Barplot - error bars calculated from SD

daisyshaw · November 16, 2021, 1:56pm

Hello,

Below I have attached some images of the graph I have made using ggplot. The top one is how it should look before the error bars are added, the second is how it looks once the error bars have been added.

I'm having a couple of problems with this - firstly, as you can see, the y axis scale is all bunched up. I have tried using ylim(), and it has worked before, but I haven't managed to get it to work this time. Does this function go on it's own line in the code, or should it be within one of the other functions?

Secondly, the error bars are all starting from the same mean value. I want them to be specific to each bar, depending on the mean data value that has been plotted. I have attached screenshots of the code alongside the plots, but I'll also add a snapshot of my data too.

Many thanks,
Daisy

JackDavison · November 16, 2021, 2:05pm

Hi Daisy,

It's a lot easier to help you if you provide a proper reproducible example:
FAQ: What's a reproducible example (reprex) and how do I create one?

You can format code properly by containing it in backticks (```).

# here is an example
iris %>%
    count(Species)

As I can't copy-paste a screenshot, I'll use iris.

library(tidyverse)

df = iris |> 
  pivot_longer(where(is.numeric), names_to = "stat") |> 
  group_by(species = Species, stat) |> 
  summarise(mean = mean(value), sd = sd(value))

ggplot(df, aes(x = stat, y = mean, ymax = mean+sd, ymin = mean-sd, fill = species)) +
  geom_col(position = position_dodge2(width = .8), width = .8) +
  geom_pointrange(position = position_dodge2(width = .8), width = .8)

I can't read your screenshot very well, but your y-axis scale is all bunched up because you've specified the breaks in scale_y_continuous. They'll be less bunched up if you change that argument. If you just want your axes to be between 0 and 100, I'd write something like

+ coord_cartesian(ylim = c(0,100))

daisyshaw · November 16, 2021, 2:32pm

Okay, thank you. I think I have created a reprex now (correct me if I'm wrong?).

library(tidyverse)
Initial_iron_experiments %>%
  ggplot(aes(x=Sample, y=Percentage_killing, fill=Iron))+
  stat_summary(fun=mean, geom="col", position="dodge", colour="black")+
  scale_y_continuous("Percentage Killing")+
  geom_errorbar(aes(x=Sample, ymin=mean(Percentage_killing)-SD, ymax=mean(Percentage_killing)+SD), position=position_dodge(0.9))+
  ylim(-50, 220)+
  labs(title="Killing effect of Pseudomonas aeruginosa on Rhizopus microsporus")+
  theme(plot.title=element_text(hjust=0.5))
#> Error in ggplot(., aes(x = Sample, y = Percentage_killing, fill = Iron)): object 'Initial_iron_experiments' not found

Thank you, yes that looks a lot better. Is there another way I can change it so the y axis values show all the 10s values from 0 to 100, i.e., 0,10,20 etc. ?

JackDavison:

ggplot(df, aes(x = stat, y = mean, ymax = mean+sd, ymin = mean-sd, fill = species)) +
  geom_col(position = position_dodge2(width = .8), width = .8) +
  geom_pointrange(position = position_dodge2(width = .8), width = .8)

Which part of this codes for the error bars that you have?

JackDavison · November 16, 2021, 2:55pm

That's your code (which I can now more easily read!) though we could use (a bit of) your data too;

You can copy the output of dput(your_data) to a post, which can then be copy-pasted straight into RStudio to get your data back. If your data is huge, dput(head(your_data)) might also do the job.

For this, I'd maybe use seq(0,100,10):

> seq(0,100,10)
 [1]   0  10  20  30  40  50  60  70  80  90 100

So I calculated my summary statistics before I piped into ggplot(). An example of how to use "stat" functions would be (again using iris):

library(tidyverse)

df = iris |> 
  pivot_longer(where(is.numeric), names_to = "stat")

df %>%
  ggplot(aes(x=stat, y=value, fill=Species))+
  stat_summary(fun=mean, geom="col", position=position_dodge2(width = .9), colour="black")+
  stat_summary(
    geom = "errorbar",
    aes(group = Species),
    fun.data = mean_sdl,
    fun.args = list(mult = 1),
    position = position_dodge2(width = .9)
  ) +
  scale_y_continuous("Percentage Killing", breaks = seq(0,100,10))+
  labs(title="Killing effect of Pseudomonas aeruginosa on Rhizopus microsporus")+
  theme(plot.title=element_text(hjust=0.5))

Note that I use fun.args = list(mult = 1) to do one standard deviation from the mean; the default is 2.

daisyshaw · November 16, 2021, 3:13pm

This is my data (sorry there's quite a bit, but I'm sure it's better if I provide it all).

> dput(Initial_iron_experiments)
structure(list(Sample = c("PA01C", "PA01C", "PA01C", "PA01C", 
"PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", 
"PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", 
"PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", 
"PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", 
"PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", "PA01C", 
"PA01C", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", 
"PA01S", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", 
"PA01S", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", 
"PA01S", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", "PA01S", 
"PA01S", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", 
"PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", 
"PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", 
"PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", 
"PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", 
"PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14C", "PA14S", 
"PA14S", "PA14S", "PA14S", "PA14S", "PA14S", "PA14S", "PA14S", 
"PA14S", "PA14S", "PA14S", "PA14S", "PA14S", "PA14S", "PA14S", 
"PA14S", "PA14S", "PA14S", "PA14S", "PA14S", "PA14S", "PA14S", 
"PA14S", "PA14S", "PA14S", "PA14S", "PA14S"), Percentage_killing = c(76.4044943820225, 
74.7191011235955, 83.314606741573, 96.7977528089888, 92.1641791044776, 
69.7761194029851, 93.5074626865672, 80.0746268656716, 96.969696969697, 
95.4545454545455, 98.1818181818182, 97.8787878787879, 92.6108374384236, 
-491.133004926108, 97.487684729064, 97.192118226601, 98.6547085201794, 
95.9641255605381, 98.6547085201794, 96.2331838565022, 51.123595505618, 
74.7191011235955, 72.5280898876404, 82.6404494382023, 97.7611940298507, 
96.6417910447761, 98.3208955223881, 97.5373134328358, 93.9393939393939, 
96.969696969697, 98.030303030303, 98.3333333333333, 98.5221674876847, 
95.5665024630542, 21.6748768472906, 97.487684729064, 95.9641255605381, 
98.6547085201794, 97.4439461883408, 99.5964125560538, 100, 100, 
99.0909090909091, 99.0909090909091, 94.0886699507389, 98.5221674876847, 
95.8620689655172, 98.2266009852217, 71.7488789237668, 83.8565022421525, 
96.7713004484305, 95.4260089686099, 15.7303370786517, 54.4943820224719, 
66.9662921348315, 72.3595505617978, 59.0909090909091, 62.1212121212121, 
84.3939393939394, 86.0606060606061, 54.1871921182266, 26.1083743842365, 
81.0837438423645, 80.64039408867, 79.8206278026906, 75.7847533632287, 
77.8026905829596, 82.914798206278, 84.8314606741573, 62.9213483146067, 
93.5955056179775, 83.4831460674157, 96.6417910447761, 75.3731343283582, 
90.9328358208955, 95.8582089552239, 69.6969696969697, 81.8181818181818, 
88.3333333333333, 92.5757575757576, 95.5665024630542, 97.0443349753695, 
67.6354679802956, 99.1133004926108, 100, 95.9641255605381, 99.5964125560538, 
97.0403587443946, 93.2584269662921, 96.6292134831461, 96.9662921348315, 
96.2921348314607, 94.4029850746269, 80.9701492537313, 96.0820895522388, 
96.0820895522388, 90.9090909090909, 100, 94.3939393939394, 99.2424242424242, 
92.6108374384236, 95.5665024630542, 97.487684729064, 98.2266009852217, 
97.3094170403587, 93.2735426008969, 98.1165919282511, 90.8520179372197, 
100, 100, 100, 100, 74.8768472906404, 89.6551724137931, 89.064039408867, 
90.9852216748769, 100, 100, 100, 100, 89.8876404494382, 92.4157303370787, 
91.2359550561798, 98.4848484848485, 100, 100, 100, 63.0541871921182, 
97.0443349753695, 80.1970443349754, 97.7832512315271, 100, 100, 
100, 100), Iron = c("100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", 
"100 µl/ml iron", "100 µl/ml iron", "100 µl/ml iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron", "No iron", "No iron", "No iron", "No iron", 
"No iron", "No iron"), SD = c(127.213656720742, 127.213656720742, 
127.213656720742, 127.213656720742, 127.213656720742, 127.213656720742, 
127.213656720742, 127.213656720742, 127.213656720742, 127.213656720742, 
127.213656720742, 127.213656720742, 127.213656720742, 127.213656720742, 
127.213656720742, 127.213656720742, 127.213656720742, 127.213656720742, 
127.213656720742, 127.213656720742, 19.4318548191016, 19.4318548191016, 
19.4318548191016, 19.4318548191016, 19.4318548191016, 19.4318548191016, 
19.4318548191016, 19.4318548191016, 19.4318548191016, 19.4318548191016, 
19.4318548191016, 19.4318548191016, 19.4318548191016, 19.4318548191016, 
19.4318548191016, 19.4318548191016, 19.4318548191016, 19.4318548191016, 
19.4318548191016, 19.4318548191016, 8.01827204126994, 8.01827204126994, 
8.01827204126994, 8.01827204126994, 8.01827204126994, 8.01827204126994, 
8.01827204126994, 8.01827204126994, 8.01827204126994, 8.01827204126994, 
8.01827204126994, 8.01827204126994, 20.6372786443555, 20.6372786443555, 
20.6372786443555, 20.6372786443555, 20.6372786443555, 20.6372786443555, 
20.6372786443555, 20.6372786443555, 20.6372786443555, 20.6372786443555, 
20.6372786443555, 20.6372786443555, 20.6372786443555, 20.6372786443555, 
20.6372786443555, 20.6372786443555, 11.1514836638692, 11.1514836638692, 
11.1514836638692, 11.1514836638692, 11.1514836638692, 11.1514836638692, 
11.1514836638692, 11.1514836638692, 11.1514836638692, 11.1514836638692, 
11.1514836638692, 11.1514836638692, 11.1514836638692, 11.1514836638692, 
11.1514836638692, 11.1514836638692, 11.1514836638692, 11.1514836638692, 
11.1514836638692, 11.1514836638692, 4.05359503648241, 4.05359503648241, 
4.05359503648241, 4.05359503648241, 4.05359503648241, 4.05359503648241, 
4.05359503648241, 4.05359503648241, 4.05359503648241, 4.05359503648241, 
4.05359503648241, 4.05359503648241, 4.05359503648241, 4.05359503648241, 
4.05359503648241, 4.05359503648241, 4.05359503648241, 4.05359503648241, 
4.05359503648241, 4.05359503648241, 7.54494046194481, 7.54494046194481, 
7.54494046194481, 7.54494046194481, 7.54494046194481, 7.54494046194481, 
7.54494046194481, 7.54494046194481, 7.54494046194481, 7.54494046194481, 
7.54494046194481, 7.54494046194481, 9.90259358423519, 9.90259358423519, 
9.90259358423519, 9.90259358423519, 9.90259358423519, 9.90259358423519, 
9.90259358423519, 9.90259358423519, 9.90259358423519, 9.90259358423519, 
9.90259358423519, 9.90259358423519, 9.90259358423519, 9.90259358423519, 
9.90259358423519)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-135L))

As you can see, I calculated the SD for each variable in excel before importing it into RStudio.

Would it be easier to try and do it how you have done here ^, or should I be able to work with the already calculated SD values?

JackDavison · November 16, 2021, 3:24pm

Thanks, got your data now!

You can do it however you like really once you've imported your data, though there might be argument for doing it more reproducibly in R rather than doing it in Excel.

One issue I see with the way you are plotting right now is the use of mean(Percentage_killing) + SD - it looks to me like all the error bars are centered around the mean of the whole data, not per-bar. See here:

I think the way I'd do this is below. Note that I've also added percentage labels to your y-axis as a bonus - can always turn these off if you want!

plot_iron = Initial_iron_experiments |> 
  group_by(Sample, Iron) |> 
  summarise(Percentage_killing = mean(Percentage_killing),
            SD = first(SD))

plot_iron %>%
  ggplot(aes(x=Sample, y=Percentage_killing, fill=Iron))+
  geom_col(position=position_dodge(0.9), 
           colour="black") +
  geom_point(position=position_dodge(0.9)) +
  geom_errorbar(aes(x = Sample, 
                    ymin = Percentage_killing - SD,
                    ymax = Percentage_killing + SD), 
                position = position_dodge(0.9),
                width = .5) +
  coord_cartesian(ylim = c(0,120)) +
  scale_y_continuous("Percentage Killing", 
                     breaks = seq(-100,200,10),
                     expand = expansion(0,.1),
                     labels = scales::label_percent(scale = 1))+
  labs(title="Killing effect of Pseudomonas aeruginosa on Rhizopus microsporus")+
  theme(plot.title=element_text(hjust=0.5))

daisyshaw · November 16, 2021, 3:31pm

Thank you Jack this is incredibly helpful and it is all looking good on my end now! Thanks again for all of your help

system · November 23, 2021, 3:32pm

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