mmparker

library(dplyr)

df_list <- list(
    mutate(mtcars[1:5, ], jurisdiction = 'New Guernsey'),
    mutate(iris[1:5, ], mean = mean(Sepal.Length)),
    mutate(beaver1[1:5, ], jurisdiction = 'New Guernsey', mean = mean(temp))
)

mmparker

library(dplyr)
library(rvest)

# Get the list of files to work on
files_to_process <- list.files('r:/shared documents/', 
                               pattern = 'html', 
                               full.names = TRUE)

mmparker

# Two main things I can't figure out:
# 1. How do I get the name of each function to prefix its log entry?
#    e.g., INFO:testmodule:outsideFun:{msg} instead of just INFO:testmodule:{msg}
# 2. How do I get bound values to persist through subsequent functions?
#    e.g., user:matt.parker and outsideArg:foo should appear in both 
#   outsideFun and insideFun log messages

import logging

mmparker

options(stringsAsFactors = FALSE,
        scipen = 9999)

library(shiny)
library(ggplot2)
library(dplyr)

mmparker

library(dplyr)

# Set up a temp sqlite database
db <- src_sqlite(tempfile(), create = TRUE)
iris2 <- copy_to(db, iris)


# if_else with named true and false on a data.frame -> no problem
iris %>% mutate(Sepal.Size = if_else(Sepal.Length > 5, 
                                     true = "big", 

mmparker

# Always
options(stringsAsFactors = FALSE)


library(tidyverse)


# Setting the ggplot2 theme

mmparker

# Setup
options(stringsAsFactors = FALSE)
library(lubridate)
library(ggplot2)

mmparker

# This is an example of how to apply the lagging function from this
# StackOverflow answer: http://stackoverflow.com/a/30874772/143319
# to grouped data. In short:
# 1. Use rxSplit() to put each group in its own XDF file
# 2. Use lapply() to iterate over the list of XDF files



# Pick a sample dataset
xdfPath <- file.path(rxGetOption("sampleDataDir"), "DJIAdaily.xdf")

mmparker

Spanish Vocabulary for Statistical Computing

This is a little cheatsheet for anyone interested in writing about or teaching statistics in Spanish. Please feel free to expand and correct.

Statistics (La Estadistica)

Values: valores

mmparker

# Pick some dynamic date - here's the start of the current month
start_date <- format(Sys.Date(), "%Y-%m-01")

# Then use paste0() to construct a query that includes it
# Here's an example to get all the positive QFTs this month
# (I have probably misremembered the field names).
# I'm pretty sure you have to put a # on each side of the
# date in order for Access to recognize it as a date.
test_query <- sqlQuery(tbdb, 
    paste0("SELECT person_id, test_date