2018 ASHS Annual Conference

Basic questions such as: disease variation in a field, disease progression throughout the season, effect of nearby covariates, and the progression of desirable genotypes, are normally answered by analyzing the data through statistical software such as SAS, JMP, SPSS, or R. However, it is often the case that searching through the long outputs of these programs for one or two p-values is less informative and harder to comprehend than a basic R-script utilizing R/plotly to visualize the data. We investigated the use of interactive 3D plots generated by R/plotly to answer the following important inquiries: Which genotypes to use as parents in this year’s crosses based on best performance in our environment throughout the year. Whether the distribution of flower yield or disease incidence was affected by the presence of a dirt road running by one side of the field or by partial flooding of the field due to Hurricane Harvey. Outputs are interactive and can help identify outliers and specific individuals in large datasets without having to search for specific values within spreadsheets with thousands of rows.