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JESSE SADLER
The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to COURSES - JESSE SADLER - JESSE SADLER Cultural and Intellectual History of Modern Europe, Eighteenth Century Taught: Fall 2016 and Spring 2015 PROJECTS - JESSE SADLER - JESSE SADLER debkeepr is an R package that provides an interface for working with non-decimal currencies that use the tripartite system of pounds, shillings, and pence. The package includes functions to apply arithmetic and financial operations to single or multiple values and to analyze account books that use either single-entry bookkeeping or double-entry bookkeeping with the latter providing the name INTRODUCTION TO GIS WITH R Introduction to GIS with R through the sp and sf packages. The output lists the different Spatial classes and shows that the basis for all Spatial objects is the bbox and proj4string slots. The proj4string provides the CRS for an object through a PROJ definition, while the bbox slot provides a matrix of the minimum and maximum coordinates for the object. The Spatial subclasses add slots to the AN EXPLORATION OF SIMPLE FEATURES FOR R With this general definition of Simple Features in mind, we can look at how the sf package implements the standard through the sf class of object. 3 At its most basic, an sf object is a collection of simple features that includes attributes and geometries in the form of a data frame. In other words, it is a data frame (or tibble) with rows of features, columns of attributes, and a special INTRODUCTION TO NETWORK ANALYSIS WITH R 1. This post will provide an introduction to working with networks in R, using the example of the network of cities in the correspondence of Daniel van der Meulen in 1585. There are a number of applications designed for network analysis and the creation of network graphs suchas
OVERVIEW OF ALL PAGES WITH THE TAG #TAGS r 7. View all. Introducing debkeepr Great Circles with R An Exploration of Simple Features for R Introduction to GIS with R Introduction to Network Analysis with R Geocoding with R Excel vs R: A Brief Introduction to R.GEOCODING WITH R
In the previous post I discussed some reasons to use R instead of Excel to analyze and visualize data and provided a brief introduction to the R programming language. That post used an example of letters sent to the sixteenth-century merchant Daniel van der Meulen in 1585. One aspect missing from the analysis was a geographical visualizationof the data.
GREAT CIRCLES WITH R Great circles with R. In Geocoding with R and Introduction to GIS with R I demonstrated different ways to make maps that showed the sources and destinations of letters sent to the sixteenth-century merchant Daniel van der Meulen in 1585. This post will show how to create great circle lines to connect the sources and destinations of the letters. There are a number of resources on making great EXCEL VS R: A BRIEF INTRODUCTION TO R 3. In a spreadsheet program the analysis directly manipulates the only copy of the raw data. In contrast, with R you import the data, creating an object that is a copy of the raw data. 4. All manipulations to the data are done on this copy, and the original data are never altered in any way.JESSE SADLER
The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to COURSES - JESSE SADLER - JESSE SADLER Cultural and Intellectual History of Modern Europe, Eighteenth Century Taught: Fall 2016 and Spring 2015 PROJECTS - JESSE SADLER - JESSE SADLER debkeepr is an R package that provides an interface for working with non-decimal currencies that use the tripartite system of pounds, shillings, and pence. The package includes functions to apply arithmetic and financial operations to single or multiple values and to analyze account books that use either single-entry bookkeeping or double-entry bookkeeping with the latter providing the name INTRODUCTION TO GIS WITH R Introduction to GIS with R through the sp and sf packages. The output lists the different Spatial classes and shows that the basis for all Spatial objects is the bbox and proj4string slots. The proj4string provides the CRS for an object through a PROJ definition, while the bbox slot provides a matrix of the minimum and maximum coordinates for the object. The Spatial subclasses add slots to the AN EXPLORATION OF SIMPLE FEATURES FOR R With this general definition of Simple Features in mind, we can look at how the sf package implements the standard through the sf class of object. 3 At its most basic, an sf object is a collection of simple features that includes attributes and geometries in the form of a data frame. In other words, it is a data frame (or tibble) with rows of features, columns of attributes, and a special INTRODUCTION TO NETWORK ANALYSIS WITH R 1. This post will provide an introduction to working with networks in R, using the example of the network of cities in the correspondence of Daniel van der Meulen in 1585. There are a number of applications designed for network analysis and the creation of network graphs suchas
OVERVIEW OF ALL PAGES WITH THE TAG #TAGS r 7. View all. Introducing debkeepr Great Circles with R An Exploration of Simple Features for R Introduction to GIS with R Introduction to Network Analysis with R Geocoding with R Excel vs R: A Brief Introduction to R.GEOCODING WITH R
In the previous post I discussed some reasons to use R instead of Excel to analyze and visualize data and provided a brief introduction to the R programming language. That post used an example of letters sent to the sixteenth-century merchant Daniel van der Meulen in 1585. One aspect missing from the analysis was a geographical visualizationof the data.
GREAT CIRCLES WITH R Great circles with R. In Geocoding with R and Introduction to GIS with R I demonstrated different ways to make maps that showed the sources and destinations of letters sent to the sixteenth-century merchant Daniel van der Meulen in 1585. This post will show how to create great circle lines to connect the sources and destinations of the letters. There are a number of resources on making great EXCEL VS R: A BRIEF INTRODUCTION TO R 3. In a spreadsheet program the analysis directly manipulates the only copy of the raw data. In contrast, with R you import the data, creating an object that is a copy of the raw data. 4. All manipulations to the data are done on this copy, and the original data are never altered in any way. COURSES - JESSE SADLER - JESSE SADLER Cultural and Intellectual History of Modern Europe, Eighteenth Century Taught: Fall 2016 and Spring 2015 OVERVIEW OF ALL PAGES WITH THE TAG #TAGS r 7. View all. Introducing debkeepr Great Circles with R An Exploration of Simple Features for R Introduction to GIS with R Introduction to Network Analysis with R Geocoding with R Excel vs R: A Brief Introduction to R. GIS - JESSE SADLER - JESSE SADLER The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to BY WAY OF INTRODUCTION By Way of Introduction. In this introductory post I want to lay out the reasons for the creation of this website, to discuss some content that I will be looking to create, and to set some goals for the site. First though, I should first introduce myself. I am a historian of early modern Europe. My research investigates merchant families andthe
THE ITALIAN RENAISSANCE History 3334 introduces students to one of the most distinctive time periods in European history, the Italian Renaissance. The course concentrates on the Italian peninsula from the rise of urban communes in the 12th century to Spanish and Imperial political dominance in the16th century.
INTRODUCING DEBKEEPR Introducing debkeepr. After an extensive period of iteration and a long but rewarding process of learning about package development, I am pleased to announce the release of my first R package. The package is called debkeepr, and it derives directly from my historical research on early modern merchants. debkeepr provides an interface for working NEW KINDS OF PROJECTS: DH 2.0 AND CODING New kinds of Projects: DH 2.0 and Coding. In the process of learning about how I could use digital technologies to better organize my research, I quickly started to think about how I might extend these skills to produce new kinds of outputs. I was familiar with the concept of digital humanities, but the step from an internal processof
SADLER 1200 SYLLABUS Thursday, October 3: Slavery and the slave trade Reading: Francesca Trivellato, The Familiarity of Strangers: The Sephardic Diaspora, Livorno, and Cross-Cultural Trade in the Early Modern Period, 177–193 Olaudah Equiano, The Interesting Narrative of the Life of Olaudah Equiano Written by Himself, 1789 Assignment: Why do you think Olaudah Equiano wrote about his life? THE ESTATE OF JAN DELLA FAILLE DE OUDE, 1582–1617 The Estate of Jan della Faille de Oude. As Jan della Faille de Oude lay in sick in bed on the 21st of October, 1582, he dictated his last will and testament to a notary from Antwerp. Though born into a peasant family in the Flemish village of Wevelgem, by the time that Jan de Oude, or Jan the Elder, made his testament, he had risen tobecome
THE CORRESPONDENCE NETWORK OF DANIEL VAN DER MEULEN, 1578 Daniel van der Meulen (1554–1600), the fifth child and youngest son of Jan van der Meulen and Elizabeth Zeghers, was born in Antwerp in 1554. The Van der Meulens participated in regional trade from Antwerp, mainly with the fairs in Frankfurt and Strassbourg. The prospects of the Van der Meulens improved over the course of Daniel’s childhoodJESSE SADLER
The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to COURSES - JESSE SADLER - JESSE SADLER Cultural and Intellectual History of Modern Europe, Eighteenth Century Taught: Fall 2016 and Spring 2015 PROJECTS - JESSE SADLER - JESSE SADLER debkeepr is an R package that provides an interface for working with non-decimal currencies that use the tripartite system of pounds, shillings, and pence. The package includes functions to apply arithmetic and financial operations to single or multiple values and to analyze account books that use either single-entry bookkeeping or double-entry bookkeeping with the latter providing the nameJESSE SADLER
Current position Lecturer, Virginia Tech Education Ph.D. History, University of California, Los Angeles, March 2015 Dissertation: Family in Revolt: The Van der Meulen and Della Faille Families in the Dutch Revolt Dissertation Committee: Margaret Jacob (Chair), David Sabean, Teofilo Ruiz, and Peter Arnade M.A. University of California, Los Angeles, 2008 B.A. University of California, San Diego INTRODUCTION TO GIS WITH R Introduction to GIS with R through the sp and sf packages. The output lists the different Spatial classes and shows that the basis for all Spatial objects is the bbox and proj4string slots. The proj4string provides the CRS for an object through a PROJ definition, while the bbox slot provides a matrix of the minimum and maximum coordinates for the object. The Spatial subclasses add slots to the GIS - JESSE SADLER - JESSE SADLER The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to AN EXPLORATION OF SIMPLE FEATURES FOR R With this general definition of Simple Features in mind, we can look at how the sf package implements the standard through the sf class of object. 3 At its most basic, an sf object is a collection of simple features that includes attributes and geometries in the form of a data frame. In other words, it is a data frame (or tibble) with rows of features, columns of attributes, and a specialGEOCODING WITH R
In the previous post I discussed some reasons to use R instead of Excel to analyze and visualize data and provided a brief introduction to the R programming language. That post used an example of letters sent to the sixteenth-century merchant Daniel van der Meulen in 1585. One aspect missing from the analysis was a geographical visualizationof the data.
INTRODUCTION TO NETWORK ANALYSIS WITH R 1. This post will provide an introduction to working with networks in R, using the example of the network of cities in the correspondence of Daniel van der Meulen in 1585. There are a number of applications designed for network analysis and the creation of network graphs suchas
THE CORRESPONDENCE NETWORK OF DANIEL VAN DER MEULEN, 1578INTERACTIVE LEAFLET MAP OF THE CORRESPONDENCE NETWORKSANKEY DIAGRAM OF THE CORRESPONDENCE NETWORKSHINY VERSION OF THE LEAFLET MAP THAT INCLUDES REAL-TIME FILTERING OF DATESSEE MORE ON JESSESADLER.COMJESSE SADLER
The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to COURSES - JESSE SADLER - JESSE SADLER Cultural and Intellectual History of Modern Europe, Eighteenth Century Taught: Fall 2016 and Spring 2015 PROJECTS - JESSE SADLER - JESSE SADLER debkeepr is an R package that provides an interface for working with non-decimal currencies that use the tripartite system of pounds, shillings, and pence. The package includes functions to apply arithmetic and financial operations to single or multiple values and to analyze account books that use either single-entry bookkeeping or double-entry bookkeeping with the latter providing the nameJESSE SADLER
Current position Lecturer, Virginia Tech Education Ph.D. History, University of California, Los Angeles, March 2015 Dissertation: Family in Revolt: The Van der Meulen and Della Faille Families in the Dutch Revolt Dissertation Committee: Margaret Jacob (Chair), David Sabean, Teofilo Ruiz, and Peter Arnade M.A. University of California, Los Angeles, 2008 B.A. University of California, San Diego INTRODUCTION TO GIS WITH R Introduction to GIS with R through the sp and sf packages. The output lists the different Spatial classes and shows that the basis for all Spatial objects is the bbox and proj4string slots. The proj4string provides the CRS for an object through a PROJ definition, while the bbox slot provides a matrix of the minimum and maximum coordinates for the object. The Spatial subclasses add slots to the GIS - JESSE SADLER - JESSE SADLER The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to AN EXPLORATION OF SIMPLE FEATURES FOR R With this general definition of Simple Features in mind, we can look at how the sf package implements the standard through the sf class of object. 3 At its most basic, an sf object is a collection of simple features that includes attributes and geometries in the form of a data frame. In other words, it is a data frame (or tibble) with rows of features, columns of attributes, and a specialGEOCODING WITH R
In the previous post I discussed some reasons to use R instead of Excel to analyze and visualize data and provided a brief introduction to the R programming language. That post used an example of letters sent to the sixteenth-century merchant Daniel van der Meulen in 1585. One aspect missing from the analysis was a geographical visualizationof the data.
INTRODUCTION TO NETWORK ANALYSIS WITH R 1. This post will provide an introduction to working with networks in R, using the example of the network of cities in the correspondence of Daniel van der Meulen in 1585. There are a number of applications designed for network analysis and the creation of network graphs suchas
THE CORRESPONDENCE NETWORK OF DANIEL VAN DER MEULEN, 1578INTERACTIVE LEAFLET MAP OF THE CORRESPONDENCE NETWORKSANKEY DIAGRAM OF THE CORRESPONDENCE NETWORKSHINY VERSION OF THE LEAFLET MAP THAT INCLUDES REAL-TIME FILTERING OF DATESSEE MORE ON JESSESADLER.COM GIS - JESSE SADLER - JESSE SADLER The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services.However, the stretching of latitudes towards the poles distorts the size of land masses, making those closer to OVERVIEW OF ALL PAGES WITH THE TAG #TAGS r 7. View all. Introducing debkeepr Great Circles with R An Exploration of Simple Features for R Introduction to GIS with R Introduction to Network Analysis with R Geocoding with R Excel vs R: A Brief Introduction to R. INTRODUCING DEBKEEPR The examples for division in the Encyclopedia Britannica include the division of pounds, shillings, and pence, as well as the division of weight measured in terms of hundredweight, quarters, and pounds. A hundredweight consisted of four quarters and there were 28 pounds (or two stones) in a quarter. While debkeepr was created with pounds, shillings, and pence values in mind, the ONE YEAR ANNIVERSARY One Year Anniversary. It was a year ago today that I launched this website with a post introducing myself and the goals for the blog. When I launched the site, I was at the beginning of teaching myself about digital humanities and how to code in R. Building this Hugo site was part of my learning process. I learned about the command line, Git EXCEL VS R: A BRIEF INTRODUCTION TO R The power of R comes with the manipulation of objects through the use of functions. Functions take in objects, possibly with a list of arguments to specify how the function works, and return another object. The objects and instructions for the functions are included within parentheses following the function name, and all instructions are separated by a comma. 9 You can think of objects as NEW KINDS OF PROJECTS: DH 2.0 AND CODING New kinds of Projects: DH 2.0 and Coding. In the process of learning about how I could use digital technologies to better organize my research, I quickly started to think about how I might extend these skills to produce new kinds of outputs. I was familiar with the concept of digital humanities, but the step from an internal processof
GREAT CIRCLES WITH R Great circles with R. In Geocoding with R and Introduction to GIS with R I demonstrated different ways to make maps that showed the sources and destinations of letters sent to the sixteenth-century merchant Daniel van der Meulen in 1585. This post will show how to create great circle lines to connect the sources and destinations of the letters. There are a number of resources on making great THE CORRESPONDENCE NETWORK OF DANIEL VAN DER MEULEN, 1578 Daniel van der Meulen (1554–1600), the fifth child and youngest son of Jan van der Meulen and Elizabeth Zeghers, was born in Antwerp in 1554. The Van der Meulens participated in regional trade from Antwerp, mainly with the fairs in Frankfurt and Strassbourg. The prospects of the Van der Meulens improved over the course of Daniel’s childhood THE ESTATE OF JAN DELLA FAILLE DE OUDE, 1582–1617 The Estate of Jan della Faille de Oude. As Jan della Faille de Oude lay in sick in bed on the 21st of October, 1582, he dictated his last will and testament to a notary from Antwerp. Though born into a peasant family in the Flemish village of Wevelgem, by the time that Jan de Oude, or Jan the Elder, made his testament, he had risen tobecome
JESSESADLER.COM
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JESSE SADLER
------------------------- A blog about early modern history and digital humanities INTRODUCING DEBKEEPR AN R PACKAGE FOR THE ANALYSIS OF NON-DECIMAL CURRENCIES Posted on September 18, 2018 After an extensive period of iteration and a long but rewarding process of learning about package development, I am pleased to announce the release of my first R package. The package is called debkeepr , and it derives directly from my historical research on early modern merchants. debkeepr
provides an interface for working with non-decimal currencies that use the tripartite system of pounds, shillings, and pence that was used throughout Europe in the medieval and early modern periods. The package includes functions to apply arithmetic and financial operations to single or multiple values and to analyze account books that use double-entry bookkeepingwith
the latter providing the basis for the name of debkeepr. In a later post I plan to write about the package development process, but here I want to discuss the motivation behind the creation of the package and provide some examples for how debkeepr can help those who encounter non-decimal currencies in their research. You can install debkeepr from GitHub right now with devtools , and I am planning to submit the package to CRAN soon. Feedback is always welcome and any bug reports or feature requests can be made on GitHub.
# install.packages("devtools") devtools::install_github("jessesadler/debkeepr") POUNDS, SHILLINGS, AND PENCE: LSD MONETARY SYSTEMS The system of expressing monetary values in the form of pounds, shillings, and pence dates back to the Carolingian Empire and the change from the use of gold coins that derived from the late Roman Empire to silver pennies that had taken place by the eighth century. Needing ways to count larger quantities of the new silver denarius , people began to define a solidus , originally a gold coin introduced by the Emperor Constantine, as a unit of account equivalent to 12 denarii. For even larger valuations, the denarius was further defined in relation to a pound or libra of silver. Though the actual number of coins struck from a pound of silver differed over time, the rate of 240 coins lasted long enough to create the custom of counting coins in dozens (solidi) and scores of dozens (librae). The librae, solidi, and denarii (lsd)monetary system was
translated into various European languages, and though the ratios between the three units often differed by region and period, the basic structure of the system remained in place until decimalization began following the French Revolution. 1#r
#debkeepr
ONE YEAR ANNIVERSARYSOME REFLECTIONS
Posted on June 6, 2018 It was a year ago today that I launched this website with a postintroducing myself
and the goals
for the blog. When I launched the site, I was at the beginning of teaching myself about digital humanities and how to code in R. Building this Hugo site was part of my learning process. I learned about the command line, Git, and GitHub, not to mention a bit of HTML and CSS to get the website up and going. I also wanted to share my progress and provide information for others who might want to go down a similar path. Over the past year I have gone from a coding newbie to actively working on two digital humanities projects and closing in on the launch of my first R package that makes it easier to work with historical non-decimal currencies.1 After writing a couple of posts on my thoughts about digitalhumanities , I have
concentrated on writing posts about using R to analyze and visualize historical data. I have always tried to adopt the perspective of a newcomer to code and to R, since I was in that position not all that long ago. The rsats community has done an admirable job in trying to make learning R as approachable as possible, but there is no way around the fact that learning to code is a daunting task. My posts have mainly focused on using R for GIS and network analysissince these
are the two topics most pertinent to the digital humanities projects I have been working on. Writing posts on these topics proved to be the best way for me to learn not only how to create maps and network graphs with R but also about the fields of GIS and network analysis ingeneral.
Even if no one read my posts they would have been useful endeavors for all that I learned through writing them. However, the most gratifying aspect of writing this blog has been hearing that the content has proven useful to others in their efforts to learn R. It has been particularly amazing and amusing to hear from people in the sciences and think about biologists and chemists learning to code by analyzing letters from a sixteenth-century merchant in the Low Countries, which is the
data set
that I have used in my posts. I never could have imagined that this website and my blog posts would be read by very many people, and I have been amazed by the steady stream of visitors that continue to read the website daily. The digital humanities and rstats communities have been amazingly open and welcoming, and I never could have gotten this far nor had this much fun without the kindness of the people in these communities. I have received very generous encouragement and increased visibility through tweeting out my posts from Maëlle Salmon, Mara Averick
, Hadley Wickham
, and the great R4DS community among many others. To these individuals and to the entire community that has come to read my blog I can only say thank you. In a nice little coincidence this is the 11th post to the blog and pushes my first introductory post to the second page of blog entries. I am looking forward to the next year of continuing to learn more about digital humanities and R and writing about my experiences on this blog, pushing more and more posts to the second page and beyond. ------------------------- * I am hoping to have a version of this package out in the nextcouple of weeks.
GREAT CIRCLES WITH R THREE METHODS WITH SP AND SF Posted on March 28, 2018 In 1569 the Flemish cartographer and mathematician Gerardus Mercatorpublished a new
world map under the title “New and more complete representation of the terrestrial globe properly adapted for use in navigation.” The title of the map points to Mercator’s main claim for its usefulness, which he expounded upon in the map’s legends. Mercator presented his map as not only an accurate representation of the known world, but also as a particularly useful map for the purposes of navigation. As described in the third legend,
Mercator aimed to maintain conformity to the shape of land masses even towards the poles and to have straight lines on the map accurately represent directionality. To achieve his goals Mercator used aprojection
in which lines of longitude and latitude were made perpendicular at all values by increasing the distance between degrees of latitude as they reach the pole.1 Mercator’s projection had the benefit that straight lines drawn on the map are rhumb lines, lines of constant bearing that pass every degree of longitude at the same angle. Theoretically this simplified oceanic navigation; a ship captain could draw a straight line from one port to another, calculate the bearing, and maintain that bearing along the voyage. However, 16th-century navigators used magnetic courses and not longitude and latitude values as Mercator’s map assumed.2 An accurate means to measure longitude at sea was only discovered in the second half of the 18th century with the development of the sextant and later the marine chronometer.3 World Map by Gerardus Mercator, 1569 The Mercator projection was designed with certain uses in mind. Mercator’s emphasis on perpendicular lines of longitude and latitude and the equivalence of straight lines and rhumb lines were meant to simplify navigation and have recently proved useful for online mapping services . However, the stretching of latitudes towards the poles distorts the size of land masses , making those closer to the poles appear larger than those near the equator. The stress on rhumb lines in Mercator’s map also highlights the difference between lines of constant bearing (rhumb or loxodrome lines ) and the shortest distance between two points (great circles). Due to
Earth’s ellipsoidal nature, the shortest distance between two points is not necessarily a straight line. For instance, to fly from Los Angeles to Amsterdam, one would not want to fly in a straight line of constant bearing at 78 degrees. Instead, you would want to make an arc to the north to take advantage of the ellipsoidal shape of the Earth. By flying along the great circle from Los Angeles to Amsterdam one would travel 1120 kilometers less than flying along the rhumb line.#dh-2.0 #r
AN EXPLORATION OF SIMPLE FEATURES FOR R BUILDING SFG, SFC, AND SF OBJECTS FROM THE SF PACKAGE Posted on March 12, 2018My previous post
provided an introduction to the spand sf
packages,
showing how the two packages represent spatial data in R. There I discussed the creation of Spatial and sf objects from data with longitude and latitude values and the process of making maps with the two packages. In this post I will go further into the details of the sf package by examining the structure of sf objects and how the package implements the Simple Features open standard . It is certainly not necessary to know the ins and outs of sf objects and the Simple Features standard to use the package — it has taken me long enough to get my head around much of this — but a better knowledge of the structure and vocabulary of sf objects is helpful for understanding the effects of the plethora of sf functions. There are a variety of good resources that discuss the structure of sf objects. The most comprehensive are the package vignette Simple Features for Rand
the overview in Chapter 2 of the working book _Geocomputation with R_ by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow.
This post is based on these sources, as well as my own sleuthing through the code for the sf package. Before diving in, let’s take a step back to provide some background to the package. The sf package implements the Simple Features standardin R. The Simple
Features standard is widely used by GIS software such as PostGIS , GeoJSON , and ArcGIS to represent geographic vector data. The sf package is designed to bring spatial analysis in R in line with these other systems.1 The standard defines a simple feature as a representation of a real world object by a point or points that may or may not be connected by straight line segments to form lines or polygons. A simple feature can contain both a geometry that includes points, any connecting lines, and a coordinate reference system to identify its location of Earth and attributes to describe the object, such as a name, values, color, etc. The sf package takes advantage of the wide use of Simple Features by linking directly to the GDAL, GEOS , and PROJ
libraries that provide the back end for reading spatial data, making geographic calculations, and handling coordinatereference systems.2
#r
INTRODUCTION TO GIS WITH R SPATIAL DATA WITH THE SP AND SF PACKAGES Posted on February 7, 2018 The geographic visualization of data makes up one of the major branches of the Digital Humanities toolkit. There are a plethora of tools that can visualize geographic information from full-scale GIS applications such as ArcGIS and QGIS to web-based tools like Google maps to any number of programing languages. There are advantages and disadvantages to these different types of tools. Using a command-line interface has a steep learning curve , but it has the benefit of enabling approaches to analysis and visualization that are customizable, transparent, and reproducible.1 My own interest in coding and R began with my desire to dip my toes into geographic information systems (GIS)and
create maps of an early modern correspondence network. The goal of this post is to introduce the basic landscape of working with spatial data in R from the perspective of a non-specialist. Since the early 2000s, an active community of R developers has built a wide variety of packages to enable R to interface with geographic data. The extent of the geographic capabilities of R is readily apparent from the many packages listed in the CRAN task view for spatial data.2
In my previous post on geocoding with RI showed the use of
the ggmap
package to geocode data and create maps using the ggplot2 system. This post will build off of the location data obtained there to introduce the two main R packages that have standardized the use of spatial data in R. Thespand sf
packages use
different methodologies for integrating spatial data into R. The sp package introduced a coherent set of classes and methods for handling spatial data in 2005.3 The package remains the backbone of many packages that provide GIS capabilities in R. The sf package implementsthe simple features
open
standard for the representation of geographic vector data in R. The package first appeared on CRAN at the end of 2016 and is under very active development. The sf package is meantto supersede sp
,
implementing ways to store spatial data in R that integrate with the tidyverse workflow of the packages developed by Hadley Wickham and others . There are a number of good resources on working with spatial data in R. The best sources for information about the sp and sf packages that I have found are Roger Bivand, Edzer Pebesma, and Virgilio Gómez-Rubio, _Applied Spatial Data Analysis with R_ (2013) and the working book Robin Lovelace, Jakub Nowosad, Jannes Muenchow, _Geocomputation with R_ , which concentrate on sp and sf respectively. The vignettes for sf are also very helpful. The perspective that I adopt in this post is slightly different from these resources. In addition to more explicitly comparing sp and sf, this post approaches the two packages from the starting point of working with geocoded data with longitude and latitude values that must be transformed into spatial data. It takes the point of view of someone getting into GIS and does not assume that you are working with data that is already in a spatial format. In other words, this post provides information that I wish I knew as I learned to work with spatial data in R. Therefore, I begin the post with a general overview of spatial data and how sp and sf implement the representation of spatial data in R. The second half of the post uses an example of mapping the locations of letters sent to a Dutch merchant in 1585 to show how to create, work with, and plot sp and sf objects. I highlight the differences between the two packages and ultimately discuss some reasons why the R spatial community is moving towards the use of the sf package.#dh-2.0
#r
INTRODUCTION TO NETWORK ANALYSIS WITH R CREATING STATIC AND INTERACTIVE NETWORK GRAPHS Posted on October 25, 2017 Over a wide range of fields network analysis has become an increasingly popular tool for scholars to deal with the complexity of the interrelationships between actors of all sorts. The promise of network analysis is the placement of significance on the relationships between actors, rather than seeing actors as isolated entities. The emphasis on complexity, along with the creation of a variety of algorithms to measure various aspects of networks, makes network analysis a central tool for digital humanities.1 This post will provide an introduction to working with networks in R , using the example of the network of cities in the correspondence of Daniel van der Meulenin 1585.
There are a number of applications designed for network analysis and the creation of network graphs such as gephi and cytoscape . Though not specifically designed for it, R has developed into a powerful tool for network analysis. The strength of R in comparison to stand-alone network analysis software is three fold. In the first place, R enables reproducible research that is not possible with GUI applications. Secondly, the data analysis power of R provides robust tools for manipulating data to prepare it for network analysis. Finally, there is an ever growing range of packages designed to make R a complete network analysis tool. Significant network analysis packages for R include the statnet suite of packages and igraph . In addition, Thomas Lin Pedersen has recently released thetidygraph
and
ggraph
packages that leverage the power of igraph in a manner consistent with the tidyverse workflow. R can also be used to make interactive network graphs with the htmlwidgets framework that translates R code to JavaScript. This post begins with a short introduction to the basic vocabulary of network analysis, followed by a discussion of the process for getting data into the proper structure for network analysis. The network analysis packages have all implemented their own object classes. In this post, I will show how to create the specific object classes for the statnet suite of packages with the networkpackage,
as well as for igraph and tidygraph, which is based on the igraph implementation. Finally, I will turn to the creation of interactive graphs with the vizNetworkand networkD3
packages.
#dh-2.0 #r
GEOCODING WITH R
USING GGMAP TO GEOCODE AND MAP HISTORICAL DATA Posted on October 13, 2017 In the previous post I discussed some reasons to use R instead of Excel to analyze and visualize data and provided a brief introduction to the R programming language. That post used an example of letters sent to the sixteenth-century merchant Daniel van der Meulenin 1585. One
aspect missing from the analysis was a geographical visualization of the data. This post will provide an introduction to geocoding and mapping location data using the ggmappackage for
R, which enables the creation of maps with ggplot . There are a number of websites that can help geocode location data and even create maps.1 You could also use a full-scale geographic information systems (GIS) application such as QGIS or ArcGIS . However, an active developer community has made it possible to complete a full range of geographic analysis from geocoding data to the creation of publication-ready maps with R.2 Geocoding and mapping data with R instead of a web or GIS application brings the general advantages of using a programming language in analyzing and visualizing data. With R, you can write the code once and use it over and over, while also providing a record of all your steps in the creation of a map.3 This post will merely scratch the surface of the mapping capabilities of R and will not enter into the domain of the more complex specific geographic packages available for R.4 Instead, it will build on thedplyr and ggplot
skills discussed in my brief introduction to R . The example of geocoding and mapping with R will also provide another opportunity to show the advantages of coding. In particular, geocoding is a good example of how code can simplify the workflow for entering data. Instead of dealing with separate spreadsheets to store information about the letters and geographic information, coding makes it possible to create the geographic information directly from the letters data. The code to find the longitude and latitude of locations can be saved as a R script and rerun if new data is added to ensure that the information is always kept up to date.#dh-2.0
#r
EXCEL VS R: A BRIEF INTRODUCTION TO R WITH EXAMPLES USING DPLYR AND GGPLOT Posted on October 2, 2017 Quantitative research often begins with the humble process of counting. Historical documents are never as plentiful as a historian would wish, but counting words, material objects, court cases, etc. can lead to a better understanding of the sources and the subject under study. When beginning the process of counting, the first instinct is to open a spreadsheet. The end result might be the production of tables and charts created in the very same spreadsheet document. In this post, I want to show why this spreadsheet-centric workflow is problematic and recommend the use of a programming language such as R as an alternative for both analyzing and visualizing data. There is no doubt that the learning curve for R is much steeper than producing one or two charts in a spreadsheet. However, there are real long-term advantages to learning a dedicated data analysis tool like R. Such advice to learn a programming language can seem both daunting and vague, especially if you do not really understand what it means to code. For this
reason, after discussing why it is preferable to analyze data with R instead of a spreadsheet program, this post provides a brief introduction to R, as well as an example of analysis and visualization of historical data with R.1 The draw of the spreadsheet is strong. As I first thought about ways to keep track of and analyze the thousands of letters in the Daniel van der Meulen Archive, I
automatically opened up Numbers — the spreadsheet software I use most often — and started to think about what columns I would need to create to document information about the letters. Whether one uses Excel, Numbers, Google Sheets or any other spreadsheet program, the basic structure and capabilities are well known. They all provide more-or-less aesthetically pleasing ways to easily enter data, view subsets of the data, and rearrange the rows based on the values of the various columns. But, of course, spreadsheet programs are more powerful than this, because you can add in your own programatic logic into cells to combine them in seemingly endless ways and produce graphs and charts from the results. The spreadsheet, after all, was the first killer app.
With great power, there must also come great responsibility . Or, in the case of the spreadsheet, with great power there must also come great danger. The danger of the spreadsheet derives from its very structure. The mixture of data entry, analysis, and visualization makes it easy to confuse cells that contain raw data from those that are the product of analysis. The nature of defining programatic logic — such as which cells are to be added together — by mouse clicks means that a mistaken click or drag action can lead to errors or the overwriting of data. You only need to think about the dread of the moment when you go to close a spreadsheet and the program asks whether you would like to save changes. It makes you wonder. Do I want to save? What changes did I make? Because the logic in a spreadsheet is all done through mouse clicks, there is no way to effectively track what changes have been made either in one session or in the production of a chart. Excel mistakes can have wide-ranging consequences, as the controversy around the paper of Carmen Reinhart and Kenneth Rogoff on national debt madeclear
.2
#dh-2.0
#r
MY APPROACH TO DIGITAL HUMANITIES Posted on August 18, 2017 Digital humanities holds the promise of increasing the means by which scholars are able to analyze and present data. Though some sentiments about the significance of digital humanities might be overblown, there is no doubt that the more ways we have to analyze sources the better . Learning a variety of the tools that make up the rather nebulous universe of digital humanities is like learning a new language. It opens up new possibilities that were previously closed or necessitated the expertise of others. This frames digital humanities as a collection of skills rather than a means to a predetermined end. I have adopted this perspective in learning about the possibilities opened by digital humanities and working on a digital humanities project. I am hardly
the first person to take these steps, but I hope that by explaining my thought process I can set a basis for future posts on digitalhumanities.
If there has been one guiding force in my approach to digital humanities, it is to learn skills and tools in the process of production. In a way, this is simply the application of critical thinking to how I create my scholarly work. Instead of doing things in what seems the de facto manner, I have sought to question if there is either a more efficient way or a way in which I could gain or improve my competency. The goal of efficiency was particularly significant in thinking about how to organize my research and writing (DH 1.0), while
learning new skills has been more important in the production of digital humanities projects (DH 2.0). This may not be
the easiest way to complete a project in the short run, but by doing things the hard way, I am looking to open up new opportunities for future projects. With this theoretical approach in mind, let me now discuss a few concrete principles of my digital humanities practices concerning text, applications, and producing digital humanitiesprojects.
#dh-1.0
#dh-2.0
NEW KINDS OF PROJECTS: DH 2.0 AND CODING Posted on August 16, 2017 In the process of learning about how I could use digital technologies to better organize my research, I quickly started to think about how I might extend these skills to produce new kinds of outputs.1 I was familiar with the concept of digital humanities, but the step from an internal process of organizing research and writing to production seemed both too nebulous and difficult. Digital humanities also seemed to concentrate on the visual. This was intriguing, but did not present itself as the most pressing need for a graduate student who was writing a dissertation on sibling relations among 16th-century merchants. It took years for me to move from what I am calling DH 1.0 to DH 2.0, to move from research methodology to making what could properly be termed digital humanities projects. This post presents an introduction to how I eventually took this step and why I decided to learn to code instead of other available solutions.2#dh-2.0
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