While costs of operating a container fleet have increased, the surge in freight rates has not served just to cover rising expenses.
Since the start of the pandemic, the shipping industry has had to struggle with port closures and congestions, labor shortages, difficulties with capacity utilization, as well as a lack of new shipping containers. Due to its complexity and transcontinental nature, container shipping was hit especially hard by the COVID-19 pandemic. The COVID-19 pandemic turned out to be an event of such a magnitude to either bring to halt whole industries and supply chains, or severely reduce their efficiency. that need to function properly for the whole system to work. The global supply chain is a fragile system consisting of numerous links and disruption to one can send cascading effects down the chain. In September 2022, the global freight rate index stood at 4,000 U.S. The year 2021 saw an especially steep increase in global freight rates, reaching a record price of nearly 10,400 U.S. I show an example of having used a random forest procedure in Statistica using R in the screenshot above.Monthly container freight rate index worldwide 2019-2022Ĭontainer freight rates increased dramatically between January 2019 and September 2022. The advantage that the R integration brings then, is allowing me to have advanced statistical procedures, like Random Forests, or even graphing abilities like ggplot2, without having to pay extra. For example, since I now have Statistica Advanced, it does allow me to do some cool multivariate procedures, but I can’t generate random forests unless I get Statistica Data Miner. Main advantage: Being a commercial product, the good folks at Statsoft aren’t just going to give you the product with all of the statistical procedures they came up with for free. It would be nice if I could modify data from R within a spreadsheet, but that seems to be out of the question. So for those variables it seems that you have to resort to referring to them by their column numbers, instead of name.įunctionality: So far, it looks like data only flows from the Statistica spreadsheet, to R, back to the Statistica report output, or a new Statistica spreadsheet. The only catch seems to be variables with spaces in them. As you can see in the screenshot, if you want to access the active dataset to do something with it, you treat it as a dataframe labelled ActiveDataSet, and then you can use the $ sign and type the variable name of your statistica dataset like you would with R. Typing in R syntax into Statistica is, thankfully, pretty easy. The upshot of this is that you should probably be careful how much data you’re passing to an R procedure from Statistica so that you don’t max out your memory. What you will notice is that any time you run an R function in statistica, the R connector program starts taking up more and more memory, representing the fact that data is being passed from Statistica to R to be processed.
Statistica 8 free download windows 7#
Memory Usage: When you actively use the R integration in Statistica, take a look at your memory usage (I’m using a windows 7 computer for work). Statistica put a white paper on their website explaining the process. You have to make sure you get the rscproxy package in R and that it is installed in the R Home directory that sits in your program files folder. Then you have to make sure that R is installed using administrator mode.
You have to make sure you run the installation in administrator mode.
Statistica 8 free download download#
There is a COM server application you have to download and install. Setup/Installation: The setup and installation of the R integration was really annoying. So once we got our copies of Version 11 Advanced, I went ahead and learned how the interface works. In speaking with my supervisor about Statistica, she mentioned that it can interface with R. So, after reading up on different commercial stats packages, I determined that getting Statistica would be best for my supervisor and I (she’s insanely busy and wouldn’t have the time for the learning curve to learn Revolution R, if we were to buy that).
I kept trying to practice working with R packages like bigmemory and ffdf, but nothing was completely satisfying my need to be able to handle a big dataset with different data types in different columns. I’ve been spending a lot of time in the last month or so doing projects at work not statistics related, hence the lack of posts! In the interim, I had to do some serious research on handling datasets bigger than the last one I worked with (the one that kept threatening to max out my 8 gigs of RAM!).
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