I have been using the xarinagn package developed by Yihui Xie for a while now and have been using that to create presentations within my company. I really like what xaringan offers to the R users. I had modified the YAML of the markdown page a little so that I could have my own css file and my layout which displays company name and logo at the footer. I was copying and pasting the css file and logo file to all the presentation folder I would create.
In this blog post, I will be showing some capabilities of dataRetrieval package by USGS to extract water quality data.
To install the dataRetrieval package use the following command:
devtools::install_github(repo = "USGS-R/dataRetrieval") The main function I will be discussing is readWQPdata. The users can search the data using various options as shown below:
bBox = Bounding box that uses the coordinates of lower left corner and upper right corner lat / long = lat / long will be specified by the user if they are interested to see if any data is available within radial distance.
I have been following Chris Albon on Twitter and have seen some really nice looking machine learning cards on his Twitter. While one can go to his website and buy all the cards he has produced. However, I was curious to see if I could download those flash cards in R. So, I started looking for a R package that would help to download the tweets by Chris Albon. I ended up using rtweet package for my analysis.
I come to these situations where I have to work with spatial datasets frequently. Sometimes the datasets is in lat-long format and sometimes on UTM (Universal transverse Mercator) coordinate or state plane coordinate system.
I extensively use EFDC (Environmental Fluid Dynamic Code) model to do environmental modeling and have to convert the data into UTM coordinate system. I had used CORPSCON in the past but as an R enthusiast I wanted to dig deeper in R and finad a way to do the transformation in R environment.
In this blogpost, I am going to take the earthquake data from April 2015 to May 2016 throughout the World. Then we will process the data in usable form and look at the data on Nepal. Later, we will also dive into the casualties from this devastating earthquake. We will look at how many people died, got injured from which district, using ggplot2, wordcloud and leaflet package.
This is my first blog on data science topics. In this blog post, I will show you how to import the data from fantasy premier league into R and perform exploratory data analysis. ————-
The questions that came up in my mind before doing this analysis are:
How has Wayne Rooney been performing for the last several years ? How do Romelu Lukaku, Sergio Aguero, Harry Kane line up side by side ?
