You would first have to pivot the data to be in long format, then you could make the graph using the pivoted values as the y-axis values and the pivoted variable names as the fill variable. Here’s an example.

Original Data

library(dplyr)
library(tidyr)
library(ggplot2)
dat <- tibble::tribble(
  ~Station , ~Salinity    , ~CentricD    , ~PennateD    , ~Dinoflag    , ~MarineFlag  , ~Cilliates   ,
"A3"      , 18.3        , 181000      , 26500       , 1000        , 15500       , 2250        ,
"A6"      , 27.4        , 584666.6667 , 4666.666667 , 11666.66667 , 0           , 61333.33333 ,
"A8"      , 25.7        , 625071.4286 , 2000        , 74000       , 294.1176471 , 1907.563025 ,
"B"       , 29.77785714 , 503693.8776 , 2000        , 6642.857143 , 7642.857143 , 5622.44898  ,
"C"       , 31.283      , 266991.5966 , 5285.714286 , 10714.28571 , 71352.94118 , 12067.22689 ,
"D"       , 32.21625    , 349375      , 6437.5      , 6142.857143 , 39651.78571 , 4339.285714 ,
"E"       , 32.23       , 379200      , 466.6666667 , 3714.285714 , 12228.57143 , 4504.761905 ,
"F"       , 32.8        , 559000      , 0           , 333.3333333 , 0           , 11000       ,
"G"       , 33.185      , 209276.7857 , 2125        , 5714.285714 , 27937.5     , 3062.5      ,
"H"       , 33.67       , 98714.28571 , 1812.5      , 7125        , 6410.714286 , 7750        ,
"I"       , 34.33294118 , 113302.521  , 1764.705882 , 40142.85714 , 5588.235294 , 9260.504202 ,
"J"       , 34.537      , 68142.85714 , 1000        , 12842.85714 , 20228.57143 , 5271.428571 )

Here, I use pivot_longer() from tidyr. This will plot the raw values by station and phytoplankton. Note, that if you are providing the y value directly (and not calculating it from the data), you need to use stat="identity" in geom_bar().

dat %>% 
  pivot_longer(CentricD:Cilliates, names_to = "phyto", values_to = "val") %>% 
  ggplot(aes(x=Station, y=val, fill = phyto)) + 
  geom_bar(stat="identity") + 
  theme_bw()

If you would rather percentagize the figures so each bar has the same height, you could make the percentage variable by Station first and then plot that variable instead.

dat %>% 
  pivot_longer(CentricD:Cilliates, names_to = "phyto", values_to = "val") %>% 
  group_by(Station) %>% 
  mutate(pct = val/sum(val)) %>% 
  ggplot(aes(x=Station, y=pct, fill = phyto)) + 
  geom_bar(stat="identity") + 
  theme_bw()

^{Created on 2024-12-04 with reprex v2.1.0}