Ingresar y preparar datos con R

Ingresar/Preparar los datos

data types are integer, numeric (real numbers), logical (TRUE or FALSE), and character (alphanumeric strings)
data frame is a table of data that combines vectors (columns) of different types (e.g.
character, factor, and numeric data). hybrid of two simpler data structures: lists, which can mix arbitrary types of data but have no other structure, and matrices, which have rows and columns but usually contain only one data type (typically numeric).

stack and unstack are simple but basic functions — stack converts from wide to long format and unstack from long to wide; they aren’t
reshape is very flexible and preserves more information than stack/unstack,
but its syntax is tricky: if long and wide are variables holding the
data in the examples above, then
library(reshape): melt, cast, and recast functions, which are similar to reshape but sometimes easier to use

ˆ Is there the right number of observations overall? Is there the right number of observations in each level for factors?
Do the summaries of the numeric variables — mean, median, etc. — look reasonable? Are the minimum and maximum values about what you expected?
Are there reasonable numbers of NAs in each column? If not (especially if you have extra mostly-NA columns), you may want to go back a few steps and look at using count.fields or ill=FALSE to identify rows with extra fields . . .

str: tells you about the structure of an R variable
class: prints out the class (numeric, factor, Date, logical,etc.) of a variable.
head: prints out the beginning of a data frame;
table: command for cross-tabulation
NAs: identificarlos

Análisis exploratorio de los datos

Ecología estadística