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Today I wanted to see if I could create a slideshow of pictures from the gganatogram package. I wanted to combine them with the gganimate package, but I have not figured out how to get that to work. (In particular, the gganatogram() function seems to return a different list layout than ggplot objects.) library(gganatogram) ## Loading required package: ggpolypath ## Loading required package: ggplot2 library(gganimate) library(profvis) N <- 25 # number of cell samples num_cell_parts <- nrow(cell_key$cell) # randomly select a random number of cell parts part_picker <- sample(1:num_cell_parts, sample(1:num_cell_parts, 1)) cell_num <- rep(1, length(part_picker)) this_cell <- cell_key[['cell']][part_picker, ] cell_samples <- cbind(this_cell, cell_num) for(j in 2:N){ part_picker <- sample(1:num_cell_parts, sample(1:num_cell_parts, 1)) cell_num <- rep(j, length(part_picker)) this_cell <- cbind( cell_key[['cell']][part_picker, ], cell_num) cell_samples <- rbind(cell_samples, this_cell) # figure_list[j] <- gganatogram(data = this_cell, # outline = FALSE, fillOutline='steelblue', organism="cell", fill="colour") + # theme_void() + # coord_fixed() png(filename = paste0(j, ".

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Today, I hope to present a quick glimpse at the phenomenon called the “Curse of Dimensionality”. For this demonstration, I am simply calculating how much random data stays within two standard deviations (in the Euclidean norm) as we go from one dimension to higher dimensions. Random Data Here are 10 vectors of 100 random numbers each sampled from the standard normal distribution stored as a matrix … X <- matrix(rnorm(1000), nrow = 100, ncol = 10) … and as a data frame.

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In this post, I want to run an example of absorbing states in stochastic processes. This example is based on Example 3.29 in Introduction to Stochastic Processes in R by Robert Dobrow. Data The data I have assembled is based on IRDS reports from my own University of California at Merced. # weights weights <- c(20, 70, 0, 0, 10, 0, 0, 5, 89, 0, 6, 0, 0, 0, 3, 94, 3, 0, 0, 0, 0, 24, 1, 76, 0, 0, 0, 0, 100, 0, 0, 0, 0, 0, 0, 100) sparse_weights <- weights[weights > 0] # transition matrix (row stochastic) P <- matrix(weights, nrow = 6, byrow = TRUE)*0.

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Today I finally read a tutorial on gganimate, and here I will build my first example. My hope is to eventually simulate traversal on a directed graph. library(tidyverse) library(gganimate) Create Node Data For this example, I will have 3 nodes (located at 3 vertices of a triangle), 100 dots distributed among the nodes, and a new placement over 21 states in time. N <- 100 # number of dots T <- 20 # number of iterations node_names <- c("Node 1", "Node 2", "Node 3") iter <- rep(0, N) node <- rep("Node 1", N) x <- 10 + rnorm(N, 0, 2) y <- 20 + rnorm(N, 0, 2) states <- data.

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Teaching

I am a teaching instructor for the following courses at the University of California at Merced:

  • Math 15: first semester data science for life science students
  • Bio 18: second semester data science for life science students
  • Bio 184 (TBD): Python for DNA analysis

Contact

  • dsollberger@ucmerced.edu
  • Derek Sollberger
    School of Natural Sciences
    5200 North Lake Road
    Merced, CA, 95343
  • email for appointment