R/mutate_variable_na.R
mutate_variable_na_number.Rd
This function adds a new column to the variable_info
slot of a mass_dataset object,
which contains the count of NA (Not Available) values for each variable according to the samples specified.
mutate_variable_na_number(object, according_to_samples = "all")
A modified mass_dataset object with an updated variable_info
slot.
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.33
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2024-09-06 08:49:54
##calculate NA number according to all the samples
object2 =
mutate_variable_na_number(object = object)
colnames(extract_variable_info(object))
#> [1] "variable_id" "mz" "rt"
colnames(extract_variable_info(object2))
#> [1] "variable_id" "mz" "rt" "na_number"
object2@variable_info_note
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
#> 4 na_number na_number
##calculate NA number according to only QC samples
object3 <-
mutate_variable_na_number(object = object2,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object3
#> --------------------
#> massdataset version: 1.0.33
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2024-09-06 08:49:54
#> mutate_variable_na_number ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_na_number() 2024-09-06 08:49:54.768686
#> 2 massdataset mutate_variable_na_number() 2024-09-06 08:49:54.771292
colnames(extract_variable_info(object3))
#> [1] "variable_id" "mz" "rt" "na_number" "na_number.1"
object3@variable_info_note
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
#> 4 na_number na_number
#> 5 na_number.1 na_number.1