Subsetting and modifying ir objects
Subsetting works as for data frames
Since ir objects are data frames, subsetting and
modifying works the same way as for data frames. For example, specific
rows (= measurements) can be filtered:
ir_sample_data[5:10, ]The advantage of storing spectra as list columns is that filtering spectral data and metadata and other data can be performed simultaneously.
One exception is that while subsetting, one must not remove the
spectra column. If it is removed, the ir class
attribute is dropped:
d1 <- ir_sample_data
class(d1[, setdiff(colnames(d), "id_sample")])
#> [1] "ir" "tbl_df" "tbl" "data.frame"
d1$spectra <- NULL
class(d1)
#> [1] "tbl_df" "tbl" "data.frame"Another exception is that when the spectra column
contains unsupported elements (e.g. wrong column names, additional
columns, duplicated “x axis values”), the object also loses its
ir class:
d2 <- ir_sample_data
d2$spectra[[1]] <- rep(d2$spectra[[1]], 2)
class(d2)
#> [1] "tbl_df" "tbl" "data.frame"
d3 <- ir_sample_data
colnames(d3$spectra[[1]]) <- c("a", "b")
class(d3)
#> [1] "tbl_df" "tbl" "data.frame"Tidyverse methods are supported
Tidyverse methods for manipulating ir objects are also supported. For
example, we can use mutate to add new variables and we can
use pipes (%>%) to make coding and reading code
easier:
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
d <- ir_sample_data
d <-
d %>%
mutate(a = rnorm(n = nrow(.)))
head(ir_sample_data)Or, a another example, we can summarize spectra for some defined
groups (here the maximum intensity value for each “x axis value” and
unique sample_type value):
library(purrr)
library(ggplot2)
d2 <-
d %>%
group_by(sample_type) %>%
summarize(
spectra = {
res <- map_dfc(spectra, function(.x) .x[, 2, drop = TRUE])
spectra[[1]] %>%
dplyr::mutate(
y =
res %>%
rowwise() %>%
mutate(y = max(c_across(everything()))) %>%
pull(y)
) %>%
list()
},
.groups = "drop"
)
plot(d2) +
facet_wrap(~ sample_type)
