how to set my barplots results in ascending order?

[OSDIAZ]

Anyone know how to set the results in my graph in ascending order?

First: Bottom : Un oxyphotobacteria, next Un cyanobacteria, nostocaceae.

My script:
library(readxl)
Family <- read_excel("~/RSTUDIO/Datos_cianobacterias/Cianobacterias_familia_sin3h5c.xlsx")

data<- Family

attach(Family)
rwnames <- index
data <- as.data.frame(data[,-1])
rownames(data) <- rwnames

Metadata<- read.csv("~/RSTUDIO/Datos_cianobacterias/Metadata-final-sin3h5c.csv", row.names=1)

#SUBSET DE SAMPLING POINTS
CAJ1 <- subset(data, Metadata$SamplingPoint == "CAJ-1", select = c(Un Caenarcaniphilales:Un Cyanobacteria))
CAJ2 <- subset(data, Metadata$SamplingPoint == "CAJ-2", select = c(Un Caenarcaniphilales:Un Cyanobacteria))
CAJ3 <- subset(data, Metadata$SamplingPoint == "CAJ-3", select = c(Un Caenarcaniphilales:Un Cyanobacteria))
CAJ4 <- subset(data, Metadata$SamplingPoint == "CAJ-4", select = c(Un Caenarcaniphilales:Un Cyanobacteria))
CAJ5 <- subset(data, Metadata$SamplingPoint == "CAJ-5", select = c(Un Caenarcaniphilales:Un Cyanobacteria))

CAJ1 <- data.frame(CAJ1)
CAJ1_counts <- colSums(CAJ1)
Counts <- unname(CAJ1_counts)
CAJ1_counts <- data.frame(CAJ1_counts)
CAJ1_counts <- t(CAJ1_counts)
total <- sum(Counts)
rel_ab <- CAJ1_counts/total
Others <- rel_ab[,colMeans(rel_ab)<.01]
Others <- sum(Others)
rel_ab <- rel_ab[,colMeans(rel_ab)>=.01]
rel_ab <- data.frame(t(rel_ab), Others)
rel_ab_P <- t(rel_ab)
abundance <- c("abundance")
rel_ab_P <- data.frame(rel_ab_P)
write.csv(rel_ab_P, file = "~/RSTUDIO/Datos_cianobacterias/CAJ1.csv")
CAJ1 <- read.csv("~/RSTUDIO/Datos_cianobacterias/CAJ1.csv")

#CAJ2
CAJ2 <- data.frame(CAJ2)
CAJ2_counts <- colSums(CAJ2)
Counts <- unname(CAJ2_counts)
CAJ2_counts <- data.frame(CAJ2_counts)
CAJ2_counts <- t(CAJ2_counts)
total <- sum(Counts)
rel_ab <- CAJ2_counts/total
Others <- rel_ab[,colMeans(rel_ab)<.01]
Others <- sum(Others)
rel_ab <- rel_ab[,colMeans(rel_ab)>=.01]
rel_ab <- data.frame(t(rel_ab), Others)
rel_ab_P <- t(rel_ab)
abundance <- c("abundance")
rel_ab_P <- data.frame(rel_ab_P)
write.csv(rel_ab_P, file = "~/RSTUDIO/Datos_cianobacterias/CAJ2.csv")
CAJ2 <- read.csv("~/RSTUDIO/Datos_cianobacterias/CAJ2.csv")

#CAJ3
CAJ3 <- data.frame(CAJ3)
CAJ3_counts <- colSums(CAJ3)
Counts <- unname(CAJ3_counts)
CAJ3_counts <- data.frame(CAJ3_counts)
CAJ3_counts <- t(CAJ3_counts)
total <- sum(Counts)
rel_ab <- CAJ3_counts/total
Others <- rel_ab[,colMeans(rel_ab)<.01]
Others <- sum(Others)
rel_ab <- rel_ab[,colMeans(rel_ab)>=.01]
rel_ab <- data.frame(t(rel_ab), Others)
rel_ab_P <- t(rel_ab)
abundance <- c("abundance")
rel_ab_P <- data.frame(rel_ab_P)
write.csv(rel_ab_P, file = "~/RSTUDIO/Datos_cianobacterias/CAJ3.csv")
CAJ3 <- read.csv("~/RSTUDIO/Datos_cianobacterias/CAJ3.csv")

#CAJ4
CAJ4 <- data.frame(CAJ4)
CAJ4_counts <- colSums(CAJ4)
Counts <- unname(CAJ4_counts)
CAJ4_counts <- data.frame(CAJ4_counts)
CAJ4_counts <- t(CAJ4_counts)
total <- sum(Counts)
rel_ab <- CAJ4_counts/total
Others <- rel_ab[,colMeans(rel_ab)<.01]
Others <- sum(Others)
rel_ab <- rel_ab[,colMeans(rel_ab)>=.01]
rel_ab <- data.frame(t(rel_ab), Others)
rel_ab_P <- t(rel_ab)
abundance <- c("abundance")
rel_ab_P <- data.frame(rel_ab_P)
write.csv(rel_ab_P, file = "~/RSTUDIO/Datos_cianobacterias/CAJ4.csv")
CAJ4 <- read.csv("~/RSTUDIO/Datos_cianobacterias/CAJ4.csv")

#CAJ5
CAJ5 <- data.frame(CAJ5)
CAJ5_counts <- colSums(CAJ5)
Counts <- unname(CAJ5_counts)
CAJ5_counts <- data.frame(CAJ5_counts)
CAJ5_counts <- t(CAJ5_counts)
total <- sum(Counts)
rel_ab <- CAJ5_counts/total
Others <- rel_ab[,colMeans(rel_ab)<.01]
Others <- sum(Others)
rel_ab <- rel_ab[,colMeans(rel_ab)>=.01]
rel_ab <- data.frame(t(rel_ab), Others)
rel_ab_P <- t(rel_ab)
abundance <- c("abundance")
rel_ab_P <- data.frame(rel_ab_P)
write.csv(rel_ab_P, file = "~/RSTUDIO/Datos_cianobacterias/CAJ5.csv")
CAJ5 <- read.csv("~/RSTUDIO/Datos_cianobacterias/CAJ5.csv")

getPalette = colorRampPalette(c("cyan3","hotpink","gold3","forestgreen","darkred","mediumpurple1","mediumvioletred","lightsalmon3","gray58","coral1","deepskyblue1","springgreen3","orchid1","yellow4"))
palette2 <- getPalette(14)
library(ggplot2)
ggplot() +geom_bar(aes(y = rel_ab_P100, x= "CAJ1", fill = X), data = CAJ1,
stat="identity", width = .5)+ geom_bar(aes(y = rel_ab_P100, x= "CAJ2", fill = X), data = CAJ2,
stat="identity",width=.5)+
geom_bar(aes(y = rel_ab_P100, x= "CAJ3", fill = X), data = CAJ3,
stat="identity", width = .5)+
geom_bar(aes(y = rel_ab_P100, x= "CAJ4", fill = X), data = CAJ4,
stat="identity", width = .5)+
geom_bar(aes(y = rel_ab_P*100, x= "CAJ5", fill = X), data = CAJ5,
stat="identity", width = .5)+
theme_classic()+
theme(legend.title = element_blank())+
ylab("Relative Abundance (%)")+
xlab(" ")+
scale_fill_manual(values = palette2)

[andresrcs]

Could you please turn this into a minimal REPRoducible EXample (reprex)? we don't have access to your local files.

[EconomiCurtis]

Here's one solution, explicitly select the order of levels in the aes arguement,

How to control ordering of stacked bar chart using identity on ggplot2

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As Andres pointed out, it'll be easier to take your example and offer a suggest if you provide a reproducible example. What's wrong with what you provided so far? Be sure to format your code (FAQ: How to format your code), and you refer to example data that no one can access.

I'd cut out all your steps from library(readxl) to CAJ5 and just focus on the ggplot with a fake toy data example (Best Practices: how to prepare your own data for use in a `reprex` if you can’t, or don’t know how to reproduce a problem with a built-in dataset?).