Tagged: SCIENTIA – Science

Near-death experiences ‘explained’


  • Image copyright BBC News

“Neurons in the brain may go into overdrive around the point of death”

Near-death experiences ‘explained’ in this BBC News Article


R & Weather Data


The weather has changed in Sacramento and now the daily lows are higher than some of the daily highs during winter.

Can I use R to determine how many days in the winter of 2012/2013 that the high temperature of the day was less than the low yesterday (59 degrees F)?


Although I was unable to get the eample RJSONIO code in the first article to work, I was did sign up for an api key from weather underground and pull historical weather data using the weather underground example.

I was able to get the code in the second article to work, and no API Key was needed.

Article on Importing Weather Data into R

  • Title: Getting Historical Weather Data in R and SAP HANA
  • Posted by: Jitender Aswani
  • allthingsr on blogspot
    • weather underground api link
      • I signed up for a weather underground api key
        • [X] sign up for account
        • [X] select a plan for the api key
          • I selected the free “stratus” plan
          • https://i2.wp.com/icons.wxug.com/logos/images/wundergroundLogo_4c.jpg
    • [X] try code from article
      • [X] install.packages(“RJSONIO”)
        • [X] library(“RJSONIO”)
      • [X] install.packages(“rjson”)
        • may not be needed
      • [X] tried code, but fail due to invalid api key
        • [X] tried weather underground example
  • [X] Give up for now 2013.04.30 07:39
  • [ ] modify code for Sacramento (SAC)

Another Article on Importing Weather Data into R


wunder_station_daily <- function(station, date)
  base_url <- 'http://www.wunderground.com/weatherstation/WXDailyHistory.asp?'

                                        # parse date
  m <- as.integer(format(date, '%m'))
  d <- as.integer(format(date, '%d'))
  y <- format(date, '%Y')

                                        # compose final url
  final_url <- paste(base_url,
  'ID=', station,
  '&month=', m,
  '&day=', d,
  '&year=', y,
  '&format=1', sep='')

                                        # reading in as raw lines from the web server
                                        # contains <br> tags on every other line
  u <- url(final_url)
  the_data <- readLines(u)

                                        # only keep records with more than 5 rows of data
  if(length(the_data) > 5 )
                                        # remove the first and last lines
        the_data <- the_data[-c(1, length(the_data))]

                                        # remove odd numbers starting from 3 --> end
        the_data <- the_data[-seq(3, length(the_data), by=2)]

                                        # extract header and cleanup
        the_header <- the_data[1]
        the_header <- make.names(strsplit(the_header, ',')[[1]])

                                        # convert to CSV, without header
        tC <- textConnection(paste(the_data, collapse='\n'))
        the_data <- read.csv(tC, as.is=TRUE, row.names=NULL, header=FALSE, skip=1)

                                        # remove the last column, created by trailing comma
        the_data <- the_data[, -ncol(the_data)]

                                        # assign column names
        names(the_data) <- the_header

                                        # convert Time column into properly encoded date time
        the_data$Time <- as.POSIXct(strptime(the_data$Time, format='%Y-%m-%d %H:%M:%S'))

                                        # remove UTC and software type columns
        the_data$DateUTC.br. <- NULL
        the_data$SoftwareType <- NULL

                                        # sort and fix rownames
        the_data <- the_data[order(the_data$Time), ]
        row.names(the_data) <- 1:nrow(the_data)

                                        # done

Pull Data

                                        # be sure to load the function from above first
                                        # get a single day's worth of (hourly) data
w <- wunder_station_daily('KCAANGEL4', as.Date('2011-05-05'))

                                        # get data for a range of dates
date.range <- seq.Date(from=as.Date('2009-1-01'), to=as.Date('2011-05-06'), by='1 day')

                                        # pre-allocate list
l <- vector(mode='list', length=length(date.range))

                                        # loop over dates, and fetch data
for(i in seq_along(date.range))
  l[[i]] <- wunder_station_daily('KCAANGEL4', date.range[i])

                                        # stack elements of list into DF, 
                                        # filling missing columns with NA
d <- ldply(l)

                                        # save to CSV
write.csv(d, file=gzfile('KCAANGEL4.csv.gz'), row.names=FALSE)


  • Worked fine, and no API Key required [2013.04.30 08:03]

Other Historical Weather Data Links