Data and code for "Color naming across languages reflects color use"
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output
7_subjs_double_half_grid.csv
English_WCS.csv
Munsell_WCS_codes.csv
README.md
Spanish_WCS.csv
Spanish_open.csv
Tsimane2014_focal_colors_object_colors.csv
Tsimane2015_2_focal_colors_object_colors.csv
Tsimane_WCS.csv
USColorChipLabels nov6 2014.csv
allfigs.R
cc.rda
chip.csv
color labeling tsimane english spanish oct 2015 v4.csv
contours.py
demographics2014.csv
demographics2015.csv
empirical_focal3.csv
english_objects.csv
english_rts.csv
foreground_background_pixels.csv
lang.csv
s0-18_big_grid.csv
s19_66_half_grid.csv
sRGB_Munsell320.csv
sedivy_data_fixed.csv
spanish_objects.csv
sparse_chips_to_use.rda
term.csv
tsimane_objects.csv
tsimane_rts.csv

README.md

Tsimane' Color Project

Data and standalone analysis pipeline for generating figures and data tables for the color paper:

Edward Gibson, Richard Futrell, Julian Jara-Ettinger, Kyle Mahowald, Leon Bergen, Sivalogeswaran Ratnasingam, Mitchell Gibson, Steven T. Piantadosi, and Bevil Conway. 2017. Color naming across languages reflects color use. Proceedings of the National Academy of Sciences 114(40): 10785-10790.

The data is anonymized: participant names and locations are replaced with numeric codes.

The analysis is done with a large R script and then a small python script. To install the R dependencies, open the R interpreter and run install.packages(c("MASS", "lme4", "reshape2", "plyr", "stringr", "hexbin", "Hmisc", "tidyverse")). To install the python dependencies, on the command line do pip install pandas, pip install scipy, pip install matplotlib.

Now to run the analysis, on the command line, do: Rscript allfigs.R, then python contours.py. Results will be stored in the directory output.