Using Data Base in gstlearn

\frametitle{Preamble}

In this preamble, we load the gstlearn library.

\frametitle{Main Classes}

This is the (non-exhaustive) list of classes (of objects) in gstlearn:

Db, DbGrid: numerical data base
DirParam, VarioParam and Vario: experimental variograms
Model: variogram model
Neigh: neighborhood
Anam: gaussian anamorphosis
Polygon: 2-D polygonal shapes
Rule: lithotype rule for thresholds used for truncated plurigaussian models

\frametitle{Loading CSV File}

You should download the ASCII file called Scotland_Temperatures.csv (organized as a CSV file) and store it on your disk in the current working directory. In this example, the file (called filecsv) is provided as a CSV format file. We load it into a data frame (names datcsv) using the relevant R-command.

dlfile = "https://soft.minesparis.psl.eu/gstlearn/data/Scotland/Scotland_Temperatures.csv"
filecsv = "Scotland_Temperatures.csv"
download.file(dlfile, filecsv)
datcsv = read.csv(filecsv)

We can check the contents of the data frame (by simply typing its name) and see that it contains four columns (respectively called Longitude, Latitude, Elevation, January_temp) and 236 rows (header line excluded).

Note that the last column contains several values called MISS: this corresponds to the absence of information.

\frametitle{Creating Db File}

We now want to load this information in order to obtain a data base of the gstlearn package (or Db) that will be called dat. This operation can be performed directly by reading the CSV file again and load it directly into a Db.

Note that we introduce a CSVformat description where we can specifiy the specificities of the file to be read, in particular we can tell how to spell the conventional value used for coding missing information.

csv = CSVformat_create(flagHeader=TRUE, naString = "MISS")
dat = Db_createFromCSV(filecsv, csv=csv)
dat

## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 0
## Number of Columns            = 5
## Maximum Number of UIDs       = 5
## Total number of samples      = 236
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = Longitude - Locator = NA
## Column = 2 - Name = Latitude - Locator = NA
## Column = 3 - Name = Elevation - Locator = NA
## Column = 4 - Name = January_temp - Locator = NA

\frametitle{Importing Db File}

A last solution is to import it directly from the set of demonstration files (provided together with the package and called fileNF) and stored in a specific format (Neutral file).

These NF (or neutral file) are currently used for serialization of the gstlearn objects. They will probably be replaced in the future by a facility backuping the whole workspace in one step.

Note that the contents of the Db is slightly different from the result obtained when reading from CSV. Essentially, some variables have a Locator field defined, some do not. This concept will be described later in this chapter and the difference can be ignored.

dlfile = "https://soft.minesparis.psl.eu/gstlearn/data/Scotland/Scotland_Temperatures.NF"
fileNF = "Scotland_Temperatures.NF"
download.file(dlfile, fileNF)
dat = Db_createFromNF(fileNF)
dat

## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 5
## Maximum Number of UIDs       = 5
## Total number of samples      = 236
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = Longitude - Locator = x1
## Column = 2 - Name = Latitude - Locator = x2
## Column = 3 - Name = Elevation - Locator = NA
## Column = 4 - Name = January_temp - Locator = z1

\frametitle{Db class}

Typing the name of the object automatically launches the display action. It is equivalent to simply typing the name of the object (at the end of a chunk in a RMarkdown file).

dat$display()

## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 5
## Maximum Number of UIDs       = 5
## Total number of samples      = 236
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = Longitude - Locator = x1
## Column = 2 - Name = Latitude - Locator = x2
## Column = 3 - Name = Elevation - Locator = NA
## Column = 4 - Name = January_temp - Locator = z1

## NULL

There, we can check that the 4 initial fields have been considered, in addition to a firs one, automatically called rank, for a total of 5 columns (the information regarding UID will not be addressed in this chapter).

We can check that each field is assigned to a numbered Column. Finally the total number of samples is 236 as expected.

In addition, some interesting information tells you that this data base corresponds to a 2-D dimension one: this will be described later together with the use of the Locator information.

\frametitle{Db class}

To get more information on the contents of the Db, it is possible to use the DbStringFormat option and to use use through the display method. There are several ways to specify the type of information that is searched for (see the documentation of this class for details): typically here we ask for statistics but restrict them to a list of variables

dbfmt = DbStringFormat_createFromFlags(flag_stats=TRUE, names=c("Elevation", "January_temp"))
dat$display(dbfmt)

## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 5
## Maximum Number of UIDs       = 5
## Total number of samples      = 236
## 
## Data Base Statistics
## --------------------
## 4 - Name Elevation - Locator NA
##  Nb of data          =        236
##  Nb of active values =        236
##  Minimum value       =      2.000
##  Maximum value       =    800.000
##  Mean value          =    146.441
##  Standard Deviation  =    165.138
##  Variance            =  27270.713
## 5 - Name January_temp - Locator z1
##  Nb of data          =        236
##  Nb of active values =        151
##  Minimum value       =      0.600
##  Maximum value       =      5.200
##  Mean value          =      2.815
##  Standard Deviation  =      1.010
##  Variance            =      1.020
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = Longitude - Locator = x1
## Column = 2 - Name = Latitude - Locator = x2
## Column = 3 - Name = Elevation - Locator = NA
## Column = 4 - Name = January_temp - Locator = z1

## NULL