Sites Report

A summary of which sites meet quality assessment checks to be included in the final map.

(code) import libraries, functions, & data

if (!requireNamespace("librarian", quietly = TRUE)) {
  # If not installed, install the package
  install.packages("librarian")
}

librarian::shelf(
  ggplot2,
  dplyr
)
source("R/getData.R")
data <- getData()

show unique providers

unique_sources <- unique(data$Source)
print(unique_sources)

 [1] "AOML"                  "DERM"                  "BROWARD"              
 [4] "DEP"                   "FIU"                   "21FLWQA"              
 [7] "BBAP"                  "Miami Beach"           "Miami Beach  Outfalls"
[10] "Miami Beach Re-Sample" "BBWW"                  "Palm Beach"

show % of clean data per site

# Count for raw dataframe
raw_counts <- getRawData() %>%
  group_by(Source) %>%
  summarise(Count = n(), .groups = 'drop') %>%
  mutate(Condition = "Before Cleaning")

New names:
• `` -> `...1`

Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `Value = as.numeric(Value)`.
Caused by warning:
! NAs introduced by coercion

show % of clean data per site

# Count for cleaned dataframe
cleaned_counts <- getData() %>%
  group_by(Source) %>%
  summarise(Count = n(), .groups = 'drop') %>%
  mutate(Condition = "After Cleaning")

combined_counts <- bind_rows(raw_counts, cleaned_counts)

ggplot(combined_counts, aes(x = Source, y = Count, fill = Condition)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Number of Rows Per Source Before and After Cleaning",
       x = "Source",
       y = "Number of Rows",
       fill = "Condition") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for better readability