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Add A ggplot Panel Column

Source: R/dilplots-ggplot.R
add_ggplot_panel.Rd

Create a column which contains a list of ggplot suited for a pdf report.

Usage

add_ggplot_panel(
  curve_table,
  curve_summary = NULL,
  dilution_table = lifecycle::deprecated(),
  dilution_summary = lifecycle::deprecated(),
  grouping_variable = c("Curve_Name", "Curve_Batch_Name"),
  curve_batch_var = "Curve_Batch_Name",
  curve_batch_col = c("#377eb8", "#4daf4a", "#9C27B0", "#BCAAA4", "#FF8A65", "#EFBBCF"),
  dil_batch_var = lifecycle::deprecated(),
  dil_batch_col = lifecycle::deprecated(),
  conc_var = "Concentration",
  conc_var_units = "%",
  conc_var_interval = 50,
  signal_var = "Signal",
  have_plot_title = TRUE,
  plot_summary_table = TRUE,
  plot_first_half_lin_reg = FALSE,
  plot_last_half_lin_reg = FALSE
)

Arguments

curve_table

Output given from the function create_curve_table(). It is in long table format with columns indicating at least the lipid/transition name, the concentration and signal. Other columns may be present if it is used to group the curve together.

curve_summary

The summary table generated by function summarise_curve_table() and/or evaluate_linearity() but it can also be any generic data frame or tibble. If there is no input given in this, the program will create one using the function summarise_curve_table() and evaluate_linearity() with grouping_variable, conc_var and signal_var as inputs. Default: NULL

dilution_table

[Deprecated] dilution_table was renamed to curve_table.

dilution_summary

[Deprecated] dilution_summary was renamed to curve_summary.

grouping_variable

A character vector of column names in curve_tableto indicate how each curve should be grouped by. Default: c("Curve_Name", "Curve_Batch_Name")

curve_batch_var

Column name in curve_table to indicate the group name of each curve batch, used to colour the points in the curve plot. Default: 'Curve_Batch_Name'

curve_batch_col

A vector of colours to be used for the curve batch group named given in curve_batch_var. Default: c("#377eb8", "#4daf4a", "#9C27B0", "#BCAAA4", "#FF8A65", "#EFBBCF")

dil_batch_var

[Deprecated] dil_batch_var was renamed to curve_batch_var.

dil_batch_col

[Deprecated] dil_batch_col was renamed to curve_batch_col.

conc_var

Column name in curve_table to indicate concentration. Default: 'Concentration'

conc_var_units

Unit of measure for conc_var. Default: '%'

conc_var_interval

Distance between two tick labels in the curve plot. Default: 50

signal_var

Column name in curve_table to indicate signal. Default: 'Area'

have_plot_title

Indicate if you want to have a plot title in the ggplot plot. Default: TRUE

plot_summary_table

Indicate if you want to plot the summary table in the ggplot plot. Default: TRUE

plot_first_half_lin_reg

Decide if we plot an extra regression line that best fits the first half of conc_var curve points. Default: FALSE

plot_last_half_lin_reg

Decide if we plot an extra regression line that best fits the last half of conc_var curve points. Default: FALSE

Value

A table with columns from grouping variable

and a new column panel created containing a ggplot curve plot in each row. This column is used to create the plot figure in the pdf report.

Examples


# Data Creation
concentration <- c(
  10, 20, 25, 40, 50, 60,
  75, 80, 100, 125, 150,
  10, 25, 40, 50, 60,
  75, 80, 100, 125, 150
)

curve_batch_name <- c(
  "B1", "B1", "B1", "B1", "B1",
  "B1", "B1", "B1", "B1", "B1", "B1",
  "B2", "B2", "B2", "B2", "B2",
  "B2", "B2", "B2", "B2", "B2"
)

sample_name <- c(
  "Sample_010a", "Sample_020a",
  "Sample_025a", "Sample_040a", "Sample_050a",
  "Sample_060a", "Sample_075a", "Sample_080a",
  "Sample_100a", "Sample_125a", "Sample_150a",
  "Sample_010b", "Sample_025b",
  "Sample_040b", "Sample_050b", "Sample_060b",
  "Sample_075b", "Sample_080b", "Sample_100b",
  "Sample_125b", "Sample_150b"
)

curve_1_saturation_regime <- c(
  5748124, 16616414, 21702718, 36191617,
  49324541, 55618266, 66947588, 74964771,
  75438063, 91770737, 94692060,
  5192648, 16594991, 32507833, 46499896,
  55388856, 62505210, 62778078, 72158161,
  78044338, 86158414
)

curve_2_good_linearity <- c(
  31538, 53709, 69990, 101977, 146436, 180960,
  232881, 283780, 298289, 344519, 430432,
  25463, 63387, 90624, 131274, 138069,
  205353, 202407, 260205, 292257, 367924
)

curve_3_noise_regime <- c(
  544, 397, 829, 1437, 1808, 2231,
  3343, 2915, 5268, 8031, 11045,
  500, 903, 1267, 2031, 2100,
  3563, 4500, 5300, 8500, 10430
)

curve_4_poor_linearity <- c(
  380519, 485372, 478770, 474467, 531640, 576301,
  501068, 550201, 515110, 499543, 474745,
  197417, 322846, 478398, 423174, 418577,
  426089, 413292, 450190, 415309, 457618
)

curve_batch_annot <- tibble::tibble(
  Sample_Name = sample_name,
  Curve_Batch_Name = curve_batch_name,
  Concentration = concentration
)

curve_data <- tibble::tibble(
  Sample_Name = sample_name,
  `Curve_1` = curve_1_saturation_regime,
  `Curve_2` = curve_2_good_linearity,
  `Curve_3` = curve_3_noise_regime,
  `Curve_4` = curve_4_poor_linearity
)

# Create curve table
curve_table <- create_curve_table(
  curve_batch_annot = curve_batch_annot,
  curve_data_wide = curve_data,
  common_column = "Sample_Name",
  signal_var = "Signal",
  column_group = "Curve_Name"
)

# Create curve statistical summary
curve_summary <- curve_table |>
  summarise_curve_table(
    grouping_variable = c(
      "Curve_Name",
      "Curve_Batch_Name"
    ),
    conc_var = "Concentration",
    signal_var = "Signal"
  ) |>
  dplyr::arrange(.data[["Curve_Name"]]) |>
  evaluate_linearity(grouping_variable = c(
    "Curve_Name",
    "Curve_Batch_Name"
  ))

# Create a ggplot table
ggplot_table <- add_ggplot_panel(
  curve_table,
  curve_summary = curve_summary,
  grouping_variable = c("Curve_Name",
                        "Curve_Batch_Name"),
  curve_batch_var = "Curve_Batch_Name",
  conc_var = "Concentration",
  conc_var_units = "%",
  conc_var_interval = 50,
  signal_var = "Signal"
)

ggplot_list <- ggplot_table$panel

ggplot_list[[1]]


ggplot_list[[2]]


ggplot_list[[3]]