Running into error when plotting alpha diversity indexes #365
Description
Here is the error
> ps_obj <- as_phyloseq(obj,
+ otu_table = presence_data,
+ otu_id_col = "#OTU ID",
+ sample_data = sample_data,
+ sample_id_col = "SampleID")
Error in as_phyloseq(obj, otu_table = presence_data, otu_id_col = "#OTU ID", :
OTU table does not have an OTU ID column named "#OTU ID". Use the "otu_id_col" option if it is named something else.
Here is the full console results up to the error
> #Data quality control
> library(metacoder)
> obj$data$otu_counts <- zero_low_counts(obj, "otu_counts", min_count = 10,
+ other_cols = TRUE) # keep OTU_ID column
No `cols` specified, so using all numeric columns:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
No counts found less than 10.
Warning message:
The following columns will be replaced in the output:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
>
> print(obj)
<Taxmap>
1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
3 data sets:
otu_counts:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 22 0 0 0 0 0 0 0 12
2 avu cb5168e06325147… 13 10 10 0 0 0 12 14 13
3 aet 3968cb5b7766a80… 89 13 15 16 22 40 31 14 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_rarefied:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 14 0 0 0 0 0 0 0 7
2 avu cb5168e06325147… 9 10 6 0 0 0 7 3 6
3 aet 3968cb5b7766a80… 49 9 9 12 9 31 18 4 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_props:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 0.000851 0 0 0 0 0 0 0 0.000387
2 avu cb5168e06325147… 0.000503 0.000359 0.000310 0 0 0 0.000474 0.000440 0.000420
3 aet 3968cb5b7766a80… 0.00344 0.000466 0.000465 0.000544 0.000741 0.00161 0.00122 0.000440 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
0 functions:
>
> no_reads <- rowSums(obj$data$otu_counts[, sample_data$SampleID]) == 0
> sum(no_reads) # when `sum` is used on a TRUE/FALSE vector it counts TRUEs
[1] 0
>
> obj <- filter_obs(obj, "otu_counts", ! no_reads, drop_taxa = TRUE)
> print(obj)
<Taxmap>
1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
3 data sets:
otu_counts:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 22 0 0 0 0 0 0 0 12
2 avu cb5168e06325147… 13 10 10 0 0 0 12 14 13
3 aet 3968cb5b7766a80… 89 13 15 16 22 40 31 14 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_rarefied:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 14 0 0 0 0 0 0 0 7
2 avu cb5168e06325147… 9 10 6 0 0 0 7 3 6
3 aet 3968cb5b7766a80… 49 9 9 12 9 31 18 4 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_props:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 0.000851 0 0 0 0 0 0 0 0.000387
2 avu cb5168e06325147… 0.000503 0.000359 0.000310 0 0 0 0.000474 0.000440 0.000420
3 aet 3968cb5b7766a80… 0.00344 0.000466 0.000465 0.000544 0.000741 0.00161 0.00122 0.000440 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
0 functions:
>
>
> hist(colSums(obj$data$otu_counts[, sample_data$SampleID]))
>
> obj$data$otu_rarefied <- rarefy_obs(obj, "otu_counts", other_cols = TRUE)
No `cols` specified, so using all numeric columns:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
Rarefying to 16141 since that is the lowest sample total.
Warning messages:
1: In vegan::rrarefy(t(count_table), sample = sample_size) :
function should be used for observed counts, but smallest count is 10
2: The following columns will be replaced in the output:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
> print(obj)
<Taxmap>
1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
3 data sets:
otu_counts:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 22 0 0 0 0 0 0 0 12
2 avu cb5168e06325147… 13 10 10 0 0 0 12 14 13
3 aet 3968cb5b7766a80… 89 13 15 16 22 40 31 14 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_rarefied:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 14 0 0 0 0 0 0 0 4
2 avu cb5168e06325147… 7 5 5 0 0 0 8 7 6
3 aet 3968cb5b7766a80… 60 7 6 11 15 23 18 11 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_props:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 0.000851 0 0 0 0 0 0 0 0.000387
2 avu cb5168e06325147… 0.000503 0.000359 0.000310 0 0 0 0.000474 0.000440 0.000420
3 aet 3968cb5b7766a80… 0.00344 0.000466 0.000465 0.000544 0.000741 0.00161 0.00122 0.000440 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
0 functions:
>
> no_reads <- rowSums(obj$data$otu_rarefied[, sample_data$SampleID]) == 0
> obj <- filter_obs(obj, "otu_rarefied", ! no_reads)
> print(obj)
<Taxmap>
1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
3 data sets:
otu_counts:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 22 0 0 0 0 0 0 0 12
2 avu cb5168e06325147… 13 10 10 0 0 0 12 14 13
3 aet 3968cb5b7766a80… 89 13 15 16 22 40 31 14 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_rarefied:
# A tibble: 2,619 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 14 0 0 0 0 0 0 0 4
2 avu cb5168e06325147… 7 5 5 0 0 0 8 7 6
3 aet 3968cb5b7766a80… 60 7 6 11 15 23 18 11 0
# ℹ 2,616 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_props:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 0.000851 0 0 0 0 0 0 0 0.000387
2 avu cb5168e06325147… 0.000503 0.000359 0.000310 0 0 0 0.000474 0.000440 0.000420
3 aet 3968cb5b7766a80… 0.00344 0.000466 0.000465 0.000544 0.000741 0.00161 0.00122 0.000440 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
0 functions:
>
>
> obj$data$otu_props <- calc_obs_props(obj, "otu_counts", other_cols = TRUE)
No `cols` specified, so using all numeric columns:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
Calculating proportions from counts for 72 columns for 2620 observations.
Warning message:
The following columns will be replaced in the output:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
> print(obj)
<Taxmap>
1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
3 data sets:
otu_counts:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 22 0 0 0 0 0 0 0 12
2 avu cb5168e06325147… 13 10 10 0 0 0 12 14 13
3 aet 3968cb5b7766a80… 89 13 15 16 22 40 31 14 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_rarefied:
# A tibble: 2,619 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 14 0 0 0 0 0 0 0 4
2 avu cb5168e06325147… 7 5 5 0 0 0 8 7 6
3 aet 3968cb5b7766a80… 60 7 6 11 15 23 18 11 0
# ℹ 2,616 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
otu_props:
# A tibble: 2,620 × 74
taxon_id `#OTU ID` `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bnk 8ffd3dc50d6274b… 0.000851 0 0 0 0 0 0 0 0.000387
2 avu cb5168e06325147… 0.000503 0.000359 0.000310 0 0 0 0.000474 0.000440 0.000420
3 aet 3968cb5b7766a80… 0.00344 0.000466 0.000465 0.000544 0.000741 0.00161 0.00122 0.000440 0
# ℹ 2,617 more rows
# ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
# `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
0 functions:
>
>
>
> ########################
> library(vegan)
> rarecurve(t(obj$data$otu_counts[, "DR80X-1"]), step = 20,
+ sample = min(colSums(obj$data$otu_counts[, sample_data$SampleID])),
+ col = "blue", cex = 1.5)
Warning message:
In rarecurve(t(obj$data$otu_counts[, "DR80X-1"]), step = 20, sample = min(colSums(obj$data$otu_counts[, :
most observed count data have counts 1, but smallest count is 10
>
> counts_to_presence(obj, "otu_rarefied")
No `cols` specified, so using all numeric columns:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
# A tibble: 2,619 × 73
taxon_id `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3` `DR40X-4` `DR40X-5` `DR40X-6`
<chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
1 bnk TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
2 avu TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
3 aet TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
4 bnm TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
5 als FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
6 bnn FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
7 alu TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
8 bnp FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
9 bnr TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
10 awh FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# ℹ 2,609 more rows
# ℹ 60 more variables: `DR20X-1` <lgl>, `DR20X-2` <lgl>, `DR20X-3` <lgl>, `DR20X-4` <lgl>, `DR20X-5` <lgl>, `DR20X-6` <lgl>,
# `DR80M-1` <lgl>, `DR80M-2` <lgl>, `DR80M-3` <lgl>, `DR80M-4` <lgl>, `DR80M-5` <lgl>, `DR80M-6` <lgl>, `DR40M-1` <lgl>,
# `DR40M-2` <lgl>, `DR40M-3` <lgl>, `DR40M-4` <lgl>, `DR40M-5` <lgl>, `DR40M-6` <lgl>, `DR20M-1` <lgl>, `DR20M-2` <lgl>,
# `DR20M-3` <lgl>, `DR20M-4` <lgl>, `DR20M-5` <lgl>, `DR20M-6` <lgl>, `RC80X-1` <lgl>, `RC80X-2` <lgl>, `RC80X-3` <lgl>,
# `RC80X-4` <lgl>, `RC80X-5` <lgl>, `RC80X-6` <lgl>, `RC40X-1` <lgl>, `RC40X-2` <lgl>, `RC40X-3` <lgl>, `RC40X-4` <lgl>,
# `RC40X-5` <lgl>, `RC40X-6` <lgl>, `RC20X-1` <lgl>, `RC20X-2` <lgl>, `RC20X-3` <lgl>, `RC20X-4` <lgl>, `RC20X-5` <lgl>, …
# ℹ Use `print(n = ...)` to see more rows
> # Apply the function
> presence_data <- counts_to_presence(obj, "otu_rarefied")
No `cols` specified, so using all numeric columns:
DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6
> packageVersion("phyloseq")
[1] ‘1.41.1’
>
> # Example of converting to a phyloseq object (if needed)
> ps_obj <- as_phyloseq(obj,
+ otu_table = presence_data,
+ otu_id_col = "#OTU ID",
+ sample_data = sample_data,
+ sample_id_col = "SampleID")
Error in as_phyloseq(obj, otu_table = presence_data, otu_id_col = "#OTU ID", :
OTU table does not have an OTU ID column named "#OTU ID". Use the "otu_id_col" option if it is named something else.
>