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R/inspect.ft.R
In respR: Import, Process, Analyse, and Calculate Rates from Respirometry Data
Defines functions
plot.inspect.ft
mean.inspect.ft
summary.inspect.ft
print.inspect.ft
inspect.ft
Documented in
inspect.ft
mean.inspect.ft
plot.inspect.ft
print.inspect.ft
summary.inspect.ft
#' Explore and visualise flowthrough respirometry data and check for errors
#'
#' `inspect.ft` is a data exploration and preparation function that visualises
#' flowthrough respirometry data, checks it for common issues, and prepares it
#' for use in later functions in `respR`, such as [`calc_rate.ft()`].
#'
#' `inspect.ft` is intended to be specific to *flowthrough* respirometry data.
#' In flowthrough respirometry (also known as 'open flow' or 'continuous flow'
#' respirometry) rather than calculating a rate from a changing oxygen
#' concentration recording in a sealed chamber, instead the difference (i.e.
#' 'oxygen delta') between the inflowing and outflowing oxygen concentrations of
#' a respirometer receiving water at a constant flow rate is used to calculate
#' an oxygen consumption or production rate, typically after it has reached a
#' steady state. Therefore, in general, regions of stable oxygen delta values
#' (difference between outflow and inflow oxygen) are of interest. `inspect.ft`
#' visualises and prepares the data for use in [`calc_rate.ft()`]. By specifying
#' data types in this function and saving the output, they do not need to be
#' specified in later functions.
#'
#' ## Inputs
#'
#' Given an input data frame `x`, the function scans the columns specified via
#' the `time`, `out.oxy`, `in.oxy` or `delta.oxy` inputs. Columns are specified
#' by using the column number (e.g. `time = 1`), or by name (e.g. `time =
#' "Time.Hrs"`). If no columns are specified, by default the function assumes
#' the first column is `time`, and all others are `delta.oxy` oxygen data.
#' However, best practice is to use the inputs to specify particular columns.
#'
#' The `x` input must contain at least two data types; a single column of
#' numeric `time` data, with *either* a column of paired `out.oxy`
#' concentrations (i.e. the exhalent or 'downstream' concentrations), *or* a
#' column of already calculated `delta.oxy` values, that is the difference
#' between outflow and inflow concentrations, or the outflow concentration
#' corrected by a background recording from a 'blank' or empty chamber.
#'
#' **out.oxy input option**: If an `out.oxy` column has been specified, in order
#' to calculate the oxygen delta (and therefore a rate in [`calc_rate.ft()`])
#' there must also be an inflow oxygen concentration input (i.e. the inhalent or
#' 'upstream' concentration). This will generally be a column of paired `in.oxy`
#' concentrations, in which case the paired values of `out.oxy` and `in.oxy` are
#' used to calculate the oxygen `delta.oxy`, which is saved in the output and
#' used to determine a rate in [`calc_rate.ft()`]. Alternatively, if the inflow
#' oxygen concentration is a known, generally unvarying value (such as fully
#' air-saturated water from a header tank) this can be entered as a single value
#' via `in.oxy.value` and this is used to calculate the `delta.oxy`.
#'
#' **delta.oxy input option**: If delta oxygen values have already been
#' calculated, these can be entered via the `delta.oxy` input, and these are
#' prepared and saved for rate calculations in `calc_rate.ft`.
#'
#' ## Check for numeric data
#'
#' `respR` requires data be in the form of paired values of numeric time and
#' oxygen. All columns are checked that they contain numeric data before any
#' other checks are performed. If any of the inspected columns do not contain
#' numeric data the remaining checks for that column are skipped, and the
#' function exits returning `NULL`, printing the summary of the checks. No plot
#' is produced. Only when all inspected columns pass this numeric check can the
#' resulting output object be saved and passed to other `respR` functions.
#'
#' ## Other checks
#'
#' The `time` column is checked for missing (`NA/NaN`) values, infinite values
#' both positive and negative (`Inf/-Inf`), that values are sequential, that
#' there are no duplicate times, and that it is numerically evenly-spaced.
#' Oxygen columns are checked for missing (`NA/NaN`) and infinite values
#' (`Inf/-Inf`). See **Failed Checks** section for what it means for analyses if
#' these checks result in warnings. If the output is assigned, the specified
#' columns are saved to a `list` object for use in later functions such as
#' [`calc_rate.ft()`]. A plot is also produced.
#'
#' ## Plot
#'
#' If `plot = TRUE`, entered data is plotted against both time (bottom, blue
#' axis) and row index (top, red axis), depending on the inputs:
#'
#' - a single `out.oxy` column with either a paired `in.oxy` column or
#' `in.oxy.value`: a two panel plot. The top plot is both outflow (green points)
#' and inflow (turquoise points) oxygen. The bottom plot is the oxygen delta
#' (black points) between outflow and inflow oxygen, essentially a unitless
#' oxygen uptake or production rate.
#'
#' - a single `delta.oxy` column: a one panel plot of oxygen delta values.
#'
#' - multiple `out.oxy` or `delta.oxy` columns: a grid plot of all `delta.oxy`
#' data (either as entered or calculated from `out.oxy` and `in.oxy`). Specific
#' delta plots can be examined individually by using the `pos` input (e.g.
#' `plot(x, pos = 2)`). Y-axes are not equal.
#'
#' - unspecified columns: all columns are plotted assuming `time` is in column
#' 1, and all others are oxygen `delta.oxy` data. Y-axes are not equal.
#'
#' In delta plots, that is those plotting `delta.oxy` values, either directly
#' entered or calculated, consistent oxygen uptake or production rates will be
#' represented by flat or level regions. The `width` input may help with
#' selecting regions from which to extract rates, and can be passed in the main
#' function call or using `plot()` on the output object. This smooths delta
#' oxygen values by calculating a rolling mean across the data. See **Additional
#' plotting options** below.
#'
#' ***Note:*** Since `respR` is primarily used to examine oxygen consumption,
#' the delta oxygen and rate plots are by default plotted on a reverse y-axis.
#' In `respR` oxygen uptake rates are negative since they represent a negative
#' slope of oxygen against time. In these plots the axis is reversed so that
#' higher uptake rates (i.e. more negative) will be higher on these plots. If
#' you are interested instead in oxygen production rates, which are positive,
#' the `rate.rev = FALSE` input can be passed in either the `inspect.ft` call,
#' or when using `plot()` on the output object. In this case, the delta and rate
#' values will be plotted numerically, and higher oxygen *production* rates will
#' be higher on the plot.
#'
#' ## Plot an additional data source
#'
#' Using the `add.data` input an additional data source, for example
#' temperature, can be plotted alongside the oxygen timeseries. This should be
#' either a column number (e.g. `add.data = 3`) or name (e.g. `add.data =
#' "Temperature"`) indicating a column in the input `x` data frame sharing the
#' same time data. None of the data checks are performed on this column; it is
#' simply to give a basic visual aid in the plot to, for example, help decide if
#' regions of the data should be used or not used because this parameter was
#' variable. Values are saved in the output as a vector under `$add.data`. It is
#' plotted in blue on a separate y-axis on the main timeseries plot. It is *not*
#' plotted if multiple oxygen columns are inspected. See examples.
#'
#' ## Additional plotting options
#'
#' The `width` input may help with selecting regions from which to extract
#' rates. This smooths delta oxygen values by calculating a rolling mean across
#' the data, and should be a value between 0 and 1 representing a proportion of
#' the total data width. If left as the default `NULL` no smoothing is
#' performed. This is a visual aid which only affects plotted values and does
#' not alter output delta oxygen values.
#'
#' If the legend or labels obscure part of the plot, they can be suppressed via
#' `legend = FALSE` in either the `inspect.ft` call, or when using `plot()` on
#' the output object. Suppress console output messages with `quiet = TRUE`. If
#' multiple columns have been inspected, the `pos` input can be used to examine
#' each `out.oxy`~`in.oxy`~`del.oxy` dataset. If axis labels (particularly
#' y-axis) are difficult to read, `las = 2` can be passed to make axis labels
#' horizontal. In addition, `oma` (outer margins, default `oma = c(0.4, 1, 1.5,
#' 0.4)`), and `mai` (inner margins, default `mai = c(0.3, 0.15, 0.35, 0.15)`)
#' can be used to adjust plot margins.
#'
#' ## Multiple data columns
#'
#' For a quick overview of larger experiments, multiple columns of `out.oxy`,
#' `in.oxy` and `delta.oxy` can be inspected, but must share the same numeric
#' time data column specified by the `time` input. Note, multiple column
#' inspection is chiefly intended to be exploratory functionality to provide a
#' quick overview of larger datasets. While the output will contain all data
#' columns in `$dataframe` and `$data`, subsequent functions such as
#' [`calc_rate.ft()`] will use only the first `delta.oxy` column for calculating
#' rates. Best practice is to inspect and assign each individual experiment or
#' column pair as separate `inspect.ft` objects. See Examples.
#'
#' If multiple `out.oxy` columns are specified, `in.oxy` can be a single column
#' (if for example all chambers are supplied from the same header tank), in
#' which case it is used to calculate an oxygen delta for all `out.oxy` columns.
#' A single `in.oxy.value` in the same units as `out.oxy` can also be specified.
#' There can also be multiple `in.oxy` columns, in which case it is assumed each
#' `out.oxy` column is paired with each `in.oxy` at the same position, and used
#' to calculate the oxygen `delta.oxy`. In this case, `out.oxy` and `in.oxy`
#' must have equal numbers of columns.
#'
#' ## Failed Checks
#'
#' The most important data check in `inspect.ft` is that all data columns are
#' numeric. If any column fails this check, the function skips the remaining
#' checks for that column, the function exits returning `NULL`, and no output
#' object or plot is produced.
#'
#' The other failed check that requires action is the check for infinite values
#' (`Inf/-Inf`). Some oxygen sensing systems add these in error when
#' interference or data dropouts occur. Infinite values will cause problems when
#' it comes to calculating rates, so need to be removed. If found, locations of
#' these are printed and can be found in the output object under `$locs`. Note,
#' these values are not plotted, so special note should be taken of the warnings
#' and console printout.
#'
#' The remaining data checks in `inspect.ft` are mainly exploratory and help
#' diagnose and flag potential issues with the data that might affect rate
#' calculations. For instance, long experiments may have had sensor dropouts the
#' user is unaware of. Some might not be major issues. For instance, an uneven
#' time warning can result from using decimalised minutes, which is a completely
#' valid time metric, but happens to be numerically unevenly spaced. As an
#' additional check, if uneven time is found, the minimum and maximum intervals
#' in the time data are in the console output, so a user can see immediately if
#' there are large gaps in the data.
#'
#' If some of these checks produce warnings, it should *generally* not hinder
#' analysis of the data. `respR` has been coded to rely on linear regressions on
#' exact data values, and not make assumptions about data spacing or order.
#' Therefore issues such as missing or NA/NaN values, duplicate or
#' non-sequential time values, or uneven time spacing should not cause any
#' erroneous results, as long as they do not occur over large regions of the
#' data. `inspect.ft` however outputs locations (row numbers) of where these
#' issues occur (located in the `$locs` element of the output), allowing users
#' to amend them before analysis. We would recommend that to be completely
#' confident in any results from analysis of such data, and avoid obscure
#' errors, these issues be addressed before proceeding.
#'
#' ## Background control or "blank" experiments
#'
#' For experiments in which the specimen data is to be background corrected by a
#' concurrently-run control experiment, `inspect.ft` can be used by specifying
#' the specimen experiment as `out.oxy`, and the "blank" as the `in.oxy` input.
#' In this way, any variations in oxygen in the specimen data due to background
#' microbial activity, or for any other reason such as fluctuations in inflow
#' oxygen, are accounted for in the delta oxygen calculations, and therefore in
#' the rate calculated in [`calc_rate.ft()`]. See the vignettes on the website
#' for examples.
#'
#' If the background recordings are experiments with their own outflow and
#' inflow recordings, which show a generally consistent oxygen delta due to
#' microbial activity, this can be saved as a separate `inspect.ft` object, a
#' background rate calculated in [`calc_rate.ft()`], and this used in
#' [`adjust_rate.ft()`] as the `by` input to perform background adjustments to
#' specimen rates.
#'
#' **Note**: All background calculations should be from experiments done at the
#' *same flow rate* as the specimen experiments to be corrected.
#'
#' ## S3 Generic Functions
#'
#' Saved output objects can be used in the generic S3 functions `plot()`,
#' `print()` and `summary()`.
#'
#' - `plot()`: plots the result.
#'
#' - `print()`: prints a summary of the checks performed on the data. If issues
#' are found, locations (row numbers) are printed (up to first 20 occurrences).
#'
#' - `summary()`: simple wrapper for `print()` function. See above.
#'
#' ## More
#'
#' For additional help, documentation, vignettes, and more visit the `respR`
#' website at <https://januarharianto.github.io/respR/>
#'
#' @return Output is a `list` object of class `inspect.ft` containing input
#' parameters and data, data check summaries, and metadata, which can be
#' passed to [`calc_rate.ft()`] to determine rates. If there are failed checks
#' or warnings, the row locations of the potentially problematic data can be
#' found in `$locs`.
#'
#' @param x `data.frame` containing columns of `time` and `out.oxy` or
#' `delta.oxy` concentrations, and optionally `in.oxy`.
#' @param time integer or string. Defaults to `1`. Specifies the column of the
#' Time data as either a column number or the name.
#' @param out.oxy integer(s) or string(s). Defaults to `NULL`. Specifies the
#' column(s) of the outflow oxygen data as either column numbers or names.
#' @param in.oxy integer(s) or string(s). Defaults to `NULL`. Specifies the
#' column(s) of the inflow oxygen data as either column numbers or names.
#' @param in.oxy.value numeric value. Defaults to `NULL`. If there is no
#' continuous `in.oxy` data, this specifies a fixed value of oxygen
#' concentration for inflowing water in same units as `out.oxy`, and is used
#' with `out.oxy` to calculate a `delta.oxy`.
#' @param delta.oxy integer(s) or string(s). Defaults to all non-time columns if
#' no other inputs given. Specifies the column number(s) or name(s) of delta
#' oxygen data, for when the user has already calculated the difference
#' between outflow and inflow oxygen (should be negative values for oxygen
#' uptake). If this is used, `out.oxy` and `in.oxy` should be NULL.
#' @param plot logical. Defaults to TRUE. Plots the data. See Details.
#' @param add.data integer or string. Defaults to `NULL`. Specifies the column
#' number or name of an optional additional data source that will be plotted
#' in blue alongside the full oxygen timeseries.
#' @param ... Allows additional plotting controls to be passed, such as `legend
#' = FALSE`, `quiet = TRUE`, `rate.rev = FALSE` and `pos`.
#'
#' @importFrom data.table data.table
#' @export
#'
#' @examples
#'
#' # Inspect outflow and inflow oxygen data
#' x <- inspect.ft(flowthrough.rd, time = 1, out.oxy = 2,
#' in.oxy = 3)
#' print(x)
#' plot(x)
#'
#' # Inspect outflow oxygen data with inflow oxygen as a known value in
#' # the same units
#' x <- inspect.ft(flowthrough.rd, time = 1, out.oxy = 2,
#' in.oxy.value = 8.90)
#'
#' # Inspect already calculated delta oxygen data
#' inspect.ft(flowthrough.rd, time = 1, delta.oxy = 4)
#'
#' # inspect multiple columns for a quick overview
#' inspect.ft(flowthrough_mult.rd, time = 1, delta.oxy = 10:12)
#'
#' # Inspect outflow and use a blank control chamber as background
#' # correction
#' #
#' # This experiment has increasing background respiration over time.
#' # Inspecting outflow oxygen with inflow header tank concentrations
#' # suggests specimen rates (bottom delta.oxy plot) are increasing.
#' inspect.ft(flowthrough_sim.rd, time = 1,
#' out.oxy = 2, in.oxy = 4)
#'
#' # However, inspecting with recordings from a concurrent blank
#' # control accounts for this and shows specimen rates are level
#' # when background is taken into account.
#' inspect.ft(flowthrough_sim.rd, time = 1,
#' out.oxy = 2, in.oxy = 3)
inspect.ft <- function(x, time = NULL, out.oxy = NULL, in.oxy = NULL,
in.oxy.value = NULL, delta.oxy = NULL, plot = TRUE,
add.data = NULL, ...) {
## Save function call for output
call <- match.call()
# Extract data ------------------------------------------------------------
df <- as.data.frame(x)
# Input checks ------------------------------------------------------------
## stop if not df
if (!is.data.frame(x)) stop("inspect.ft: 'x' must be data.frame object.", call. = FALSE)
## Apply time column default
if(is.null(time)) {
message("inspect.ft: Applying column default of 'time = 1'")
time <- 1
}
## only one of in.oxy or in.oxy.value should be entered
if(!is.null(in.oxy) && !is.null(in.oxy.value))
stop("inspect.ft: Only one of 'in.oxy' or 'in.oxy.value' can be entered.", call. = FALSE)
## if in.oxy entered, out.oxy must be entered
if(!is.null(in.oxy) && (is.null(out.oxy)))
stop("inspect.ft: An 'in.oxy' input requires paired 'out.oxy' column(s).", call. = FALSE)
## if out.oxy entered, one of in.oxy or in.oxy.value must be entered
if(!is.null(out.oxy) && (is.null(in.oxy) && is.null(in.oxy.value)))
stop("inspect.ft: With 'out.oxy' data, paired 'in.oxy' columns or an 'in.oxy.value' is required.", call. = FALSE)
## if out.oxy entered, in.oxy must be either same no. of columns or a single column
if(!is.null(out.oxy) && !(is.null(in.oxy))){
ncol_o <- length(out.oxy)
ncol_i <- length(in.oxy)
if(ncol_o != ncol_i && ncol_i != 1)
stop("inspect.ft: With 'out.oxy' data, 'in.oxy' must be a single column or an equal number of paired columns.", call. = FALSE)
}
# if delta.oxy entered, out.oxy, in.oxy & in.oxy.value should be NULL
if(!is.null(delta.oxy) && (!is.null(out.oxy) || !is.null(in.oxy) || !is.null(in.oxy.value)))
stop("inspect.ft: With 'delta.oxy' data, 'out.oxy', 'in.oxy' and 'in.oxy.value' should be NULL.", call. = FALSE)
# Apply input defaults ----------------------------------------------------
## After all that, if these are still NULL assume all non-time are delta.oxy
if (is.null(out.oxy) && is.null(delta.oxy)) {
message("inspect.ft: Applying column default of all non-time column(s) as 'delta.oxy'")
listcols <- seq.int(1, ncol(x))
delta.oxy <- listcols[!listcols %in% time]
}
# Final input checks ------------------------------------------------------
# parse strings to column numbers
if(is.character(time)) time <- column.id(time, df, "inspect.ft: ")
if(is.character(out.oxy)) out.oxy <- column.id(out.oxy, df, "inspect.ft: ")
if(is.character(in.oxy)) in.oxy <- column.id(in.oxy, df, "inspect.ft: ")
if(is.character(delta.oxy)) delta.oxy <- column.id(delta.oxy, df, "inspect.ft: ")
## check for duplicate column numbers
inputs <- list(time, out.oxy, in.oxy, delta.oxy)
if(column.conflict(inputs)) {
dupe_cols <- column.conflict(inputs, id = TRUE)
stop(glue::glue("inspect.ft: Input columns conflict. Column(s) {glue::glue_collapse(dupe_cols, \", \", last = \" and \")} are entered more than once."), call. = FALSE)
}
## check column inputs are valid
column.val(time, req = TRUE, max = 1, range = c(1, ncol(x)),
msg = "inspect.ft: 'time' -")
column.val(out.oxy, req = FALSE, max = ncol(x)-1, range = c(1, ncol(x)),
msg = "inspect.ft: 'out.oxy' -")
column.val(in.oxy, req = FALSE, max = ncol(x)-1, range = c(0, ncol(x)),
msg = "inspect.ft: 'in.oxy' -")
column.val(delta.oxy, req = FALSE, max = ncol(x)-1, range = c(0, ncol(x)),
msg = "inspect.ft: 'delta.oxy' -")
# Extract data ------------------------------------------------------------
## time
time.all <- lapply(1:length(df[time]), function(z) df[time][[z]])
names(time.all) <- names(df[time])
## out oxy
# if no input, null
if(is.null(out.oxy)) {
out.oxy.all <- NULL
} else {
out.oxy.all <- lapply(1:length(df[out.oxy]), function(z) df[out.oxy][[z]])
names(out.oxy.all) <- names(df[out.oxy])
}
## in.oxy
# if no input, null
if(is.null(in.oxy)) {
in.oxy.all <- NULL
} else {
in.oxy.all <- lapply(1:length(df[in.oxy]), function(z) df[in.oxy][[z]])
names(in.oxy.all) <- names(df[in.oxy])
}
## in.oxy conc value - if present, make into vector same length as out.oxy
if(!is.null(in.oxy.value)) {
in.oxy.all <- list(rep(in.oxy.value, nrow(df)))
names(in.oxy.all) <- "in.oxy.value"
}
## If no delta input - make it NULL for now,
## We will calculate it after checks if out.oxy and in.out non-numeric check passes.
## Otherwise failures here if non-numeric data
if(is.null(delta.oxy)) {
del.oxy.all <- NULL
## otherwise extract it
} else {
del.oxy.all <- lapply(1:length(df[delta.oxy]), function(z) df[delta.oxy][[z]])
names(del.oxy.all) <- names(df[delta.oxy])
}
## Additional data
if(!is.null(add.data)) {
# if column name, get number
if(is.character(add.data)) add.data <- column.id(add.data, df, "inspect.ft: ")
input.val(add.data, num = TRUE, int = TRUE, req = FALSE,
max = 1, min = 1, range = c(1, ncol(df)),
msg = "inspect.ft: 'add.data' -")
add.data <- df[[add.data]]
}
## Do Data Checks
time_results <- check_timeseries(time.all, "time")
if(is.null(out.oxy)) out.oxy_results <- NULL else
out.oxy_results <- check_timeseries(out.oxy.all, "oxygen")
if(is.null(in.oxy)) in.oxy_results <- NULL else
in.oxy_results <- check_timeseries(in.oxy.all, "oxygen")
if(is.null(delta.oxy)) del.oxy_results <- NULL else
del.oxy_results <- check_timeseries(del.oxy.all, "oxygen")
# issue warnings
# time
if (any(unlist(time_results[[1]][1,])))
warning("inspect.ft: Time column not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo output returned.", call. = F)
if (any(unlist(time_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Time column. Remove or replace before proceeding.", call. = F)
if (any(unlist(time_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Time column.", call. = F)
if (any(unlist(time_results[[1]][4,]) == "TRUE"))
warning("inspect.ft: Non-sequential Time values found.", call. = F)
if (any(unlist(time_results[[1]][5,]) == "TRUE"))
warning("inspect.ft: Duplicate Time values found.", call. = F)
if (any(unlist(time_results[[1]][6,]) == "TRUE"))
warning("inspect.ft: Time values are not evenly-spaced (numerically).", call. = F)
# out.oxy
if (any(unlist(out.oxy_results[[1]][1,])))
warning("inspect.ft: Oxygen column(s) not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo output returned.", call. = F)
if (any(unlist(out.oxy_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Oxygen column(s). Remove or replace before proceeding.", call. = F)
if (any(unlist(out.oxy_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Oxygen column(s).", call. = F)
# in.oxy
if (any(unlist(in.oxy_results[[1]][1,])))
warning("inspect.ft: Oxygen column(s) not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo input returned.", call. = F)
if (any(unlist(in.oxy_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Oxygen column(s). Remove or replace before proceeding.", call. = F)
if (any(unlist(in.oxy_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Oxygen column(s).", call. = F)
# del.oxy
if (any(unlist(del.oxy_results[[1]][1,])))
warning("inspect.ft: Oxygen column(s) not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo output returned.", call. = F)
if (any(unlist(del.oxy_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Oxygen column(s). Remove or replace before proceeding.", call. = F)
if (any(unlist(del.oxy_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Oxygen column(s).", call. = F)
# combine results
checks <- cbind(time_results[[1]], out.oxy_results[[1]], in.oxy_results[[1]], del.oxy_results[[1]])
locs_raw <- cbind(time_results[[2]], out.oxy_results[[2]], in.oxy_results[[2]], del.oxy_results[[2]])
# output
locs <- lapply(1:ncol(locs_raw), function(z) locs_raw[, z])
names(locs) <- colnames(locs_raw)
# if in.oxy and out.oxy non-numeric checks have passed, now it's ok to calculate delta.oxy
if(is.null(delta.oxy) && !(any(unlist(out.oxy_results[[1]][1,]))) && !(any(unlist(in.oxy_results[[1]][1,])))) {
del.oxy.all <- mapply(function(p,q) p-q,
p = out.oxy.all,
q = in.oxy.all,
SIMPLIFY = FALSE)
names(del.oxy.all) <- sapply(1:length(del.oxy.all), function(p) glue::glue("delta.oxy.calc.{p}"))
## otherwise make dummy list so next part doesn't error.
## It won't be returned anyway
} else if (is.null(delta.oxy)) {
del.oxy.all <- mapply(function(p,q) rep(NA, length(p)),
p = out.oxy.all,
q = in.oxy.all,
SIMPLIFY = FALSE)
#names(del.oxy.all) <- sapply(1:length(del.oxy.all), function(p) glue::glue("delta.oxy.calc.{p}"))
}
# save new data frame and create output object
if(is.null(out.oxy.all) && is.null(in.oxy.all)) {
dataframe <- data.table::data.table(cbind(data.frame(time.all),
del.oxy.all))
} else {
dataframe <- data.table::data.table(cbind(data.frame(time.all),
out.oxy.all,
in.oxy.all,
del.oxy.all))
}
out <- list(call = call,
dataframe = dataframe,
add.data = add.data,
inputs = list(df = x,
time = time,
out.oxy = out.oxy,
in.oxy = in.oxy,
in.oxy.value = in.oxy.value,
delta.oxy = delta.oxy,
plot = plot,
add.data = add.data),
data = list(time = time.all,
out.oxy = out.oxy.all,
in.oxy = in.oxy.all,
delta.oxy = del.oxy.all),
checks = checks,
locs_raw = locs_raw,
locs = locs)
class(out) <- "inspect.ft"
# if no errors occur, send out a good message :D
if (!any(na.omit(unlist(checks) == "TRUE")))
message("inspect.ft: No issues detected while inspecting data frame.") else
message("inspect.ft: Data issues detected. For more information use print().")
# Not all functions should print on assigning, but this one should
print(out)
# If any inspected columns found to be non-numeric, print, but don't return object
# If this is the case, it can't be used in later fns anyway, so no point
# And skip plotting - avoids awkward code in plot() to handle the object having non-numeric data
# Warnings issued above.
if(any(unlist(checks[1,]))) {
return(invisible(NULL))
} else {
if (plot) plot(out, quiet = TRUE, ...)
## invisible prevents it printing twice on assigning
return(invisible(out))
}
}
#' Print inspect.ft objects
#' @param x inspect.ft object
#' @param ... Pass additional inputs
#' @keywords internal
#' @return Print to console. No returned value.
#' @export
print.inspect.ft <- function(x, ...) {
cat("\n# print.inspect.ft # --------------------\n")
checks <- x$checks
locs <- x$locs_raw
# rename content:
tab <- checks
tab[tab == "TRUE"] <- "WARN"
tab[tab == "FALSE"] <- "pass"
tab[is.na(tab)] <- "-"
# print table
print(as.data.frame(tab), quote = FALSE)
cat("\n")
# highlight locations that did not pass the tests (but only for 2-col dfs):
# No need to do this for numeric check - it's all or none
if (checks[, 1][[2]] != "skip" && checks[, 1][[2]]) {
xinf <- locs[, 1][[2]]
cat("Inf/-Inf Time data locations: ")
if (length(xinf) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xinf, 20))
}
if (checks[, 1][[3]] != "skip" && checks[, 1][[3]]) {
xnan <- locs[, 1][[3]]
cat("NA/NaN Time data locations: ")
if (length(xnan) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xnan, 20))
}
if (checks[, 1][[4]] != "skip" && checks[, 1][[4]]) {
xseq <- locs[, 1][[4]]
cat("Non-sequential Time data locations ")
if (length(xseq) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xseq, 20))
}
if (checks[, 1][[5]] != "skip" && checks[, 1][[5]]) {
xdup <- locs[, 1][[5]]
cat("Duplicate Time data locations ")
if (length(xdup) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xdup, 20))
}
if (checks[, 1][[6]] != "skip" && checks[, 1][[6]]) {
xevn <- locs[, 1][[6]]
cat("Uneven Time data locations ")
if (length(xevn) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xevn, 20))
cat("Minimum and Maximum intervals in uneven Time data: \n")
print(range(diff(nainf.omit(x$dataframe[[1]]))))
}
for(i in 2:ncol(checks)) { ## for multiple columns
# use ncol(checks) because x$dataframe may have extra delta oxy calculated columns added
if (checks[, i][[2]] != "skip" && checks[, i][[2]]) {
yinf <- locs[,i][[2]]
cat("Inf/-Inf locations ")
if (length(yinf) > 20) cat("(first 20 shown) ")
cat("in Oxygen column:", names(x$dataframe)[i], "\n")
print(head(yinf, 20))
}
if (checks[, i][[3]] != "skip" && checks[, i][[3]]) {
ynan <- locs[,i][[3]]
cat("NA/NaN locations ")
if (length(ynan) > 20) cat("(first 20 shown) ")
cat("in Oxygen column:", names(x$dataframe)[i], "\n")
print(head(ynan, 20))
}
}
cat("-----------------------------------------\n")
return(invisible(x))
}
#' Summarise inspect.ft objects
#' @param object inspect.ft object
#' @param ... Pass additional inputs
#' @keywords internal
#' @return Print to console. No returned value.
#' @export
summary.inspect.ft <- function(object, ...) {
print(object)
}
#' Average inspect.ft object rates
#' @param x calc_rate.bg object
#' @param ... Pass additional inputs
#' @keywords internal
#' @return Print to console. No returned value.
#' @export
mean.inspect.ft <- function(x, ...){
message("inspect.ft: mean() is not available for 'inspect.ft' objects.")
return(invisible(x))
}
#' Plot inspect.ft objects
#' @param x inspect.ft object
#' @param width numeric. Smoothing factor (rolling mean) for delta oxygen values
#' as proportion of total data length (0 to 1)
#' @param pos integer. Which result to plot.
#' @param quiet logical. Suppress console output.
#' @param legend logical. Suppress labels and legends.
#' @param rate.rev logical. Control direction of y-axis in delta oxygen plot.
#' @param ... Pass additional plotting parameters
#' @keywords internal
#' @return A plot. No returned value.
#' @export
plot.inspect.ft <- function(x, width = NULL, pos = NULL, quiet = FALSE,
legend = TRUE, rate.rev = TRUE, ...) {
parorig <- par(no.readonly = TRUE) # save original par settings
on.exit(par(parorig)) # revert par settings to original
if (!quiet)
cat("\n# plot.inspect.ft # ---------------------\n")
# extract data
dt <- x$dataframe
time <- x$data$time
out.oxy <- x$data$out.oxy
in.oxy <- x$data$in.oxy
if(length(in.oxy) == 1) in.oxy <- rep(in.oxy, length(out.oxy)) # makes plotting easier
del.oxy <- x$data$delta.oxy
if(is.null(width)) win <- 1 else
win <- length(del.oxy[[1]]) * width
del.oxy.smooth <- lapply(del.oxy, function(z) roll::roll_mean(z, width = win, min_obs = 1))
# Apply default plotting params
par(oma = oma_def,
mai = mai_def,
las = las_def,
mgp = mgp_def,
tck = tck_def,
pch = pch_def,
cex = cex_def)
# Multi column plot -------------------------------------------------------
## if multiple columns or only delta.oxy data inspected, just plot grid of all delta.oxy
if (length(del.oxy) > 1 || is.null(out.oxy)){
if(!quiet && is.null(pos) && length(del.oxy) > 1)
cat("plot.inspect.ft: Plotting all delta oxygen columns. To plot a specific dataset use 'pos'.\n")
if(!is.null(x$add.data))
message("plot.inspect.ft: Additional data source cannot be plotted for multiple columns.")
if(is.null(pos))
pos <- seq.int(1, length(del.oxy))
if(any(pos > length(del.oxy)))
stop("plot.inspect.ft: Invalid 'pos' input: only ", length(del.oxy), " data inputs found.", call. = FALSE)
if (!quiet && length(pos) == 1)
cat('plot.inspect.ft: Plotting delta.oxy data from position', pos, 'of', length(del.oxy), '... \n')
par(mfrow = n2mfrow(length(pos)),
ps = 10,
cex = 1,
cex.main = 1,
...)
lapply(pos, function(z) {
ylim <- grDevices::extendrange(r = range(del.oxy[[z]], na.rm = TRUE), f = 0.05) ## add a little more space
if(rate.rev) ylim <- rev(ylim) ## reverse y-axis
plot(data.frame(time, del.oxy.smooth[[z]]),
axes = FALSE,
ylim = ylim,
xlab = "",
ylab = "",
cex = 0.5,
col = ftcol_del,
panel.first = grid())
box()
axis(side = 2)
axis(side = 1, col.lab = "blue", col.axis = "blue")
# plot invisibly - to add row index x-axis
par(new = TRUE, ...)
plot(data.frame(1:length(unlist(time)), del.oxy[[z]]),
xlab = "",
ylab = "",
pch = "",
cex = .5,
axes = FALSE
)
axis(side = 3, col.axis = "red")
title(main = glue::glue("Column: {names(del.oxy)[z]}"), line = 1.2,
adj = 0)
})
if(legend && length(pos) == 1) legend("topright",
"Row Index",
text.col = "red",
bg = "gray90",
cex = 0.6)
if(legend && length(pos) == 1) legend("bottomright",
"Time",
text.col = "blue",
bg = "gray90",
cex = 0.6)
mtext("inspect.ft: Inspecting Selected Columns",
outer = TRUE, cex = 1.2, line = 0.3, font = 2)
# Single dataset ----------------------------------------------------------
} else if (length(x$data$delta.oxy) == 1 || !is.null(pos)) {
if(is.null(pos)) pos <- 1
if(pos > length(x$data$delta.oxy))
stop("plot.inspect.ft: Invalid 'pos' input: only ", length(x$data$out.oxy), " data inputs found.", call. = FALSE)
if (!quiet)
cat('plot.inspect.ft: Plotting inspect.ft dataset from position', pos, 'of', length(x$data$delta), '... \n')
m <- rbind(c(1,1,1), c(1,1,1), c(2,2,2))
layout(m)
par(mai = mai_def_top_ext, # this one needs extra space
ps = 10,
cex = 1,
cex.main = 1,
...)
par(...) # to allow oma and mai to be overridden
## ylim for outflow and inflow plots - plus 10%
ylim <- range(range(nainf.omit(out.oxy[[pos]])), range(nainf.omit(in.oxy[[pos]]))) ## so all on same axes
buffer <- diff(ylim)*0.1
ylim <- c(ylim[1] - buffer, ylim[2] + buffer) ## add a little more space
# Outflow plot ------------------------------------------------------------
plot(unlist(time),
out.oxy[[pos]],
xlab = "",
ylab = "",
ylim = ylim,
cex = .5,
col = ftcol_out,
axes = FALSE,
col.lab = "blue",
col.axis = "blue",
panel.first = grid())
axis(side = 2)
points(unlist(time),
in.oxy[[pos]],
xlab = "",
ylab = "",
ylim = ylim,
cex = .5,
col = ftcol_in)
# plot invisibly - to add row index x-axis
par(new = TRUE)
plot(seq(1, nrow(dt)),
out.oxy[[pos]],
xlab = "",
ylab = "",
pch = "",
cex = .5,
axes = FALSE)
axis(side = 3, col.axis = "red")
# if add.data plot it too
if(!is.null(x$add.data)) {
par(new = TRUE, mgp = c(0, 0.13, 0))
plot(seq(1, nrow(dt)),
x$add.data,
ylim = grDevices::extendrange(x$add.data, f = 1),
xlab = "",
ylab = "",
pch = ".",
cex = .5,
axes = FALSE,
col = "blue")
axis(side = 4, col.axis = "blue")
par(mgp = mgp_def) # set back default mgp
par(...) # or allow custom one to overwrite it
}
box()
if(legend) legend("topright",
"Row Index",
text.col = "red",
bg = "gray90",
cex = 0.6)
if(legend) legend("right",
legend = c("Inflow", "Outflow"),
pch = pch_def,
col = c(ftcol_in, ftcol_out),
bg = "white",
cex = 0.8)
mtext("Outflow ~ Inflow Oxygen",
outer = TRUE, cex = 1.2, line = 0.3, font = 2)
# Delta plot --------------------------------------------------------------
## ylim - plus 10%
ylim <- range(nainf.omit(del.oxy[[pos]])) ## so all on same axes
buffer <- diff(ylim)*0.1
ylim <- c(ylim[1] - buffer, ylim[2] + buffer) ## add a little more space
if(rate.rev) ylim <- rev(ylim) ## reverse y-axis
plot(unlist(time),
del.oxy.smooth[[pos]],
xlab = "",
ylab = "",
ylim = ylim, # reverse y axis
cex = .5,
col = ftcol_del,
axes = FALSE,
panel.first = grid())
axis(side = 2) # simply to put yaxis lab colour back to black
axis(side = 1,
col.lab = "blue",
col.axis = "blue")
box()
if(legend) legend("bottomright",
"Time",
text.col = "blue",
bg = "gray90",
cex = 0.6)
mtext("Delta Oxygen",
outer = FALSE, cex = 1.2, line = 1.2, font = 2)
mtext(glue::glue("(i.e. Unitless Rate)"),
outer = FALSE, cex = 1, line = 0.3, font = 2)
}
if (!quiet){
cat("-----------------------------------------\n")
}
return(invisible(x))
}
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Defines functions plot.inspect.ft mean.inspect.ft summary.inspect.ft print.inspect.ft inspect.ft
Documented in inspect.ft mean.inspect.ft plot.inspect.ft print.inspect.ft summary.inspect.ft
#' Explore and visualise flowthrough respirometry data and check for errors
#'
#' `inspect.ft` is a data exploration and preparation function that visualises
#' flowthrough respirometry data, checks it for common issues, and prepares it
#' for use in later functions in `respR`, such as [`calc_rate.ft()`].
#'
#' `inspect.ft` is intended to be specific to *flowthrough* respirometry data.
#' In flowthrough respirometry (also known as 'open flow' or 'continuous flow'
#' respirometry) rather than calculating a rate from a changing oxygen
#' concentration recording in a sealed chamber, instead the difference (i.e.
#' 'oxygen delta') between the inflowing and outflowing oxygen concentrations of
#' a respirometer receiving water at a constant flow rate is used to calculate
#' an oxygen consumption or production rate, typically after it has reached a
#' steady state. Therefore, in general, regions of stable oxygen delta values
#' (difference between outflow and inflow oxygen) are of interest. `inspect.ft`
#' visualises and prepares the data for use in [`calc_rate.ft()`]. By specifying
#' data types in this function and saving the output, they do not need to be
#' specified in later functions.
#'
#' ## Inputs
#'
#' Given an input data frame `x`, the function scans the columns specified via
#' the `time`, `out.oxy`, `in.oxy` or `delta.oxy` inputs. Columns are specified
#' by using the column number (e.g. `time = 1`), or by name (e.g. `time =
#' "Time.Hrs"`). If no columns are specified, by default the function assumes
#' the first column is `time`, and all others are `delta.oxy` oxygen data.
#' However, best practice is to use the inputs to specify particular columns.
#'
#' The `x` input must contain at least two data types; a single column of
#' numeric `time` data, with *either* a column of paired `out.oxy`
#' concentrations (i.e. the exhalent or 'downstream' concentrations), *or* a
#' column of already calculated `delta.oxy` values, that is the difference
#' between outflow and inflow concentrations, or the outflow concentration
#' corrected by a background recording from a 'blank' or empty chamber.
#'
#' **out.oxy input option**: If an `out.oxy` column has been specified, in order
#' to calculate the oxygen delta (and therefore a rate in [`calc_rate.ft()`])
#' there must also be an inflow oxygen concentration input (i.e. the inhalent or
#' 'upstream' concentration). This will generally be a column of paired `in.oxy`
#' concentrations, in which case the paired values of `out.oxy` and `in.oxy` are
#' used to calculate the oxygen `delta.oxy`, which is saved in the output and
#' used to determine a rate in [`calc_rate.ft()`]. Alternatively, if the inflow
#' oxygen concentration is a known, generally unvarying value (such as fully
#' air-saturated water from a header tank) this can be entered as a single value
#' via `in.oxy.value` and this is used to calculate the `delta.oxy`.
#'
#' **delta.oxy input option**: If delta oxygen values have already been
#' calculated, these can be entered via the `delta.oxy` input, and these are
#' prepared and saved for rate calculations in `calc_rate.ft`.
#'
#' ## Check for numeric data
#'
#' `respR` requires data be in the form of paired values of numeric time and
#' oxygen. All columns are checked that they contain numeric data before any
#' other checks are performed. If any of the inspected columns do not contain
#' numeric data the remaining checks for that column are skipped, and the
#' function exits returning `NULL`, printing the summary of the checks. No plot
#' is produced. Only when all inspected columns pass this numeric check can the
#' resulting output object be saved and passed to other `respR` functions.
#'
#' ## Other checks
#'
#' The `time` column is checked for missing (`NA/NaN`) values, infinite values
#' both positive and negative (`Inf/-Inf`), that values are sequential, that
#' there are no duplicate times, and that it is numerically evenly-spaced.
#' Oxygen columns are checked for missing (`NA/NaN`) and infinite values
#' (`Inf/-Inf`). See **Failed Checks** section for what it means for analyses if
#' these checks result in warnings. If the output is assigned, the specified
#' columns are saved to a `list` object for use in later functions such as
#' [`calc_rate.ft()`]. A plot is also produced.
#'
#' ## Plot
#'
#' If `plot = TRUE`, entered data is plotted against both time (bottom, blue
#' axis) and row index (top, red axis), depending on the inputs:
#'
#' - a single `out.oxy` column with either a paired `in.oxy` column or
#' `in.oxy.value`: a two panel plot. The top plot is both outflow (green points)
#' and inflow (turquoise points) oxygen. The bottom plot is the oxygen delta
#' (black points) between outflow and inflow oxygen, essentially a unitless
#' oxygen uptake or production rate.
#'
#' - a single `delta.oxy` column: a one panel plot of oxygen delta values.
#'
#' - multiple `out.oxy` or `delta.oxy` columns: a grid plot of all `delta.oxy`
#' data (either as entered or calculated from `out.oxy` and `in.oxy`). Specific
#' delta plots can be examined individually by using the `pos` input (e.g.
#' `plot(x, pos = 2)`). Y-axes are not equal.
#'
#' - unspecified columns: all columns are plotted assuming `time` is in column
#' 1, and all others are oxygen `delta.oxy` data. Y-axes are not equal.
#'
#' In delta plots, that is those plotting `delta.oxy` values, either directly
#' entered or calculated, consistent oxygen uptake or production rates will be
#' represented by flat or level regions. The `width` input may help with
#' selecting regions from which to extract rates, and can be passed in the main
#' function call or using `plot()` on the output object. This smooths delta
#' oxygen values by calculating a rolling mean across the data. See **Additional
#' plotting options** below.
#'
#' ***Note:*** Since `respR` is primarily used to examine oxygen consumption,
#' the delta oxygen and rate plots are by default plotted on a reverse y-axis.
#' In `respR` oxygen uptake rates are negative since they represent a negative
#' slope of oxygen against time. In these plots the axis is reversed so that
#' higher uptake rates (i.e. more negative) will be higher on these plots. If
#' you are interested instead in oxygen production rates, which are positive,
#' the `rate.rev = FALSE` input can be passed in either the `inspect.ft` call,
#' or when using `plot()` on the output object. In this case, the delta and rate
#' values will be plotted numerically, and higher oxygen *production* rates will
#' be higher on the plot.
#'
#' ## Plot an additional data source
#'
#' Using the `add.data` input an additional data source, for example
#' temperature, can be plotted alongside the oxygen timeseries. This should be
#' either a column number (e.g. `add.data = 3`) or name (e.g. `add.data =
#' "Temperature"`) indicating a column in the input `x` data frame sharing the
#' same time data. None of the data checks are performed on this column; it is
#' simply to give a basic visual aid in the plot to, for example, help decide if
#' regions of the data should be used or not used because this parameter was
#' variable. Values are saved in the output as a vector under `$add.data`. It is
#' plotted in blue on a separate y-axis on the main timeseries plot. It is *not*
#' plotted if multiple oxygen columns are inspected. See examples.
#'
#' ## Additional plotting options
#'
#' The `width` input may help with selecting regions from which to extract
#' rates. This smooths delta oxygen values by calculating a rolling mean across
#' the data, and should be a value between 0 and 1 representing a proportion of
#' the total data width. If left as the default `NULL` no smoothing is
#' performed. This is a visual aid which only affects plotted values and does
#' not alter output delta oxygen values.
#'
#' If the legend or labels obscure part of the plot, they can be suppressed via
#' `legend = FALSE` in either the `inspect.ft` call, or when using `plot()` on
#' the output object. Suppress console output messages with `quiet = TRUE`. If
#' multiple columns have been inspected, the `pos` input can be used to examine
#' each `out.oxy`~`in.oxy`~`del.oxy` dataset. If axis labels (particularly
#' y-axis) are difficult to read, `las = 2` can be passed to make axis labels
#' horizontal. In addition, `oma` (outer margins, default `oma = c(0.4, 1, 1.5,
#' 0.4)`), and `mai` (inner margins, default `mai = c(0.3, 0.15, 0.35, 0.15)`)
#' can be used to adjust plot margins.
#'
#' ## Multiple data columns
#'
#' For a quick overview of larger experiments, multiple columns of `out.oxy`,
#' `in.oxy` and `delta.oxy` can be inspected, but must share the same numeric
#' time data column specified by the `time` input. Note, multiple column
#' inspection is chiefly intended to be exploratory functionality to provide a
#' quick overview of larger datasets. While the output will contain all data
#' columns in `$dataframe` and `$data`, subsequent functions such as
#' [`calc_rate.ft()`] will use only the first `delta.oxy` column for calculating
#' rates. Best practice is to inspect and assign each individual experiment or
#' column pair as separate `inspect.ft` objects. See Examples.
#'
#' If multiple `out.oxy` columns are specified, `in.oxy` can be a single column
#' (if for example all chambers are supplied from the same header tank), in
#' which case it is used to calculate an oxygen delta for all `out.oxy` columns.
#' A single `in.oxy.value` in the same units as `out.oxy` can also be specified.
#' There can also be multiple `in.oxy` columns, in which case it is assumed each
#' `out.oxy` column is paired with each `in.oxy` at the same position, and used
#' to calculate the oxygen `delta.oxy`. In this case, `out.oxy` and `in.oxy`
#' must have equal numbers of columns.
#'
#' ## Failed Checks
#'
#' The most important data check in `inspect.ft` is that all data columns are
#' numeric. If any column fails this check, the function skips the remaining
#' checks for that column, the function exits returning `NULL`, and no output
#' object or plot is produced.
#'
#' The other failed check that requires action is the check for infinite values
#' (`Inf/-Inf`). Some oxygen sensing systems add these in error when
#' interference or data dropouts occur. Infinite values will cause problems when
#' it comes to calculating rates, so need to be removed. If found, locations of
#' these are printed and can be found in the output object under `$locs`. Note,
#' these values are not plotted, so special note should be taken of the warnings
#' and console printout.
#'
#' The remaining data checks in `inspect.ft` are mainly exploratory and help
#' diagnose and flag potential issues with the data that might affect rate
#' calculations. For instance, long experiments may have had sensor dropouts the
#' user is unaware of. Some might not be major issues. For instance, an uneven
#' time warning can result from using decimalised minutes, which is a completely
#' valid time metric, but happens to be numerically unevenly spaced. As an
#' additional check, if uneven time is found, the minimum and maximum intervals
#' in the time data are in the console output, so a user can see immediately if
#' there are large gaps in the data.
#'
#' If some of these checks produce warnings, it should *generally* not hinder
#' analysis of the data. `respR` has been coded to rely on linear regressions on
#' exact data values, and not make assumptions about data spacing or order.
#' Therefore issues such as missing or NA/NaN values, duplicate or
#' non-sequential time values, or uneven time spacing should not cause any
#' erroneous results, as long as they do not occur over large regions of the
#' data. `inspect.ft` however outputs locations (row numbers) of where these
#' issues occur (located in the `$locs` element of the output), allowing users
#' to amend them before analysis. We would recommend that to be completely
#' confident in any results from analysis of such data, and avoid obscure
#' errors, these issues be addressed before proceeding.
#'
#' ## Background control or "blank" experiments
#'
#' For experiments in which the specimen data is to be background corrected by a
#' concurrently-run control experiment, `inspect.ft` can be used by specifying
#' the specimen experiment as `out.oxy`, and the "blank" as the `in.oxy` input.
#' In this way, any variations in oxygen in the specimen data due to background
#' microbial activity, or for any other reason such as fluctuations in inflow
#' oxygen, are accounted for in the delta oxygen calculations, and therefore in
#' the rate calculated in [`calc_rate.ft()`]. See the vignettes on the website
#' for examples.
#'
#' If the background recordings are experiments with their own outflow and
#' inflow recordings, which show a generally consistent oxygen delta due to
#' microbial activity, this can be saved as a separate `inspect.ft` object, a
#' background rate calculated in [`calc_rate.ft()`], and this used in
#' [`adjust_rate.ft()`] as the `by` input to perform background adjustments to
#' specimen rates.
#'
#' **Note**: All background calculations should be from experiments done at the
#' *same flow rate* as the specimen experiments to be corrected.
#'
#' ## S3 Generic Functions
#'
#' Saved output objects can be used in the generic S3 functions `plot()`,
#' `print()` and `summary()`.
#'
#' - `plot()`: plots the result.
#'
#' - `print()`: prints a summary of the checks performed on the data. If issues
#' are found, locations (row numbers) are printed (up to first 20 occurrences).
#'
#' - `summary()`: simple wrapper for `print()` function. See above.
#'
#' ## More
#'
#' For additional help, documentation, vignettes, and more visit the `respR`
#' website at <https://januarharianto.github.io/respR/>
#'
#' @return Output is a `list` object of class `inspect.ft` containing input
#' parameters and data, data check summaries, and metadata, which can be
#' passed to [`calc_rate.ft()`] to determine rates. If there are failed checks
#' or warnings, the row locations of the potentially problematic data can be
#' found in `$locs`.
#'
#' @param x `data.frame` containing columns of `time` and `out.oxy` or
#' `delta.oxy` concentrations, and optionally `in.oxy`.
#' @param time integer or string. Defaults to `1`. Specifies the column of the
#' Time data as either a column number or the name.
#' @param out.oxy integer(s) or string(s). Defaults to `NULL`. Specifies the
#' column(s) of the outflow oxygen data as either column numbers or names.
#' @param in.oxy integer(s) or string(s). Defaults to `NULL`. Specifies the
#' column(s) of the inflow oxygen data as either column numbers or names.
#' @param in.oxy.value numeric value. Defaults to `NULL`. If there is no
#' continuous `in.oxy` data, this specifies a fixed value of oxygen
#' concentration for inflowing water in same units as `out.oxy`, and is used
#' with `out.oxy` to calculate a `delta.oxy`.
#' @param delta.oxy integer(s) or string(s). Defaults to all non-time columns if
#' no other inputs given. Specifies the column number(s) or name(s) of delta
#' oxygen data, for when the user has already calculated the difference
#' between outflow and inflow oxygen (should be negative values for oxygen
#' uptake). If this is used, `out.oxy` and `in.oxy` should be NULL.
#' @param plot logical. Defaults to TRUE. Plots the data. See Details.
#' @param add.data integer or string. Defaults to `NULL`. Specifies the column
#' number or name of an optional additional data source that will be plotted
#' in blue alongside the full oxygen timeseries.
#' @param ... Allows additional plotting controls to be passed, such as `legend
#' = FALSE`, `quiet = TRUE`, `rate.rev = FALSE` and `pos`.
#'
#' @importFrom data.table data.table
#' @export
#'
#' @examples
#'
#' # Inspect outflow and inflow oxygen data
#' x <- inspect.ft(flowthrough.rd, time = 1, out.oxy = 2,
#' in.oxy = 3)
#' print(x)
#' plot(x)
#'
#' # Inspect outflow oxygen data with inflow oxygen as a known value in
#' # the same units
#' x <- inspect.ft(flowthrough.rd, time = 1, out.oxy = 2,
#' in.oxy.value = 8.90)
#'
#' # Inspect already calculated delta oxygen data
#' inspect.ft(flowthrough.rd, time = 1, delta.oxy = 4)
#'
#' # inspect multiple columns for a quick overview
#' inspect.ft(flowthrough_mult.rd, time = 1, delta.oxy = 10:12)
#'
#' # Inspect outflow and use a blank control chamber as background
#' # correction
#' #
#' # This experiment has increasing background respiration over time.
#' # Inspecting outflow oxygen with inflow header tank concentrations
#' # suggests specimen rates (bottom delta.oxy plot) are increasing.
#' inspect.ft(flowthrough_sim.rd, time = 1,
#' out.oxy = 2, in.oxy = 4)
#'
#' # However, inspecting with recordings from a concurrent blank
#' # control accounts for this and shows specimen rates are level
#' # when background is taken into account.
#' inspect.ft(flowthrough_sim.rd, time = 1,
#' out.oxy = 2, in.oxy = 3)
inspect.ft <- function(x, time = NULL, out.oxy = NULL, in.oxy = NULL,
in.oxy.value = NULL, delta.oxy = NULL, plot = TRUE,
add.data = NULL, ...) {
## Save function call for output
call <- match.call()
# Extract data ------------------------------------------------------------
df <- as.data.frame(x)
# Input checks ------------------------------------------------------------
## stop if not df
if (!is.data.frame(x)) stop("inspect.ft: 'x' must be data.frame object.", call. = FALSE)
## Apply time column default
if(is.null(time)) {
message("inspect.ft: Applying column default of 'time = 1'")
time <- 1
}
## only one of in.oxy or in.oxy.value should be entered
if(!is.null(in.oxy) && !is.null(in.oxy.value))
stop("inspect.ft: Only one of 'in.oxy' or 'in.oxy.value' can be entered.", call. = FALSE)
## if in.oxy entered, out.oxy must be entered
if(!is.null(in.oxy) && (is.null(out.oxy)))
stop("inspect.ft: An 'in.oxy' input requires paired 'out.oxy' column(s).", call. = FALSE)
## if out.oxy entered, one of in.oxy or in.oxy.value must be entered
if(!is.null(out.oxy) && (is.null(in.oxy) && is.null(in.oxy.value)))
stop("inspect.ft: With 'out.oxy' data, paired 'in.oxy' columns or an 'in.oxy.value' is required.", call. = FALSE)
## if out.oxy entered, in.oxy must be either same no. of columns or a single column
if(!is.null(out.oxy) && !(is.null(in.oxy))){
ncol_o <- length(out.oxy)
ncol_i <- length(in.oxy)
if(ncol_o != ncol_i && ncol_i != 1)
stop("inspect.ft: With 'out.oxy' data, 'in.oxy' must be a single column or an equal number of paired columns.", call. = FALSE)
}
# if delta.oxy entered, out.oxy, in.oxy & in.oxy.value should be NULL
if(!is.null(delta.oxy) && (!is.null(out.oxy) || !is.null(in.oxy) || !is.null(in.oxy.value)))
stop("inspect.ft: With 'delta.oxy' data, 'out.oxy', 'in.oxy' and 'in.oxy.value' should be NULL.", call. = FALSE)
# Apply input defaults ----------------------------------------------------
## After all that, if these are still NULL assume all non-time are delta.oxy
if (is.null(out.oxy) && is.null(delta.oxy)) {
message("inspect.ft: Applying column default of all non-time column(s) as 'delta.oxy'")
listcols <- seq.int(1, ncol(x))
delta.oxy <- listcols[!listcols %in% time]
}
# Final input checks ------------------------------------------------------
# parse strings to column numbers
if(is.character(time)) time <- column.id(time, df, "inspect.ft: ")
if(is.character(out.oxy)) out.oxy <- column.id(out.oxy, df, "inspect.ft: ")
if(is.character(in.oxy)) in.oxy <- column.id(in.oxy, df, "inspect.ft: ")
if(is.character(delta.oxy)) delta.oxy <- column.id(delta.oxy, df, "inspect.ft: ")
## check for duplicate column numbers
inputs <- list(time, out.oxy, in.oxy, delta.oxy)
if(column.conflict(inputs)) {
dupe_cols <- column.conflict(inputs, id = TRUE)
stop(glue::glue("inspect.ft: Input columns conflict. Column(s) {glue::glue_collapse(dupe_cols, \", \", last = \" and \")} are entered more than once."), call. = FALSE)
}
## check column inputs are valid
column.val(time, req = TRUE, max = 1, range = c(1, ncol(x)),
msg = "inspect.ft: 'time' -")
column.val(out.oxy, req = FALSE, max = ncol(x)-1, range = c(1, ncol(x)),
msg = "inspect.ft: 'out.oxy' -")
column.val(in.oxy, req = FALSE, max = ncol(x)-1, range = c(0, ncol(x)),
msg = "inspect.ft: 'in.oxy' -")
column.val(delta.oxy, req = FALSE, max = ncol(x)-1, range = c(0, ncol(x)),
msg = "inspect.ft: 'delta.oxy' -")
# Extract data ------------------------------------------------------------
## time
time.all <- lapply(1:length(df[time]), function(z) df[time][[z]])
names(time.all) <- names(df[time])
## out oxy
# if no input, null
if(is.null(out.oxy)) {
out.oxy.all <- NULL
} else {
out.oxy.all <- lapply(1:length(df[out.oxy]), function(z) df[out.oxy][[z]])
names(out.oxy.all) <- names(df[out.oxy])
}
## in.oxy
# if no input, null
if(is.null(in.oxy)) {
in.oxy.all <- NULL
} else {
in.oxy.all <- lapply(1:length(df[in.oxy]), function(z) df[in.oxy][[z]])
names(in.oxy.all) <- names(df[in.oxy])
}
## in.oxy conc value - if present, make into vector same length as out.oxy
if(!is.null(in.oxy.value)) {
in.oxy.all <- list(rep(in.oxy.value, nrow(df)))
names(in.oxy.all) <- "in.oxy.value"
}
## If no delta input - make it NULL for now,
## We will calculate it after checks if out.oxy and in.out non-numeric check passes.
## Otherwise failures here if non-numeric data
if(is.null(delta.oxy)) {
del.oxy.all <- NULL
## otherwise extract it
} else {
del.oxy.all <- lapply(1:length(df[delta.oxy]), function(z) df[delta.oxy][[z]])
names(del.oxy.all) <- names(df[delta.oxy])
}
## Additional data
if(!is.null(add.data)) {
# if column name, get number
if(is.character(add.data)) add.data <- column.id(add.data, df, "inspect.ft: ")
input.val(add.data, num = TRUE, int = TRUE, req = FALSE,
max = 1, min = 1, range = c(1, ncol(df)),
msg = "inspect.ft: 'add.data' -")
add.data <- df[[add.data]]
}
## Do Data Checks
time_results <- check_timeseries(time.all, "time")
if(is.null(out.oxy)) out.oxy_results <- NULL else
out.oxy_results <- check_timeseries(out.oxy.all, "oxygen")
if(is.null(in.oxy)) in.oxy_results <- NULL else
in.oxy_results <- check_timeseries(in.oxy.all, "oxygen")
if(is.null(delta.oxy)) del.oxy_results <- NULL else
del.oxy_results <- check_timeseries(del.oxy.all, "oxygen")
# issue warnings
# time
if (any(unlist(time_results[[1]][1,])))
warning("inspect.ft: Time column not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo output returned.", call. = F)
if (any(unlist(time_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Time column. Remove or replace before proceeding.", call. = F)
if (any(unlist(time_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Time column.", call. = F)
if (any(unlist(time_results[[1]][4,]) == "TRUE"))
warning("inspect.ft: Non-sequential Time values found.", call. = F)
if (any(unlist(time_results[[1]][5,]) == "TRUE"))
warning("inspect.ft: Duplicate Time values found.", call. = F)
if (any(unlist(time_results[[1]][6,]) == "TRUE"))
warning("inspect.ft: Time values are not evenly-spaced (numerically).", call. = F)
# out.oxy
if (any(unlist(out.oxy_results[[1]][1,])))
warning("inspect.ft: Oxygen column(s) not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo output returned.", call. = F)
if (any(unlist(out.oxy_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Oxygen column(s). Remove or replace before proceeding.", call. = F)
if (any(unlist(out.oxy_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Oxygen column(s).", call. = F)
# in.oxy
if (any(unlist(in.oxy_results[[1]][1,])))
warning("inspect.ft: Oxygen column(s) not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo input returned.", call. = F)
if (any(unlist(in.oxy_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Oxygen column(s). Remove or replace before proceeding.", call. = F)
if (any(unlist(in.oxy_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Oxygen column(s).", call. = F)
# del.oxy
if (any(unlist(del.oxy_results[[1]][1,])))
warning("inspect.ft: Oxygen column(s) not numeric. Other column checks skipped. \nData cannot be analysed by respR functions if not numeric. \nNo output returned.", call. = F)
if (any(unlist(del.oxy_results[[1]][2,]) == "TRUE"))
warning("inspect.ft: Inf/-Inf values detected in Oxygen column(s). Remove or replace before proceeding.", call. = F)
if (any(unlist(del.oxy_results[[1]][3,]) == "TRUE"))
warning("inspect.ft: NA/NaN values detected in Oxygen column(s).", call. = F)
# combine results
checks <- cbind(time_results[[1]], out.oxy_results[[1]], in.oxy_results[[1]], del.oxy_results[[1]])
locs_raw <- cbind(time_results[[2]], out.oxy_results[[2]], in.oxy_results[[2]], del.oxy_results[[2]])
# output
locs <- lapply(1:ncol(locs_raw), function(z) locs_raw[, z])
names(locs) <- colnames(locs_raw)
# if in.oxy and out.oxy non-numeric checks have passed, now it's ok to calculate delta.oxy
if(is.null(delta.oxy) && !(any(unlist(out.oxy_results[[1]][1,]))) && !(any(unlist(in.oxy_results[[1]][1,])))) {
del.oxy.all <- mapply(function(p,q) p-q,
p = out.oxy.all,
q = in.oxy.all,
SIMPLIFY = FALSE)
names(del.oxy.all) <- sapply(1:length(del.oxy.all), function(p) glue::glue("delta.oxy.calc.{p}"))
## otherwise make dummy list so next part doesn't error.
## It won't be returned anyway
} else if (is.null(delta.oxy)) {
del.oxy.all <- mapply(function(p,q) rep(NA, length(p)),
p = out.oxy.all,
q = in.oxy.all,
SIMPLIFY = FALSE)
#names(del.oxy.all) <- sapply(1:length(del.oxy.all), function(p) glue::glue("delta.oxy.calc.{p}"))
}
# save new data frame and create output object
if(is.null(out.oxy.all) && is.null(in.oxy.all)) {
dataframe <- data.table::data.table(cbind(data.frame(time.all),
del.oxy.all))
} else {
dataframe <- data.table::data.table(cbind(data.frame(time.all),
out.oxy.all,
in.oxy.all,
del.oxy.all))
}
out <- list(call = call,
dataframe = dataframe,
add.data = add.data,
inputs = list(df = x,
time = time,
out.oxy = out.oxy,
in.oxy = in.oxy,
in.oxy.value = in.oxy.value,
delta.oxy = delta.oxy,
plot = plot,
add.data = add.data),
data = list(time = time.all,
out.oxy = out.oxy.all,
in.oxy = in.oxy.all,
delta.oxy = del.oxy.all),
checks = checks,
locs_raw = locs_raw,
locs = locs)
class(out) <- "inspect.ft"
# if no errors occur, send out a good message :D
if (!any(na.omit(unlist(checks) == "TRUE")))
message("inspect.ft: No issues detected while inspecting data frame.") else
message("inspect.ft: Data issues detected. For more information use print().")
# Not all functions should print on assigning, but this one should
print(out)
# If any inspected columns found to be non-numeric, print, but don't return object
# If this is the case, it can't be used in later fns anyway, so no point
# And skip plotting - avoids awkward code in plot() to handle the object having non-numeric data
# Warnings issued above.
if(any(unlist(checks[1,]))) {
return(invisible(NULL))
} else {
if (plot) plot(out, quiet = TRUE, ...)
## invisible prevents it printing twice on assigning
return(invisible(out))
}
}
#' Print inspect.ft objects
#' @param x inspect.ft object
#' @param ... Pass additional inputs
#' @keywords internal
#' @return Print to console. No returned value.
#' @export
print.inspect.ft <- function(x, ...) {
cat("\n# print.inspect.ft # --------------------\n")
checks <- x$checks
locs <- x$locs_raw
# rename content:
tab <- checks
tab[tab == "TRUE"] <- "WARN"
tab[tab == "FALSE"] <- "pass"
tab[is.na(tab)] <- "-"
# print table
print(as.data.frame(tab), quote = FALSE)
cat("\n")
# highlight locations that did not pass the tests (but only for 2-col dfs):
# No need to do this for numeric check - it's all or none
if (checks[, 1][[2]] != "skip" && checks[, 1][[2]]) {
xinf <- locs[, 1][[2]]
cat("Inf/-Inf Time data locations: ")
if (length(xinf) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xinf, 20))
}
if (checks[, 1][[3]] != "skip" && checks[, 1][[3]]) {
xnan <- locs[, 1][[3]]
cat("NA/NaN Time data locations: ")
if (length(xnan) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xnan, 20))
}
if (checks[, 1][[4]] != "skip" && checks[, 1][[4]]) {
xseq <- locs[, 1][[4]]
cat("Non-sequential Time data locations ")
if (length(xseq) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xseq, 20))
}
if (checks[, 1][[5]] != "skip" && checks[, 1][[5]]) {
xdup <- locs[, 1][[5]]
cat("Duplicate Time data locations ")
if (length(xdup) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xdup, 20))
}
if (checks[, 1][[6]] != "skip" && checks[, 1][[6]]) {
xevn <- locs[, 1][[6]]
cat("Uneven Time data locations ")
if (length(xevn) > 20) cat("(first 20 shown) ")
cat("in column:", names(x$dataframe)[1], "\n")
print(head(xevn, 20))
cat("Minimum and Maximum intervals in uneven Time data: \n")
print(range(diff(nainf.omit(x$dataframe[[1]]))))
}
for(i in 2:ncol(checks)) { ## for multiple columns
# use ncol(checks) because x$dataframe may have extra delta oxy calculated columns added
if (checks[, i][[2]] != "skip" && checks[, i][[2]]) {
yinf <- locs[,i][[2]]
cat("Inf/-Inf locations ")
if (length(yinf) > 20) cat("(first 20 shown) ")
cat("in Oxygen column:", names(x$dataframe)[i], "\n")
print(head(yinf, 20))
}
if (checks[, i][[3]] != "skip" && checks[, i][[3]]) {
ynan <- locs[,i][[3]]
cat("NA/NaN locations ")
if (length(ynan) > 20) cat("(first 20 shown) ")
cat("in Oxygen column:", names(x$dataframe)[i], "\n")
print(head(ynan, 20))
}
}
cat("-----------------------------------------\n")
return(invisible(x))
}
#' Summarise inspect.ft objects
#' @param object inspect.ft object
#' @param ... Pass additional inputs
#' @keywords internal
#' @return Print to console. No returned value.
#' @export
summary.inspect.ft <- function(object, ...) {
print(object)
}
#' Average inspect.ft object rates
#' @param x calc_rate.bg object
#' @param ... Pass additional inputs
#' @keywords internal
#' @return Print to console. No returned value.
#' @export
mean.inspect.ft <- function(x, ...){
message("inspect.ft: mean() is not available for 'inspect.ft' objects.")
return(invisible(x))
}
#' Plot inspect.ft objects
#' @param x inspect.ft object
#' @param width numeric. Smoothing factor (rolling mean) for delta oxygen values
#' as proportion of total data length (0 to 1)
#' @param pos integer. Which result to plot.
#' @param quiet logical. Suppress console output.
#' @param legend logical. Suppress labels and legends.
#' @param rate.rev logical. Control direction of y-axis in delta oxygen plot.
#' @param ... Pass additional plotting parameters
#' @keywords internal
#' @return A plot. No returned value.
#' @export
plot.inspect.ft <- function(x, width = NULL, pos = NULL, quiet = FALSE,
legend = TRUE, rate.rev = TRUE, ...) {
parorig <- par(no.readonly = TRUE) # save original par settings
on.exit(par(parorig)) # revert par settings to original
if (!quiet)
cat("\n# plot.inspect.ft # ---------------------\n")
# extract data
dt <- x$dataframe
time <- x$data$time
out.oxy <- x$data$out.oxy
in.oxy <- x$data$in.oxy
if(length(in.oxy) == 1) in.oxy <- rep(in.oxy, length(out.oxy)) # makes plotting easier
del.oxy <- x$data$delta.oxy
if(is.null(width)) win <- 1 else
win <- length(del.oxy[[1]]) * width
del.oxy.smooth <- lapply(del.oxy, function(z) roll::roll_mean(z, width = win, min_obs = 1))
# Apply default plotting params
par(oma = oma_def,
mai = mai_def,
las = las_def,
mgp = mgp_def,
tck = tck_def,
pch = pch_def,
cex = cex_def)
# Multi column plot -------------------------------------------------------
## if multiple columns or only delta.oxy data inspected, just plot grid of all delta.oxy
if (length(del.oxy) > 1 || is.null(out.oxy)){
if(!quiet && is.null(pos) && length(del.oxy) > 1)
cat("plot.inspect.ft: Plotting all delta oxygen columns. To plot a specific dataset use 'pos'.\n")
if(!is.null(x$add.data))
message("plot.inspect.ft: Additional data source cannot be plotted for multiple columns.")
if(is.null(pos))
pos <- seq.int(1, length(del.oxy))
if(any(pos > length(del.oxy)))
stop("plot.inspect.ft: Invalid 'pos' input: only ", length(del.oxy), " data inputs found.", call. = FALSE)
if (!quiet && length(pos) == 1)
cat('plot.inspect.ft: Plotting delta.oxy data from position', pos, 'of', length(del.oxy), '... \n')
par(mfrow = n2mfrow(length(pos)),
ps = 10,
cex = 1,
cex.main = 1,
...)
lapply(pos, function(z) {
ylim <- grDevices::extendrange(r = range(del.oxy[[z]], na.rm = TRUE), f = 0.05) ## add a little more space
if(rate.rev) ylim <- rev(ylim) ## reverse y-axis
plot(data.frame(time, del.oxy.smooth[[z]]),
axes = FALSE,
ylim = ylim,
xlab = "",
ylab = "",
cex = 0.5,
col = ftcol_del,
panel.first = grid())
box()
axis(side = 2)
axis(side = 1, col.lab = "blue", col.axis = "blue")
# plot invisibly - to add row index x-axis
par(new = TRUE, ...)
plot(data.frame(1:length(unlist(time)), del.oxy[[z]]),
xlab = "",
ylab = "",
pch = "",
cex = .5,
axes = FALSE
)
axis(side = 3, col.axis = "red")
title(main = glue::glue("Column: {names(del.oxy)[z]}"), line = 1.2,
adj = 0)
})
if(legend && length(pos) == 1) legend("topright",
"Row Index",
text.col = "red",
bg = "gray90",
cex = 0.6)
if(legend && length(pos) == 1) legend("bottomright",
"Time",
text.col = "blue",
bg = "gray90",
cex = 0.6)
mtext("inspect.ft: Inspecting Selected Columns",
outer = TRUE, cex = 1.2, line = 0.3, font = 2)
# Single dataset ----------------------------------------------------------
} else if (length(x$data$delta.oxy) == 1 || !is.null(pos)) {
if(is.null(pos)) pos <- 1
if(pos > length(x$data$delta.oxy))
stop("plot.inspect.ft: Invalid 'pos' input: only ", length(x$data$out.oxy), " data inputs found.", call. = FALSE)
if (!quiet)
cat('plot.inspect.ft: Plotting inspect.ft dataset from position', pos, 'of', length(x$data$delta), '... \n')
m <- rbind(c(1,1,1), c(1,1,1), c(2,2,2))
layout(m)
par(mai = mai_def_top_ext, # this one needs extra space
ps = 10,
cex = 1,
cex.main = 1,
...)
par(...) # to allow oma and mai to be overridden
## ylim for outflow and inflow plots - plus 10%
ylim <- range(range(nainf.omit(out.oxy[[pos]])), range(nainf.omit(in.oxy[[pos]]))) ## so all on same axes
buffer <- diff(ylim)*0.1
ylim <- c(ylim[1] - buffer, ylim[2] + buffer) ## add a little more space
# Outflow plot ------------------------------------------------------------
plot(unlist(time),
out.oxy[[pos]],
xlab = "",
ylab = "",
ylim = ylim,
cex = .5,
col = ftcol_out,
axes = FALSE,
col.lab = "blue",
col.axis = "blue",
panel.first = grid())
axis(side = 2)
points(unlist(time),
in.oxy[[pos]],
xlab = "",
ylab = "",
ylim = ylim,
cex = .5,
col = ftcol_in)
# plot invisibly - to add row index x-axis
par(new = TRUE)
plot(seq(1, nrow(dt)),
out.oxy[[pos]],
xlab = "",
ylab = "",
pch = "",
cex = .5,
axes = FALSE)
axis(side = 3, col.axis = "red")
# if add.data plot it too
if(!is.null(x$add.data)) {
par(new = TRUE, mgp = c(0, 0.13, 0))
plot(seq(1, nrow(dt)),
x$add.data,
ylim = grDevices::extendrange(x$add.data, f = 1),
xlab = "",
ylab = "",
pch = ".",
cex = .5,
axes = FALSE,
col = "blue")
axis(side = 4, col.axis = "blue")
par(mgp = mgp_def) # set back default mgp
par(...) # or allow custom one to overwrite it
}
box()
if(legend) legend("topright",
"Row Index",
text.col = "red",
bg = "gray90",
cex = 0.6)
if(legend) legend("right",
legend = c("Inflow", "Outflow"),
pch = pch_def,
col = c(ftcol_in, ftcol_out),
bg = "white",
cex = 0.8)
mtext("Outflow ~ Inflow Oxygen",
outer = TRUE, cex = 1.2, line = 0.3, font = 2)
# Delta plot --------------------------------------------------------------
## ylim - plus 10%
ylim <- range(nainf.omit(del.oxy[[pos]])) ## so all on same axes
buffer <- diff(ylim)*0.1
ylim <- c(ylim[1] - buffer, ylim[2] + buffer) ## add a little more space
if(rate.rev) ylim <- rev(ylim) ## reverse y-axis
plot(unlist(time),
del.oxy.smooth[[pos]],
xlab = "",
ylab = "",
ylim = ylim, # reverse y axis
cex = .5,
col = ftcol_del,
axes = FALSE,
panel.first = grid())
axis(side = 2) # simply to put yaxis lab colour back to black
axis(side = 1,
col.lab = "blue",
col.axis = "blue")
box()
if(legend) legend("bottomright",
"Time",
text.col = "blue",
bg = "gray90",
cex = 0.6)
mtext("Delta Oxygen",
outer = FALSE, cex = 1.2, line = 1.2, font = 2)
mtext(glue::glue("(i.e. Unitless Rate)"),
outer = FALSE, cex = 1, line = 0.3, font = 2)
}
if (!quiet){
cat("-----------------------------------------\n")
}
return(invisible(x))
}
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