WORK IN PROGRESS

Download Data

The download function (fcn_download) itself it not a matter of this exercise.
It merely downloads from the service below the earth quake data from the years. I’ve done that already and so, I got all the data already - it takes quite a while to download it, be aware of that.

rm(list = ls()); cat("\014")


##---------------
## Download
##
## source: http://service.iris.edu/fdsnws/event/docs/1/builder/
## Data available since 1966
##---------------

source("./000_analysis/fcn_download.R")
fcn_download("./010_data/", 2016:2016) # the function first checks if the file 2016.txt already exists, or not. If yes, it won't start the download. 

# fcn_download <- function(directory, id_year){
#
#         directory <- "./010_data"
#         
#         start_url <- "http://service.iris.edu/fdsnws/event/1/query?starttime=1966-01-01T00:00:00&endtime=1966-12-31T23:59:59&orderby=time-asc&format=text&nodata=404"
#         
#         for (i in id_year){
#                 
#                 destfile <- paste(directory, "/", i, ".txt", sep = "") 
#                 url_i <- gsub(as.character("1966"), as.character(i), start_url) 
#              
#                 if (!file.exists(destfile)) {
#                      download.file(url_i, destfile, method = "auto")
#                 }
#         }
# }

Loading Data

Let’s explore the data from the years 2011 to 2016:

##---------------
## Loading Data
##---------------

files_loc <- list.files("./010_data/", full.names = TRUE)



# specify the year
# loading year data
source("./000_analysis/fcn_loading.R")
years <- 2012:2016
tdat <- fcn_loading("./010_data/", years)

# fcn_loading <- function(directory, id_year){
#         
#         files_loc <- list.files(directory, full.names = TRUE)
#         
#         data <- data.frame()
#         for (i in id_year){
#                 data <- rbind(data, read.table(paste(directory, i, ".txt", sep = "") # "/",
#                                                , sep = "|"
#                                                , header = TRUE
#                                                , comment.char = ""
#                                                , quote = ""
#                                                )
#                         )
#         }
#         data
# }


dim(tdat)
#> [1] 1151446      13

We have over 1.1 million data points here.

Packages

require(lubridate)
require(dplyr)
require(RColorBrewer)

Exploring the Data - Heat Map

tdat$Time <- ymd_hms(tdat$Time)
source("http://blog.revolutionanalytics.com/downloads/calendarHeat.R")
# Plot the calendar graph to view the missing data pattern
green_color_ramp = brewer.pal(20, "YlGnBu")   #OrRd  #YlGnBu  #Greens  #PuRd
calendarHeat(tdat$Time, tdat$Magnitude, 
             varname=paste("Magnitude.....", min(years), "-", max(years)), color="green_color_ramp")

This heat map gives quite a good overview. However, the size of this map in this report isn’t optimal. To be updated.

Explore the Data Points

We can nicely see the tectonic plates, respectively the earthquake’s distribution is highly correlated to the plate’s boundaries.

plot(tdat$Longitude, tdat$Latitude)

WORK IN PROGRESS