Relative abundance_Out of 100%

[ari_sh70]

Hello everyone,

I have one question regarding the relative frequency plot. I am trying to perform a stacked plot for my microbiome data, but in y axis (relative frequencies) I get much more than 100. The sum of each bacteria (relative frequency) is 100 but I don't know why it goes way higher than 100 in the plot. I have read other posts but I couldn't figured it out. I really appreciate if you can help me out, please.

The first 10 rows of the data are the following:

The codes that I am using are:

library(tidyverse)
library(readxl)
library(glue)
library(ggtext)
library(patchwork)
library(reshape2)
library(ggtext)

pc = read.csv("L2_16S_R2.csv", header = TRUE)
#convert data frame from a "wide" format to a "long" format
pcm = melt(pc, id = c("Vineyard"))
View(pcm)
str(pcm)
pcm %>%
  group_by(Vineyard, variable) %>%
  summarize(value = sum(value), .groups="drop") %>%
  group_by(Vineyard, variable) %>%
  summarize(mean_value = mean(value), .groups="drop") %>%
  mutate(variable=str_replace(variable,
                           "(.*)_unclassified", "Unclassified *\\1*"),
         variable = str_replace(variable,
                             "^(\\S*)$", "*\\1*")) %>% 
  ggplot(aes(x=Vineyard, y=mean_value, fill= variable)) +
  geom_col() +
  labs(x = NULL,
       y = "Mean relative abundance (%)") + 
  theme_classic() +
  theme(axis.text.x = element_markdown(),
        legend.text = element_markdown())

  

And the plot is:

Thank you

[nirgrahamuk]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[ari_sh70]

Thank you for the link.
I try to follow based on the guideline.

head(pc)
Vineyard Bacteria_unclassified Acidobacteria Actinobacteria Bacteroidetes
1   MOL_PI              5.081867      4.644098       4.453394      3.192632
2   MOL_PI              2.494538      3.162059       1.746438      1.328089
3   MOL_PI              1.930898      2.045947       2.262010      2.412740
4   MOL_PI              2.374574      3.004277       1.625022      1.920817
5   MOL_PI              1.515035      2.045939       2.150442      3.603883
6   MOL_PI              1.828937      1.717337       1.654520      3.223426
  Chlamydiae Chloroflexi Fibrobacteres Firmicutes Gemmatimonadetes Planctomycetes
1   2.796760    2.328822      1.969988   1.969880         1.949909       0.949891
2   1.531040    1.995751      1.503860   2.151369         1.887314       2.536497
3   1.541752    1.155944      2.039573   1.313711         2.166829       2.271800
4   1.404445    1.529284      1.852650   1.399901         2.892016       1.909484
5   1.342512    1.371413      1.141715   1.205557         1.287703       2.616590
6   1.355212    1.049989      2.117746   1.190188         1.566800       1.943042
  Proteobacteria Verrucomicrobia
1       0.762190        0.000000
2       2.079625        1.696870
3       2.197808        2.155778
4       2.037909        2.108571
5       2.014559        2.521974
6       2.068532        2.519107

#convert data frame from a "wide" format to a "long" format
pcm = melt(pc, id = c("Vineyard"))

 Vineyard              variable    value
1   MOL_PI Bacteria_unclassified 5.081867
2   MOL_PI Bacteria_unclassified 2.494538
3   MOL_PI Bacteria_unclassified 1.930898
4   MOL_PI Bacteria_unclassified 2.374574
5   MOL_PI Bacteria_unclassified 1.515035
6   MOL_PI Bacteria_unclassified 1.828937

The code that I am using for the plot is:

pcm %>%
  group_by(Vineyard, variable) %>%
  summarize(value = sum(value), .groups="drop") %>%
  group_by(Vineyard, variable) %>%
  summarize(mean_value = mean(value), .groups="drop") %>%
  mutate(variable=str_replace(variable,
                           "(.*)_unclassified", "Unclassified *\\1*"),
         variable = str_replace(variable,
                             "^(\\S*)$", "*\\1*")) %>% 
  ggplot(aes(x=Vineyard, y=mean_value, fill= variable)) +
  geom_col() +
  labs(x = NULL,
       y = "Mean relative abundance (%)") + 
  theme_classic() +
  theme(axis.text.x = element_markdown(),
        legend.text = element_markdown())