Category Archives: getting started

Getting started – revision of dataframe input, new on looking at data type

This post assumes that you have read and worked through examples in previous posts concerning: 1. how to read in .csv files as dataframes; 2. data structure basics, including discussion of vectors, matrices, and dataframes. We will be working with … Continue reading

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Getting started – it can be worrying

Creative commons – flickr – Florida Memory: Bridge over Homosassa river One reason I am writing the blog is that when I started things were pretty mysterious. I knew I had to travel down the road but I wasn’t sure … Continue reading

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Getting started – why bother?

Creative commons – flickr – Florida Memory: View of the Blue Hole swiming area at Florida Caverns State Park I sometimes recall an article by Jerry Fodor in, I think, the New York Review of Books where he asked himself … Continue reading

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Getting started – referring to dataframe elements by place, name or condition – selecting elements to subset data

In the previous post, we revised how to read in dataframes (see also how to read in .csv files as dataframes), using the item norms database as an example (see also here), and we also revised how to get summary … Continue reading

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Getting started – basics: vectors and matrices; creating them; manipulating them; referring to specific elements in them

We looked previously at data structures like vectors, and data types or modes, as in numeric, character string or logical. I’ll recap some of the previous post on vectors and then move on to talking about data structures like matrices … Continue reading

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Getting started – recapping basics, adding foundational knowledge

This post assumes that you have installed R and R-studio, see here and here if not. Getting started with data analysis can require a shift in mind-set. Home base for most people is a spreadsheet Most people find it difficult to … Continue reading

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Getting started – selecting data, wrangling data – early (basic) moves

This post and the few following will switch focus from the ML subject scores database to a database built out of normative data about word attributes, which actually comes in a number of different parts (downloadable at the links). Remember that … Continue reading

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