tjmahr

library("devtools")
install_github("sckott/cowsay")
library("cowsay")
say("HAPPY BIRTHDAY", "cat")

##  ----- 
##  HAPPY BIRTHDAY 
##  ------ 
##     \   
##      \

tjmahr

# Code for formatting numbers (for my knitr docs)

library("magrittr")
library("dplyr", warn.conflicts = FALSE)
library("broom")
library("stringr")

# Fixed width integers (like for track or image numbers in filenames)
sprintf("%03.f", seq_len(20))
# [1] "001" "002" "003" "004" "005" "006" "007" "008" "009" "010"

tjmahr

library("dplyr")
library("readr")

# Download a tbl from a db connection and write to a csv
backup_tbl <- function(tbl_name, src, output_dir) {
  # Try to download the tbl, defaulting to an empty data-frame
  try_tbl <- failwith(data_frame(), tbl)
  df <- collect(try_tbl(src, tbl_name))

  output_file <- file.path(output_dir, paste0(tbl_name, ".csv"))

tjmahr

# Download all the 'See also: Artist: "Title"' entries from 
# Pitchfork's Top 200 songs of the 80's
# http://pitchfork.com/features/staff-lists/9700-the-200-best-songs-of-the-1980s/
library("rvest")
library("stringr")
library("dplyr", warn.conflicts = FALSE)
library("curl")

# Given url, get see-also paragraphs and text matching "See also*"
scrape_see_also_nodes <- function(url) {

tjmahr

Good idea or bad idea for YAML metadata?

---
title: "My report"
author: "Tristan Mahr"
output: html_document
params:
  knitr_chunks: !r 
 knitr::opts_chunk$set(comment = "#>", 

tjmahr

# Function factory
make_adder <- function(x) {
  function() x + 1
}

# Create list of functions with Map
funs <- Map(make_adder, 1:10)

unlist(Map(function(f) f(), funs))
# [1]  2  3  4  5  6  7  8  9 10 11

tjmahr

get_p_stars <- function(ps) {
  symnum(ps, na = FALSE, cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),
         symbols = c("***", "**", "*", ".", " "), legend = FALSE)
}

#> get_p_stars(c(.00001, .001, .002, .01, .02, .05, .06, .1, .2))
#> [1] *** *** **  **  *   *   .   .      

tjmahr

library("readr")
library("dplyr")
library("tidyr")
library("lubridate")

# Load the visit history, parse the timestamp as a Date
mdr <- read_csv("C:/Users/trist/Downloads/longitudinal_missing.csv") %>%
  mutate(Date = mdy_hm(DateTime) %>% as.Date)

# for each study/subject, find difference between latest and earliest date

tjmahr

compute_bias <- function(looks, window, t_col = "Time", aoi_col = "GazeByImageAOI", sample_rate = NULL) {
  # Create filtering expression by plugging in the name of the "Time" column
  interp <- lazyeval::interp
  pred <- interp(~ between(x, min(w), max(w)), x = as.name(t_col), w = window)
  
  # Keep just looks in range defined by window
  subtrial <- looks %>% filter_(pred)
  
  # Make sure that there is only set of looks in the data
  window_times <- subtrial[[t_col]]

tjmahr

sample_n_groups <- function(df, n, group_keys = as.character(groups(df))) {
  # Remember original grouping
  group_keys_orig <- as.character(groups(df))
  stopifnot(length(group_keys) != 0)

  df_sub <- df %>% 
    # Keep just the grouping columns
    group_by_(.dots = group_keys) %>% 
    select(one_of(group_keys)) %>% 
    distinct %>%