# Posts

### Random Walks (draft)

library("gganimate") library("ggplot2") p = 1/2 # parameters p <- 0.5 Tmax = 60 # start at the origin x = 0 y = 0 t = 0 df <- data.frame(x,y,t) # random walk for(i in 1:Tmax){ t <- t + 1 if(runif(1) < p){ x <- x - 1 } else { x <- x + 1 } # reset process if dot leaves view if(abs(x) > 10){ x <- 0 } this_step <- data.

### Valentine's Day 2019

In this short project, I hope to draw a heart through an animation of appearing dots. library("gganimate") library("ggforce") library("tidyverse") Draw a Heart Here I will place two circles centered at $(\pm 1, 1)$ with the same radius $r = \sqrt{2}$. circles <- data.frame( x0 = c(-1,1), y0 = rep(1,2), r = rep(1, 2) ) right_pt <- (sqrt(2) + 1) / sqrt(2) left_pt <- -1*right_pt f <- function(x){abs(x) - sqrt(2)} ggplot(data.frame(x = c(left_pt, right_pt)), aes(x)) + coord_fixed() + geom_circle(aes(x0 = x0, y0 = y0, r = r), data = circles, inherit.

### Introduction to R Workshop

Packages workshop_packages <-c("ggplot2", "mosaic", "gganimate") install.packages(workshop_packages) library("gganimate") ## Loading required package: ggplot2 library("ggplot2") library("mosaic") ## Loading required package: dplyr ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union ## Loading required package: lattice ## Loading required package: ggformula ## Loading required package: ggstance ## ## Attaching package: 'ggstance' ## The following objects are masked from 'package:ggplot2': ## ## geom_errorbarh, GeomErrorbarh ## ## New to ggformula?

### Introduction to R Workshop

Packages workshop_packages <-c("ggplot2", "mosaic", "gganimate") install.packages(workshop_packages) library("gganimate") ## Loading required package: ggplot2 library("ggplot2") library("mosaic") ## Loading required package: dplyr ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union ## Loading required package: lattice ## Loading required package: ggformula ## Loading required package: ggstance ## ## Attaching package: 'ggstance' ## The following objects are masked from 'package:ggplot2': ## ## geom_errorbarh, GeomErrorbarh ## ## New to ggformula?

### gganatogram and gganimate

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, ".

### Curse of Dimensionality

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.

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.

### My First gganimate Plot

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.

### ggplot2

http://rpubs.com/dsollberger/activelearning

### Plotly

http://rpubs.com/dsollberger/scc

### Shiny Dashboard

https://dsollberger.shinyapps.io/SHW3/

### Flex Dashboard

https://dsollberger.shinyapps.io/shw7/

### Shiny and htmlwidgets

https://dsollberger.shinyapps.io/SAMPLe/

### Supreme Court Confirmations (1967-present)

Introduction Following up on Rachel Wellford’s tweet about Senate votes for Supreme Court confirmations, I decided to try to graph the data. Below, I have a ggplot picture with decent labeling a searchable datatable a plotly interactive graph The data came from Senate.gov. I chose to focus on 1967 onward because it appeared that voting procedures were slightly different before Thurgood Marshall’s nomination process.