Multiple Stacked Bar Charts with ggplot()

Question

I'm a newbie to R and I'm stuck on creating the following bar plot in ggplot2:

Here is the code I have so far:

#Read in data
parameter_results<- readRDS("param_results_2014.RDS")

#list of parameter names
parameters <- sort(readRDS("parameters.RDS"))

bar_plot <- function(parameter) {
  parameter_df <- parameter_results %>%
    select(results = parameter) %>%  #keep only column for the parameter you want to plot
    filter(results != "Not Applicable") %>% 
    count(results) %>%    
    mutate(prop = prop.table(n), perc = paste0(round(prop * 100),"%"))
  color_code <- c("Attaining" = "#99FF99","Non Attaining" =  "#FF9999", "Insufficient Information" =  "#FFFF99")

  values <- vector(mode = "numeric", length = nrow(parameter_df))
  labs <- vector(mode = "character", length = nrow(parameter_df))
  colors <- vector(mode = "character", length = nrow(parameter_df))
  for (i in seq_along(1:nrow(parameter_df))) {
    values[[i]] <- parameter_df$prop[[i]] * 100
    labs[[i]] <- parameter_df$perc[i]
    colors[[i]] <- color_code[[parameter_df$results[[i]]]]
  }

  stacked_bar<-ggplot(parameter_df,aes(x=parameter,y=n,fill = fct_inorder(results)))+
    geom_bar(stat = "identity", width = 0.5,color="black") +
    blank_theme + theme(legend.title=element_blank()) +
    ggtitle("Figure ES-2: Statewide Designated Use Assessment Results, 2014") + 
    xlab("Designated Uses")+
    ylab("Number of Assessment Units")+
    theme(plot.title = element_text(hjust = 0.5,vjust=10))   +
    scale_fill_manual(values = c("Attaining" = "#99FF99","Non Attaining" = "#FF9999","Insufficient Information" = "#FFFF99"))      
}

bar_plot()
bar_ALG <-bar_plot('ALG')

My dataset looks like the following:

 A tibble: 958 x 89
   WMA   Waterbody  Name      `Biological (Caus~ `Biological Trout~ DO     `DO Trout` Temperature  `Temperature Tr~ pH    
   <chr> <chr>      <chr>     <chr>              <chr>              <chr>  <chr>      <chr>        <chr>            <chr> 
 1 15    020403020~ Absecon ~ Attaining          Not Applicable     Attai~ Not Appli~ Attaining    Not Applicable   Attai~
 2 15    020403020~ Absecon ~ Insufficient Info~ Not Applicable     Non A~ Not Appli~ Attaining    Not Applicable   Insuf~
 3 15    020403020~ Absecon ~ Attaining          Not Applicable     Insuf~ Not Appli~ Insufficien~ Not Applicable   Non A~
 4 15    020403020~ Absecon ~ Attaining          Not Applicable     Attai~ Not Appli~ Attaining    Not Applicable   Attai~
 5 14    020403011~ Albertso~ Non Attaining      Not Applicable     Attai~ Not Appli~ Attaining    Not Applicable   Non A~
 6 11    020401052~ Alexauke~ Attaining          Attaining          Insuf~ Attaining  Insufficien~ Non Attaining    Non A~
 7 11    020401052~ Alexauke~ Attaining          Attaining          Insuf~ Attaining  Insufficien~ Non Attaining    Non A~
 8 17    020402060~ Alloway ~ Non Attaining      Not Applicable     Attai~ Not Appli~ Attaining    Not Applicable   Attai~
 9 17    020402060~ Alloway ~ Insufficient Info~ Not Applicable     Attai~ Not Appli~ Attaining    Not Applicable   Insuf~
10 17    020402060~ Alloway ~ Insufficient Info~ Not Applicable     Insuf~ Not Appli~ Insufficien~ Not Applicable   Insuf~

parameter_df:

parameter_df
## # A tibble: 2 x 4
##                    results     n      prop  perc
##                      <chr> <int>     <dbl> <chr>
## 1                Attaining   454 0.5443645   54%
## 2 Insufficient Information   380 0.4556355   46%

Each parameter has its own column… and each row of the data table contains the assessment values for a given location for each parameter. My question is what do I need to do to the dataset or the function in order to have each parameter plotted like the graph above?

This is the plot I'm getting:

Answer 1

Avoid running graph iteratively across the parameters but run on entire dataframe, parameter_results. However, first consider transforming the data with tidyr::gather and dplyr::group_by to calculate category tabs:

library(dplyr)
library(tidyr)
library(ggplot2)

# RESHAPE WIDE TO LONG
rdf <- parameter_results %>%
  gather(value = colnames(parameter_results)) %>%
  setNames(c("parameter", "results"))

# GROUP BY PARAMETER CALCULATIONS
graph_df <- rdf %>%
  group_by(parameter) %>%
  filter(results != "Not Applicable") %>% 
  count(results) %>%    
  mutate(prop = prop.table(n), 
         perc = paste0(round(prop * 100),"%"))

color_code <- c("Attaining"="#99FF99", "Non Attaining"="#FF9999", 
                "Insufficient Information"="#FFFF99")

# GRAPH ALL PARAMETERS TOGETHER AT ONCE
ggplot(graph_df, aes(x=parameter, y=n, fill = results)) +
  geom_bar(stat = "identity", width = 0.5,color="black") +
  theme(legend.title=element_blank()) +
  ggtitle("Figure ES-2: Statewide Designated Use Assessment Results, 2014") + 
  xlab("Designated Uses")+
  ylab("Number of Assessment Units") +
  theme(legend.position="bottom", plot.title = element_text(hjust=0.5, vjust=10)) +
  scale_fill_manual(values = color_code) 

Input (using random data of 200, assuming parameters_results to be a similar structure)

categ <- c("Attaining", "Insufficient Information", "Non Attaining", "Not Applicable")

set.seed(555)
parameter_results <- data.frame(
  Acquatic_Life_Gen = sample(categ, 200, replace=TRUE),
  Acquatic_Life_Trout = sample(categ, 200, replace=TRUE),
  Recreation = sample(categ, 200, replace=TRUE),
  Water_Supply = sample(categ, 200, replace=TRUE),
  Shellfish_Harvest = sample(categ, 200, replace=TRUE),
  Fish_Consumption = sample(categ, 200, replace=TRUE)
)

Output