JoFrhwld

# I don't understand why or how this
# plus what's in Makevars works, but it's
# the only way I can get rstan & brms installed\

# see also: https://github.com/stan-dev/rstan/issues/1070#issuecomment-1570565599

# renv::init(bare = TRUE)
Sys.setenv(MAKEFLAGS = "-j4") # four cores used
install.packages(c("Rcpp", "RcppEigen", "RcppParallel", "StanHeaders"), type = "source")
install.packages("rstan", type = "source")

JoFrhwld

#!/bin/sh
x="Version: 1.0
RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default
 
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8

JoFrhwld

import pandas as pd

def align_chunks(X_df, Y_df, A_idx_list, B_idx_list, state, **kwargs):
    """
        Given X_df and Y_df, return the dataframes (A_df and B_df) with the
        smallest possible span of overlapping transcription chunks. 

        Function operates recursively.
    """

JoFrhwld

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

Source:

https://edition.cnn.com/election/2016/results/exit-polls

```{r}

JoFrhwld

---
title: "Building up and evaluating models"
output:
  html_notebook:
    code_folding: none
    css: ../custom.css
    theme: flatly
    toc: yes
    toc_depth: 3
    toc_float: yes

JoFrhwld

---
title: "Building up and evaluating models"
output:
  html_notebook:
    code_folding: none
    css: ../custom.css
    theme: flatly
    toc: yes
    toc_depth: 3
    toc_float: yes

JoFrhwld

library(tweenr)
library(gganimate)
library(ggplot2)
library(tidyverse)

#' define the 4 states
state1 <- data_frame(mean = 0, 
                     sd = 1)
state2 <- data_frame(mean = 0, 
                     sd = 3)

JoFrhwld

library(tidyverse)

replicates <- (1:100000)%>%
                map(~sample(faithful$waiting, replace = T))%>%
                map(mean)%>%
                simplify()

data_frame(replicates = replicates)%>%
  ggplot(aes(replicates))+
    stat_density()

JoFrhwld

library(tidyverse)

replicates <- (1:100000)%>%
                map(~sample(faithful$waiting, replace = T))%>%
                map(mean)%>%
                simplify()

data_frame(replicates = replicates)%>%
  ggplot(aes(replicates))+
    stat_density()

JoFrhwld

#' I often have individual speaker data files in a nested directory structure.
#' But I also often want to read all speaker's data into R in one big data frame.
#' Here's my current best recipe.

library(tidyverse)

#' glob for the file list. This is dependent on good directory naming practices
all_files <- Sys.glob("path/speakerid*/*.csv")

df <- data_frame(file = all_files) %>%               # make a column of all of the file paths