FactoMineR (version 2.2)

textual: Text mining

Description

Calculates the number of occurence of each words and a contingence table

Usage

textual(tab, num.text, contingence.by=1:ncol(tab), 
    maj.in.min = TRUE, sep.word=NULL)

Arguments

tab

a data frame with one textual variable

num.text

indice of the textual variable

contingence.by

a list with the indices of the variables for which a contingence table is calculated by default a contingence table is calculated for all the variables (except the textual one). A contingence table can also be calculated for couple of variables. If contingence.by is equal to num.text, then the contingence table is calculated for each row of the data table

maj.in.min

boolean, if TRUE majuscule are transformed in minuscule

sep.word

a string with all the characters which correspond to separator of words

Value

Returns a list including:

cont.table

the contingence table with in rows the categories of the categorical variables (or the couple of categories), and in column the words, and in each cell the number of occurence

nb.words

a data.frame with all the words and for each word, the number of lists in which it is present, and the number of occurence

See Also

CA, descfreq

Examples

# NOT RUN {
data(poison.text)
res.text <- textual(poison.text, num.text = 3, contingence.by = 1)
descfreq(res.text$cont.table)
## Contingence table for the couple of variable sick-sex
res.text2 <- textual(poison.text, num.text = 3, contingence.by = list(c(1,2)))
descfreq(res.text2$cont.table)
## Contingence table for sex, sick and the couple of variable sick-sex
res.text2 <- textual(poison.text, num.text = 3, contingence.by = list(1,2,c(1,2)))
# }

Run the code above in your browser using DataCamp Workspace