JoFrhwld

library(babynames)
library(dplyr)
library(ggplot2)

lifetables %>%
  mutate(decade = year)%>%
  group_by(decade)%>%
  mutate(prob_alive = lx/100000,
         study_year = year + x)->prob_people

JoFrhwld

library(lme4)

mod <-  lmer(F1_n ~ plt_vclass * Decade_c * freq_c + (plt_vclass + freq_c| File) + (Decade_c|word), 
             data = ays_to_test)

boot_fun <- function(mod){
  # x is a named vector
  x <- fixef(mod)
  
  #out is a longer named vector

JoFrhwld

libarary(plyr)
library(dplyr)
library(ggplot2)


baseball %>%
  group_by(year)%>%
  summarise(r=sum(r)) %>%
  ggplot(., aes(year, r)) +
    geom_point()

JoFrhwld

#' SQL Load
#' 
#' This is function meant to be used along with ldply to read data in using sqldf.
#' 
#' @param x the path to a file to be read
#' @param selection the columns to return. Defaults to \code{"*"}
#' @param condition conditions defining which data rows to load in SQL
#' @param file.format an argument to be passed to \code{sqldf}. 
#' Defaults to assume a tab-delimited file with a header row.
#' See \code{?sqldf} for more info

JoFrhwld

theKingOfFrance = None
bald = ["JeanLucPicard", "StoneColdSteveAustin"]

print("Jean: The King of France is bald.")
if theKingOfFrance in bald:
    print("Jaques: It's true!")
elif theKingOfFrance not in bald:
    print("Jaques: It's false!")
    
print("")

JoFrhwld

data.frame(SES = c("High","Middle","Low"),
		   Applied = c(0.79, 0.59, 0.5),
		   Admitted = c(0.67, 0.47, 0.42),
		   Enrolled = c(0.53, 0.28, 0.32)) -> data
		   
ggplot(data, aes(SES)) + 
	geom_point(aes(y = Applied, color = "Applied"))+
	geom_line(aes(y = Applied, group = 1, color = "Applied"))+
	geom_point(aes(y = Admitted/Applied, color = "Applied and Admitted"))+
	geom_line(aes(group = 1, y = Admitted/Applied, color = "Applied and Admitted"))+

JoFrhwld

multi_match <- function(x, table){
    # returns initial indicies of all substrings in table which match x
    if(length(table) < length(x)){
		return(NA)
	}else{
		check_mat <- matrix(nrow = length(x), ncol = length(table))
		for(i in 1:length(x)){
			check_mat[i,] <- table %in% x[i]
		}
		out <- vector(length = length(table))

JoFrhwld

syllabify <- function(trans){
    require(stringr)

    segments <- unlist(str_split(trans, " "))
	nuclei_string <- "A|E|I|O|U|@"
	nucs <- grep(nuclei_string, segments)
	n <- length(nucs)

	r_colored <- nucs[grep("R", segments[nucs])]

JoFrhwld

library(ggplot2)
library(grid)

bg <- data.frame(xmin = 0, xmax = 180, ymin = 0, ymax = 180)
bg_col <- rgb(0.8,0,0)

bars <- data.frame(xmin = c(35, 35), xmax = c(145, 145), ymin = c(45, 100), ymax = c(80, 135), groups = c("first", "second") )
bars_col <- rgb(0.9, 0.56, 0.56)

JoFrhwld

# http://www.cdc.gov/nchs/data/nhsr/nhsr010.pdf

n_fem = 604
h_fem = 162.2
se_fem = 0.34
sd_fem = se_fem * sqrt(n_fem)

n_mal = 591
h_mal = 176.6
se_mal = 0.38