Olorgesailie.sub {archdata} | R Documentation |

##
Stone tool subclasses, Olorgesailie, Kenya

### Description

The data represent the number of specimens in each of 16 artifact subclasses recovered from 19 localities at the Lower Paleolithic site of Olorgesailie as described in Isaac (1977).

### Usage

`data(Olorgesailie.sub)`

### Format

A data frame with 19 observations showing the stratum, locality and the number of specimens for each of 16 stone artifact types.

`Strat`

stratum: `Lower`

, `Middle`

, `Upper`

`Locality`

Locality

`HA`

Number of handaxes

`PHA`

Number of pick-like handaxes

`CHA`

Number of chisel handaxes

`CL`

Number of cleavers

`KN`

Number of knives

`BLCT`

Number of broken large cutting tools

`PAT`

Number of picks and trièdres

`CH`

Number of choppers

`CS`

Number of core scrapers

`LFS`

Number of large flake scrapers

`CB`

Number of core bifaces

`OLT`

Number of other large tools

`SSS`

Number of small scrapers simple

`SSNP`

Number of small scrapers nosed point

`OST`

Number of other small tools

`SP`

Number of spheroids

### Details

The data come from Table E1 in Isaac (1977: 239). The `Locality`

contains the column headings in the original table. The `rownames`

are the same as those in `Olorgesailie.maj`

. The attribute `Variables`

in the data frame includes the full variable names. Potts (2011) provides updated information on the site complex.

### Source

Isaac, Glynn Ll. 1977. *Olorgesailie: Archeological Studies of a Middle Pleistocene Lake Basin in Kenya*. The University of Chicago Press.

### References

Carlson, David L. 2017. *Quantitative Methods in Archaeology Using R*. Cambridge University Press, pp 280-293.

Potts, R. 2011. Olorgesailie–Retrospective and current synthesis. In *Casting the net wide: papers in honor of Glynn Isaac and his approach to human origins research*, edited by J. Sept and D. Pilbeam, pp 1–20. American School of Prehistoric Research Monographs in Archaeology and Paleoanthropology.

### Examples

```
data(Olorgesailie.sub)
# Chi square after removing the first two columns and simulating the p
# value since there are a number of very small expected values
chisq.test(Olorgesailie.sub[,3:18], simulate.p.value=TRUE)
# Compute percentages over the localities
Olor.pct <- prop.table(as.matrix(Olorgesailie.sub[,3:18]), 1)*100
boxplot(Olor.pct, cex.axis=.7)
```

[Package

*archdata* version 1.2-1

Index]