Group-level visualization of group-level results

Usage

Visualize_Scatter(
  dfResults,
  dfBounds = NULL,
  strGroupCol = NULL,
  strGroupLabel = NULL,
  strUnit = "days",
  vColors = c("#999999", "#FADB14", "#FF4D4F")
)

Arguments

dfResults

data.frame A stacked summary of analysis pipeline output. Created by passing a list of results returned by Summarize() to BindResults(). Expected columns: GroupID, GroupLevel, Numerator, Denominator, Metric, Score, Flag, MetricID, StudyID, SnapshotDate.

dfBounds

data.frame Set of predicted percentages/rates and upper- and lower-bounds across the full range of sample sizes/total exposure values for reporting. Created by passing dfResults and dfMetrics to MakeBounds(). Expected columns: Threshold, Denominator, Numerator, Metric, MetricID, StudyID, SnapshotDate.

strGroupCol

character name of stratification column for facet wrap Default: NULL

strGroupLabel

character name of group, used for labeling axes. Default: NULL

strUnit

character exposure time unit. Default: days

vColors

character vector of hex colors for plotting boundaries/thresholds. Index 1: mean; index 2: first threshold boundary; index 3: second threshold boundary.

Value

group-level plot object.

Examples

## Filter sample data to only one metric
reportingResults_filter <- reportingResults %>%
  dplyr::filter(MetricID == "kri0001")

reportingBounds_filter <- reportingBounds %>%
  dplyr::filter(MetricID == "kri0001")

## Output- filtered to one snapshot date
Visualize_Scatter(
  dfResults = reportingResults_filter %>%
    dplyr::filter(SnapshotDate == max(SnapshotDate)),
  dfBounds = reportingBounds_filter %>%
    dplyr::filter(SnapshotDate == max(SnapshotDate))
)
#> Warning: Removed 253 rows containing missing values or values outside the scale range
#> (`geom_line()`).


## Create Faceted output on snapshot date
Visualize_Scatter(
  dfResults = reportingResults_filter,
  dfBounds = reportingBounds_filter,
  strGroupCol = "SnapshotDate",
  strGroupLabel = "Snapshot Date"
)
#> Warning: Removed 3035 rows containing missing values or values outside the scale range
#> (`geom_line()`).


## Custom Colors
Visualize_Scatter(
  dfResults = reportingResults_filter %>%
    dplyr::filter(SnapshotDate == max(SnapshotDate)),
  dfBounds = reportingBounds_filter %>%
    dplyr::filter(SnapshotDate == max(SnapshotDate)),
  vColors = c("#F4E7E7", "#C17070", "#981212")
)
#> Warning: Removed 253 rows containing missing values or values outside the scale range
#> (`geom_line()`).