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

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

dgrtwo

"""
DrupalPasswordHasher

To use, put this in any app and add to your settings.py, something like this:

PASSWORD_HASHERS = (
    'django.contrib.auth.hashers.PBKDF2PasswordHasher',
    'myproject.myapp.drupal_hasher.DrupalPasswordHasher',
    'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
    'django.contrib.auth.hashers.BCryptPasswordHasher',

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  # 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() %>%

dgrtwo

library(tidyverse)
library(lubridate)

questions <- readr::read_csv("~/Repositories/stacklite/questions.csv.gz")
question_tags <- readr::read_csv("~/Repositories/stacklite/question_tags.csv.gz")

r_questions <- question_tags %>%
  filter(Tag == "r")

r_questions_by_month <- questions %>%

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#' Convert a dplyr expression to a list of step objects
separate_steps <- function(expr, iscall=FALSE) {
  if (iscall) {
    call <- expr
  } else {
    call <- match.call()[["expr"]]
  }
  
  len <- length(call)
  if (len == 1) {

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#' Add a column counting or tallying observations within groups
#'
#' \code{add_tally} adds a column named "n" (or similar) to a table based on the number
#' of items within each group. These functions are to \code{tally} and
#' \code{count} as \code{mutate} is to \code{summarise}: they add an additional
#' column. They tally within the groups of the current data, and do not change them.
#'
#' @param x A table
#' @param wt (Optional) If omitted, will count the number of rows. Otherwise, use a weighted tally
#' @param sort Whether to sort the result in descending order of n

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grouply <- function(f, ...) {
  groups <- lazyeval::lazy_dots(...)
  
  function(tbl, ...) {
    dplyr::group_by_(tbl, .dots = groups) %>%
      f(...) %>%
      dplyr::ungroup()
  }
}