statVisual (version 1.1.9)

Hist: Compare Groups Based on Histograms

Description

Compare groups based on histograms.

Usage

Hist(
    data, 
    y, 
    group = NULL, 
    fill = group, 
    border.color = NULL, 
    inner.color = NULL, 
    theme_classic = TRUE, 
    bins = NULL, 
    binwidth = NULL, 
    alpha = 0.8, 
    xlab = y, 
    ylab = "count", 
    group.lab = group, 
    title = "Histogram", 
    addThemeFlag = TRUE,
    ...)

Arguments

data

A data frame. Rows are subjects; Columns are variables describing the subjects.

y

character. The column name of data that indicates the variable, for which the histogram will be drawn. The string y can also indicate a function of the variable, e.g., $\log (y)$.

group

character. The column name of data that indicates the subject groups. The histogram will be drawn for each of the subject group. It also indicates the border colors of the histograms.

fill

character. The column name of data that indicates the subject groups. It indicates the inside colors of the histograms.

border.color

Histogram border color, only available when group & fill are NULL.

inner.color

Histogram inside color, only available when group & fill are NULL.

theme_classic

logical. Use classic background without grids (default: TRUE).

bins

integer. number of bins of histogram (default: 30).

binwidth

Bin width of histogram.

alpha

Transparency of histogram inside color.

xlab

x axis label

ylab

y axis label

group.lab

label of group variable

title

title of the plot

addThemeFlag

logical. Indicates if light blue background and white grid should be added to the figure.

…

other input parameters for facet & theme

Value

A list with the following 9 elements. data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Examples

# NOT RUN {
data(esSim)
print(esSim)

# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])

# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])

# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])

# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]

# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))

statVisual(type = 'Hist', 
       data = pDat, 
       y = 'probe1', 
       group = 'grp') 

Hist(
     data = pDat, 
     y = 'probe1', 
     group = 'grp') 


# }