thomasp85

library(ggraph)
library(gganimate)
library(igraph)
# Data from http://konect.uni-koblenz.de/networks/sociopatterns-infectious
infect <- read.table('out.sociopatterns-infectious', skip = 2, sep = ' ', stringsAsFactors = FALSE)
infect$V3 <- NULL
names(infect) <- c('from', 'to', 'time')
infect$timebins <- as.numeric(cut(infect$time, breaks = 100))

# We want that nice fading effect so we need to add extra data for the trailing

thomasp85

Verifying that +thomasp85 is my blockchain ID. https://onename.com/thomasp85

thomasp85

#devtools::install_github('thomasp85/ggforce')
#devtools::install_github("dgrtwo/gganimate")
#devtools::install_github('thomasp85/ggraph')
#devtools::install_github("hadley/ggplot2")

library(ggraph)
library(ggforce)
library(gganimate)
library(ggplot2)
library(igraph)

thomasp85

library(tweenr) # Available on CRAN
library(ggforce) # Install from thomasp85/ggforce
library(gganimate) # Install from dgrtwo/gganimate
set.seed(2)
x <- sample(9,20, prob=c(1,2,3,4,5,4,3,2,1), replace=T)
df <- data.frame(x = x, y = 15)
dfs <- list(df)
for(i in seq_len(nrow(df))) {
    dftemp <- tail(dfs, 1)
    dftemp[[1]]$y[i] <- sum(dftemp[[1]]$x[seq_len(i)] == dftemp[[1]]$x[i])

thomasp85

library(ggforce) # need github version
library(tweenr)
library(animation)

# Define the different states
controls <- list(
    data.frame(
        x = runif(6),
        y = runif(6),
        col = 'steelblue',

thomasp85

Keybase proof

I hereby claim:

• I am thomasp85 on github.
• I am thomasp85 (https://keybase.io/thomasp85) on keybase.
• I have a public key ASDRGUror8ScAXuoC-MJVs06sOwPsdpyP9RU3GrLfkKU9Qo

To claim this, I am signing this object:

thomasp85

library(future)
trim_model <- function(model, predictor = predict, ..., ignore_warnings = TRUE) {
  # Cache the correct output
  true_pred <- predictor(model, ...)
  
  # Treat prediction warnings as errors?
  if (!ignore_warnings) {
    old_ops <- options(warn = 2)
    on.exit(options(old_ops))
  }

thomasp85

---
title: "R Notebook"
output: html_notebook
---

# Difference in behavior between ggplot2 and patchwork plots in Notebooks

Consider the following ggplot

```{r}

thomasp85

library(tidygraph)
library(particles)
library(jsonlite)
library(magick)

# Prepare text polygons
text <- read_json('text.json')
par_text <- text$layers[[3]]$paths
on_text <- text$layers[[2]]$paths
cran_text <- text$layers[[1]]$paths

thomasp85

library(magick)
library(reshape2)
library(dplyr)
library(tidygraph)
library(particles)
library(animation)

plot_fun <- function(sim) {
  df <- as_tibble(sim)
  plot(df$x, df$y, col = df$color, pch = '.', axes = FALSE, xlim = c(-100, 317), ylim = c(-268, 100), xlab = NA, ylab = NA)