P < 0.05 Significant Notes

To all my readers, sorry for the large time gap in between posts! I decided to switch up my career and become a Software Engineer! So for the last few months, I was in a coding Bootcamp specifically Hack Reactor. It was probably some of the hardest months of my life, but In just three months I was able to learn so much! More then I would have ever learned on my own. It was such an experience. This Bootcamp is not for the faint of heart. You are coding 6 days a week, with 12 - 13 hour days AND then you would go home and work more. In just the first 6 weeks of the program, I learned the basic technologies to become a full stack software engineer. Technology like JavaScript, React, AnugalrJS, NodeJS, Express, SQL and NON-SQL Databases. And in the remaining 6 weeks, we were able to apply our newly found knowledge into two capstone projects. The first project was called Front-End Capstone. Where we had a few weeks to really dive into our front-end skills to create websites that looked a

Hello everyone! I am sorry it took me so long to write this next post. As in my last post you can see I have been dealing with a lot on my plate. However, I am excited to share some more R tips to making your code run faster and more efficiently. In this post we will be working with vectors and matrices. 1. Loose the loop between matrices.  How to run your code without a loop and using the apply()  function. Now I am only going to show you the basic form of this function. The apply function helps save lots of computation time and makes your code so much more cleaner. For example, lets say we have a matrix and we want to find the mean of each column. You can't just use the mean() function, because that will give you the mean of the whole matrix. And you can do it column by column, but that takes time so to automate it one would think to create a loop. # Example of loop of calculations done on a column. m <- matrix ( c ( 1 : 100000 ), nrow = 20 ) MeanCol = n

Hello everyone! I am sorry that I have yet to post the R shortcuts part 2. I was hit with several deadlines then my apartment was destroyed by Hurricane Harvey. I am finally back at work and have my living situation under control. I am shooting for that next week to be able to post R shortcuts Part 2! Thanks for staying with me!

The goal of running code is usually to make tasks easier, faster, and more efficient. Sometimes however, it may feel like you are always waiting for your code to run. We will go through a few fun tips that can help speed up that process. 1. Preset your vector or Matrix destinations. Believe it or not that presetting your vector destinations saves your code a lot of running time. This is because when you run a loop or a function and you don't preset your destination R has to hold those variables in its cache memory, however, if you have preset vector ready for variables the values will be routed to that vector. Take this simple example below for instance: # Creating a destination with non-specific number of variables for output vector X = c () # Start timing the loop Strt <- Sys.time () for (i in 1 : 100000 ){ X[i] = i } print ( Sys.time () - Strt) # Print how long it takes to run the loop ## Time difference of 8.179752 secs Ok lets try it with a m

This week we are going finish our Creating your own .gif fun. So last blog we were able to make a gif using ImagJ and R. Using this method has its pro's and con's. Pro's you can use ImageJ to edit your images as you see fit. Con's you have to do additional steps outside of R. So today's post we will use R and ImageMagic. These steps will allow you to create a .gif without leaving R for additional steps.  Now I saved this post for last because it it slightly more complicated to install ImageMagic. It took me several weeks searching bog post, stack flow posts, and any other tutorials to figure out how to download and run ImageMagic properly. When most people do posts on creating .gifs they leave out the importance of downloading and running ImageMagic. And even if they did it was either for Windows or Mac but never both. SO.... I will be take the time to explain how to install it for both Mac and Windows (I'm sorry Linux users I'm not there yet). ~~~~~~~~~~~

Ok this week we are going to start something fun! Gifs are the new fun way to express:  how people feel, a situation, or just for fun.  But gifs can be used for your science and math also! Say you have a presentation or a project where you would like to show the changes to your graph in a rolling images rather then a mosaic of images. Well you can do this in R! I have found two easy ways of doing this. Both are relatively easy to do, however you have to do a bit of leg work before you get to the fun part.  When I was researching this for a project I was working on, I found that the most important part was downloading the packages needed to execute the .gifs. However in most tutorials out there would glaze over this part, as if you should already know how to download the packages needed. Since they aren't as strait forward as we would like, I will go over that first then show you how you can make a fun easy .gif for your work. I will brake up this blog into two parts to cover the tw

Open Source Platform. To any data manager, data scientist, or coder when you hear or see those words you think gold mind. Open source platforms are the best thing to utilize for your work. First its free! Second its peer reviewed (in most cases)! Third it gives you the ability to use it as you please! And the list goes on... I think you get my point. For me, having open source tools and resources are essential to my work. I have little to no budget at my disposal for my projects. So when I need other resources to enhance the data set, open resources are my only option. And lucky for me there are tons of data sets, platforms, and tools out there that help me with my work every day. More and more people are learning how to code, but not as many as should. I understand the hesitation behind this. In my generation, scientists used paid programs like Jump, SPSS, Systat, SigmaPlot, MatLab, etc. Because it was the trend... if you had to pay for it it must be the best. Right? Also why

Hello everyone! Welcome to my blog P < 0.05 Significant Notes ! This bog will be an outlet for me to share fun or interesting notes on coding, data management and any thing science related that comes my way. A little about me... My current work is focused on the Antarctic Weddell seals. However there are several projects in my lab that I help with so sometimes I will post larval fish or qPCR. So I hope that you will enjoy my blog and subscribe to follow my weekly posts.