Problem with "names" function

Hello everyone, I hope you are very well.

When I use the "names" function, it helps me classify my samples by name, but at the same time I feel that it fills in the space where they are placed in the graph, there is some way that the "names" function can respect the line of each sample, without filling in the entire space that ranges from the lowest to the highest? ..

library(phyloseq)
library(tidyverse)
library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.5-6
library(readxl)
clase <-  tibble::tribble(
                          ~OTU, ~July, ~July, ~July, ~July, ~July,  ~July, ~August, ~August, ~August, ~August, ~September, ~September, ~September,
               "Acidobacteria",    0L,    0L,    0L,    0L,    0L,     9L,      0L,      4L,      0L,      0L,         0L,         0L,         0L,
              "Actinobacteria",  389L,   33L,   31L,  797L, 3864L,  9570L,    878L,    338L,   4450L,   1819L,      2093L,      1163L,      1988L,
             "Armatimonadetes",    0L,    0L,    0L,    0L,    0L,     0L,      0L,      0L,      0L,      0L,         0L,         0L,         0L,
                        "BRC1",    0L,    0L,    0L,    0L,    0L,     0L,      0L,      0L,      0L,      0L,         0L,         0L,         0L,
               "Bacteroidetes", 5936L, 4927L, 1035L, 1690L, 5390L, 10966L,   3329L,  12756L,  15453L,  10255L,     15262L,      3485L,      6123L,
            "Saccharibacteria",    0L,    0L,    0L,    0L,    4L,     0L,      0L,      3L,      0L,      5L,         0L,         0L,         0L,
                  "Chlamydiae",    0L,    0L,    0L,    3L,   67L,   148L,      0L,      5L,     61L,    208L,        67L,        60L,         0L,
                 "Chloroflexi",    0L,    0L,    0L,    0L,   22L,   129L,      0L,     76L,      0L,    243L,        37L,       119L,        25L,
               "Cloacimonetes",    0L,    0L,    0L,    0L,    0L,     0L,      0L,      0L,      0L,      0L,         0L,         0L,         0L,
               "Cyanobacteria",    0L,    0L,    0L,    0L,    0L,    37L,      2L,    761L,     22L,    102L,         0L,        12L,       163L
            )



data <- clase
data # data is the name of your matrix
#> # A tibble: 10 x 14
#>    OTU    July  July  July  July  July  July August August August August
#>    <chr> <int> <int> <int> <int> <int> <int>  <int>  <int>  <int>  <int>
#>  1 Acid~     0     0     0     0     0     9      0      4      0      0
#>  2 Acti~   389    33    31   797  3864  9570    878    338   4450   1819
#>  3 Arma~     0     0     0     0     0     0      0      0      0      0
#>  4 BRC1      0     0     0     0     0     0      0      0      0      0
#>  5 Bact~  5936  4927  1035  1690  5390 10966   3329  12756  15453  10255
#>  6 Sacc~     0     0     0     0     4     0      0      3      0      5
#>  7 Chla~     0     0     0     3    67   148      0      5     61    208
#>  8 Chlo~     0     0     0     0    22   129      0     76      0    243
#>  9 Cloa~     0     0     0     0     0     0      0      0      0      0
#> 10 Cyan~     0     0     0     0     0    37      2    761     22    102
#> # ... with 3 more variables: September <int>, September <int>, September <int>

attach(clase)
rwnames <- OTU
data <- data.matrix(data[,-1])
rownames(data) <- rwnames
data <- t(data)
S <- specnumber(data)

raremax <- min(rowSums(data))
Srare <- rarefy(data, raremax)

#Plot rarefaction results
par(mfrow = c(1,2))
plot(S, Srare, xlab = "Observed No. of Species", 
     ylab = "Rarefied No. of Species",
     main = " plot(rarefy(data, raremax))")
abline(0, 1)
rarecurve(data, step = 20, 
          sample = raremax, 
          col = "blue", 
          cex = 0.6,
          main = "rarecurve()")


rarecurve(data[1:13,], step = 20, sample = raremax, col = "blue", cex =      0.6,
          main = "rarecurve() on subset of data")


out <- rarecurve(data, step = 20, sample = raremax, label = T)


rare <- lapply(out, function(x){
  b <- as.data.frame(x)
  b <- data.frame(OTU = b[,1], raw.read = rownames(b))
  b$raw.read <- as.numeric(gsub("N", "",  b$raw.read))
  return(b)
})

names(rare) <- rownames(data)


rare <- map_dfr(rare, function(x){
  z <- data.frame(x)
  return(z)
}, .id = "sample")

head(rare)
#>   sample      OTU raw.read
#> 1   July 1.000000        1
#> 2   July 1.736880       21
#> 3   July 1.926538       41
#> 4   July 1.979576       61
#> 5   July 1.994345       81
#> 6   July 1.998441      101

library(ggplot2)
write.csv(rare, file = "~/RSTUDIO/Bacteria-total/bacteria-clase-total-otu-rarefaction.csv")
#agregar manual el tratamiento
rarefaction <- read.csv("~/RSTUDIO/Bacteria-total/bacteria-clase-total-otu-rarefaction.csv", row.names=1)
#plot
#generar una escala de colores 
colourCount = length(table(rarefaction$sample))
getPalette = colorRampPalette(c("yellowgreen","darkturquoise", "coral"))

rarcu <- ggplot(rarefaction, aes(x=raw.read, y=OTU, colour = factor (Sample)))+
  geom_line(size=0.5)+
  geom_point(size=0.5)

ggplot(data = rarefaction)+
  theme_classic()+
  theme(legend.title = element_blank())+
  geom_line(aes(x = raw.read, y = OTU, color = sample))+
  scale_x_continuous(labels =  scales::scientific_format())+
  ylab("Observed Phylum Number")+
  xlab("Sequences Number")+
  scale_x_continuous(limits = c(0, 20000), breaks = c(0, 5000, 10000, 15000, 20000))
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
#> Warning: Removed 44 row(s) containing missing values (geom_path).

Created on 2020-05-15 by the reprex package (v0.3.0)

Since you did not specified a grouping variable, geom_line inherits the default grouping by the colour, it means that for ggplot there are only three groups instead of 13. You need to create a variable with a identity for each different set of values within each month. A possible solution:

This code adds a new variable called group, then increase a unit every time variable raw.read == 1

rarefaction$group = rep(1, nrow(rarefaction) )
n = 0
for (i in 1:nrow(rarefaction) ) {
if (rarefaction$raw.read[i] == 1) {
n = n + 1
}
rarefaction$group[i] <- n
}

Finally, you have to specify the parameter 'group' in the aes()

ggplot(rarefaction, aes(x = raw.read, y = OTU, colour = sample,
group = group ) ) +
geom_point() +
geom_line()

Thank you so much as always Andrés

This topic was automatically closed 7 days after the last reply. New replies are no longer allowed.

Thank you very much, using that function it worked, and now the graph looks better, although I am still not very convinced by the lines, I feel that they still look like weaves between them.

One last question JossChavez. , it is possible to establish the number of the axis of the Y, cause in my whole data (here its just a pice of it), I have approximately 29 OTU (the number of species that appear on the axis of the y) and when I obtain the graph shows up to 20 OTU, I would like the actual number of OTU to appear on the Y axis. Is it possible to set the axis number?. Sorry I'm very new in R and programming.

Thanks in advance

library(phyloseq)
library(tidyverse)
library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.5-6
library(readxl)
clase <- tibble::tribble(
  ~OTU, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`,  ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`,
  "Alveolata",     0L,     0L,     0L,    36L,     0L,  6455L,    78L,    16L,     0L,     0L,     0L,     0L,    75L,     0L,     0L,    36L,   118L,  3799L,     0L,     0L,   297L,   230L,   198L,   761L,     0L,   227L,     0L,   510L,    40L,     0L,     0L,     0L,     0L,    20L,     0L,    30L,   363L,    45L,  2382L,     0L,     0L,     0L,     0L,  2069L,     0L,     0L,      0L,     0L,     0L,     0L,    74L,   140L,   186L,     0L,   183L,   277L,     0L,    99L,   648L,  2744L,   747L,    67L,     0L,     0L,    31L,     0L,     0L,     0L,  1099L,     0L,    41L,     0L,     0L,   113L,   469L,    37L,     0L,     0L,     0L,     0L,   111L,   816L,     0L,     0L,   342L,     0L,     0L,     0L,   874L,     0L,   107L,   531L,     0L,
  "Amoebozoa",     0L,     0L,    44L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    52L,     0L,     3L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    16L,     0L,     0L,     0L,     0L,      0L,     0L,     0L,    13L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,   673L,     0L,   130L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    38L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,  3953L,
  "Rhizaria",     0L,     0L,     0L,    44L,     0L,     0L,     0L,    66L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,    31L,     0L,   119L,     0L,     0L,    12L,     0L,     0L,    77L,    26L,   105L,     0L,     0L,     0L,    53L,     0L,     0L,     0L,     0L,    12L,     7L,   150L,    12L,     0L,     0L,     0L,    10L,     0L,     0L,    67L,    68L,      0L,    73L,     0L,     0L,    56L,     0L,    30L,     7L,    34L,     6L,     0L,     0L,     0L,     0L,   654L,    39L,     0L,     0L,     0L,     0L,    96L,    24L,   677L,     0L,     0L,     0L,     0L,     0L,     0L,     5L,     0L,    12L,     0L,     0L,     0L,     0L,     0L,    20L,     0L,     8L,     0L,    19L,   312L,     0L,     0L,   389L,     0L,
  "Stramenopiles",    35L,  1414L,  5272L,  2379L,   106L,  2066L,   393L,  7429L,   208L,  4266L, 25094L,   560L,   655L,   874L,  3673L,   267L,   699L,   482L,  1935L,  2221L,    84L,   297L,   401L,  1264L,   654L,  1370L,   439L,   305L,  1502L,   727L, 49170L,   223L,  2674L,  1436L,   449L,   684L,   867L,   511L,  5752L,   953L,  1053L,    83L,   710L,   385L,  1370L,  5062L, 122478L,   647L,    49L,   577L,  1176L,  1833L,   127L,   247L, 18175L,  7793L,  1270L,  9834L,   425L,  2067L,  4058L,   855L, 20948L,  1980L,  1991L,  1023L,  1342L,  1757L,  4962L,  1306L,   192L,    96L,   432L,   420L,  1914L,   316L,   312L,  9520L,   462L, 22327L,  1038L,   298L,  1702L,   157L,   844L,   319L,   106L,  7008L,  1002L, 13285L,   390L,  1724L,   705L,
  "Un Eukaryota",  2150L,   862L,   395L,   201L,   617L,  7707L,   631L,  1955L,  2196L,  2616L,  4347L,   830L,   257L,  4696L,  2135L,  6700L,  5863L,  1686L,  2718L,    36L,   361L,   656L,   591L,  3106L,   553L,  2973L,   146L,   787L, 10534L,  2460L,    86L,   166L,     7L,  9024L,  3332L,  7256L,  6716L,   800L,  2268L,   744L,   723L,   247L,  3242L,  1103L,  7087L, 26475L,     68L,    82L,   117L,   605L, 11458L,  1959L,  1265L,   760L,  1256L, 10297L,  1552L,  5664L,  1050L,  2236L, 13820L, 10785L,  3914L,  3169L,   810L,   677L, 10142L, 12313L,  6801L,   205L,   898L,    95L,   313L,   362L,  4736L,  2484L,  3719L,   327L,   394L,    91L,  1078L,  1073L,   883L,  1943L,   631L,   187L,     3L,  2244L, 12976L,  3626L,  1062L, 30639L,  1086L
)

data <- clase
data # data is the name of your matrix
#> # A tibble: 5 x 94
#>   OTU    `18S` `18S` `18S` `18S` `18S` `18S` `18S` `18S` `18S` `18S` `18S` `18S`
#>   <chr>  <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 Alveo~     0     0     0    36     0  6455    78    16     0     0     0     0
#> 2 Amoeb~     0     0    44     0     0     0     0     0     0     0    52     0
#> 3 Rhiza~     0     0     0    44     0     0     0    66     0     0     0     0
#> 4 Stram~    35  1414  5272  2379   106  2066   393  7429   208  4266 25094   560
#> 5 Un Eu~  2150   862   395   201   617  7707   631  1955  2196  2616  4347   830
#> # ... with 81 more variables: `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>, `18S` <int>,
#> #   `18S` <int>, `18S` <int>, `18S` <int>

attach(clase)
rwnames <- OTU
data <- data.matrix(data[,-1])
rownames(data) <- rwnames
data <- t(data)
S <- specnumber(data)

raremax <- min(rowSums(data))
Srare <- rarefy(data, raremax)

#Plot rarefaction results
par(mfrow = c(1,2))
plot(S, Srare, xlab = "Observed No. of Species", 
     ylab = "Rarefied No. of Species",
     main = " plot(rarefy(data, raremax))")
abline(0, 1)
rarecurve(data, step = 20, 
          sample = raremax, 
          col = "blue", 
          cex = 0.6,
          main = "rarecurve()")


rarecurve(data[1:93,], step = 20, sample = raremax, col = "blue", cex =      0.6,
          main = "rarecurve() on subset of data")


out <- rarecurve(data, step = 20, sample = raremax, label = T)


#Clean the list up a bit:

rare <- lapply(out, function(x){b <- as.data.frame(x) 
b <- data.frame(clase = b[,1], raw.read = rownames(b)) 
b$raw.read <- as.numeric(gsub("N", "",  b$raw.read)) 
return(b)})
#label list

names(rare) <- rownames(data)


#convert to data frame:

rare <- map_dfr(rare, function(x){
  z <- data.frame(x) 
  return(z)
}, .id = "Sample")

write.csv(rare, file = "~/RSTUDIO/Bacteria-total/bacteria-clase-total-otu-rarefaction.csv")
#agregar manual el tratamiento
rarefaction <- read.csv("~/RSTUDIO/Bacteria-total/bacteria-clase-total-otu-rarefaction.csv", row.names=1)
df1 <- rarefaction


#ALTO

library(phyloseq)
library(tidyverse)
library(vegan)
library(readxl)
clase <- tibble::tribble(
  ~OTU, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`,
  "Acidobacteria",     0L,     0L,     0L,     0L,     0L,     9L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,    19L,     0L,    50L,     0L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,     0L,     0L,     0L,     0L,     0L,    36L,     0L,     0L,     0L,    10L,     0L,    11L,    27L,     0L,     9L,     0L,     0L,     0L,     0L,     0L,     0L,    55L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,    50L,     0L,     0L,     0L,     0L,     5L,     2L,    34L,    19L,   164L,     3L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     6L,     0L,     0L,     0L,     0L,    32L,     0L,     0L,    82L,    63L,
  "Actinobacteria",   389L,    33L,    31L,   797L,  3864L,  9570L,   878L,   338L,  4450L,  1819L,  2093L,  1163L,  1988L,   719L,  2646L,  4894L,   771L,    90L,    42L,   533L,  2440L,  1357L,  3194L,    12L, 10548L,  2370L,   669L,  3716L,  4786L,    82L,  1482L,  5804L,  4329L,    34L,     0L,  2174L,    93L,  2141L,  3592L,  1346L,  7088L,  4521L,   808L,   338L,  4087L,  6805L,  1020L,  3670L,  2199L,    95L,    54L,   427L,  2870L,   331L,  3372L,   636L,  3906L,  8550L,   277L,  9361L,  4837L,  6891L,  8241L,  3089L,    90L,    62L,   659L, 13399L,   669L,  2628L,  1421L,  2630L,  2003L,  1218L,   668L,   107L,  5767L,  2243L,   615L,   239L,    17L,   157L,   216L,  6913L,  3364L,   336L,   437L,   109L,  1321L,  1131L,   980L,  7694L,   179L,  7413L,  9547L,  4713L,
  "Armatimonadetes",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,
  "BRC1",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    20L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    21L,     0L,     0L,     0L,     0L,     0L,     0L,     7L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     3L,    15L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,
  "Bacteroidetes",  5936L,  4927L,  1035L,  1690L,  5390L, 10966L,  3329L, 12756L, 15453L, 10255L, 15262L,  3485L,  6123L,  9240L, 11010L, 14675L,  6400L,   298L,  3043L,  2099L,  3060L,  4345L,  4240L,   525L, 15187L, 13848L,  5335L,  6041L,  5490L,   508L,  9433L, 28803L,  6488L,  3842L,   302L,  8097L,  9782L, 13500L,  1058L,  8161L, 16490L, 19264L,  4630L,  7699L,  3957L, 10869L,  6708L, 40145L,  5246L,  1954L,  3696L, 23605L, 15779L, 14385L,   142L,  1906L, 15517L, 22258L, 10028L,  7378L,  4426L, 17895L,  1204L, 14137L,  3145L,  8091L,  1632L,  9577L, 20608L, 45241L,  2551L,  3660L, 11260L, 14090L,  2150L, 10691L,  6029L, 11502L,  2311L,  2757L,    86L,   781L,  3277L,  5658L,  8732L,  4174L,  1650L, 13487L, 28515L, 11051L, 17648L,  1852L,  6891L, 11030L, 19868L, 29711L,
  "Saccharibacteria",     0L,     0L,     0L,     0L,     4L,     0L,     0L,     3L,     0L,     5L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     6L,     0L,     0L,     0L,     0L,     0L,    11L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     6L,     0L,     2L,     0L,     0L,     3L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     5L,     0L,     0L,     0L,     0L,     0L,     7L,    10L,
  "Chlamydiae",     0L,     0L,     0L,     3L,    67L,   148L,     0L,     5L,    61L,   208L,    67L,    60L,     0L,     3L,    36L,   108L,     0L,     6L,     0L,     0L,    24L,    14L,    22L,     0L,   298L,   254L,    26L,    84L,    30L,     0L,    15L,   126L,     0L,     0L,     0L,     0L,     0L,    29L,    20L,     0L,   168L,   122L,    76L,     0L,    22L,   156L,    15L,    40L,    29L,     0L,     0L,     0L,    22L,     4L,     4L,     0L,    54L,   283L,     3L,   122L,    52L,   252L,    93L,     4L,     0L,     2L,     0L,    56L,     5L,   112L,    31L,     9L,   210L,   104L,    39L,     0L,    64L,    10L,     4L,     0L,     0L,     3L,     9L,    17L,   101L,     0L,     7L,     0L,    75L,    38L,    59L,    94L,     0L,    32L,   146L,    69L,
  "Chloroflexi",     0L,     0L,     0L,     0L,    22L,   129L,     0L,    76L,     0L,   243L,    37L,   119L,    25L,    35L,   124L,   118L,   281L,   359L,     0L,     0L,     0L,    35L,     0L,     0L,   440L,    87L,     0L,     0L,     0L,     0L,    18L,   223L,    56L,     0L,     0L,     2L,     0L,    44L,    74L,     0L,    93L,    90L,    44L,     0L,    18L,    52L,    32L,   250L,     0L,     6L,     0L,   314L,   108L,   160L,     0L,     6L,    81L,   440L,    24L,    96L,   253L,   338L,   310L,    67L,     0L,    56L,     2L,   241L,   125L,   393L,    47L,  1222L,   202L,    68L,     0L,    65L,    45L,     2L,    28L,     0L,     0L,     0L,     0L,    74L,     0L,    10L,     7L,     4L,   135L,   111L,     0L,   228L,     0L,    35L,   633L,   289L,
  "Cloacimonetes",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     3L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    12L,    17L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,
  "Cyanobacteria",     0L,     0L,     0L,     0L,     0L,    37L,     2L,   761L,    22L,   102L,     0L,    12L,   163L,     3L,    81L,   111L,   131L,   645L,     0L,     0L,     0L,     0L,     0L,     0L,    52L,    63L,     0L,     0L,     8L,     0L,    69L,   153L,    25L,     0L,     0L,    27L,    17L,    55L,    92L,    26L,    66L,     0L,     0L,     0L,     0L,     4L,    67L,   388L,   349L,     0L,     0L,   128L,    63L,   285L,    34L,     8L,    16L,     0L,    31L,    12L,   145L,    97L,   798L,   137L,     0L,     2L,     0L,    24L,   266L,   150L,    77L,  5931L,    66L,    14L,     0L,    47L,    62L,     4L,    51L,     0L,     0L,     0L,   177L,     3L,    20L,     0L,     2L,     0L,    28L,     0L,     0L,   360L,     0L,    18L,   509L,   645L,
  "Elusimicrobia",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,
  "Firmicutes",    39L,   371L,   467L,   918L,   550L,    79L,   182L,  1582L,  3115L,  3272L,  1058L,  2009L,   551L,   357L,   402L,   656L,   792L,   121L,     0L,   805L,     3L,     2L,     0L,     0L,  4229L,  2930L,   403L,   998L,    63L,   131L,  1780L,  4215L,  2657L,   207L,   222L,    23L,    21L,   265L,   628L,  5899L,  3364L,  1670L,   710L,   720L,    34L,    53L,  5536L,  3207L,  2263L,   287L,   465L,  2224L,  1899L,   241L,  1198L,  2157L,  1789L,   736L,   315L,  3956L,   689L,    74L,  3726L,  1788L,   451L,   308L,  1401L,  3413L,   528L,  2398L,  2548L,    73L,  1430L,   539L,  1118L,  2410L,    25L,   449L,   101L,   557L,  2796L,   106L,  2018L,  1568L,     2L,   124L,   163L,   616L,  1295L,   862L,  1550L,  1752L,    63L,    46L,  3455L,  4248L,
  "Fusobacteria",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,
  "Gemmatimonadetes",     0L,     0L,     0L,     0L,     0L,    80L,     0L,     0L,    12L,     4L,     0L,    10L,     0L,     0L,     3L,    19L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    15L,     0L,     0L,    13L,     0L,     0L,     0L,    36L,     0L,     0L,     0L,     0L,     0L,     9L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    48L,     3L,     8L,     0L,     0L,     0L,     0L,    13L,     0L,     0L,     0L,     6L,    11L,     0L,     0L,     4L,    33L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    23L,    20L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     9L,     0L,     0L,     0L,     0L,     0L,     0L,    19L,     0L,    20L,    36L,    14L,
  "Hydrogenedentes",     0L,     0L,     0L,     0L,     7L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,    10L,    19L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,   250L,     0L,     0L,     0L,     0L,     0L,     9L,    24L,     0L,     0L,     0L,     0L,     0L,    11L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    10L,     9L,     6L,     0L,     0L,     0L,     0L,    19L,     0L,     0L,     0L,     0L,     0L,     0L,    32L,     0L,     0L,    12L,     9L,     0L,     0L,     0L,     5L,    68L,    23L,    18L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     5L,     0L,     0L,     0L,     0L,    16L,     0L,     0L,    29L,     3L,
  "Verrucomicrobia",   108L,     0L,    50L,     0L,   949L,  2781L,   318L,   397L,   953L,   651L,   811L,   566L,   117L,   304L,   523L,  1272L,   469L,    42L,     0L,     0L,   349L,   456L,   791L,     0L,  3072L,   833L,   222L,  1019L,   743L,     0L,   350L,  1170L,     5L,    28L,    25L,   471L,    48L,   796L,   562L,    95L,  1027L,  1162L,   391L,    58L,   517L,  2086L,   266L,  1583L,    85L,     0L,     0L,   216L,   757L,   222L,   295L,     0L,   974L,  2708L,   111L,  1706L,  1167L,  2037L,  1319L,   686L,     0L,   111L,     0L,   714L,   654L,  1105L,   549L,   111L,   566L,   543L,   311L,   112L,  1421L,   576L,   105L,   100L,     4L,    29L,    52L,   658L,  1017L,     2L,   171L,    34L,   474L,   551L,   541L,  1398L,    51L,  1307L,  2395L,   885L,
  "Un Bacteria",    24L,    54L,    11L,     0L,   334L,  1223L,    60L,   127L,   718L,   496L,   130L,   193L,    76L,    72L,   110L,   793L,   121L,   174L,     0L,     0L,   175L,   200L,   181L,     0L,  2145L,   410L,    42L,   139L,   478L,     0L,   114L,   751L,   239L,     4L,     0L,   197L,    66L,   480L,   222L,   172L,   685L,   188L,   106L,     9L,   180L,   962L,   180L,   261L,   158L,     0L,    65L,   196L,   327L,   123L,    55L,    23L,   690L,  1022L,    62L,   685L,   749L,  1024L,   715L,   254L,    10L,    21L,     0L,   805L,   384L,   431L,   216L,   632L,   355L,   146L,    56L,    68L,   295L,   283L,   128L,    18L,     0L,     4L,    18L,   455L,   432L,     7L,    27L,    16L,   410L,   343L,   193L,   774L,    36L,   309L,  1392L,   958L
)



data <- clase
data # data is the name of your matrix
#> # A tibble: 17 x 97
#>    OTU   `16S` `16S` `16S` `16S` `16S` `16S` `16S` `16S` `16S` `16S` `16S` `16S`
#>    <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#>  1 Acid~     0     0     0     0     0     9     0     4     0     0     0     0
#>  2 Acti~   389    33    31   797  3864  9570   878   338  4450  1819  2093  1163
#>  3 Arma~     0     0     0     0     0     0     0     0     0     0     0     0
#>  4 BRC1      0     0     0     0     0     0     0     0     0     0     0     0
#>  5 Bact~  5936  4927  1035  1690  5390 10966  3329 12756 15453 10255 15262  3485
#>  6 Sacc~     0     0     0     0     4     0     0     3     0     5     0     0
#>  7 Chla~     0     0     0     3    67   148     0     5    61   208    67    60
#>  8 Chlo~     0     0     0     0    22   129     0    76     0   243    37   119
#>  9 Cloa~     0     0     0     0     0     0     0     0     0     0     0     0
#> 10 Cyan~     0     0     0     0     0    37     2   761    22   102     0    12
#> 11 Elus~     0     0     0     0     0     0     0     0     0     0     0     0
#> 12 Firm~    39   371   467   918   550    79   182  1582  3115  3272  1058  2009
#> 13 Fuso~     0     0     0     0     0     0     0     0     0     0     0     0
#> 14 Gemm~     0     0     0     0     0    80     0     0    12     4     0    10
#> 15 Hydr~     0     0     0     0     7     0     0     0     0     0     0     0
#> 16 Verr~   108     0    50     0   949  2781   318   397   953   651   811   566
#> 17 Un B~    24    54    11     0   334  1223    60   127   718   496   130   193
#> # ... with 84 more variables: `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>, `16S` <int>,
#> #   `16S` <int>

attach(clase)
#> The following object is masked from clase (pos = 3):
#> 
#>     OTU
rwnames <- OTU
data <- data.matrix(data[,-1])
rownames(data) <- rwnames
data <- t(data)
S <- specnumber(data)

raremax <- min(rowSums(data))
Srare <- rarefy(data, raremax)

#Plot rarefaction results
par(mfrow = c(1,2))
plot(S, Srare, xlab = "Observed No. of Species", 
     ylab = "Rarefied No. of Species",
     main = " plot(rarefy(data, raremax))")
abline(0, 1)
rarecurve(data, step = 20, 
          sample = raremax, 
          col = "blue", 
          cex = 0.6,
          main = "rarecurve()")


rarecurve(data[1:96,], step = 20, sample = raremax, col = "blue", cex =      0.6,
          main = "rarecurve() on subset of data")


out <- rarecurve(data, step = 20, sample = raremax, label = T)


#Clean the list up a bit:

rare <- lapply(out, function(x){b <- as.data.frame(x) 
b <- data.frame(clase = b[,1], raw.read = rownames(b)) 
b$raw.read <- as.numeric(gsub("N", "",  b$raw.read)) 
return(b)})
#label list

names(rare) <- rownames(data)




#convert to data frame:

rare <- map_dfr(rare, function(x){
  z <- data.frame(x) 
  return(z)
}, .id = "Sample")

write.csv(rare, file = "~/RSTUDIO/Bacteria-total/bacteria-clase-total-otu-rarefactioNn.csv")
#agregar manual el tratamiento
rarefactioNn <- read.csv("~/RSTUDIO/Bacteria-total/bacteria-clase-total-otu-rarefactioNn.csv", row.names=1)
df2 <- rarefactioNn

df=rbind(df1,df2)

df$group = rep(1, nrow(df) )
n = 0
for (i in 1:nrow(df) ) {
  if (df$raw.read[i] == 1) {
    n = n + 1
  }
  df$group[i] <- n
}



library(ggplot2)


#plot
#generar una escala de colores 

rarcu <- ggplot(data=df, aes(x=raw.read, y=clase, color = factor (Sample), group = group ))+
  geom_line(size=0.1)+
  geom_point(size=0.1)

rarcu + theme_classic()+
  theme(legend.title = element_blank())+
  scale_x_continuous(labels =  scales::scientific_format())+
  ylab("Number of phyla observed")+
  xlab("Sequencing depth")+
  scale_x_continuous(limits = c(0, 100000), breaks = c(0, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000))
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
#> Warning: Removed 1129 row(s) containing missing values (geom_path).
#> Warning: Removed 1129 rows containing missing values (geom_point).

Created on 2020-05-16 by the reprex package (v0.3.0)

If is it ok for you :smiley: Do you think you could tell me if the combination analysis of both data.frames (16S, 18S) is correct? I would appreciate it infinitely.

Which one of the plots do you want to modify? I get loss among all the plots showed. Regardless the 18S and 16S, it seems ok to me but what is your goal for the analysis?

Is this what you mean about the y-axis? (I have simplified and cleaned your code a bit)

library(phyloseq)
library(tidyverse)
library(vegan)

clase_18s <- tibble::tribble(
    ~OTU, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`,  ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`, ~`18S`,
    "Alveolata",     0L,     0L,     0L,    36L,     0L,  6455L,    78L,    16L,     0L,     0L,     0L,     0L,    75L,     0L,     0L,    36L,   118L,  3799L,     0L,     0L,   297L,   230L,   198L,   761L,     0L,   227L,     0L,   510L,    40L,     0L,     0L,     0L,     0L,    20L,     0L,    30L,   363L,    45L,  2382L,     0L,     0L,     0L,     0L,  2069L,     0L,     0L,      0L,     0L,     0L,     0L,    74L,   140L,   186L,     0L,   183L,   277L,     0L,    99L,   648L,  2744L,   747L,    67L,     0L,     0L,    31L,     0L,     0L,     0L,  1099L,     0L,    41L,     0L,     0L,   113L,   469L,    37L,     0L,     0L,     0L,     0L,   111L,   816L,     0L,     0L,   342L,     0L,     0L,     0L,   874L,     0L,   107L,   531L,     0L,
    "Amoebozoa",     0L,     0L,    44L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    52L,     0L,     3L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    16L,     0L,     0L,     0L,     0L,      0L,     0L,     0L,    13L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,   673L,     0L,   130L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    38L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,  3953L,
    "Rhizaria",     0L,     0L,     0L,    44L,     0L,     0L,     0L,    66L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,    31L,     0L,   119L,     0L,     0L,    12L,     0L,     0L,    77L,    26L,   105L,     0L,     0L,     0L,    53L,     0L,     0L,     0L,     0L,    12L,     7L,   150L,    12L,     0L,     0L,     0L,    10L,     0L,     0L,    67L,    68L,      0L,    73L,     0L,     0L,    56L,     0L,    30L,     7L,    34L,     6L,     0L,     0L,     0L,     0L,   654L,    39L,     0L,     0L,     0L,     0L,    96L,    24L,   677L,     0L,     0L,     0L,     0L,     0L,     0L,     5L,     0L,    12L,     0L,     0L,     0L,     0L,     0L,    20L,     0L,     8L,     0L,    19L,   312L,     0L,     0L,   389L,     0L,
    "Stramenopiles",    35L,  1414L,  5272L,  2379L,   106L,  2066L,   393L,  7429L,   208L,  4266L, 25094L,   560L,   655L,   874L,  3673L,   267L,   699L,   482L,  1935L,  2221L,    84L,   297L,   401L,  1264L,   654L,  1370L,   439L,   305L,  1502L,   727L, 49170L,   223L,  2674L,  1436L,   449L,   684L,   867L,   511L,  5752L,   953L,  1053L,    83L,   710L,   385L,  1370L,  5062L, 122478L,   647L,    49L,   577L,  1176L,  1833L,   127L,   247L, 18175L,  7793L,  1270L,  9834L,   425L,  2067L,  4058L,   855L, 20948L,  1980L,  1991L,  1023L,  1342L,  1757L,  4962L,  1306L,   192L,    96L,   432L,   420L,  1914L,   316L,   312L,  9520L,   462L, 22327L,  1038L,   298L,  1702L,   157L,   844L,   319L,   106L,  7008L,  1002L, 13285L,   390L,  1724L,   705L,
    "Un Eukaryota",  2150L,   862L,   395L,   201L,   617L,  7707L,   631L,  1955L,  2196L,  2616L,  4347L,   830L,   257L,  4696L,  2135L,  6700L,  5863L,  1686L,  2718L,    36L,   361L,   656L,   591L,  3106L,   553L,  2973L,   146L,   787L, 10534L,  2460L,    86L,   166L,     7L,  9024L,  3332L,  7256L,  6716L,   800L,  2268L,   744L,   723L,   247L,  3242L,  1103L,  7087L, 26475L,     68L,    82L,   117L,   605L, 11458L,  1959L,  1265L,   760L,  1256L, 10297L,  1552L,  5664L,  1050L,  2236L, 13820L, 10785L,  3914L,  3169L,   810L,   677L, 10142L, 12313L,  6801L,   205L,   898L,    95L,   313L,   362L,  4736L,  2484L,  3719L,   327L,   394L,    91L,  1078L,  1073L,   883L,  1943L,   631L,   187L,     3L,  2244L, 12976L,  3626L,  1062L, 30639L,  1086L
)

clase_16s <- tibble::tribble(
    ~OTU, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`, ~`16S`,
    "Acidobacteria",     0L,     0L,     0L,     0L,     0L,     9L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,    19L,     0L,    50L,     0L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,     0L,     0L,     0L,     0L,     0L,    36L,     0L,     0L,     0L,    10L,     0L,    11L,    27L,     0L,     9L,     0L,     0L,     0L,     0L,     0L,     0L,    55L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,    50L,     0L,     0L,     0L,     0L,     5L,     2L,    34L,    19L,   164L,     3L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     6L,     0L,     0L,     0L,     0L,    32L,     0L,     0L,    82L,    63L,
    "Actinobacteria",   389L,    33L,    31L,   797L,  3864L,  9570L,   878L,   338L,  4450L,  1819L,  2093L,  1163L,  1988L,   719L,  2646L,  4894L,   771L,    90L,    42L,   533L,  2440L,  1357L,  3194L,    12L, 10548L,  2370L,   669L,  3716L,  4786L,    82L,  1482L,  5804L,  4329L,    34L,     0L,  2174L,    93L,  2141L,  3592L,  1346L,  7088L,  4521L,   808L,   338L,  4087L,  6805L,  1020L,  3670L,  2199L,    95L,    54L,   427L,  2870L,   331L,  3372L,   636L,  3906L,  8550L,   277L,  9361L,  4837L,  6891L,  8241L,  3089L,    90L,    62L,   659L, 13399L,   669L,  2628L,  1421L,  2630L,  2003L,  1218L,   668L,   107L,  5767L,  2243L,   615L,   239L,    17L,   157L,   216L,  6913L,  3364L,   336L,   437L,   109L,  1321L,  1131L,   980L,  7694L,   179L,  7413L,  9547L,  4713L,
    "Armatimonadetes",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,
    "BRC1",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    20L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    21L,     0L,     0L,     0L,     0L,     0L,     0L,     7L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     8L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     3L,    15L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,
    "Bacteroidetes",  5936L,  4927L,  1035L,  1690L,  5390L, 10966L,  3329L, 12756L, 15453L, 10255L, 15262L,  3485L,  6123L,  9240L, 11010L, 14675L,  6400L,   298L,  3043L,  2099L,  3060L,  4345L,  4240L,   525L, 15187L, 13848L,  5335L,  6041L,  5490L,   508L,  9433L, 28803L,  6488L,  3842L,   302L,  8097L,  9782L, 13500L,  1058L,  8161L, 16490L, 19264L,  4630L,  7699L,  3957L, 10869L,  6708L, 40145L,  5246L,  1954L,  3696L, 23605L, 15779L, 14385L,   142L,  1906L, 15517L, 22258L, 10028L,  7378L,  4426L, 17895L,  1204L, 14137L,  3145L,  8091L,  1632L,  9577L, 20608L, 45241L,  2551L,  3660L, 11260L, 14090L,  2150L, 10691L,  6029L, 11502L,  2311L,  2757L,    86L,   781L,  3277L,  5658L,  8732L,  4174L,  1650L, 13487L, 28515L, 11051L, 17648L,  1852L,  6891L, 11030L, 19868L, 29711L,
    "Saccharibacteria",     0L,     0L,     0L,     0L,     4L,     0L,     0L,     3L,     0L,     5L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     6L,     0L,     0L,     0L,     0L,     0L,    11L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     6L,     0L,     2L,     0L,     0L,     3L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     5L,     0L,     0L,     0L,     0L,     0L,     7L,    10L,
    "Chlamydiae",     0L,     0L,     0L,     3L,    67L,   148L,     0L,     5L,    61L,   208L,    67L,    60L,     0L,     3L,    36L,   108L,     0L,     6L,     0L,     0L,    24L,    14L,    22L,     0L,   298L,   254L,    26L,    84L,    30L,     0L,    15L,   126L,     0L,     0L,     0L,     0L,     0L,    29L,    20L,     0L,   168L,   122L,    76L,     0L,    22L,   156L,    15L,    40L,    29L,     0L,     0L,     0L,    22L,     4L,     4L,     0L,    54L,   283L,     3L,   122L,    52L,   252L,    93L,     4L,     0L,     2L,     0L,    56L,     5L,   112L,    31L,     9L,   210L,   104L,    39L,     0L,    64L,    10L,     4L,     0L,     0L,     3L,     9L,    17L,   101L,     0L,     7L,     0L,    75L,    38L,    59L,    94L,     0L,    32L,   146L,    69L,
    "Chloroflexi",     0L,     0L,     0L,     0L,    22L,   129L,     0L,    76L,     0L,   243L,    37L,   119L,    25L,    35L,   124L,   118L,   281L,   359L,     0L,     0L,     0L,    35L,     0L,     0L,   440L,    87L,     0L,     0L,     0L,     0L,    18L,   223L,    56L,     0L,     0L,     2L,     0L,    44L,    74L,     0L,    93L,    90L,    44L,     0L,    18L,    52L,    32L,   250L,     0L,     6L,     0L,   314L,   108L,   160L,     0L,     6L,    81L,   440L,    24L,    96L,   253L,   338L,   310L,    67L,     0L,    56L,     2L,   241L,   125L,   393L,    47L,  1222L,   202L,    68L,     0L,    65L,    45L,     2L,    28L,     0L,     0L,     0L,     0L,    74L,     0L,    10L,     7L,     4L,   135L,   111L,     0L,   228L,     0L,    35L,   633L,   289L,
    "Cloacimonetes",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     3L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    12L,    17L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,
    "Cyanobacteria",     0L,     0L,     0L,     0L,     0L,    37L,     2L,   761L,    22L,   102L,     0L,    12L,   163L,     3L,    81L,   111L,   131L,   645L,     0L,     0L,     0L,     0L,     0L,     0L,    52L,    63L,     0L,     0L,     8L,     0L,    69L,   153L,    25L,     0L,     0L,    27L,    17L,    55L,    92L,    26L,    66L,     0L,     0L,     0L,     0L,     4L,    67L,   388L,   349L,     0L,     0L,   128L,    63L,   285L,    34L,     8L,    16L,     0L,    31L,    12L,   145L,    97L,   798L,   137L,     0L,     2L,     0L,    24L,   266L,   150L,    77L,  5931L,    66L,    14L,     0L,    47L,    62L,     4L,    51L,     0L,     0L,     0L,   177L,     3L,    20L,     0L,     2L,     0L,    28L,     0L,     0L,   360L,     0L,    18L,   509L,   645L,
    "Elusimicrobia",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,
    "Firmicutes",    39L,   371L,   467L,   918L,   550L,    79L,   182L,  1582L,  3115L,  3272L,  1058L,  2009L,   551L,   357L,   402L,   656L,   792L,   121L,     0L,   805L,     3L,     2L,     0L,     0L,  4229L,  2930L,   403L,   998L,    63L,   131L,  1780L,  4215L,  2657L,   207L,   222L,    23L,    21L,   265L,   628L,  5899L,  3364L,  1670L,   710L,   720L,    34L,    53L,  5536L,  3207L,  2263L,   287L,   465L,  2224L,  1899L,   241L,  1198L,  2157L,  1789L,   736L,   315L,  3956L,   689L,    74L,  3726L,  1788L,   451L,   308L,  1401L,  3413L,   528L,  2398L,  2548L,    73L,  1430L,   539L,  1118L,  2410L,    25L,   449L,   101L,   557L,  2796L,   106L,  2018L,  1568L,     2L,   124L,   163L,   616L,  1295L,   862L,  1550L,  1752L,    63L,    46L,  3455L,  4248L,
    "Fusobacteria",     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     2L,     0L,
    "Gemmatimonadetes",     0L,     0L,     0L,     0L,     0L,    80L,     0L,     0L,    12L,     4L,     0L,    10L,     0L,     0L,     3L,    19L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    15L,     0L,     0L,    13L,     0L,     0L,     0L,    36L,     0L,     0L,     0L,     0L,     0L,     9L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    48L,     3L,     8L,     0L,     0L,     0L,     0L,    13L,     0L,     0L,     0L,     6L,    11L,     0L,     0L,     4L,    33L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    23L,    20L,     2L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     9L,     0L,     0L,     0L,     0L,     0L,     0L,    19L,     0L,    20L,    36L,    14L,
    "Hydrogenedentes",     0L,     0L,     0L,     0L,     7L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     4L,    10L,    19L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,   250L,     0L,     0L,     0L,     0L,     0L,     9L,    24L,     0L,     0L,     0L,     0L,     0L,    11L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,    10L,     9L,     6L,     0L,     0L,     0L,     0L,    19L,     0L,     0L,     0L,     0L,     0L,     0L,    32L,     0L,     0L,    12L,     9L,     0L,     0L,     0L,     5L,    68L,    23L,    18L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     0L,     5L,     0L,     0L,     0L,     0L,    16L,     0L,     0L,    29L,     3L,
    "Verrucomicrobia",   108L,     0L,    50L,     0L,   949L,  2781L,   318L,   397L,   953L,   651L,   811L,   566L,   117L,   304L,   523L,  1272L,   469L,    42L,     0L,     0L,   349L,   456L,   791L,     0L,  3072L,   833L,   222L,  1019L,   743L,     0L,   350L,  1170L,     5L,    28L,    25L,   471L,    48L,   796L,   562L,    95L,  1027L,  1162L,   391L,    58L,   517L,  2086L,   266L,  1583L,    85L,     0L,     0L,   216L,   757L,   222L,   295L,     0L,   974L,  2708L,   111L,  1706L,  1167L,  2037L,  1319L,   686L,     0L,   111L,     0L,   714L,   654L,  1105L,   549L,   111L,   566L,   543L,   311L,   112L,  1421L,   576L,   105L,   100L,     4L,    29L,    52L,   658L,  1017L,     2L,   171L,    34L,   474L,   551L,   541L,  1398L,    51L,  1307L,  2395L,   885L,
    "Un Bacteria",    24L,    54L,    11L,     0L,   334L,  1223L,    60L,   127L,   718L,   496L,   130L,   193L,    76L,    72L,   110L,   793L,   121L,   174L,     0L,     0L,   175L,   200L,   181L,     0L,  2145L,   410L,    42L,   139L,   478L,     0L,   114L,   751L,   239L,     4L,     0L,   197L,    66L,   480L,   222L,   172L,   685L,   188L,   106L,     9L,   180L,   962L,   180L,   261L,   158L,     0L,    65L,   196L,   327L,   123L,    55L,    23L,   690L,  1022L,    62L,   685L,   749L,  1024L,   715L,   254L,    10L,    21L,     0L,   805L,   384L,   431L,   216L,   632L,   355L,   146L,    56L,    68L,   295L,   283L,   128L,    18L,     0L,     4L,    18L,   455L,   432L,     7L,    27L,    16L,   410L,   343L,   193L,   774L,    36L,   309L,  1392L,   958L
)

rarefy_df <- function(data) {

    rwnames <- data$OTU
    data <- data.matrix(data[,-1])
    rownames(data) <- rwnames
    data <- t(data)
    raremax <- min(rowSums(data))
    out <- rarecurve(data, step = 20, sample = raremax, label = T)
    
    rare <- lapply(out, function(x){
        b <- as.data.frame(x)
        b <- data.frame(clase = b[,1], raw.read = rownames(b))
        b$raw.read <- as.numeric(gsub("N", "",  b$raw.read))
        return(b)
        })
    names(rare) <- rownames(data)
    
    rare <- map_dfr(rare, function(x){
        z <- data.frame(x) 
        return(z)
    }, .id = "Sample")
    return(rare)
}

df1 <- rarefy_df(clase_16s)
df2 <- rarefy_df(clase_18s)

df <- rbind(df1, df2) %>% 
    mutate(group = if_else(raw.read == 1, row_number(), NA_integer_)) %>% 
    fill(group, .direction = "down") %>%
    mutate(group = as.numeric(as.factor(group)))

df %>% 
    ggplot(aes(x = raw.read, y = clase, color = Sample, group = group)) +
    geom_line(size = 0.1) +
    labs(x = "Sequencing depth",
         y = "Number of phyla observed") +
    scale_x_continuous(limits = c(0, 100000),
                       breaks = c(0, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000),
                       labels =  scales::scientific_format()) +
    scale_y_continuous(breaks = 1:14) +
    theme_classic() +
    theme(legend.title = element_blank())

The purpose of this analysis is to compare the read obtained in both genes (16S, AND 18S), it is assumed that the sequencing equipment must have given me at least 100,000 readings, but not all microorganisms (OTU) reached that amount of reads. But this analysis also makes sense with what I did, because in the sequencing I added 70 percent of the 16S gene sample and 30 percent of the 18S gene, therefore if I expected less of the 18S gene, and had to represent it.

Rarefaction allows the calculation of species richness for a given number of individual samples, based on the construction of so-called rarefaction curves. This curve is a plot of the number of species as a function of the number of samples.

Thank you for all your help