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Summary top abundant tableGrobs for plots.

Source: R/plotting-functions.R
top_abund_tableGrob.Rd

Produce summary tableGrobs as R graphics. For this functionality the suggested package gridExtra is required. To visualize the resulting object:

gridExtra::grid.arrange(tableGrob)

Usage

top_abund_tableGrob(
  df,
  id_cols = mandatory_IS_vars(),
  quant_col = "fragmentEstimate_sum_PercAbundance",
  by = "TimePoint",
  alluvial_plot = NULL,
  top_n = 10,
  tbl_cols = "GeneName",
  include_id_cols = FALSE,
  digits = 2,
  perc_symbol = TRUE,
  transform_by = NULL
)

Arguments

df

A data frame

id_cols

Character vector of id column names. To plot after alluvial, these columns must be the same as the alluvia argument of integration_alluvial_plot.

quant_col

Column name holding the quantification value. To plot after alluvial, these columns must be the same as the plot_y argument of integration_alluvial_plot.

by

The column name to subdivide tables for. The function will produce one table for each distinct value in by. To plot after alluvial, these columns must be the same as the plot_x argument of integration_alluvial_plot.

alluvial_plot

Either NULL or an alluvial plot for color mapping between values of y.

top_n

Integer. How many rows should the table contain at most?

tbl_cols

Table columns to show in the final output besides quant_col.

include_id_cols

Logical. Include id_cols in the output?

digits

Integer. Digits to show for the quantification column

perc_symbol

Logical. Show percentage symbol in the quantification column?

transform_by

Either a function or a purrr-style lambda. This function is applied to the column by before separating columns. If NULL no function is applied. Useful to modify column order in final table.

Value

A tableGrob object

See also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), fisher_scatterplot(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_cis_overtime_heatmap()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
aggreg <- aggregate_values_by_key(
    x = integration_matrices,
    association_file = association_file,
    value_cols = c("seqCount", "fragmentEstimate")
)
abund <- compute_abundance(x = aggreg)
grob <- top_abund_tableGrob(abund)
gridExtra::grid.arrange(grob)


# with transform
grob <- top_abund_tableGrob(abund, transform_by = ~ as.numeric(.x))