# calc_n_samples

##### Count the number of samples

For a given table in a `taxmap`

object, count the number of
samples (i.e. columns) with greater than a minimum value.

##### Usage

```
calc_n_samples(obj, data, cols = NULL, groups = "n_samples",
other_cols = FALSE, out_names = NULL, drop = FALSE,
more_than = 0, dataset = NULL)
```

##### Arguments

- obj
A

`taxmap`

object- data
The name of a table in

`obj$data`

.- cols
The columns in

`data`

to use. By default, all numeric columns are used. Takes one of the following inputs:- TRUE/FALSE:
All/No columns will used.

- Character vector:
The names of columns to use

- Numeric vector:
The indexes of columns to use

- Vector of TRUE/FALSE of length equal to the number of columns:
Use the columns corresponding to

`TRUE`

values.

- groups
Group multiple columns per treatment/group. This should be a vector of group IDs (e.g. character, integer) the same length as

`cols`

that defines which samples go in which group. When used, there will be one column in the output for each unique value in`groups`

.- other_cols
Preserve in the output non-target columns present in the input data. New columns will always be on the end. The "taxon_id" column will be preserved in the front. Takes one of the following inputs:

- NULL:
No columns will be added back, not even the taxon id column.

- TRUE/FALSE:
All/None of the non-target columns will be preserved.

- Character vector:
The names of columns to preserve

- Numeric vector:
The indexes of columns to preserve

- Vector of TRUE/FALSE of length equal to the number of columns:
Preserve the columns corresponding to

`TRUE`

values.

- out_names
The names of count columns in the output. Must be the same length and order as

`cols`

(or`unique(groups)`

, if`groups`

is used).- drop
If

`groups`

is not used, return a vector of the results instead of a table with one column.- more_than
A sample must have greater than this value for it to be counted as present.

- dataset
DEPRECIATED. use "data" instead.

##### Value

A tibble

##### See Also

Other calculations: `calc_group_mean`

,
`calc_group_median`

,
`calc_group_rsd`

,
`calc_group_stat`

,
`calc_obs_props`

,
`calc_prop_samples`

,
`calc_taxon_abund`

,
`compare_groups`

,
`counts_to_presence`

,
`rarefy_obs`

, `zero_low_counts`

##### Examples

```
# NOT RUN {
# Parse data for example
x = parse_tax_data(hmp_otus, class_cols = "lineage", class_sep = ";",
class_key = c(tax_rank = "taxon_rank", tax_name = "taxon_name"),
class_regex = "^(.+)__(.+)$")
# Count samples with at least one read
calc_n_samples(x, data = "tax_data")
# Count samples with at least 5 reads
calc_n_samples(x, data = "tax_data", more_than = 5)
# Return a vector instead of a table
calc_n_samples(x, data = "tax_data", drop = TRUE)
# Only use some columns
calc_n_samples(x, data = "tax_data", cols = hmp_samples$sample_id[1:5])
# Return a count for each treatment
calc_n_samples(x, data = "tax_data", groups = hmp_samples$body_site)
# Rename output columns
calc_n_samples(x, data = "tax_data", groups = hmp_samples$body_site,
out_names = c("A", "B", "C", "D", "E"))
# Preserve other columns from input
calc_n_samples(x, data = "tax_data", other_cols = TRUE)
calc_n_samples(x, data = "tax_data", other_cols = 2)
calc_n_samples(x, data = "tax_data", other_cols = "otu_id")
# }
# NOT RUN {
# }
```

*Documentation reproduced from package metacoder, version 0.3.3, License: GPL-2 | GPL-3*