Reputation: 13
Here is what it looks like after those edits - lines but no boxes.
Reproducible code:
df <- data.frame(SampleID = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L),
.Label = c("C004", "C005", "C007", "C009", "C010",
"C011", "C013", "C027", "C028", "C029",
"C030", "C031", "C032", "C033", "C034",
"C035", "C036", "C042", "C043", "C044",
"C045", "C046", "C047", "C048", "C049",
"C058", "C086"), class = "factor"),
Sequencing.Depth = c(1L, 2612L, 5223L, 7834L, 10445L, 13056L, 15667L, 18278L,
20889L, 23500L),
Observed.OTUs = c(1, 213, 289.5, 338, 377.8, 408.9, 434.4, 453.8, 472.1, NA),
Mange = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
.Label = c("N", "Y"), class = "factor"),
SpeciesCode = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L),
.Label = c("Cla", "Ucin", "Vvu"), class = "factor"))
Upvotes: 1
Views: 118
Reputation: 16178
In your aes
, you can use interaction
of your x values and your categorical values for plotting boxplot on a continuous x axis and pass position = "identity"
in order to place them on the precise x values and not to be dodged.
Here to add the line connecting each boxplot, I calculate mean per Species per x values using dplyr
directly inggplot
but you can calculate outside and generate a second dataframe.
So, as your x values are pretty spread from 1 to 23500, you will have to modify the width
of the geom_boxplot
in order to see a box and not a single line:
library(ggplot2)
library(dplyr)
ggplot(df,aes(x = Xvalues, y = Yvalues, color = Species,
group = interaction(Species, Xvalues)))+
geom_boxplot(position = "identity", width = 1000)+
geom_line(data = df %>%
group_by(Xvalues, Species) %>%
summarise(Mean = mean(Yvalues)),
aes(x = Xvalues, y = Mean,
color = Species, group = Species))
So, apply to your dataset (based on informations you provided in your code), you should try something like:
library(ggplot2)
library(dplyr)
ggplot(observedotusrare,
aes(x=Sequencing.Depth, y=Observed.OTUs,
color=SpeciesCode,
group = interaction(Sequencing.Depth, SpeciesCode))) +
geom_boxplot(position = "identity", width = 1000) +
geom_line(data = observedotusrare %>%
group_by(Sequencing.Depth, SpeciesCode) %>%
summarise(Mean = mean(Observed.OTUs, na.rm = TRUE)),
aes(x = Sequencing.Depth, y = Mean,
color = SpeciesCode, group = SpeciesCode))
Does it answer your question ?
Reproducible example
df <- data.frame(Xvalues = rep(c(10,2000,23500), each = 30),
Species = rep(rep(LETTERS[1:3], each = 10),3),
Yvalues = c(rnorm(10,1,1),
rnorm(10,5,1),
rnorm(10,8,1),
rnorm(10,5,1),
rnorm(10,8,1),
rnorm(10,12,1),
rnorm(10,20,1),
rnorm(10,30,1),
rnorm(10,50,1)))
Upvotes: 2