mplourde

library(dplyr)
library(ggplot2)

iris %>%
    group_by(Species) %>%
    summarize(
        lower=quantile(Sepal.Length, .25),
        upper=quantile(Sepal.Length, .75),
        med=median(Sepal.Length),
        avg=mean(Sepal.Length),

mplourde

/*
In this example I create two matrices in the same manner, extract the diagonal, and then turn the nx1 
matrix of the diagonal values into a diagonal matrix by calling `diag` again. When the initial matrix contains all 1s, 
it works as expected. However, when the initial matrix contains all zeros, the second `diag` does not convert the nx1 
matrix of diagonal values to a diagonal matrix. I can work around this behavior, but I thought I should bring it to your attention.
*/

val systemml_alg = 
"""
fileOut1 = ""

mplourde

sql_query(cnxn, "
  SELECT MIN(media_week_id) 'min_media_week',
    MAX(media_week_id) 'max_media_week'
  FROM availability_estimation_inputs
")

mplourde

#' @export
add_time_sig <- function(D) {

    daypart_cols <- names(D)[grepl('hour_', names(D))]
    
    # NOT CURRENTLY USED
    #D <- D %>% 
    #    mutate_(
    #        week_sig = ~ifelse(get_media_month(log_date) != get_media_month(detail_start),
    #            get_media_week(detail_end),

mplourde

get_p_id <- function(p_id, connection) {
    sqlQuery(connection, sprintf(    
                "select p.id 'proposal_id',
                        pd.id 'proposal_detail_id',
                        mm.media_month,
                        b.name 'MVPD',             
                        n.code 'network',
                        mpi.post_log_recieved_date 'log_date',
                        pd.start_date 'detail_start',
                        pd.end_date 'detail_end',

mplourde

vsel <- function (y, lag.max = 10, type = c("const", "trend", "both", 
    "none"), season = NULL, exogen = NULL) 
{
    browser()
    y <- as.matrix(y)
    if (any(is.na(y))) 
        stop("\nNAs in y.\n")
    colnames(y) <- make.names(colnames(y))
    K <- ncol(y)
    lag.max <- abs(as.integer(lag.max))

mplourde

# create instance, create key, install awscli, do awscli configure, 

export AWS_ACCESS_KEY_ID=XX
export AWS_SECRET_ACCESS_KEY=XX
export SPARK_HOME=~/spark-1.4.1-bin-hadoop2.6

$SPARK_HOME/ec2/spark-ec2 --key-pair=XX \
--instance-type=m1.large \
--zone us-east-1a \
--identity-file=XX.pem \

mplourde

x <- ggplot(mtcars, aes(x=mpg, y=disp, label=carb)) +
    geom_point() +
    geom_text(y=Inf, vjust=-.3)
g <- ggplotGrob(x)

g$layout$clip[g$layout$name == "panel"] = "off"

grid.draw(g)

mplourde

ui <- fluidPage(
    selectInput('plot_type', 'Plot type', choices=c('point', 'line')),
    sliderInput('n', 'N plots', value=1, min=1, max=100),
    uiOutput('plots')
)


server <- function(input, output, session) {
    output$plots <- renderUI({
        ptype <- input$plot_type

mplourde

# example1 (using grobs instead of grid.*)
vp <- vpTree(
    parent=viewport(layout=grid.layout(nrow=2, ncol=2), name='parent'),
    children=vpList(
        viewport(layout.pos.row=1, layout.pos.col=1, name='vp1'),
        viewport(layout.pos.row=1, layout.pos.col=2, name='vp2')
    )
)
pushViewport(vp)
seekViewport('parent')