tablefreq: Create a table of frequencies

Description

Create a table of frequencies

Usage

tablefreq(data, vars = NULL, freq = ~1)
## S3 method for class 'tablefreq':
update(object, ...)

Arguments

data

an object that can be coerced as a matrix or a data frame. It must contain all variables in vars and in freq

vars

variables to count unique values of. It may be a numeric vector or character vector

freq

a one-sided, single term formula specifying frequency weights

object

a tablefreq object

...

more data

Value

A data frame or matrix with label and freq columns. When it is possible, the last column is named freq and it represents the frequency counts of the cases. This object of class tablefreq, has two attributes:
freqFormula used to create the frequency table
colweightsName of the column with the weighting counts

Details

Based on the count function, it can natively work with matrices and may be updated.

It creates a frequency table of the data, or just of the columns specified in vars. If you provide a freq formula, the cases are weighted by the result of the formula. Any variables in the formula are removed from the data set.

See count for further information. count uses data frames; tablefreq also works with matrices.

Examples

Run this code

tablefreq(iris[,c(1:5)])
tablefreq(iris, c("Sepal.Length","Species"))
a <- tablefreq(iris,freq=~Sepal.Length+Petal.Length)
head(tablefreq(a, freq=~Sepal.Width))

tt <- tablefreq(iris[,c(1:2,5)],freq=~Sepal.Width)

data <- iris[1:10,c(1:2,5)]
chunk1 <- iris[c(11:20),]
chunk2 <- iris[-c(1:20),]
a <- tablefreq(data,freq=~Sepal.Width)
a <- update(a,chunk1)
a <- update(a,chunk2)
all.equal(a, tt)

##
## Graphs
##
if(require(ggplot2) && require(hflights)){
  library(dplyr)

  ## One variable
  ## Bar plot
  tt <- as.data.frame(tablefreq(hflights[,"ArrDelay"]))
  p <- ggplot() + geom_bar(aes(x=x, y=freq), data=tt, stat="identity")
  print(p)

  ## Histogram
  p <- ggplot() + geom_histogram(aes(x=x, weight= freq), data = tt)
  print(p)

  ## Density
  tt <- tt[complete.cases(tt),] ## remove missing values
  tt$w <- tt$freq / sum(tt$freq) ## weights must sum 1
  p <- ggplot() + geom_density(aes(x=x, weight= w), data = tt)
  print(p)

  ##
  ## Two distributions
  ##
  ## A numeric and a factor variable
  td <- tablefreq(hflights[,c("TaxiIn","Origin")])
  td <- td[complete.cases(td),]

  ## Bar plot
  p <- ggplot() + geom_bar(aes(x=TaxiIn, weight= freq, colour = Origin),
                           data = td, position ="dodge")
  print(p)

  ## Density
  ## compute the relative frequencies for each group
  td <- td %.% group_by(Origin) %.%
               mutate( ngroup= sum(freq), wgroup= freq/ngroup)
  p <- ggplot() + geom_density(aes(x=TaxiIn, weight=wgroup, colour = Origin),
                               data = td)
  print(p)

  ## For each group, plot its values
  p <- ggplot() + geom_point(aes(x=Origin, y=TaxiIn, size=freq),
                             data = td, alpha= 0.6)
  print(p)

  ## Two numeric variables
  tc <- tablefreq(hflights[,c("TaxiIn","TaxiOut")])
  tc <- tc[complete.cases(tc),]
  p <- ggplot() + geom_point(aes(x=TaxiIn, y=TaxiOut, size=freq),
                             data = tc, color = "red", alpha=0.5)
  print(p)

  ## Two factors
  tf <- tablefreq(hflights[,c("UniqueCarrier","Origin")])
  tf <- tf[complete.cases(tf),]

  ## Bar plot
  p <- ggplot() + geom_bar(aes(x=Origin, fill=UniqueCarrier, weight= freq),
                           data = tf)
  print(p)
}