This dataset contains all data and code necessary to reproduce the analysis described under the heading "Experiment 3" in the manuscript: Taliercio, E., Eickholt, D., Read, Q. D., Carter, T., Waldeck, N., & Fallen, B. (2023). Parental choice and seed size impact the uprightness of progeny from interspecific Glycine hybridizations. Crop Science. https://doi.org/10.1002/csc2.21015 The attached files are: G_max_G_soja_seedweight_seedcolor_analysis.Rmd: RMarkdown notebook containing all analysis code. The CSV data files should be placed in a subdirectory called data within the working directory from which the notebook is rendered. G_max_G_soja_seedweight_seedcolor_analysis.html: Rendered HTML output from RMarkdown notebook, including figures, tables, and explanatory text. counts_seedwt.csv: CSV file containing the number of progeny selected and average 100-seed weight data for each combination of cross, size class, and replicate. Columns are: F3_location: text identifier of F3 nursery location, either "CLA" or "FF" plot: numeric ID of plot pop: numeric ID of population max: name of G. max parent soja: name of G. soja parent F2_location: text identifier of F2 nursery location, either "Caswell" or "Hugo" n_planted: number of seeds planted (raw) n_selected: number of progeny selected size_ordered: seed size class, to be converted to an ordered factor size_combined: seed size class aggregated to fewer unique levels ave_100sw: average 100-seed weight for the given size class n_planted_trials: number of seeds planted rounded to nearest integer seedcolor.csv: CSV file with additional data on number of seeds of each color by population. Columns are: cross: text identifier of cross line: text identifier of line light: number of light seeds mid: number of mid-green seeds brown: number of brown seeds dark: number of dark or black seeds population: identifier of population type (F2 derived or selected) max: name of G. max parent n_total: sum of the light, mid, brown, and dark columns soja: name of G. soja parent The data processing and analysis pipeline in the RMarkdown notebook includes: Importing the data (slightly cleaned version is provided) Creating boxplots of proportion selected by cross, nursery location, and size class Fitting logistic GLMM to estimate the probability of selection as a function of parent, 100-seed weight, and their interactions Extracting and plotting random effect estimates from model Calculating and plotting estimated marginal means from model Taking contrasts between pairs of estimated marginal means and trends Calculating Bayes Factors associated with the contrasts Generating figures and tables for all above results Additional seed color analysis: importing data (slightly cleaned version is provided) Additional seed color analysis: drawing exploratory bar plot Additional seed color analysis: fitting multinomial GLM modeling the proportion of seeds with each color as a function of population Additional seed color analysis: generating expected value predictions from GLM and taking contrasts Additional seed color analysis: creating figures and tables for model results This research was funded by CRIS 6070-21220-069-00D, United Soybean Board Project # 2333-203-0101, and falls under National Program NP301.