dgrtwo

separate_sql <- function(expression) {
  s <- paste(deparse(expression), collapse = "\n")
  s <- stringr::str_replace(s, "%>%", "%>%\n  ")
  s <- stringr::str_split(s, "\n")[[1]]
  
  val <- eval(expression)
  
  list(expression = expression,
       dplyr_code = s,
       sql = as.character(dbplyr:::remote_query(val)),

dgrtwo

library(purrr)

transition_mc <- function(steps, start, mat) {
  i <- seq_len(nrow(mat))
  
  transition <- ~ sample(i, 1, prob = (i == .) %*% mat)
  
  accumulate(seq_len(steps), transition, .init = start)
}

dgrtwo

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

```{r}
library(purrr)

transition_mc <- function(steps, start, mat) {
  i <- seq_len(nrow(mat))

dgrtwo

library(tidyverse)

# Data is downloaded from here:
# https://www.kaggle.com/c/digit-recognizer
kaggle_data <- read_csv("~/Downloads/train.csv")

pixels_gathered <- kaggle_data %>%
  mutate(instance = row_number()) %>%
  gather(pixel, value, -label, -instance) %>%
  extract(pixel, "pixel", "(\\d+)", convert = TRUE)

dgrtwo

Year,Month,Questions,Tag,MonthTotal,TagTotal
2008,8,4,.htaccess,3929,57155
2008,9,12,.htaccess,14543,57155
2008,10,10,.htaccess,14951,57155
2008,11,16,.htaccess,12967,57155
2008,12,12,.htaccess,12324,57155
2009,1,24,.htaccess,16152,57155
2009,2,35,.htaccess,17889,57155
2009,3,43,.htaccess,20785,57155
2009,4,46,.htaccess,21701,57155

dgrtwo

library(readr)
library(dplyr)
library(ggplot2)

faces <- read_csv("faces.csv")

vase <- bind_rows(select(faces, x = x1, y),
                  select(faces, x = x2, y) %>% arrange(y))

faces %>%

dgrtwo

library(tidyverse)
library(broom)
library(scales)
theme_set(theme_minimal())

crossing(n = round(10 ^ seq(1, 7, .1)),
         delta = 10 ^ seq(-1, -3, -1),
         sig.level = .05,
         sd = .3) %>%
  invoke(power.t.test, ., type = "one.sample") %>%

dgrtwo

dgrtwo

library(ggplot2)
library(igraph)
library(ggraph)
library(scales)
library(ggforce)

network_theme <- theme_no_axes() +
  theme(panel.border = element_blank())
theme_set(network_theme)

dgrtwo

  # randomly select min_n_plates from each strain
  randomly_sampled_dataframe <- x %>%
    dplyr::group_by_(grouping_var, plate_var, sample_var) %>%
    tidyr::nest(.key = id_data) %>%
    dplyr::group_by_(grouping_var, plate_var) %>%
    dplyr::sample_n(min_n_id_per_plate) %>%
    tidyr::nest(.key = plate_data) %>%
    dplyr::group_by_(grouping_var) %>%
    dplyr::sample_n(min_n_plates_per_strain) %>%
    tidyr::unnest() %>%