aammd

Is there an easy way to convert a named list into a dataframe, preserving the elements of the list in a "list-column"?

    library(dplyr)
    library(magrittr)

    ## make a random matrix
    rand_mat <- function() {

aammd

using Plots

# a vector of r values
Rs=collect(0.1:0.001:3)
T = 5000

N=zeros(length(Rs), T)

#Set t0 values to 1
N[:,1] .= 1

aammd

library(shiny)
library(bslib)
library(thematic)
library(ggplot2)


thematic::thematic_shiny(font = "auto")


# take a vector, make a histogram

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library(tidyverse)
#> Warning: package 'tibble' was built under R version 4.0.2
#> Warning: package 'tidyr' was built under R version 4.0.2
#> Warning: package 'dplyr' was built under R version 4.0.2
tibble(x = 0:42,
       y1 = pbinom(x, 42, 0.3),
       y2 = 1-pbeta(0.3, x+1, 42-x)) %>% 
  ggplot(aes(x = x, y = y1)) + 
 geom_point(col = "green", size = 3) + 

aammd

---
title: "Exploring observations"
author: "Andrew, based on Tim's lecture!"
date: "18/05/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

aammd

m <- 0.086

o <- (1-m)/m
curve((sqrt(o) - sqrt(o^-1)) / sqrt(x^2 - x + 1), xlim = c(0,50))
abline(h = 1/3)

aammd

Simple data manipulations in R

Many years ago, I was introduced to R by Cam Webb . At the time, his website contained a list of common data manipulations (original here). This list dated from Cam's early experience with R, and contained the R-help mailing list responses to a series of data manipulations. For a long time, I kept this file as a handy reference. I printed it out. I recommended it to friends.

Now I have been using R for years, and the state of the art has advanced considerably. Particulary, Hadley Wickham's reshape2 and dplyr packages have transformed the way most useRs manipulate their data. I decided that it would be interesting to revisit my favourite resource and try my hand at solving these problems with tools from these two packages.

library(reshape2)
library(dplyr)

aammd

library(tidyverse)
cv <- 2.5
sigma = seq(0.2,5,by = 0.2)
list(mean = sigma / cv, sd = sigma) %>% transpose %>%
  set_names(., nm = map_chr(., lift_dl(paste, sep="_"))) %>%
  map(lift_dl(partial, .f = dnorm)) %>%
  map_df(~ tibble(x = seq(-2,3.5,0.01),
               y = .x(x)), .id = "mean_sd") %>%
 ggplot(aes(x = x, ymax = y, ymin = 0, fill = mean_sd)) + geom_ribbon() +

aammd

pwr <- function(S, a, mu = 0.086, phi=240){
  D <- S * (S - 1)
  2 * (D + 1) * (D/(phi + 1) + 1) * mu * ( 1 - mu) + ( a * mu + mu)  * (1 - a * mu - mu) * (2.8)^2 / ((D + 1)*a*mu)^2
}

pwr(6, 0.10)
curve(pwr(x, 0.4), xlim = c(3,30))

curve(pwr(x, 0.2), xlim = c(3,30), log = "y")

aammd

make_plot <- function(mydata){
  qplot(Length, Freq, data=mydata)
}