In [1]:
list.of.packages <- c("gdata","R.utils","FactoMineR","factoextra","data.table","stringr","sp","cluster","plyr",
                      "stringi","proj4","ggplot2","plotly","rgdal","rgeos","raster","randomForest")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages) > 0) install.packages(new.packages)

lapply(list.of.packages, FUN = function(X) {
    do.call("require", list(X)) 
})
Loading required package: gdata

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Loading required package: R.utils

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R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.

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Loading required package: FactoMineR

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Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

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Type rfNews() to see new features/changes/bug fixes.


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History and Data Science

In this excercise, we will emulate some of the analyses described in the "Data mining the past" vignette. This will compare the concepts that have been mapped to the Jardabok dataARC observations with those in the Saga Maps.

Directory Setup

As part of this exercise, we'll have to import csv files. The default directory to which files will be downloaded is the current working directory. If you'd like to specify a different folder, change "getwd()" to the filepath with frontslashes between quotes. (e.g. "I:/DataArc/"). We'll also define the projection we want to use. Here, we'll use the EPSG:9040 conformal conic employed by Iceland, defined according to the WGS1984 spheroid. The init file for proj4 included in this binder release does not have access to the complete list of EPSG codes, and as such the simple EPSG import has been commented out in this script. Users may employ that line if they are running the notebook or associated scripts locally. The projections, however, are identical.

In [2]:
path <- getwd()
setwd(path)
set.seed(1601720)

#proj <- CRS("+init=epsg:9040 +datum=WGS84")
proj <- CRS("+proj=lcc +lat_0=52 +lon_0=10 +lat_1=35 +lat_2=65 +x_0=4000000 +y_0=2800000
                    +datum=WGS84 +units=m +no_defs")

DataARC Preprocessing

Next, we'll import the dataARC dataset either using the Provided_Results.gz compressed CSV from all dataARC observations in Iceland as of March 3, 2021, or the arc.dataconvert function for user-obtained output jsons from the dataARC API. This will convert the dataARC API output into a parsable data.table. We'll then convert it to a data.table object, and use the trimws function to remove any whitespace before column entries. This will make the columns parsable when we compare hashes.

In [3]:
if (file.exists("Provided_Results.gz")) {
    arc <- fread("Provided_Results.gz", encoding="UTF-8")
} else {
    source("Translator.R")
    arc <- arc.dataconvert("results.json.gz")
}

setnames(arc,names(arc),c("NUMBER","ID","X","Y","DATASET","CATEGORY",
                          "ENTRY","CONCEPT","COMBINATOR","RELATED","CONTEXT"))

arc[, names(arc) := lapply(.SD,trimws,which='left')]
head(arc)
A data.table: 6 × 11
NUMBERIDXYDATASETCATEGORYENTRYCONCEPTCOMBINATORRELATEDCONTEXT
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
4631985FD9474424B939001C69230364.1-225F41CAE4B2E723864080817CID 38282 5F430A73836C1E48136B4F66:5F430A73836C1E48136B4F67:5F430A73836C1E48136B4F68:5F430A73836C1E48136B4F69:5F430A74836C1E48136B4F6E:5F430A74836C1E48136B4F76:5F430A74836C1E48136B4F7A:5F430A74836C1E48136B4F7E:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F98:5F430A74836C1E48136B4F9E:5F430A74836C1E48136B4F9F:5F430A75836C1E48136B4FA3:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FB4:5F430A75836C1E48136B4FBD:5F430A75836C1E48136B4FC5:5F430A75836C1E48136B4FC6:5F430A75836C1E48136B4FC7:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD4:5F430A76836C1E48136B4FDA:5F430A76836C1E48136B4FE4:5F430A76836C1E48136B4FEB:5F430A76836C1E48136B4FF0:5F430A76836C1E48136B4FF2:5F430A76836C1E48136B4FF5:5F430A77836C1E48136B4FFF:5F430A77836C1E48136B5001:5F430A77836C1E48136B5002:5F430A77836C1E48136B5009:5F430A77836C1E48136B5012:5F430A77836C1E48136B5018:5F430A77836C1E48136B5023:5F430A77836C1E48136B5025:5F430A78836C1E48136B5044:5F430A78836C1E48136B5057:5F430A78836C1E48136B505C:5F430A78836C1E48136B50685F41CADEB2E7238640808014:5F41CADEB2E7238640808016:5F41CADEB2E7238640808017:5F41CADDB2E723864080800B:5F41CADEB2E7238640808013:5F41CADEB2E723864080801A:5F41CADEB2E723864080801F:5F41CADEB2E7238640808020:5F41CADEB2E7238640808018:5F41CADEB2E7238640808019:5F41CADEB2E723864080800E:5F41CADEB2E7238640808012:5F41CADEB2E723864080801C:5F41CADEB2E723864080800F:5F41CADEB2E7238640808010:5F41CADEB2E7238640808023:5F41CADDB2E7238640808008:5F41CADEB2E723864080801B:5F41CADEB2E72386408080225f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
4631995FD9474424B939001C69230364.1-225F41CAE4B2E723864080817CCOUNTRY Iceland 5F430A73836C1E48136B4F66:5F430A73836C1E48136B4F67:5F430A73836C1E48136B4F68:5F430A73836C1E48136B4F69:5F430A74836C1E48136B4F6E:5F430A74836C1E48136B4F76:5F430A74836C1E48136B4F7A:5F430A74836C1E48136B4F7E:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F98:5F430A74836C1E48136B4F9E:5F430A74836C1E48136B4F9F:5F430A75836C1E48136B4FA3:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FB4:5F430A75836C1E48136B4FBD:5F430A75836C1E48136B4FC5:5F430A75836C1E48136B4FC6:5F430A75836C1E48136B4FC7:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD4:5F430A76836C1E48136B4FDA:5F430A76836C1E48136B4FE4:5F430A76836C1E48136B4FEB:5F430A76836C1E48136B4FF0:5F430A76836C1E48136B4FF2:5F430A76836C1E48136B4FF5:5F430A77836C1E48136B4FFF:5F430A77836C1E48136B5001:5F430A77836C1E48136B5002:5F430A77836C1E48136B5009:5F430A77836C1E48136B5012:5F430A77836C1E48136B5018:5F430A77836C1E48136B5023:5F430A77836C1E48136B5025:5F430A78836C1E48136B5044:5F430A78836C1E48136B5057:5F430A78836C1E48136B505C:5F430A78836C1E48136B50685F41CADEB2E7238640808014:5F41CADEB2E7238640808016:5F41CADEB2E7238640808017:5F41CADDB2E723864080800B:5F41CADEB2E7238640808013:5F41CADEB2E723864080801A:5F41CADEB2E723864080801F:5F41CADEB2E7238640808020:5F41CADEB2E7238640808018:5F41CADEB2E7238640808019:5F41CADEB2E723864080800E:5F41CADEB2E7238640808012:5F41CADEB2E723864080801C:5F41CADEB2E723864080800F:5F41CADEB2E7238640808010:5F41CADEB2E7238640808023:5F41CADDB2E7238640808008:5F41CADEB2E723864080801B:5F41CADEB2E72386408080225f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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When we import the dataARC dataset, the XY coordinates are imported as character strings since not every observation has an entry. The XY coordinates are also in latitude/longitude format, which is inappropriate for many types of spatial operations. To convert it to the projected coordinate system we defined above, we first need to convert the degrees to radians (a pecularity with the proj4string library), and then we can transform to EPSG:9040.

In [4]:
arc[,c("X","Y") := lapply(.SD,as.numeric), .SDcols = c("X","Y")] # Convert to numeric
arc[, `:=`(X = X * ..pi / 180, Y = Y * ..pi / 180)] # Convert to radians
arc[,`:=`(X = ptransform(.(X,Y),src.proj=CRS("+proj=longlat +datum=WGS84"), # Project the coordinates
                         dst.proj=proj)[[1]],
          Y = ptransform(.(X,Y),src.proj=CRS("+proj=longlat +datum=WGS84"),
                         dst.proj=proj)[[2]])]

The dataARC dataset has its concepts and constituent datasets listed as hashes. Since we'll want to work with their actual values later on, we'll import both right now.

In [5]:
concepts <- read.csv("CONCEPT_DATA_FRAME.csv",
                     stringsAsFactors = FALSE)
concepts <- as.data.table(concepts)
concepts[,names(concepts) := lapply(.SD,trimws,which='left')]

head(concepts)
A data.table: 6 × 2
HASHNAME
<chr><chr>
5F430A73836C1E48136B4F65administrative unit
5F430A73836C1E48136B4F67agricultural landscape
5F430A73836C1E48136B4F64abandoned place
5F430A73836C1E48136B4F66agricultural building
5F430A73836C1E48136B4F69animal husbandry
5F430A73836C1E48136B4F6Cavian
In [6]:
datasets <- read.csv("DATASET_HASH_OUT.csv",
                     stringsAsFactors = FALSE,
                     encoding='UTF-8')
datasets <- as.data.table(datasets)
datasets[,names(datasets) := lapply(.SD,trimws,which='left')]

datasets
A data.table: 12 × 2
HASHNAME
<chr><chr>
5fc98f37960aba3111930cbePollen Data (Iceland)
5fc98f5e960aba3111930cc0Paleoclimate Iceland
5f41cae4b2e723864080818bIcelandic Excavation Data - Key finds and archaeological data by stratigraphic units
5f41cae4b2e7238640808188Icelandic Excavation Data - Finds from each group of stratigraphic units
5f41cae4b2e723864080817fIcelandic Sagas Database
5f41cae4b2e7238640808183Northwest Icelandic Cairns
5f41cae4b2e7238640808182Icelandic Farm Histories Database
5f41cae4b2e723864080818aIcelandic Excavation Data - Unit Descriptions
5f41cae4b2e7238640808187Environmental Threats to Icelandic Archaeological Sites
5f41cae4b2e723864080817cStrategic Environmental Archaeology Database (SEAD)
5f41cae4b2e7238640808180Tephrabase
5f41cae4b2e7238640808184Zooarchaeological Data from NABO Excavations (NABONOSEAD)

Now let's pull out the observations for the Jardabok, contained in the Icelandic Farm Histories Database. We'll use the latter's acronym (fhd) as a variable name for expediency's sake. For this, we'll query the datasets data.table with "Farm Histories", and filter the dataARC entries by the resulting hash.

In [7]:
query <- "Farm Histories"
fhd_hash <- datasets[str_detect(NAME,query),HASH]
fhd_hash <- toupper(fhd_hash) # Since the dataARC hashes are all capitalized

fhd <- arc[DATASET == fhd_hash,] # Filter dataARC and assign to new variable

fhd
A data.table: 48196 × 11
NUMBERIDXYDATASETCATEGORYENTRYCONCEPTCOMBINATORRELATEDCONTEXT
<chr><chr><dbl><dbl><chr><chr><chr><chr><chr><chr><chr>
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1733185FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182PARISH_NAME Flugumýrarsókn 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733195FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182TITLE Flugumýri 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733205FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182FARM_NUMBER SK-235 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733215FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182ISLEIF_FARMS_ID 4901 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733225FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182VALUATION 100 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733235FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E72386408081821861_ADJUSTED_VALUE 55.6 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733245FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182FODDER_PRODUCTIVITY 6 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733255FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182ENTITY_NAME Biskupsstóllinn Hólar5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733265FC9E27E7C39E36162AF512313934448-29173225F41CAE4B2E7238640808182ENTITY_TYPE_ALIAS bishop 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733275FC9E27E7C39E36162AF512413946356-29180535F41CAE4B2E7238640808182START_DATE 900 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733285FC9E27E7C39E36162AF512413946356-29180535F41CAE4B2E7238640808182END_DATE 1900 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
1733295FC9E27E7C39E36162AF512413946356-29180535F41CAE4B2E7238640808182SHIRE_NAME Akrahreppur 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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1733315FC9E27E7C39E36162AF512413946356-29180535F41CAE4B2E7238640808182TITLE Frostastaðir 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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1733345FC9E27E7C39E36162AF512413946356-29180535F41CAE4B2E7238640808182VALUATION 80 5F430A73836C1E48136B4F67:5F430A74836C1E48136B4F71:5F430A74836C1E48136B4F77:5F430A74836C1E48136B4F86:5F430A74836C1E48136B4F90:5F430A74836C1E48136B4F9A:5F430A75836C1E48136B4FA0:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A76836C1E48136B4FD8:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B5011:5F430A77836C1E48136B5014:5F430A77836C1E48136B501F:5F430A77836C1E48136B5024:5F430A78836C1E48136B5045:5F430A78836C1E48136B5050 5F41CADEB2E7238640808031:5F41CADEB2E7238640808030:5F41CADEB2E7238640808035:5F41CADEB2E723864080802F5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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2213625FC9E27F7C39E36162AF5F6114134129-31114055F41CAE4B2E7238640808182ENTITY_TYPE_ALIAS king 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213635FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182START_DATE 900 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213645FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182END_DATE 1900 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213655FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182SHIRE_NAME Þverárhreppur 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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2213675FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182TITLE Vatnsendi 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213685FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182FARM_NUMBER HV-133 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213695FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182ISLEIF_FARMS_ID 1419 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213705FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182VALUATION 16 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213715FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E72386408081821861_ADJUSTED_VALUE11.9 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213725FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182ENTITY_NAME Steinun Erlendsdóttir & Þórður Þorleifsson5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2213735FC9E27F7C39E36162AF5F6214107078-31226975F41CAE4B2E7238640808182ENTITY_TYPE_ALIAS person 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035 5f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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2214025FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182SHIRE_NAME Þverárhreppur 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2214035FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182PARISH_NAME Vesturhópshólasókn 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2214045FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182TITLE Þorfinnstaðir 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2214055FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182FARM_NUMBER HV-112 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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2214075FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182VALUATION 10 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2214085FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E72386408081821861_ADJUSTED_VALUE7.1 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2214095FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182FODDER_PRODUCTIVITY2 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2214105FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182ENTITY_NAME Beneficium Vesturhópshólar 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
2214115FC9E27F7C39E36162AF5F6414117087-31286675F41CAE4B2E7238640808182ENTITY_TYPE_ALIAS beneficium 5F430A74836C1E48136B4F77:5F430A75836C1E48136B4FA5:5F430A75836C1E48136B4FA6:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FCB:5F430A76836C1E48136B4FCF:5F430A76836C1E48136B4FD0:5F430A76836C1E48136B4FD2:5F430A76836C1E48136B4FD7:5F430A77836C1E48136B5024:5F430A78836C1E48136B50455F41CADEB2E7238640808035:5F41CADFB2E72386408080405f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99

We'll do the same for the Saga Map sites.

In [8]:
query <- "Sagas"
saga_hash <- datasets[str_detect(NAME,query),HASH]
saga_hash <- toupper(saga_hash)

saga <- arc[DATASET == saga_hash,]
head(saga)
A data.table: 6 × 11
NUMBERIDXYDATASETCATEGORYENTRYCONCEPTCOMBINATORRELATEDCONTEXT
<chr><chr><dbl><dbl><chr><chr><chr><chr><chr><chr><chr>
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507175FC9E2227C39E36162AF209813711383-26485855F41CAE4B2E723864080817FNAME Ljósvetninga saga5F430A73836C1E48136B4F67:5F430A73836C1E48136B4F68:5F430A73836C1E48136B4F69:5F430A74836C1E48136B4F6E:5F430A74836C1E48136B4F7A:5F430A74836C1E48136B4F7B:5F430A74836C1E48136B4F83:5F430A74836C1E48136B4F8D:5F430A74836C1E48136B4F90:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FA9:5F430A75836C1E48136B4FBD:5F430A75836C1E48136B4FC1:5F430A75836C1E48136B4FC4:5F430A75836C1E48136B4FC9:5F430A76836C1E48136B4FD9:5F430A76836C1E48136B4FED:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B4FF9:5F430A77836C1E48136B4FFA:5F430A77836C1E48136B500B:5F430A77836C1E48136B500D:5F430A77836C1E48136B5014:5F430A77836C1E48136B5018:5F430A78836C1E48136B5035:5F430A78836C1E48136B50405F41CADEB2E723864080802C:5F41CADEB2E72386408080385f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
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507195FC9E2227C39E36162AF209813711383-26485855F41CAE4B2E723864080817FSAGANAMELjósvetninga saga5F430A73836C1E48136B4F67:5F430A73836C1E48136B4F68:5F430A73836C1E48136B4F69:5F430A74836C1E48136B4F6E:5F430A74836C1E48136B4F7A:5F430A74836C1E48136B4F7B:5F430A74836C1E48136B4F83:5F430A74836C1E48136B4F8D:5F430A74836C1E48136B4F90:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FA9:5F430A75836C1E48136B4FBD:5F430A75836C1E48136B4FC1:5F430A75836C1E48136B4FC4:5F430A75836C1E48136B4FC9:5F430A76836C1E48136B4FD9:5F430A76836C1E48136B4FED:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B4FF9:5F430A77836C1E48136B4FFA:5F430A77836C1E48136B500B:5F430A77836C1E48136B500D:5F430A77836C1E48136B5014:5F430A77836C1E48136B5018:5F430A78836C1E48136B5035:5F430A78836C1E48136B50405F41CADEB2E723864080802C:5F41CADEB2E72386408080385f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
507205FC9E2227C39E36162AF209813711383-26485855F41CAE4B2E723864080817FTYPE farm 5F430A73836C1E48136B4F67:5F430A73836C1E48136B4F68:5F430A73836C1E48136B4F69:5F430A74836C1E48136B4F6E:5F430A74836C1E48136B4F7A:5F430A74836C1E48136B4F7B:5F430A74836C1E48136B4F83:5F430A74836C1E48136B4F8D:5F430A74836C1E48136B4F90:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FA9:5F430A75836C1E48136B4FBD:5F430A75836C1E48136B4FC1:5F430A75836C1E48136B4FC4:5F430A75836C1E48136B4FC9:5F430A76836C1E48136B4FD9:5F430A76836C1E48136B4FED:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B4FF9:5F430A77836C1E48136B4FFA:5F430A77836C1E48136B500B:5F430A77836C1E48136B500D:5F430A77836C1E48136B5014:5F430A77836C1E48136B5018:5F430A78836C1E48136B5035:5F430A78836C1E48136B50405F41CADEB2E723864080802C:5F41CADEB2E72386408080385f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99
507215FC9E2227C39E36162AF209813711383-26485855F41CAE4B2E723864080817FCHAPTER 1. kafli 5F430A73836C1E48136B4F67:5F430A73836C1E48136B4F68:5F430A73836C1E48136B4F69:5F430A74836C1E48136B4F6E:5F430A74836C1E48136B4F7A:5F430A74836C1E48136B4F7B:5F430A74836C1E48136B4F83:5F430A74836C1E48136B4F8D:5F430A74836C1E48136B4F90:5F430A75836C1E48136B4FA7:5F430A75836C1E48136B4FA9:5F430A75836C1E48136B4FBD:5F430A75836C1E48136B4FC1:5F430A75836C1E48136B4FC4:5F430A75836C1E48136B4FC9:5F430A76836C1E48136B4FD9:5F430A76836C1E48136B4FED:5F430A76836C1E48136B4FF2:5F430A77836C1E48136B4FF9:5F430A77836C1E48136B4FFA:5F430A77836C1E48136B500B:5F430A77836C1E48136B500D:5F430A77836C1E48136B5014:5F430A77836C1E48136B5018:5F430A78836C1E48136B5035:5F430A78836C1E48136B50405F41CADEB2E723864080802C:5F41CADEB2E72386408080385f430a75836c1e48136b4fa7:5f430a76836c1e48136b4fd8:5f430a76836c1e48136b4fe15f430a75836c1e48136b4fa6:5f430a76836c1e48136b4fd2:5f430a76836c1e48136b4fe4:5f430a73836c1e48136b4f66:5f430a76836c1e48136b4fd7:5f430a76836c1e48136b4fd9:5f430a75836c1e48136b4fb4:5f430a76836c1e48136b4ff8:5f430a74836c1e48136b4f99

Right now, the data.table is structured such that each variable/attribute is stored in its own row under the "CATEGORY" and "ENTRY" columns, when they should instead be columns all pertaining to the same site. We need to cast those rows into new columns, reducing the number of rows in the process to one per Jardabok entry.

First select the observation types we want to keep. In the vignette, we were interested in variables that may be related to economic wellbeing (1861 Adjusted Valuation, Fodder Productivity, Owner, and Resource), so we'll fiter by those.

In [9]:
entries <- c("ENTITY_TYPE_ALIAS", "RELATIONSHIPS_LOOKUP_RESOURCE_EN", # Entries by which to filter
             "FODDER_PRODUCTIVITY", "1861_ADJUSTED_VALUE")
fhd_socio <- fhd[CATEGORY %in% entries,][,.(ID,CATEGORY,ENTRY)]  # Apply the filter, keeping only the ID and new cols

head(fhd_socio)
A data.table: 6 × 3
IDCATEGORYENTRY
<chr><chr><chr>
5FC9E27E7C39E36162AF51231861_ADJUSTED_VALUE55.6
5FC9E27E7C39E36162AF5123FODDER_PRODUCTIVITY6
5FC9E27E7C39E36162AF5123ENTITY_TYPE_ALIAS bishop
5FC9E27E7C39E36162AF51241861_ADJUSTED_VALUE28.5
5FC9E27E7C39E36162AF5124FODDER_PRODUCTIVITY7
5FC9E27E7C39E36162AF5124ENTITY_TYPE_ALIAS bishop

Now we can cast the rows into new columns, and "smush" the rows for each observation into a single row removing NAs in the process.

In [10]:
fhd_socio <- dcast(fhd_socio, ID+ENTRY~CATEGORY, value.var="ENTRY", # Cast the rows
                   fun.aggregate = function (x) paste(x,collapse = "; "))

fhd_socio <- setDT(fhd_socio)[order(ID), # And smush them
                              lapply(.SD, function(x) x[!is.na(x) & x != ""][1]), 
                              by=ID]
head(fhd_socio)
A data.table: 6 × 6
IDENTRY1861_ADJUSTED_VALUEENTITY_TYPE_ALIASFODDER_PRODUCTIVITYRELATIONSHIPS_LOOKUP_RESOURCE_EN
<chr><chr><chr><chr><chr><chr>
5FC9E27E7C39E36162AF511B17.117.1bishop2 outfield pasture
5FC9E27E7C39E36162AF511C2 33.2bishop2 outfield pasture
5FC9E27E7C39E36162AF511D0.5 9.9 bishop0.5NA
5FC9E27E7C39E36162AF511E19.919.9bishop2 outfield pasture
5FC9E27E7C39E36162AF511F15.615.6bishop2 NA
5FC9E27E7C39E36162AF512018.518.5bishop4 NA

Note that a lot of the entires are "NA"---these farmsteads did not have additional access to other resources. We'll replace the NAs with "NOTHING", and drop any remaining NAs in other columns.

In [11]:
fhd_socio[is.na(RELATIONSHIPS_LOOKUP_RESOURCE_EN), 
          RELATIONSHIPS_LOOKUP_RESOURCE_EN := "NOTHING"]
fhd_socio <- na.omit(fhd_socio)

head(fhd_socio)
A data.table: 6 × 6
IDENTRY1861_ADJUSTED_VALUEENTITY_TYPE_ALIASFODDER_PRODUCTIVITYRELATIONSHIPS_LOOKUP_RESOURCE_EN
<chr><chr><chr><chr><chr><chr>
5FC9E27E7C39E36162AF511B17.117.1bishop2 outfield pasture
5FC9E27E7C39E36162AF511C2 33.2bishop2 outfield pasture
5FC9E27E7C39E36162AF511D0.5 9.9 bishop0.5NOTHING
5FC9E27E7C39E36162AF511E19.919.9bishop2 outfield pasture
5FC9E27E7C39E36162AF511F15.615.6bishop2 NOTHING
5FC9E27E7C39E36162AF512018.518.5bishop4 NOTHING

A small subset of farmstead had access to more than one pasture or more than one site for a single type of resource

In [12]:
unique(fhd_socio$RELATIONSHIPS_LOOKUP_RESOURCE_EN)
  1. 'outfield pasture'
  2. 'NOTHING'
  3. 'use of unoccupied farm'
  4. 'pasture'
  5. 'driftwood'
  6. 'peat'
  7. 'outfield pasture; outfield pasture'
  8. 'beached whale; beached whale'
  9. 'twigs'
  10. 'turf'
  11. 'beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale'
  12. 'beached whale'
  13. 'hay'
  14. 'woodland'
  15. 'egg'
  16. 'beached whale; beached whale; beached whale; beached whale'
  17. 'ship place'
  18. 'hay; hay'
  19. 'beached whale; beached whale; beached whale'
  20. 'shieling place'
  21. 'beached whale; beached whale; beached whale; beached whale; beached whale; beached whale'
  22. 'beached whale; beached whale; beached whale; beached whale; beached whale'
  23. 'ship place; ship place'
  24. 'driftwood; driftwood'
  25. 'pasture; pasture'
  26. 'shieling place; shieling place; shieling place'
  27. 'shieling place; shieling place'
  28. 'beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale'
  29. 'driftwood; driftwood; driftwood'
  30. 'beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale; beached whale'

These will complicate our analyses later on. Since none of the sites appear to have direct access to more than one type of resource, we'll truncate the lists to only their first entry.

In [13]:
fhd_socio[, RELATIONSHIPS_LOOKUP_RESOURCE_EN := 
            list(strsplit(RELATIONSHIPS_LOOKUP_RESOURCE_EN, "; "))] # Split any multi-resource entries
fhd_socio[, RELATIONSHIPS_LOOKUP_RESOURCE_EN := 
            lapply(RELATIONSHIPS_LOOKUP_RESOURCE_EN, function(x) x[1])] # Keep only the first one
                   
unique(fhd_socio$RELATIONSHIPS_LOOKUP_RESOURCE_EN)
  1. 'outfield pasture'
  2. 'NOTHING'
  3. 'use of unoccupied farm'
  4. 'pasture'
  5. 'driftwood'
  6. 'peat'
  7. 'beached whale'
  8. 'twigs'
  9. 'turf'
  10. 'hay'
  11. 'woodland'
  12. 'egg'
  13. 'ship place'
  14. 'shieling place'

Finally we'll convert the two numeric columns to numeric type using "as.numeric"

In [14]:
fhd_socio[, c("FODDER_PRODUCTIVITY","1861_ADJUSTED_VALUE") :=
           lapply(.SD,as.numeric), .SDcols = c("FODDER_PRODUCTIVITY","1861_ADJUSTED_VALUE")]
fhd_socio <- na.omit(fhd_socio)
Warning message in lapply(.SD, as.numeric):
“NAs introduced by coercion”
Warning message in lapply(.SD, as.numeric):
“NAs introduced by coercion”

Dimensionality Reduction

We're now almost ready to perform dimensionality reduction. We have four input variables, two of them are numeric (Fodder productivity and adjusted valuation), and two of them are nominal. The appropriate method is Factor Analysis of Mixed Data, provided by the FactoMineR and factoextra libraries thorugh the FAMD function.

The function expects all numeric variables to be of numeric type, and nominal variables to be of factor type. Currently, RELATIONSHIPS_LOOKUP_RESOURCE_EN is a list class object given the truncating step we performed above. ENTITY_TYPE_ALIAS is a character class. We'll create a new data table with only the columns of interest, convert the list and characters to a factor, and perform the FAMD.

This overview of FAMD is written by the original author of both the method and R package, and provides detailed guidance as to how to interpret the output.

In [15]:
vars <- c(entries) # The entries variable already has stored the variables of interest
fhd_famd <- fhd_socio[,..vars] # Extract only the columns we want
fhd_famd[, "RELATIONSHIPS_LOOKUP_RESOURCE_EN" := # Convert list to character type
           as.character(RELATIONSHIPS_LOOKUP_RESOURCE_EN)]
fhd_famd[, c("ENTITY_TYPE_ALIAS","RELATIONSHIPS_LOOKUP_RESOURCE_EN") := 
           lapply(.SD,as.factor), # Convert character to factor
         .SDcols = c("ENTITY_TYPE_ALIAS", "RELATIONSHIPS_LOOKUP_RESOURCE_EN")]
famd <- FAMD(fhd_famd,ncp=nrow(fhd_famd),graph=FALSE) # Perform the FAMD
famd
*The results are available in the following objects:

  name          description                             
1 "$eig"        "eigenvalues and inertia"               
2 "$var"        "Results for the variables"             
3 "$ind"        "results for the individuals"           
4 "$quali.var"  "Results for the qualitative variables" 
5 "$quanti.var" "Results for the quantitative variables"

The first thing we'll look at is how much each principal dimension contributes to the total variance of the dataset. We can generally ignore dimensions that contain less than 5% of the variance

In [16]:
famd$eig
A matrix: 18 × 3 of type dbl
eigenvaluepercentage of variancecumulative percentage of variance
comp 11.70240169.457787 9.457787
comp 21.28709587.150532 16.608319
comp 31.12789966.266109 22.874427
comp 41.10106676.117037 28.991465
comp 51.03542545.752363 34.743828
comp 61.02773755.709653 40.453481
comp 71.01152125.619562 46.073043
comp 81.00000005.555556 51.628599
comp 91.00000005.555556 57.184154
comp 101.00000005.555556 62.739710
comp 111.00000005.555556 68.295265
comp 120.97504895.416938 73.712204
comp 130.96550965.363942 79.076146
comp 140.94865395.270300 84.346445
comp 150.87312554.850697 89.197142
comp 160.83708844.650491 93.847633
comp 170.71383613.965756 97.813389
comp 180.39358992.186611100.000000

It looks like with the first 13 variables (all those at or above 5%) we can capture about three-fourths of the total variance.

Next we'll look at the percent contribution to each dimesnsion from each input variable. The higher the contribution, the more closely related they are.

In [17]:
famd$var$contrib
A matrix: 4 × 18 of type dbl
Dim.1Dim.2Dim.3Dim.4Dim.5Dim.6Dim.7Dim.8Dim.9Dim.10Dim.11Dim.12Dim.13Dim.14Dim.15Dim.16Dim.17Dim.18
FODDER_PRODUCTIVITY41.384077 0.003288726 0.5341104 0.5747235 0.98273714 0.04494208 1.20192994.441518e-600.000000e+000.000000e+001.957389e-26 0.399701731 1.2197966 2.193599 1.5015851 3.128009 0.0573969446.7741030
1861_ADJUSTED_VALUE42.878437 0.353733811 0.2533428 1.4147496 0.01074523 0.53488810 0.52805085.527263e-293.866343e-271.761603e-278.312826e-27 0.001642099 0.8357081 1.141222 0.1432517 1.924446 0.9719731449.0078092
ENTITY_TYPE_ALIAS 5.53235249.78591982845.149895649.193691849.3127092247.06162588 1.52427622.851314e-264.434982e-261.658536e-269.895320e-2742.27834123543.974626723.57515141.802888650.42226749.52221900 0.8640357
RELATIONSHIPS_LOOKUP_RESOURCE_EN10.20513449.85705763554.062651248.816835049.6938084152.3585439496.74574311.000000e+021.000000e+021.000000e+021.000000e+0257.32031493553.969868673.09002856.552274744.52527749.44841092 3.3540522

We can plot these relationships for quantitative variables in a circle plot. Here this is for all combinations of the first three dimensions

In [18]:
fviz_famd_var(famd, "quanti.var", repel = TRUE,col.var = "black", axes=c(1,2))
fviz_famd_var(famd, "quanti.var", repel = TRUE,col.var = "black", axes=c(2,3))
fviz_famd_var(famd, "quanti.var", repel = TRUE,col.var = "black", axes=c(1,3))

For qualitative variables we can plot scatter plots

In [19]:
fviz_famd_var(famd, "quali.var", repel = TRUE,col.var = "black", axes=c(1,2))
fviz_famd_var(famd, "quali.var", repel = TRUE,col.var = "black", axes=c(2,3))
fviz_famd_var(famd, "quali.var", repel = TRUE,col.var = "black", axes=c(1,3))

To plot the Dimension scores themselves, let's create a new data.table that merges both the input and output values

In [20]:
famd_out <- as.data.table(famd$call$X) 
setnames(famd_out,c("1861_ADJUSTED_VALUE","ENTITY_TYPE_ALIAS","RELATIONSHIPS_LOOKUP_RESOURCE_EN"),
         c("VALUE","OWNER","RIGHTS_TO"))
famd_out[,colnames(famd$ind$coord) := as.data.table(..famd$ind$coord)]

And we plot the first three dimensions in a 3D scatterplot using ggplot2 and plotly

In [21]:
p1 <- plot_ly(x = famd_out$Dim.1, y = famd_out$Dim.2, z = famd_out$Dim.3, 
          type = "scatter3d",
          mode = "markers",
          color = famd_out$OWNER,
          text = paste("RIGHTS TO:",famd_out$RIGHTS_TO))

p1