(aes(max={{proportion_upp}},min={{proportion_low}}),
-                  position = position_dodge(width = 0.5)) +
-    scale_y_continuous(labels = scales::percent_format(accuracy = 1),
-                       breaks = scales::pretty_breaks(n = {{breaks_n}})) +
-    # scale_size_continuous(labels = scales::percent_format()) +
-    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
+                  position = position_dodge(width = 0.5)) #+
+  # scale_y_continuous(#labels = scales::percent_format(accuracy = 1),
+  # breaks = scales::pretty_breaks(n = {{breaks_n}})) #+
+  # scale_size_continuous(labels = scales::percent_format()) +
+  # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
 }
+
+
+#' #' #param category denominator level
+#' #param outcome numerator variable
+#' #export ggplot_prevalence_ii
+#' ggplot_prevalence <- function(data,category,outcome,
+#'                               proportion,proportion_upp,proportion_low,
+#'                               breaks_n=5) {
+#'   data %>%
+#'     ggplot(aes(x = {{category}},
+#'                y = {{proportion}},
+#'                color={{outcome}},
+#'                group={{outcome}})) +
+#'     # geom_point(aes(size=proportion_cv),position = position_dodge(width = 0.5)) +
+#'     geom_point(position = position_dodge(width = 0.5)) +
+#'     geom_errorbar(aes(max={{proportion_upp}},min={{proportion_low}}),
+#'                   position = position_dodge(width = 0.5)) +
+#'     scale_y_continuous(labels = scales::percent_format(accuracy = 1),
+#'                        breaks = scales::pretty_breaks(n = {{breaks_n}})) +
+#'     # scale_size_continuous(labels = scales::percent_format()) +
+#'     theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
+#' }
+#'
+#' #' describeIn ggplot_prevalence ggplot_prevalence with new arguments
+#' #' inheritParams ggplot_prevalence
+#' #' param denominator_level - denominator values column
+#' #' param numerator - numerator variable name column
diff --git a/man/ggplot_prevalence.Rd b/man/ggplot_prevalence.Rd
diff --git a/vignettes/howto-reprex.Rmd b/vignettes/howto-reprex.Rmd
@@ -453,13 +453,16 @@ outcome_01_adj_tbl %>%
 ```{r}
 outcome_01_adj_tbl %>%
   filter(str_detect(numerator,"outcome")) %>% 
-  ggplot_prevalence_ii(
+  ggplot_prevalence(
     denominator_level = denominator_level,
     numerator = numerator,
     proportion = prop,
     proportion_upp = prop_upp,
     proportion_low = prop_low) +
-  theme(axis.text.x = element_text(angle = 0, vjust = 0, hjust=0)) +
+  # theme(axis.text.x = element_text(angle = 0, vjust = 0, hjust=0)) +
+  scale_y_continuous(
+    labels = scales::percent_format(accuracy = 1),
+    breaks = scales::pretty_breaks(n = 5)) +
   # coord_flip() +
   facet_wrap(denominator~.,scales = "free") +
   # facet_grid(denominator~.,scales = "free_y") +

diff --git a/vignettes/intro.Rmd b/vignettes/intro.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "Workflow: How to use the serosurvey R package?"
+title: "Introduction: serosurvey R package?"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteEncoding{UTF-8}
@@ -25,6 +25,9 @@ knitr::opts_chunk$set(fig.width=10, fig.height=4)
 options(tidyverse.quiet = TRUE)
 ```
 
+## Introduction
+
+
 ```{r setup}
 library(serosurvey)
 ```
@@ -92,3 +95,5 @@ Here are a list of publications with interesting approaches using R:
 and post-stratification to account for test uncertainty
 with unknown specificity and sensitivity. 
 Here a [case-study](https://github.com/bob-carpenter/diagnostic-testing/blob/master/src/case-study/seroprevalence-meta-analysis.Rmd).
+
+## References