fdth-package: Frequency distribution tables, histograms and polygons
In jcfaria/fdth: Frequency Distribution Tables, Histograms and Polygons
fdth-package R Documentation
Frequency distribution tables, histograms and polygons
Description
The fdth package contains a set of functions which easily allows
the user to make frequency distribution tables (‘fdt’), its associated
histograms and frequency polygons (absolute, relative and cumulative).
The ‘fdt’ can be formatted in many ways which may be suited to
publication in many different ways (papers, books, etc).
The plot method (S3) is the histogram which can be dealt with the
easiness and flexibility of a high level function.
Details
The frequency of a particular observation is the number of times the
observation occurs in the data. The distribution of a variable is the pattern
of frequencies of the observation.
Frequency distribution table ‘fdt’ can be used for ordinal, continuous and categorical variables.
The R environment provides a set of functions (generally low level)
enabling the user to perform a ‘fdt’ and the associated graphical representation,
the histogram. A ‘fdt’ plays an important role to summarize data information and
is the basis for the estimation of probability density function used in
parametrical inference.
However, for novices or ocasional users of R, it can be laborious to
find out all necessary functions and graphical parameters to do a normalized
and pretty ‘fdt’ and the associated histogram ready for publications.
That is the aim of this package, i.e, to allow the user easily and flexibly to do
both: the ‘fdt’ and the histogram. The most common input data for univariated is
a vector. For multivariated data can be used both: a data.frame,
in this case also allowing grouping all numerical variables according to one
categorical, or matrices.
The simplest way to run ‘fdt’ and ‘fdt_cat’ is by supplying only the ‘x’
object, for example: d <- fdt(x). In this case all necessary
default values (‘breaks’ and ‘right’) ("Sturges" and FALSE
respectively) will be used, if the ‘x’ object is categorical then just use
d <- fdt_cat(x).
If the varable is of contiuos type, you can also supply:
-
‘x’ and ‘k’ (number of class intervals);
-
‘x’, ‘start’ (left endpoint of the first class interval) and
‘end’ (right endpoint of the last class interval); or
-
‘x’, ‘start’, ‘end’ and ‘h’ (class interval width).
These options make the ‘fdt’ very easy and flexible.
The ‘fdt’ and ‘fdt_cat’ object store information to be used by methods summary,
print and plot. The result of plot is a histogram or
polygon (absolute, relative or cumulative).
The methods summary, print and plot provide a reasonable
set of parameters to format and plot the ‘fdt’ object in a pretty
(and publishable) way.
Author(s)
Faria, J. C.
Allaman, I. B
Jelihovschi, E. G.
See Also
hist provided by graphics and
table, cut both provided by base.
Examples
library (fdth)
# Numerical
#======================
# Vectors: univariated
#======================
x <- rnorm(n=1e3,
mean=5,
sd=1)
(ft <- fdt(x))
# Histograms
plot(ft) # Absolute frequency histogram
plot(ft,
main='My title')
plot(ft,
x.round=3,
col='darkgreen')
plot(ft,
xlas=2)
plot(ft,
x.round=3,
xlas=2,
xlab=NULL)
plot(ft,
v=TRUE,
cex=.8,
x.round=3,
xlas=2,
xlab=NULL,
col=rainbow(11))
plot(ft,
type='fh') # Absolute frequency histogram
plot(ft,
type='rfh') # Relative frequency histogram
plot(ft,
type='rfph') # Relative frequency (%) histogram
plot(ft,
type='cdh') # Cumulative density histogram
plot(ft,
type='cfh') # Cumulative frequency histogram
plot(ft,
type='cfph') # Cumulative frequency (%) histogram
# Polygons
plot(ft,
type='fp') # Absolute frequency polygon
plot(ft,
type='rfp') # Relative frequency polygon
plot(ft,
type='rfpp') # Relative frequency (%) polygon
plot(ft,
type='cdp') # Cumulative density polygon
plot(ft,
type='cfp') # Cumulative frequency polygon
plot(ft,
type='cfpp') # Cumulative frequency (%) polygon
# Density
plot(ft,
type='d') # Density
# Summary
ft
summary(ft) # the same
print(ft) # the same
show(ft) # the same
summary(ft,
format=TRUE) # It can not be what you want to publications!
summary(ft,
format=TRUE,
pattern='%.2f') # Huumm ..., good, but ... Can it be better?
summary(ft,
col=c(1:2, 4, 6),
format=TRUE,
pattern='%.2f') # Yes, it can!
range(x) # To know x
summary(fdt(x,
start=1,
end=9,
h=1),
col=c(1:2, 4, 6),
format=TRUE,
pattern='%d') # Is it nice now?
# The fdt.object
ft[['table']] # Stores the feq. dist. table (fdt)
ft[['breaks']] # Stores the breaks of fdt
ft[['breaks']]['start'] # Stores the left value of the first class
ft[['breaks']]['end'] # Stores the right value of the last class
ft[['breaks']]['h'] # Stores the class interval
as.logical(ft[['breaks']]['right']) # Stores the right option
# Theoretical curve and fdt
y <- rnorm(1e5,
mean=5,
sd=1)
ft <- fdt(y,
k=100)
plot(ft,
type='d', # density
col=heat.colors(100))
curve(dnorm(x,
mean=5,
sd=1),
n=1e3,
add=TRUE,
lwd=3,
col='dark blue')
#=============================================
# Data.frames: multivariated with categorical
#=============================================
mdf <- data.frame(X1=rep(LETTERS[1:4], 25),
X2=as.factor(rep(1:10, 10)),
Y1=c(NA, NA, rnorm(96, 10, 1), NA, NA),
Y2=rnorm(100, 60, 4),
Y3=rnorm(100, 50, 4),
Y4=rnorm(100, 40, 4),
stringsAsFactors=TRUE)
#(ft <- fdt(mdf)) # Error message due to presence of NA values
(ft <- fdt(mdf,
na.rm=TRUE))
# Histograms
plot(ft,
v=TRUE)
plot(ft,
col=rainbow(8))
plot(ft,
type='fh')
plot(ft,
type='rfh')
plot(ft,
type='rfph')
plot(ft,
type='cdh')
plot(ft,
type='cfh')
plot(ft,
type='cfph')
# Poligons
plot(ft,
v=TRUE,
type='fp')
plot(ft,
type='rfp')
plot(ft,
type='rfpp')
plot(ft,
type='cdp')
plot(ft,
type='cfp')
plot(ft,
type='cfpp')
# Density
plot(ft,
type='d')
# Summary
ft
summary(ft) # the same
print(ft) # the same
show(ft) # the same
summary(ft,
format=TRUE)
summary(ft,
format=TRUE,
pattern='%05.2f') # regular expression
summary(ft,
col=c(1:2, 4, 6),
format=TRUE,
pattern='%05.2f')
print(ft,
col=c(1:2, 4, 6))
print(ft,
col=c(1:2, 4, 6),
format=TRUE,
pattern='%05.2f')
# Using by
levels(mdf$X1)
plot(fdt(mdf,
k=5,
by='X1',
na.rm=TRUE),
col=rainbow(5))
levels(mdf$X2)
summary(fdt(iris,
k=5),
format=TRUE,
patter='%04.2f')
plot(fdt(iris,
k=5),
col=rainbow(5))
levels(iris$Species)
summary(fdt(iris,
k=5,
by='Species'),
format=TRUE,
patter='%04.2f')
plot(fdt(iris,
k=5,
by='Species'),
v=TRUE)
#=========================
# Matrices: multivariated
#=========================
summary(fdt(state.x77),
col=c(1:2, 4, 6),
format=TRUE)
plot(fdt(state.x77))
# Very big
summary(fdt(volcano,
right=TRUE),
col=c(1:2, 4, 6),
round=3,
format=TRUE,
pattern='%05.1f')
plot(fdt(volcano,
right=TRUE))
## Categorical
x <- sample(x=letters[1:5],
size=5e2,
rep=TRUE)
(fdt.c <- fdt_cat(x))
(fdt.c <- fdt_cat(x,
sort=FALSE))
#================================================
# Data.frame: multivariated with two categorical
#================================================
mdf <- data.frame(c1=sample(LETTERS[1:3], 1e2, rep=TRUE),
c2=as.factor(sample(1:10, 1e2, rep=TRUE)),
n1=c(NA, NA, rnorm(96, 10, 1), NA, NA),
n2=rnorm(100, 60, 4),
n3=rnorm(100, 50, 4),
stringsAsFactors=TRUE)
str(mdf)
(fdt.c <- fdt_cat(mdf))
(fdt.c <- fdt_cat(mdf,
dec=FALSE))
(fdt.c <- fdt_cat(mdf,
sort=FALSE))
(fdt.c <- fdt_cat(mdf,
by='c1'))
#================================================
# Matrix: two categorical
#================================================
x <- matrix(sample(x=letters[1:10],
size=100,
rep=TRUE),
nc=2,
dimnames=list(NULL,
c('c1', 'c2')))
head(x)
(fdt.c <- fdt_cat(x))
jcfaria/fdth documentation built on Nov. 24, 2023, 4:16 a.m.
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| fdth-package | R Documentation |
Frequency distribution tables, histograms and polygons
Description
The fdth package contains a set of functions which easily allows
the user to make frequency distribution tables (‘fdt’), its associated
histograms and frequency polygons (absolute, relative and cumulative).
The ‘fdt’ can be formatted in many ways which may be suited to
publication in many different ways (papers, books, etc).
The plot method (S3) is the histogram which can be dealt with the
easiness and flexibility of a high level function.
Details
The frequency of a particular observation is the number of times the observation occurs in the data. The distribution of a variable is the pattern of frequencies of the observation.
Frequency distribution table ‘fdt’ can be used for ordinal, continuous and categorical variables.
The R environment provides a set of functions (generally low level)
enabling the user to perform a ‘fdt’ and the associated graphical representation,
the histogram. A ‘fdt’ plays an important role to summarize data information and
is the basis for the estimation of probability density function used in
parametrical inference.
However, for novices or ocasional users of R, it can be laborious to
find out all necessary functions and graphical parameters to do a normalized
and pretty ‘fdt’ and the associated histogram ready for publications.
That is the aim of this package, i.e, to allow the user easily and flexibly to do
both: the ‘fdt’ and the histogram. The most common input data for univariated is
a vector. For multivariated data can be used both: a data.frame,
in this case also allowing grouping all numerical variables according to one
categorical, or matrices.
The simplest way to run ‘fdt’ and ‘fdt_cat’ is by supplying only the ‘x’
object, for example: d <- fdt(x). In this case all necessary
default values (‘breaks’ and ‘right’) ("Sturges" and FALSE
respectively) will be used, if the ‘x’ object is categorical then just use
d <- fdt_cat(x).
If the varable is of contiuos type, you can also supply:
-
‘x’ and ‘k’ (number of class intervals);
-
‘x’, ‘start’ (left endpoint of the first class interval) and ‘end’ (right endpoint of the last class interval); or
-
‘x’, ‘start’, ‘end’ and ‘h’ (class interval width).
These options make the ‘fdt’ very easy and flexible.
The ‘fdt’ and ‘fdt_cat’ object store information to be used by methods summary,
print and plot. The result of plot is a histogram or
polygon (absolute, relative or cumulative).
The methods summary, print and plot provide a reasonable
set of parameters to format and plot the ‘fdt’ object in a pretty
(and publishable) way.
Author(s)
Faria, J. C.
Allaman, I. B
Jelihovschi, E. G.
See Also
hist provided by graphics and
table, cut both provided by base.
Examples
library (fdth)
# Numerical
#======================
# Vectors: univariated
#======================
x <- rnorm(n=1e3,
mean=5,
sd=1)
(ft <- fdt(x))
# Histograms
plot(ft) # Absolute frequency histogram
plot(ft,
main='My title')
plot(ft,
x.round=3,
col='darkgreen')
plot(ft,
xlas=2)
plot(ft,
x.round=3,
xlas=2,
xlab=NULL)
plot(ft,
v=TRUE,
cex=.8,
x.round=3,
xlas=2,
xlab=NULL,
col=rainbow(11))
plot(ft,
type='fh') # Absolute frequency histogram
plot(ft,
type='rfh') # Relative frequency histogram
plot(ft,
type='rfph') # Relative frequency (%) histogram
plot(ft,
type='cdh') # Cumulative density histogram
plot(ft,
type='cfh') # Cumulative frequency histogram
plot(ft,
type='cfph') # Cumulative frequency (%) histogram
# Polygons
plot(ft,
type='fp') # Absolute frequency polygon
plot(ft,
type='rfp') # Relative frequency polygon
plot(ft,
type='rfpp') # Relative frequency (%) polygon
plot(ft,
type='cdp') # Cumulative density polygon
plot(ft,
type='cfp') # Cumulative frequency polygon
plot(ft,
type='cfpp') # Cumulative frequency (%) polygon
# Density
plot(ft,
type='d') # Density
# Summary
ft
summary(ft) # the same
print(ft) # the same
show(ft) # the same
summary(ft,
format=TRUE) # It can not be what you want to publications!
summary(ft,
format=TRUE,
pattern='%.2f') # Huumm ..., good, but ... Can it be better?
summary(ft,
col=c(1:2, 4, 6),
format=TRUE,
pattern='%.2f') # Yes, it can!
range(x) # To know x
summary(fdt(x,
start=1,
end=9,
h=1),
col=c(1:2, 4, 6),
format=TRUE,
pattern='%d') # Is it nice now?
# The fdt.object
ft[['table']] # Stores the feq. dist. table (fdt)
ft[['breaks']] # Stores the breaks of fdt
ft[['breaks']]['start'] # Stores the left value of the first class
ft[['breaks']]['end'] # Stores the right value of the last class
ft[['breaks']]['h'] # Stores the class interval
as.logical(ft[['breaks']]['right']) # Stores the right option
# Theoretical curve and fdt
y <- rnorm(1e5,
mean=5,
sd=1)
ft <- fdt(y,
k=100)
plot(ft,
type='d', # density
col=heat.colors(100))
curve(dnorm(x,
mean=5,
sd=1),
n=1e3,
add=TRUE,
lwd=3,
col='dark blue')
#=============================================
# Data.frames: multivariated with categorical
#=============================================
mdf <- data.frame(X1=rep(LETTERS[1:4], 25),
X2=as.factor(rep(1:10, 10)),
Y1=c(NA, NA, rnorm(96, 10, 1), NA, NA),
Y2=rnorm(100, 60, 4),
Y3=rnorm(100, 50, 4),
Y4=rnorm(100, 40, 4),
stringsAsFactors=TRUE)
#(ft <- fdt(mdf)) # Error message due to presence of NA values
(ft <- fdt(mdf,
na.rm=TRUE))
# Histograms
plot(ft,
v=TRUE)
plot(ft,
col=rainbow(8))
plot(ft,
type='fh')
plot(ft,
type='rfh')
plot(ft,
type='rfph')
plot(ft,
type='cdh')
plot(ft,
type='cfh')
plot(ft,
type='cfph')
# Poligons
plot(ft,
v=TRUE,
type='fp')
plot(ft,
type='rfp')
plot(ft,
type='rfpp')
plot(ft,
type='cdp')
plot(ft,
type='cfp')
plot(ft,
type='cfpp')
# Density
plot(ft,
type='d')
# Summary
ft
summary(ft) # the same
print(ft) # the same
show(ft) # the same
summary(ft,
format=TRUE)
summary(ft,
format=TRUE,
pattern='%05.2f') # regular expression
summary(ft,
col=c(1:2, 4, 6),
format=TRUE,
pattern='%05.2f')
print(ft,
col=c(1:2, 4, 6))
print(ft,
col=c(1:2, 4, 6),
format=TRUE,
pattern='%05.2f')
# Using by
levels(mdf$X1)
plot(fdt(mdf,
k=5,
by='X1',
na.rm=TRUE),
col=rainbow(5))
levels(mdf$X2)
summary(fdt(iris,
k=5),
format=TRUE,
patter='%04.2f')
plot(fdt(iris,
k=5),
col=rainbow(5))
levels(iris$Species)
summary(fdt(iris,
k=5,
by='Species'),
format=TRUE,
patter='%04.2f')
plot(fdt(iris,
k=5,
by='Species'),
v=TRUE)
#=========================
# Matrices: multivariated
#=========================
summary(fdt(state.x77),
col=c(1:2, 4, 6),
format=TRUE)
plot(fdt(state.x77))
# Very big
summary(fdt(volcano,
right=TRUE),
col=c(1:2, 4, 6),
round=3,
format=TRUE,
pattern='%05.1f')
plot(fdt(volcano,
right=TRUE))
## Categorical
x <- sample(x=letters[1:5],
size=5e2,
rep=TRUE)
(fdt.c <- fdt_cat(x))
(fdt.c <- fdt_cat(x,
sort=FALSE))
#================================================
# Data.frame: multivariated with two categorical
#================================================
mdf <- data.frame(c1=sample(LETTERS[1:3], 1e2, rep=TRUE),
c2=as.factor(sample(1:10, 1e2, rep=TRUE)),
n1=c(NA, NA, rnorm(96, 10, 1), NA, NA),
n2=rnorm(100, 60, 4),
n3=rnorm(100, 50, 4),
stringsAsFactors=TRUE)
str(mdf)
(fdt.c <- fdt_cat(mdf))
(fdt.c <- fdt_cat(mdf,
dec=FALSE))
(fdt.c <- fdt_cat(mdf,
sort=FALSE))
(fdt.c <- fdt_cat(mdf,
by='c1'))
#================================================
# Matrix: two categorical
#================================================
x <- matrix(sample(x=letters[1:10],
size=100,
rep=TRUE),
nc=2,
dimnames=list(NULL,
c('c1', 'c2')))
head(x)
(fdt.c <- fdt_cat(x))
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