The composition and organisational patterns of Pleistocene human groups are a main research when it comes to the evolution of human behaviour. However, these studies are often limited by the restricted characteristics of the archaeological records and do not show enough resolution to make approaches with the necessary precision. The travertinic formations of the Abric Romaní site (Capellades, Barcelona, Spain) provide an ideal scenario to answer some questions about the European Middle Palaeolithic occupational patterns. The hearth-related accumulations from this site show many similarities with those generated by several contemporary forager groups, so each could represent the activity area of a specific social unit. This work contributes to the existing research by examining the faunal refits recovered in six stratigraphic units (H, I, J-Ja, K, L and M) that cover the chronological period between 44 and 55 ka. Faunal refits are analysed using the metric parameters of ethnographic hearth-related accumulations (the hearth itself and its corresponding drop and toss zones); significant relationships are found between many of these elements and the areas of influence of the hearths. In addition, connections between the activity areas from these refits are seen in several stratigraphic units. This phenomenon allows for greater diversity in the occupational patterns of this site to be identified than those recorded only from taphonomic studies. From this perspective, two main occupational models are proposed: (1) the simple model, in which isolated and unconnected hearth-related accumulations are identified (units H, L and—to a lesser extent—K) and (2) the complex model, primarily represented by the identification of several long-distance faunal refits connecting different activity areas (units I, J-Ja and M). Thus, this work provides deeper insights into the behavioural diversity of Middle Palaeolithic human groups, their social organisation and composition and their evolution in the region.
In this interactive tutorial, you will learn how to perform sophisticated dplyr techniques to carry out your data manipulation with R. First you will master the five verbs of R data manipulation with dplyr: select, mutate, filter, arrange and summarise. Next, you will learn how you can chain your dplyr operations using the pipe operator of the magrittr package. In the final section, the focus is on practicing how to subset your data using the group_by function, and how you can access data stored outside of R in a database. All said and done, you will be familiar with data manipulation tools and techniques that will allow you to efficiently manipulate data.
Functions are a fundamental building block of the R language. You’ve probably used dozens (or even hundreds) of functions written by others, but in order to take your R game to the next level, you’ll need to learn to write your own functions. This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.
It’s commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you’ll be able to go from raw data to awesome insights as quickly and painlessly as possible!
In this second part to Importing Data in R, you will take a deeper dive into the wide range of data formats out there. More specifically, you’ll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you’ll get hands-on experience with importing data from statistical software packages such SAS, STATA and SPSS.
Importing data into R to start your analyses—it should be the easiest step. Unfortunately, this is almost never the case. Data come in all sorts of formats, ranging from CSV and text files and statistical software files to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis. In this course, you will get started with learning how to read CSV and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that you will learn how to read XLS files in R using readxl and gdata.