# Simple stats and tabulation helper functions

### Description

`a.iqr(x)`

interquartile range of numeric vector

`a.qr(x)`

ratio of 3rd to 1st quartile of numeric vector

`a.proportion.test(x1,x2, y1,y2, totals=FALSE)`

compares x1/x2 to y1/y2 using fisher.test and prints the result.
totals=TRUE means the supplied x2 is in fact (x1+x2);
ditto for y2.

`a.findcorrelations(df, vars1=names(df), vars2=vars1, min.cor=0.5)`

computes corrrelation (of values and of ranks) for each pair of variables
from (vars1,vars2), sorts them by size and returns the large ones
(along with descriptive names) as a vector. Ignores NAs.

`a.printextremes(df, vars, largest=5, showalso=NULL`

given variable names a,b,c from dataframe df, prints the
tuples a,b,c with the 5 largest values of a. Ditto for b and for c.
largest can be a vector (along vars) and negative values print
smallest instead of largest. Factors are moved from vars to showalso.

### Details

Type the name of a function to see its source code for details.

### Author(s)

Lutz Prechelt prechelt@inf.fu-berlin.de

### See Also

`cor`

,
`rank`

,
`quantile`

,
`summary`

.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
set.seed(17)
base = rnorm(100)
a = floor(base*10)
b = floor(a+runif(100, -10, 11))
c=floor(base)
d=ordered(floor(b/8)) # allows for rank correlation only
df=data.frame(a=a,b=b,c=c,d=d)
a.findcorrelations(df,min.cor=0.85)
a.printextremes(iris, vars=c("Species", "Sepal.Length", "Petal.Width"),
largest=c(3, -4, -5), showalso=c("Petal.Length"))
``` |