A number of very useful datasets containing archaeological data have been collected together into a single package by David L. Carlson and Georg Roth. You can find the information about the package at the cran archive. In this notebook, we've already installed the package when the Binder was created. We will invoke it with the library
command, and then do a few example visualizations and calculations.
At the end of this notebook, we have provided the code to load up mineralogical data from Graham's 2002 thesis, and we encourage you to explore it by copying and modifying the other example code. Add cells explaining what each code block is doing; you will need to search the various functions. You should try to find their actual manual pages, as well as other tutorials and blog posts where people are using these functions (add markdown links to make your text readable).
# to find out what's in this package, run the following
?archdata
# or go to
# https://cran.rstudio.com/web/packages/archdata/archdata.pdf
library("archdata")
data("RBGlass1")
RBGlass1
Site | Al | Fe | Mg | Ca | Na | K | Ti | P | Mn | Sb | Pb |
---|---|---|---|---|---|---|---|---|---|---|---|
Mancetter | 2.51 | 0.53 | 0.56 | 6.98 | 17.44 | 0.73 | 0.09 | 0.15 | 0.58 | 0.12 | 0.03 |
Mancetter | 2.36 | 0.49 | 0.53 | 6.71 | 17.69 | 0.68 | 0.09 | 0.13 | 0.40 | 0.23 | 0.04 |
Mancetter | 2.30 | 0.36 | 0.49 | 8.10 | 15.94 | 0.68 | 0.07 | 0.13 | 0.77 | 0.00 | 0.01 |
Mancetter | 2.42 | 0.52 | 0.56 | 6.93 | 17.59 | 0.72 | 0.09 | 0.14 | 0.47 | 0.18 | 0.02 |
Mancetter | 2.32 | 0.37 | 0.51 | 7.51 | 16.27 | 0.69 | 0.07 | 0.13 | 0.21 | 0.00 | 0.02 |
Mancetter | 2.34 | 0.56 | 0.52 | 6.10 | 18.61 | 0.69 | 0.10 | 0.11 | 0.30 | 0.32 | 0.03 |
Mancetter | 2.50 | 0.46 | 0.50 | 6.83 | 17.46 | 0.79 | 0.08 | 0.15 | 0.40 | 0.06 | 0.02 |
Mancetter | 2.47 | 0.53 | 0.55 | 6.55 | 18.55 | 0.75 | 0.09 | 0.12 | 0.35 | 0.23 | 0.04 |
Mancetter | 2.41 | 0.67 | 0.62 | 6.18 | 18.33 | 0.81 | 0.12 | 0.14 | 0.52 | 0.31 | 0.07 |
Mancetter | 2.64 | 0.50 | 0.63 | 7.76 | 15.66 | 0.63 | 0.08 | 0.16 | 0.21 | 0.00 | 0.01 |
Mancetter | 2.77 | 0.58 | 0.50 | 7.33 | 16.10 | 0.68 | 0.08 | 0.14 | 0.57 | 0.00 | 0.01 |
Mancetter | 2.43 | 0.69 | 0.72 | 6.27 | 17.84 | 0.98 | 0.12 | 0.22 | 0.63 | 0.13 | 0.04 |
Mancetter | 2.50 | 0.36 | 0.53 | 8.51 | 15.46 | 0.60 | 0.07 | 0.16 | 0.45 | 0.00 | 0.01 |
Mancetter | 2.63 | 0.46 | 0.47 | 7.25 | 16.26 | 0.59 | 0.07 | 0.12 | 0.30 | 0.00 | 0.01 |
Mancetter | 2.66 | 0.41 | 0.50 | 7.35 | 17.12 | 0.63 | 0.07 | 0.15 | 0.11 | 0.00 | 0.01 |
Mancetter | 2.43 | 0.62 | 0.52 | 6.89 | 17.17 | 0.69 | 0.08 | 0.13 | 0.44 | 0.18 | 0.05 |
Mancetter | 2.55 | 0.53 | 0.52 | 7.91 | 16.20 | 0.62 | 0.07 | 0.15 | 0.38 | 0.00 | 0.01 |
Mancetter | 2.44 | 0.54 | 0.56 | 6.65 | 17.68 | 0.97 | 0.10 | 0.12 | 0.40 | 0.25 | 0.03 |
Mancetter | 2.22 | 0.34 | 0.46 | 7.08 | 16.14 | 0.63 | 0.06 | 0.15 | 0.12 | 0.00 | 0.01 |
Mancetter | 2.59 | 0.37 | 0.46 | 7.57 | 15.71 | 0.56 | 0.07 | 0.16 | 0.07 | 0.00 | 0.01 |
Mancetter | 2.45 | 0.48 | 0.55 | 6.84 | 17.73 | 0.76 | 0.09 | 0.14 | 0.62 | 0.14 | 0.05 |
Mancetter | 2.42 | 0.49 | 0.51 | 7.00 | 16.32 | 0.93 | 0.08 | 0.14 | 0.42 | 0.10 | 0.03 |
Mancetter | 2.27 | 0.38 | 0.48 | 7.88 | 16.28 | 0.52 | 0.07 | 0.14 | 0.26 | 0.00 | 0.01 |
Mancetter | 2.48 | 0.55 | 0.55 | 6.64 | 18.76 | 0.75 | 0.09 | 0.12 | 0.36 | 0.24 | 0.04 |
Leicester | 2.27 | 0.32 | 0.39 | 6.75 | 17.95 | 0.75 | 0.07 | 0.12 | 0.18 | 0.00 | 0.01 |
Leicester | 2.32 | 0.84 | 0.55 | 6.19 | 19.78 | 0.70 | 0.10 | 0.11 | 0.24 | 0.37 | 0.08 |
Mancetter | 2.46 | 0.49 | 0.54 | 6.82 | 18.07 | 0.75 | 0.08 | 0.13 | 0.60 | 0.13 | 0.04 |
Mancetter | 2.67 | 0.34 | 0.49 | 6.94 | 18.04 | 0.54 | 0.06 | 0.11 | 0.44 | 0.00 | 0.01 |
Mancetter | 2.47 | 0.42 | 0.51 | 7.57 | 17.94 | 0.76 | 0.07 | 0.14 | 0.41 | 0.00 | 0.01 |
Mancetter | 2.40 | 0.45 | 0.54 | 7.62 | 17.76 | 0.64 | 0.08 | 0.13 | 0.40 | 0.11 | 0.02 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
Leicester | 2.31 | 0.88 | 0.57 | 6.42 | 18.90 | 0.76 | 0.10 | 0.12 | 0.28 | 0.30 | 0.03 |
Leicester | 2.50 | 0.78 | 0.56 | 6.46 | 18.57 | 0.73 | 0.11 | 0.12 | 0.26 | 0.27 | 0.03 |
Leicester | 2.57 | 0.80 | 0.56 | 6.43 | 18.41 | 0.75 | 0.12 | 0.12 | 0.26 | 0.26 | 0.03 |
Leicester | 2.24 | 0.84 | 0.56 | 6.26 | 19.49 | 0.73 | 0.09 | 0.12 | 0.23 | 0.32 | 0.03 |
Leicester | 2.37 | 0.44 | 0.50 | 6.78 | 17.15 | 0.70 | 0.08 | 0.15 | 0.45 | 0.06 | 0.02 |
Leicester | 2.48 | 0.77 | 0.55 | 6.36 | 18.30 | 0.73 | 0.11 | 0.12 | 0.26 | 0.26 | 0.03 |
Leicester | 2.26 | 0.58 | 0.61 | 6.16 | 19.47 | 0.74 | 0.10 | 0.11 | 0.21 | 0.43 | 0.03 |
Leicester | 2.59 | 0.48 | 0.60 | 8.76 | 14.50 | 0.51 | 0.07 | 0.13 | 0.27 | 0.00 | 0.01 |
Leicester | 2.25 | 0.66 | 0.52 | 6.20 | 18.06 | 0.64 | 0.09 | 0.11 | 0.24 | 0.31 | 0.04 |
Leicester | 2.43 | 0.48 | 0.56 | 7.60 | 15.57 | 0.62 | 0.08 | 0.16 | 0.49 | 0.00 | 0.01 |
Leicester | 2.49 | 0.93 | 0.55 | 6.18 | 16.54 | 1.10 | 0.12 | 0.13 | 0.25 | 0.28 | 0.03 |
Leicester | 2.46 | 0.76 | 0.55 | 6.37 | 17.95 | 0.72 | 0.11 | 0.12 | 0.26 | 0.27 | 0.03 |
Leicester | 2.47 | 1.05 | 0.56 | 7.62 | 17.02 | 0.70 | 0.11 | 0.14 | 0.26 | 0.23 | 0.03 |
Leicester | 2.16 | 0.74 | 0.53 | 6.09 | 17.25 | 0.65 | 0.09 | 0.11 | 0.25 | 0.32 | 0.03 |
Leicester | 2.26 | 0.58 | 0.52 | 6.41 | 17.28 | 0.67 | 0.09 | 0.13 | 0.28 | 0.25 | 0.03 |
Leicester | 2.29 | 0.78 | 0.56 | 6.24 | 18.45 | 0.70 | 0.10 | 0.11 | 0.26 | 0.32 | 0.03 |
Leicester | 2.30 | 0.78 | 0.53 | 6.28 | 18.20 | 0.65 | 0.10 | 0.11 | 0.25 | 0.31 | 0.03 |
Leicester | 2.52 | 0.65 | 0.55 | 6.16 | 18.69 | 0.74 | 0.11 | 0.10 | 0.29 | 0.33 | 0.06 |
Leicester | 2.28 | 0.68 | 0.55 | 6.37 | 18.60 | 0.68 | 0.10 | 0.11 | 0.24 | 0.32 | 0.03 |
Leicester | 2.25 | 0.62 | 0.56 | 5.55 | 19.47 | 0.74 | 0.13 | 0.11 | 0.31 | 0.42 | 0.05 |
Leicester | 2.32 | 0.80 | 0.54 | 6.34 | 18.25 | 0.66 | 0.10 | 0.11 | 0.25 | 0.32 | 0.03 |
Leicester | 2.35 | 0.74 | 0.55 | 6.54 | 18.44 | 0.71 | 0.10 | 0.11 | 0.26 | 0.29 | 0.03 |
Leicester | 2.45 | 0.42 | 0.61 | 9.79 | 16.22 | 0.62 | 0.08 | 0.13 | 0.14 | 0.00 | 0.01 |
Leicester | 2.19 | 0.84 | 0.54 | 6.13 | 17.99 | 0.69 | 0.10 | 0.11 | 0.24 | 0.33 | 0.04 |
Leicester | 2.62 | 0.82 | 0.54 | 6.25 | 17.79 | 0.73 | 0.11 | 0.09 | 0.29 | 0.32 | 0.04 |
Leicester | 2.35 | 0.65 | 0.54 | 6.73 | 17.91 | 0.72 | 0.10 | 0.12 | 0.28 | 0.25 | 0.06 |
Leicester | 2.44 | 0.35 | 0.51 | 7.70 | 16.27 | 0.62 | 0.07 | 0.13 | 0.16 | 0.00 | 0.01 |
Leicester | 2.42 | 0.68 | 0.53 | 6.15 | 17.19 | 0.77 | 0.13 | 0.11 | 0.27 | 0.26 | 0.03 |
Leicester | 2.52 | 0.79 | 0.56 | 6.37 | 18.11 | 0.74 | 0.12 | 0.11 | 0.26 | 0.27 | 0.03 |
Leicester | 2.37 | 0.75 | 0.55 | 6.33 | 18.55 | 0.69 | 0.10 | 0.11 | 0.25 | 0.32 | 0.03 |
RBGlass1.pca <- prcomp(RBGlass1[, -1], scale.=TRUE)
Error in prcomp(RBGlass1[, -1], scale. = TRUE): object 'RBGlass1' not found Traceback: 1. prcomp(RBGlass1[, -1], scale. = TRUE)
biplot(RBGlass1.pca, xlabs=abbreviate(RBGlass1$Site, 1), cex=.75)
PitHouses
Hearths | Depth | Size | Form | Orient | Entrance |
---|---|---|---|---|---|
Two | Deep | Medium | Rectangular | Parallel Coast | None |
One | Deep | Small | Rectangular | Parallel Coast | None |
Two | Deep | Small | Rectangular | Parallel Coast | None |
None | Shallow | Small | Rectangular | Parallel Coast | One Side |
Two | Shallow | Medium | Rectangular | Parallel Coast | Front and One Side |
None | Shallow | Medium | Rectangular | Parallel Coast | Front and One Side |
Charcoal Conc | Shallow | Medium | Rectangular | Parallel Coast | One Side |
One | Deep | Medium | Rectangular | Parallel Coast | None |
One | Deep | Small | Rectangular | Parallel Coast | None |
One | Shallow | Small | Rectangular | Gabel Toward Coast | One Side |
One | Deep | Small | Rectangular | Parallel Coast | None |
Charcoal Conc | Shallow | Medium | Rectangular | Parallel Coast | None |
Charcoal Conc | Shallow | Large | Rectangular | Parallel Coast | None |
Two | Deep | Large | Rectangular | Parallel Coast | None |
None | Deep | Small | Oval | Parallel Coast | One Side |
One | Deep | Medium | Oval | Parallel Coast | None |
Charcoal Conc | Deep | Small | Rectangular | Parallel Coast | One Side |
Two | Deep | Medium | Rectangular | Parallel Coast | None |
One | Shallow | Medium | Oval | Parallel Coast | One Side |
None | Shallow | Large | Rectangular | Parallel Coast | None |
Two | Shallow | Medium | Rectangular | Parallel Coast | None |
Two | Deep | Medium | Rectangular | Parallel Coast | None |
Charcoal Conc | Shallow | Medium | Oval | Gabel Toward Coast | None |
None | Deep | Medium | Oval | Parallel Coast | None |
Two | Shallow | Medium | Rectangular | Parallel Coast | None |
Two | Shallow | Medium | Rectangular | Parallel Coast | None |
None | Shallow | Medium | Rectangular | Gabel Toward Coast | None |
Charcoal Conc | Shallow | Medium | Oval | Parallel Coast | None |
Two | Deep | Medium | Rectangular | Parallel Coast | None |
Two | Deep | Medium | Oval | Parallel Coast | None |
Two | Deep | Small | Rectangular | Parallel Coast | None |
Two | Deep | Small | Rectangular | Parallel Coast | One Side |
None | Deep | Small | Rectangular | Parallel Coast | None |
Charcoal Conc | Deep | Small | Rectangular | Gabel Toward Coast | None |
Charcoal Conc | Shallow | Medium | Rectangular | Parallel Coast | None |
One | Deep | Small | Rectangular | Parallel Coast | None |
Two | Shallow | Medium | Rectangular | Parallel Coast | None |
Charcoal Conc | Deep | Small | Rectangular | Parallel Coast | None |
Charcoal Conc | Deep | Small | Rectangular | Parallel Coast | None |
Two | Deep | Small | Rectangular | Parallel Coast | None |
One | Deep | Small | Rectangular | Parallel Coast | None |
Charcoal Conc | Deep | Small | Rectangular | Gabel Toward Coast | None |
Charcoal Conc | Deep | Small | Rectangular | Parallel Coast | None |
Charcoal Conc | Shallow | Small | Rectangular | Parallel Coast | None |
Charcoal Conc | Deep | Small | Rectangular | Gabel Toward Coast | None |
data(PitHouses)
# Crosstabulation of Hearths with Size
PitHouses.tbl <- xtabs(~Hearths+Size, PitHouses)
PitHouses.tbl
barplot(PitHouses.tbl, ylab="Frequency", main="Arctic Norway Pithouses", beside=TRUE,legend.text=TRUE, args.legend=list(title="Hearths"))
Size Hearths Small Medium Large None 3 3 1 One 6 3 0 Two 4 10 1 Charcoal Conc 8 5 1
data(Fibulae)
Fibulae
Grave | Mno | FL | BH | BFA | FA | CD | BRA | ED | FEL | C | BW | BT | FEW | Coils | Length |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
149 | 389 | 93 | 24 | 7 | 10 | 16 | 1 | 13 | 31 | 47 | 3.5 | 3.5 | NA | 4 | 114 |
190 | 615 | 21 | 7 | 6 | 9 | 6 | 5 | 2 | 11 | 10 | 3.5 | 1.7 | NA | 12 | 35 |
161 | 125 | 33 | 15 | 2 | 8 | 7 | 3 | 8 | 10 | 20 | 3.9 | 3.2 | NA | 4 | 60 |
31 | 812 | 23 | 26 | 4 | 7 | 9 | 5 | 12 | 1 | 16 | 6.2 | 7.7 | 2.8 | 4 | 74 |
49 | 798 | 20 | 23 | 2 | 8 | 7 | 1 | 8 | 5 | 16 | 7.7 | 5.2 | 2.6 | 6 | 68 |
6 | 673 | 27 | 15 | 6 | 8 | 7 | 5 | 3 | 11 | 11 | 3.7 | 3.5 | 1.8 | 4 | 55 |
Thames | Thames | 10 | 16 | 1 | 10 | 9 | 1 | 7 | 0 | 11 | 6.1 | 4.1 | 0.0 | 4 | 45 |
23 | 643 | 15 | 18 | 1 | 10 | 10 | 1 | 5 | 0 | 15 | 3.5 | 3.5 | 0.0 | 4 | 40 |
149 | 391 | 31 | 13 | 4 | 9 | 7 | 4 | 5 | 11 | 18 | 17.6 | 1.4 | 3.6 | 6 | 54 |
149 | 398 | 19 | 17 | 1 | 7 | 6 | 2 | 6 | 10 | 12 | 9.2 | 6.6 | 3.9 | 6 | 39 |
101 | 491 | 41 | 23 | 3 | 8 | 11 | 3 | 14 | 15 | 24 | 7.3 | 5.8 | 8.6 | 6 | 71 |
171 | 149 | 47 | 17 | 5 | 9 | 10 | 4 | 8 | 14 | 26 | 5.8 | 4.7 | 6.0 | 6 | 78 |
130 | 545 | 29 | 15 | 3 | 8 | 6 | 3 | 6 | 10 | 17 | 11.7 | 3.9 | 6.4 | 6 | 47 |
157 | 85 | 23 | 13 | 3 | 8 | 6 | 2 | 10 | 7 | 15 | 5.2 | 2.7 | 5.4 | 12 | 41 |
97 | 478 | 20 | 15 | 1 | 7 | 5 | 1 | 12 | 4 | 12 | 4.7 | 4.8 | 3.5 | 6 | 38 |
85 | 436 | 17 | 16 | 1 | 7 | 7 | 1 | 8 | 3 | 11 | 5.1 | 3.5 | 2.2 | 6 | 44 |
91 | 464 | 20 | 15 | 2 | 7 | 7 | 3 | 6 | 10 | 12 | 5.5 | 3.8 | 3.9 | 6 | 50 |
61 | 821 | 20 | 13 | 5 | 8 | 5 | 2 | 10 | 5 | 10 | 4.4 | 4.4 | 5.1 | 6 | 36 |
94 | 474 | 21 | 18 | 2 | 9 | 9 | 1 | 5 | 6 | 15 | 8.1 | 2.3 | 1.9 | 4 | 49 |
121 | 348 | 28 | 17 | 1 | 10 | 10 | 2 | 8 | 6 | 20 | 2.5 | 2.6 | 2.2 | 4 | 53 |
181 | 212 | 94 | 15 | 7 | 10 | 12 | 5 | 11 | 31 | 50 | 4.3 | 4.3 | NA | 6 | 128 |
68 | 587 | 22 | 18 | 1 | 8 | 7 | 1 | 5 | 8 | 17 | 8.8 | 3.0 | 2.4 | 6 | 59 |
61 | 830 | 20 | 14 | 1 | 8 | 6 | 1 | 3 | 4 | 14 | 14.3 | 1.4 | 1.7 | 6 | 44 |
130 | 549 | 22 | 15 | 3 | 8 | 7 | 3 | 13 | 1 | 17 | 5.0 | 4.6 | 2.5 | 10 | 47 |
80 | 529 | 12 | 22 | 1 | 6 | 9 | 1 | 9 | 0 | 11 | 6.8 | 6.4 | 0.0 | 4 | 45 |
149 | 399 | 27 | 15 | 1 | 8 | 10 | 2 | 9 | 11 | 19 | 8.2 | 4.0 | 7.6 | 4 | 53 |
48 | 788 | 15 | 19 | 2 | 8 | 7 | 3 | 3 | 4 | 12 | 3.7 | 3.5 | 1.9 | 4 | 56 |
44 | 752 | 10 | 10 | 2 | 10 | 6 | 2 | 2 | NA | 9 | 2.0 | 2.3 | 2.2 | 3 | 26 |
Hallstatt | Hallstatt | 9 | 13 | 3 | 10 | 4 | 4 | 9 | 0 | 8 | 9.6 | 5.0 | 0.0 | 22 | 28 |
193 | 611 | 68 | 18 | 7 | 9 | 9 | 7 | 3 | 50 | 18 | 9.3 | 6.5 | NA | 4 | 110 |
t(sapply(Fibulae[, 3:16], quantile, na.rm=TRUE))
plot(density(Fibulae$Length, bw="SJ"), main="Kernel Density Plot of Length")
0% | 25% | 50% | 75% | 100% | |
---|---|---|---|---|---|
FL | 9.0 | 19.25 | 21.50 | 28.750 | 94.0 |
BH | 7.0 | 15.00 | 15.50 | 18.000 | 26.0 |
BFA | 1.0 | 1.00 | 2.00 | 4.000 | 7.0 |
FA | 6.0 | 8.00 | 8.00 | 9.000 | 10.0 |
CD | 4.0 | 6.00 | 7.00 | 9.000 | 16.0 |
BRA | 1.0 | 1.00 | 2.00 | 3.750 | 7.0 |
ED | 2.0 | 5.00 | 8.00 | 9.750 | 14.0 |
FEL | 0.0 | 4.00 | 7.00 | 11.000 | 50.0 |
C | 8.0 | 11.25 | 15.00 | 18.000 | 50.0 |
BW | 2.0 | 4.00 | 5.65 | 8.175 | 17.6 |
BT | 1.4 | 3.05 | 3.85 | 4.775 | 7.7 |
FEW | 0.0 | 1.90 | 2.50 | 3.900 | 8.6 |
Coils | 3.0 | 4.00 | 6.00 | 6.000 | 22.0 |
Length | 26.0 | 41.75 | 49.50 | 59.750 | 128.0 |
data(EndScrapers)
xtabs(Freq~Site+Curvature, EndScrapers)
xtabs(Freq~Curvature+Sides+Site, EndScrapers)
Curvature Site Round Medium Shallow Castenet A 388 504 306 Ferrassie H 276 769 757
, , Site = Castenet A Sides Curvature Convergent Parallel Round 89 299 Medium 46 458 Shallow 28 278 , , Site = Ferrassie H Sides Curvature Convergent Parallel Round 21 255 Medium 61 708 Shallow 59 698
data(Olorgesailie.maj)
Olorgesailie.maj
# Compute percentages over the localities
Olor.pct <- prop.table(as.matrix(Olorgesailie.maj), 1)*100
boxplot(Olor.pct)
Large.cutting.tools | Heavy.duty.tools | Large.scrapers | Other.large.tools | Small.tools | Spheroids | |
---|---|---|---|---|---|---|
LS1 | 1 | 1 | 0 | 0 | 59 | 0 |
LS2 | 13 | 9 | 0 | 2 | 42 | 2 |
LS3 | 6 | 7 | 1 | 6 | 34 | 0 |
LS4 | 0 | 0 | 0 | 0 | 0 | 1 |
LS5 | 33 | 14 | 6 | 24 | 139 | 3 |
MS1a | 51 | 11 | 5 | 6 | 124 | 1 |
MS1b | 34 | 1 | 3 | 5 | 7 | 0 |
MS2a | 69 | 7 | 4 | 5 | 10 | 8 |
MS2b | 435 | 30 | 38 | 20 | 50 | 8 |
MS3 | 24 | 5 | 3 | 2 | 41 | 2 |
MS4 | 52 | 4 | 1 | 5 | 15 | 1 |
MS5 | 57 | 5 | 12 | 25 | 108 | 4 |
MS6 | 14 | 1 | 2 | 0 | 13 | 0 |
MS7 | 89 | 5 | 5 | 4 | 4 | 5 |
MS8 | 97 | 4 | 6 | 7 | 33 | 0 |
MS9 | 2 | 3 | 0 | 4 | 13 | 0 |
US1 | 51 | 4 | 7 | 8 | 74 | 3 |
US2 | 7 | 2 | 0 | 6 | 47 | 1 |
US3 | 0 | 2 | 1 | 0 | 16 | 0 |
data(Olorgesailie.sub)
Olorgesailie.sub
Strat | Locality | HA | PHA | CHA | CL | KN | BLCT | PAT | CH | CS | LFS | CB | OLT | SSS | SSNP | OST | SP | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
L1 | Lower | BBB | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 34 | 24 | 1 | 0 |
L2 | Lower | BBA | 0 | 3 | 3 | 3 | 0 | 4 | 0 | 4 | 5 | 0 | 2 | 0 | 22 | 17 | 3 | 2 |
L3 | Lower | FB | 0 | 3 | 1 | 1 | 0 | 1 | 0 | 6 | 1 | 1 | 5 | 1 | 13 | 20 | 1 | 1 |
L4 | Lower | FB/HL | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
L5 | Lower | I3 | 6 | 17 | 3 | 5 | 2 | 0 | 4 | 9 | 1 | 6 | 20 | 4 | 110 | 29 | 0 | 3 |
M1a | Middle | DE/89A-L | 1 | 36 | 0 | 1 | 4 | 9 | 2 | 7 | 2 | 5 | 4 | 2 | 78 | 45 | 1 | 1 |
M1b | Middle | DE/89A-I | 2 | 20 | 0 | 1 | 2 | 9 | 1 | 0 | 0 | 3 | 1 | 4 | 7 | 0 | 0 | 0 |
M2a | Middle | DE/89B-L | 1 | 27 | 0 | 34 | 1 | 6 | 1 | 6 | 0 | 4 | 2 | 3 | 6 | 1 | 3 | 8 |
M2b | Middle | DE/89B-I | 2 | 245 | 0 | 148 | 33 | 7 | 9 | 18 | 3 | 38 | 0 | 20 | 29 | 21 | 0 | 8 |
M3 | Middle | DE/89C | 3 | 9 | 2 | 6 | 0 | 4 | 1 | 4 | 0 | 3 | 1 | 1 | 22 | 19 | 0 | 2 |
M4 | Middle | H/6 | 7 | 21 | 10 | 4 | 6 | 4 | 1 | 3 | 0 | 1 | 2 | 3 | 10 | 4 | 1 | 1 |
M5 | Middle | H/9-A | 0 | 15 | 11 | 18 | 4 | 9 | 0 | 3 | 2 | 12 | 9 | 16 | 85 | 23 | 0 | 4 |
M6 | Middle | H/9-AM | 2 | 5 | 5 | 2 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 12 | 1 | 0 | 0 |
M7 | Middle | Mid | 3 | 27 | 15 | 34 | 4 | 6 | 1 | 4 | 0 | 5 | 0 | 4 | 1 | 1 | 2 | 5 |
M8 | Middle | Meng | 2 | 46 | 29 | 7 | 1 | 12 | 1 | 3 | 0 | 6 | 6 | 1 | 24 | 9 | 0 | 0 |
M9 | Middle | LHS | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 3 | 8 | 5 | 0 | 0 |
U1 | Upper | TrTr/M10 | 0 | 22 | 3 | 19 | 1 | 6 | 1 | 2 | 1 | 7 | 4 | 4 | 58 | 15 | 1 | 3 |
U2 | Upper | Hog | 1 | 2 | 1 | 1 | 0 | 3 | 0 | 2 | 0 | 0 | 1 | 5 | 34 | 13 | 0 | 1 |
U3 | Upper | MFS | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 5 | 9 | 2 | 0 |
chisq.test(Olorgesailie.sub[,3:18], simulate.p.value=TRUE)
Pearson's Chi-squared test with simulated p-value (based on 2000 replicates) data: Olorgesailie.sub[, 3:18] X-squared = 1717.1, df = NA, p-value = 0.0004998
In the following example, we have uploaded a file to the 'data-import' folder. It is a simple csv containing the data concerning the XRF measurements of the British School at Rome's collection of stamped brick Graham 2006. Try visualizing this data or re-running Graham's analysis.
BSR_XRF <- read.csv(file="./data-import/xrd-majors-bsr-brickstamps.csv", header=TRUE, sep=",")
BSR_XRF
Sample | Quartz | Augite | Haematite | Gehlenite | Calcite | Analcime | Muscovite | Dolomite | Anorthoclase | Sanidine | Albite |
---|---|---|---|---|---|---|---|---|---|---|---|
se 1 | 58 | 70 | 14 | 0 | 0 | 10 | 34 | 36 | 105 | 0 | 0 |
se 2 | 16 | 83 | 5 | 0 | 43 | 9 | 0 | 23 | 0 | 0 | 66 |
se 4 | 89 | 36 | 17 | 36 | 70 | 0 | 7 | 14 | 0 | 55 | 61 |
se 5 | 42 | 94 | 8 | 0 | 0 | 0 | 10 | 40 | 0 | 65 | 55 |
se 7 | 20 | 102 | 9 | 0 | 0 | 15 | 10 | 30 | 85 | 25 | 0 |
se 8 | 50 | 14 | 10 | 20 | 75 | 0 | 10 | 0 | 56 | 0 | 0 |
se 10 | 50 | 60 | 16 | 20 | 0 | 0 | 42 | 48 | 0 | 50 | 45 |
se 13 | 87 | 25 | 24 | 30 | 55 | 19 | 14 | 13 | 56 | 0 | 0 |
se 14 | 32 | 104 | 9 | 3 | 0 | 20 | 10 | 40 | 85 | 0 | 0 |
se 14 | 38 | 106 | 12 | 0 | 5 | 17 | 15 | 47 | 0 | 60 | 65 |
se 16 | 86 | 28 | 14 | 50 | 86 | 25 | 26 | 22 | 103 | 0 | 0 |
se 18 | 38 | 71 | 17 | 0 | 32 | 0 | 3 | 28 | 95 | 0 | 0 |
se 19 | 38 | 92 | 9 | 0 | 50 | 0 | 0 | 30 | 0 | 0 | 100 |
se 20 | 106 | 41 | 23 | 23 | 32 | 16 | 0 | 24 | 96 | 0 | 0 |
se 21 | 23 | 100 | 7 | 0 | 0 | 0 | 11 | 34 | 0 | 105 | 0 |
se 22 | 36 | 106 | 6 | 40 | 0 | 7 | 42 | 0 | 72 | 0 | 0 |
se 23 | 26 | 0 | 3 | 0 | 124 | 0 | 12 | 0 | 0 | 0 | 12 |
se 26 | 68 | 33 | 8 | 18 | 0 | 24 | 0 | 23 | 70 | 0 | 0 |
se 27 | 45 | 93 | 17 | 0 | 32 | 0 | 3 | 40 | 0 | 0 | 75 |
se 28 | 76 | 44 | 14 | 33 | 66 | 14 | 6 | 0 | 65 | 0 | 0 |
se 29 | 21 | 95 | 8 | 7 | 95 | 0 | 7 | 32 | 0 | 50 | 60 |
se 30 | 82 | 44 | 12 | 6 | 0 | 0 | 6 | 17 | 0 | 35 | 0 |
se 36 | 52 | 90 | 8 | 0 | 43 | 0 | 0 | 39 | 95 | 0 | 0 |
se 37 | 97 | 48 | 24 | 33 | 12 | 22 | 24 | 20 | 95 | 0 | 0 |
se 42 | 56 | 105 | 11 | 0 | 0 | 17 | 0 | 40 | 0 | 114 | 90 |
se 45 | 29 | 92 | 10 | 0 | 0 | 0 | 5 | 31 | 55 | 0 | 0 |
se 47 | 47 | 110 | 11 | 0 | 0 | 20 | 0 | 40 | 0 | 88 | 96 |
se 48 | 41 | 16 | 7 | 7 | 80 | 0 | 8 | 12 | 0 | 30 | 35 |
se 50 | 88 | 22 | 12 | 17 | 0 | 14 | 17 | 0 | 0 | 50 | 50 |
se 51 | 40 | 110 | 8 | 0 | 10 | 15 | 10 | 42 | 84 | 0 | 88 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
se 168 | 117 | 85 | 18 | 60 | 10 | 25 | 22 | 21 | 100 | 0 | 0 |
se 170 | 33 | 23 | 31 | 0 | 11 | 0 | 0 | 12 | 0 | 0 | 117 |
se 171 | 63 | 26 | 13 | 46 | 100 | 13 | 15 | 0 | 25 | 0 | 70 |
se 172 | 107 | 45 | 31 | 0 | 0 | 0 | 0 | 16 | 76 | 0 | 103 |
se 174 | 30 | 29 | 34 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 109 |
se 176 | 33 | 55 | 7 | 2 | 14 | 0 | 0 | 10 | 0 | 30 | 85 |
se 177 | 65 | 36 | 10 | 13 | 74 | 0 | 0 | 10 | 0 | 35 | 70 |
fal 1 | 105 | 0 | 10 | 60 | 30 | 26 | 33 | 0 | 90 | 0 | 0 |
fal 2 | 28 | 63 | 0 | 6 | 120 | 36 | 0 | 24 | 66 | 0 | 0 |
fal 3 | 92 | 54 | 20 | 90 | 76 | 20 | 9 | 0 | 85 | 0 | 0 |
fnv13 | 43 | 87 | 7 | 30 | 60 | 15 | 0 | 0 | 42 | 0 | 60 |
fnv14 | 92 | 91 | 13 | 0 | 110 | 9 | 0 | 32 | 70 | 0 | 0 |
fnv15 | 76 | 25 | 20 | 0 | 100 | 16 | 0 | 0 | 24 | 0 | 0 |
fnv4 | 64 | 25 | 6 | 0 | 72 | 0 | 9 | 3 | 0 | 0 | 27 |
fnv5 | 52 | 0 | 0 | 0 | 94 | 0 | 14 | 4 | 30 | 0 | 0 |
fnv6 | 72 | 22 | 10 | 0 | 75 | 0 | 16 | 0 | 23 | 0 | 0 |
fnv8 | 36 | 108 | 7 | 0 | 63 | 50 | 0 | 35 | 0 | 0 | 55 |
fnv9 | 14 | 98 | 6 | 0 | 72 | 8 | 7 | 23 | 0 | 0 | 77 |
mod 1 | 113 | 55 | 0 | 54 | 0 | 15 | 9 | 0 | 98 | 0 | 0 |
mod 2 | 37 | 120 | 7 | 43 | 0 | 10 | 0 | 27 | 24 | 0 | 0 |
mod 3 | 112 | 22 | 24 | 71 | 96 | 12 | 17 | 0 | 62 | 0 | 0 |
mod 4 | 16 | 0 | 0 | 5 | 127 | 0 | 0 | 0 | 0 | 5 | 0 |
mod 5 | 52 | 96 | 10 | 49 | 0 | 9 | 0 | 27 | 53 | 0 | 0 |
mod 6 | 57 | 105 | 6 | 60 | 0 | 6 | 0 | 20 | 36 | 0 | 0 |
mod 7 | 37 | 0 | 0 | 38 | 124 | 0 | 16 | 0 | 0 | 0 | 25 |
mod 8 | 105 | 0 | 0 | 20 | 100 | 20 | 20 | 0 | 32 | 0 | 0 |
mod 9 | 11 | 0 | 3 | 0 | 54 | 0 | 3 | 4 | 0 | 0 | 3 |
ser 1 | 89 | 38 | 9 | 12 | 79 | 0 | 12 | 28 | 0 | 0 | 70 |
ser 2 | 97 | 18 | 14 | 8 | 60 | 0 | 18 | 7 | 0 | 36 | 50 |
ser 3 | 90 | 46 | 10 | 6 | 90 | 0 | 19 | 19 | 45 | 35 | 0 |
BSR.pca <- prcomp(BSR_XRF[, -1], scale.=TRUE)
biplot(BSR.pca, xlabs=abbreviate(BSR_XRF$Sample, 1), cex=.75)