Title: Dot Plots Mimicking Violin Plots
Date: 2023-10-19
Version: 0.0.1
Maintainer: Fernando Roa <froao@ufg.br>
Description: Modifies dot plots to have different sizes of dots mimicking violin plots and identifies modes or peaks for them based on frequency and kernel density estimates (Rosenblatt, 1956) <doi:10.1214/aoms/1177728190> (Parzen, 1962) <doi:10.1214/aoms/1177704472>.
Depends: R (≥ 3.5)
Imports: gridExtra, gtools, tidyr, stringr, dplyr, ggplot2, lazyeval, magrittr, rlang, scales, tidyselect
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-10-29 22:10:54 UTC; fernando
Author: Fernando Roa [aut, cre], Mariana Pires de Campos Telles [ctb]
Repository: CRAN
Date/Publication: 2023-10-30 13:20:02 UTC

Integrates tables and plots

Description

A series of functions to get modes/peaks from discrete and continuous variables and integrate them as tables inside plots cite as in: citation("dotsViolin")

Makes a composite dot-plot and violin-plot

Description

This function makes a dot-plot and violin-plot

Usage

dots_and_violin(
  dataframe,
  colgroup,
  collabel,
  maxcountcol,
  widthdots,
  maxx,
  labelx,
  desiredorder,
  binwidth,
  adjust,
  binexp,
  fill_group = "fill_group",
  dots = TRUE,
  violin = TRUE
)

Arguments

dataframe

dataframe

colgroup

chr column to group by

collabel

label to be used in the plot

maxcountcol

numeric variable

widthdots

dotsize parameter for geom_dotplot

maxx

x axis maximum value

labelx

label for x axis

desiredorder

order for the colgroup categories

binwidth

see, plot_dotviolin

adjust

adjust param, see geom_violin

binexp

digit to modify size of bins with base 10

fill_group

2nd categorical data (use only 2 categories)

dots

boolean include dot plot

violin

boolean include violin plot

Value

A grid of ggplots that mimics a single plot

Examples

fabaceae_mode_counts <- get_modes_counts(fabaceae_clade_n_df, "clade", "parsed_n")
fabaceae_clade_n_df_count <- make_legend_with_stats(fabaceae_mode_counts, "label_count", 1, TRUE)
fabaceae_clade_n_df$label_count <- fabaceae_clade_n_df_count$label_count[match(
  fabaceae_clade_n_df$clade,
  fabaceae_clade_n_df_count$clade
)]
desiredorder1 <- unique(fabaceae_clade_n_df$clade)

dots_and_violin(
  fabaceae_clade_n_df, "clade", "label_count", "parsed_n", 2,
  30, "Chromosome haploid number", desiredorder1, 1, .85, 4,
  "ownwork",
  violin = FALSE
)

dots_and_violin(
  fabaceae_clade_n_df, "clade", "label_count", "parsed_n", 2,
  30, "Chromosome haploid number", desiredorder1, 1, .85, 4,
  dots = FALSE
)
dots_and_violin(
  fabaceae_clade_n_df, "clade", "label_count", "parsed_n", 2,
  30, "Chromosome haploid number", desiredorder1, 1, .85, 4
)

fabaceae_Cx_mode_counts_per_clade_df <- get_peaks_counts_continuous(
  fabaceae_clade_1Cx_df,
  "clade", "Cx", 2, 0.25, 1, 2
)

namecol <- "labelcountcustom"
fabaceae_clade_Cx_peaks_count_df <- make_legend_with_stats(
  fabaceae_Cx_mode_counts_per_clade_df,
  namecol, 1, TRUE
)
fabaceae_clade_1Cx_df$labelcountcustom <-
  fabaceae_clade_Cx_peaks_count_df$labelcountcustom[match(
    fabaceae_clade_1Cx_df$clade,
    fabaceae_clade_Cx_peaks_count_df$clade
  )]
desiredorder <- unique(fabaceae_clade_1Cx_df$clade)

dots_and_violin(
  fabaceae_clade_1Cx_df, "clade", "labelcountcustom", "Cx", 3,
  3, "Genome Size", desiredorder, 0.03, 0.25, 2,
  "ownwork"
)

dots_and_violin(
  fabaceae_clade_1Cx_df, "clade", "labelcountcustom", "Cx", 3,
  3, "Genome Size", desiredorder, 0.03, 0.25, 2,
  dots = FALSE
)
dots_and_violin(
  fabaceae_clade_1Cx_df, "clade", "labelcountcustom", "Cx", 3,
  3, "Genome Size", desiredorder, 0.03, 0.25, 2,
  "ownwork",
  violin = FALSE
)

Genome sizes for fabaceae

Description

fabaceae_clade_1Cx_df: parsed Cx sizes for fabaceae

Usage

fabaceae_clade_1Cx_df

Format

data.frame with columns:

name

OTU, species

clade

main fabaceae clade

Cx

genome size, Cx

See Also

get_peaks_counts_continuous

chromosomal counts for fabaceae

Description

fabaceae_clade_n_df: parsed n counts for fabaceae

Usage

fabaceae_clade_n_df

Format

data.frame with columns:

tip.label

OTU, species

clade

main fabaceae clade

parsed_n

chromosome number, n

See Also

get_modes_counts

Get peaks of a continuous variable

Description

This function allows you to get peaks for a continuous variable. Based on the kernel density function

Usage

get.peaks(x, bw, signifi, nsmall, ranks = 3)

Arguments

x

dataframe

bw

bandwidth

signifi

criteria to bin the data in number of digits

nsmall

criteria to approximate (round) data

ranks

numeric how many ranks to consider

Value

data.frame

get modes, handle ties, ignore less frequent values

Description

This function comes from an answer for a question in stackoverflow https://stackoverflow.com/questions/42698465/obtaining-3-most-common-elements-of-groups-concatenating-ties-and-ignoring-les

Usage

get_modes_counts(data, grouping_col, col2, mode_number = 3)

Arguments

data

data.frame

grouping_col

string split by this column

col2

string numerical data column

mode_number

numeric number of modes to retrieve

Value

data.frame with modes and counts per group

Examples

get_modes_counts(fabaceae_clade_n_df, "clade", "parsed_n")

Peaks of a continuous variable in a dataframe format

Description

This function allows you to get peaks and summary counts per group for a continuos variable in a dataframe format. Handles ties; least frequent is ignored, except if it is the only one, depends on get.peaks function

Usage

get_peaks_counts_continuous(
  origtable,
  grouping_col,
  columnname,
  peak_number,
  adjust1,
  signifi,
  nsmall
)

Arguments

origtable

dataframe

grouping_col

column with categories - character

columnname

column with numerical data

peak_number

number of peaks to get, see get.peaks

adjust1

bandwith adjust parameter

signifi

see get.peaks function

nsmall

see get.peaks function

Value

data.frame

Examples

get_peaks_counts_continuous(fabaceae_clade_1Cx_df, "clade", "Cx", 2, 0.25, 1, 2)

Make legends with stats

Description

This function merges all columns in a dataframe to be used as legends

Usage

make_legend_with_stats(
  data,
  namecol,
  start_column_idx = 2,
  first_justified_left = FALSE
)

Arguments

data

dataframe with columns to be merged into 1

namecol

name to be given to new column

start_column_idx

numeric index of first column to process

first_justified_left

boolean when TRUE justifies first column to the left, defaults to FALSE

Value

data.frame with combined source columns

Examples

fabaceae_mode_counts <- get_modes_counts(fabaceae_clade_n_df, "clade", "parsed_n")
fabaceae_clade_n_df_count <- make_legend_with_stats(fabaceae_mode_counts, "label_count", 1, TRUE)
fabaceae_Cx_mode_counts_per_clade_df <- get_peaks_counts_continuous(
  fabaceae_clade_1Cx_df,
  "clade", "Cx", 2, 0.25, 1, 2
)
namecol <- "labelcountcustom"
fabaceae_clade_1Cx_modes_count_df <- make_legend_with_stats(
  fabaceae_Cx_mode_counts_per_clade_df,
  namecol, 1, TRUE
)

Makes a dot-plot and violin-plot

Description

This function makes a dot-plot and violin-plot, internal function

Usage

plot_dotviolin(
  dataset,
  par,
  groupcol,
  vary,
  labelx,
  maxx,
  adjust,
  binwidth,
  fill_group = "fill_group",
  font = "mono",
  dots = TRUE,
  violin = TRUE
)

Arguments

dataset

dataframe with columns to be merged into 1

par

dot size

groupcol

categories to group

vary

numeric variable

labelx

x axis label

maxx

x axis maximum value

adjust

geom_violin adjust parameter

binwidth

geom_dotplot binwidth parameter

fill_group

2nd category with 2 options as a fill aes argument for geom_dotplot

font

font family

dots

boolean include dot plot

violin

boolean include violin plot

Value

ggplot