When finding the skew and the kurtosis, there are three different options available. Na.rm=FALSE is equivalent to describe(na.omit(x)) In a typical study, one might read the data in from the clipboard ( read.clipboard), show the splom plot of the correlations ( pairs.panels), and then describe the data. Thus, statistics for those variables marked with * should be interpreted cautiously (if at all). For instance, if education is coded 'high school', 'some college', 'finished college', then the default coding will lead to these as values of 2, 3, 1. Note that in the case of categories or factors, the numerical ordering is not necessarily the one expected. These variables are marked with an * in the row name. If the check option is TRUE, variables that are categorical or logical are converted to numeric and then described.

The range statistics (min, max, range) are most useful for data checking to detect coding errors, and should be found in early analyses of the data.Īlthough describe will work on data frames as well as matrices, it is important to realize that for data frames, descriptive statistics will be reported only for those variables where this makes sense (i.e., not for alphanumeric data). The results from describe can be used in graphics functions (e.g., error.crosses). If a grouping variable is called for in formula mode, it will also call describeBy to the processing. The describe function in the psych package is meant to produce the most frequently requested stats in psychometric and psychology studies, and to produce them in an easy to read ame. Procedures such as summary and Hmisc::describe do so. Maintainer: William Revelle basic data analysis it is vital to get basic descriptive statistics. Show the last nobs-tail cases for each variable in describeDataĪllows formula input for specific grouping variables Show the first 1:head cases for each variable in describeData quant=c(.25.75) willĭo not convert non-numerical variables to numeric, omit them instead If not NULL, will find the specified quantiles (e.g. NULL, will switch to fast mode for large (ncol * nrow > 10^7) problems, otherwise If TRUE, will do n, means, sds, min, max, ranges for an improvement in speed. Should we check for non-numeric variables? Slower but helpful. Which estimate of skew and kurtosis should be used? (See details.) Trim=.1 - trim means by dropping the top and bottom trim fraction Should the skew and kurtosis be calculated? Should the median be standard or interpolated

Rstudio descriptive statistics