# Plotting multiple mediation

This posts on multiple mediation on lavaan supplements the two previous ones (1 – introducing multiple mediator analysis with lavaan and 2 – showing an example analysis) by describing how to process lavaan’s output graphically. I discovered the handy package semPlot and I am very positive about it. I will make the example as reproducible as possible, so that each step can be repeated. Also, I am going to try to provide more explanation about the R commands I used because a friend pointed out that the description of the steps was sometimes a bit dry and abstract.
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# Four dimensions in two dimensions

This scatterplot is one of the best data visualisation I made. I like it because it concentrates a lot of information into a single visualisation. The scatterplot displays four dimensional data (i.e., four variables) using a two dimensional scatterplot. I made the first implementation in R, but because I wanted to add interactivity I switched to d3.js. Below I describe the choices I made to display the information and how I coded them in d3.js. Continue reading “Four dimensions in two dimensions”

# Streamgraphs in base::R [e.II]

Until recently I did not have a practical application in which to use streamgraphs. In fact, I still find the visualisation complex to understand, abstract and a bit too artistic. While I recognise that the strength of streamgraphs is the display of all the time seriesâ€™ values into one (possibly interactive) plot, the amount of data displayed is massive, with many streams and even more data points. Because of the amount of data displayed Continue reading “Streamgraphs in base::R [e.II]”

# Streamgraphs in base::R [e.I]

This is the first of a series of four post on producing a streamgraph in plain R code. Here I present a very simple R script plotting a streamgraph. In this post I made streamgraph in d3.js, but I wanted to be able to do the same in R, to not depend on a webpage, or without requiring additional libraries (e.g. the streamgraph htmlwidgtet is only a wrapper around d3, and does not work always smoothly).
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# Making up for univariate [DAI IVb]

This post is an extension of this one, which was (supposed to be) the final post of the coursera course ‘data analysis and interpretation’. This current post extends or complements the previous one because in that assignment I forgot to include univariate graphs in my plot. Since I only had a bivariate graph, the other reviewers failed my assignment. I was quite disappointed by their reaction, but I understood their motives. If univariate graphs get points and the absence thereof does not, I was righteously failed. Therefore, in this post I try to fix my previous mistake including three univariate graphs. The conclusion one can gather from these graphs remains unchanged and one should Continue reading “Making up for univariate [DAI IVb]”

# Loading data and frequencies [DAI -II]

Second assignment for the Coursera Data Management and Visualization ‘challenge’ (here is the introduction). I rename the data management and visualization course to challenge since it has been a bit challenging to keep up with the weekly deadlines (and this is only the second week). But I am happy I am still close to the deadline when submitting the assignments. The goal of this second assignment is to load the data set and explore it by means of some descriptive statistics. Below I adapted the sample text for this assignment.