R is an open source programming language for statistical computing and graphics, it is widely used by statisticians and data miners.Hire R Programmers
Hi, I've been working on a R script that pulls in account info from Coinbase Pro, runs some basic functions (i.e., RSI) and decides to Buy, Sell, or Do nothing and I have this set to run every 15 minutes. However, I'm having issues with the public_orderbook() function from the rgdax R package I'm using, which is key to pulling in the current asking price and initiating a buy action. I have a busy month coming up and hope that I can find someone willing to take on what should be a quick project. Please note, that I understand that these projects are typically done in Python. However, I'm a huge R enthusiast and do almost everything in R (I'm a biobehavioral researcher), so I want this to stay in R. With that said, I am not married to the rgdax library, I got this ...
Looking for a statistical analysis to identify abnormal behaviour in data. With dynamic dimensions. For example A data has following dimensions 1. Date (1-DEC , 2-DEC … 25-DEC) 2. hour (0, 1, 2 …. 23) 3. type (website, app etc.) With following measures No. Of hits We want a solution that can pick each dimension figure and try to identify anomalies For example Pick each day and compare with other days (against each dimension) , hours and types Pick each hour and compare with other hour, days and types Pick each type and compare with other types, days and hour Identify if the traffic trends and look for drops and increase systematically This is just an example .. the number of dimensions/measures should be flexible/dynamic ,
Description of the dataset: I have an experiment with 2 treatments and 10 cultivars with three replicates each (2x10x3). I did an unequal amount of measurements (>8000 observations) in each of them over several days. I would like to make a proper statistical analysis of it in R and compare my results among cultivars and treatments and, if possible, also over the days. Other variables influencing our cultivars and treatments are temperature, humidity, sunlight and location. The idea is to set up a mixed model (I need help to define the model) and run in R (I need the R code).
Attached is a fake data set of 100,000 customer records with some demographic data. In the last column is information about their favourite ice cream. However, only about 10% of customers have told us their favourite ice cream, the rest have “No Answer”. For the customers that have “No Answer” can you please create a quick predictive model which tells us what type of ice cream is likely their favourite based on the information we have about customers where we know the answer.
The project should be a detailed analysis completely build on r. it should be completed by 14th may 2022 .
I can share this directly with the candidate. Ideally, the candidate has to be in the USA. High-level requirements: 1. R analytical programming support to convert and document data import/export, business rules, econometric models, and workflows 2. Knowledge of analytical programming tools to include, but not limited to: SAS, Stata, Excel, Excel VBA, FORTRAN, GAUSS, GAMS, Git/GitHub, Python and R/R Studio (not all of them are required) 3. Demonstrate Competency in Statistical Analysis 4. Ability to document all conversion processes from legacy tools and methodologies. Documentation should be written in clear, concise “plain language” in the event the new staff is onboarded, to quickly facilitate their personal transition to utilizing R instead of niche tools they may be accu...
The project is to build statistical models on the data provided to identify drivers and gain insight. Data: The data used for the project will be publically available data. I will provide the data or links to download the data. Model: I want you to build and refine 3-4 statistical models based on methods that we can discuss once we chat Delivery: The delivery will consist of codes for data preparation, modeling, and results, all the graphs, any assumptions made, the results with output tables, etc. Timeline: I want the project delivered by June 4.
Particle Swarm Optimization (PSO) to optimize the hyper-parameters of SVR, KNN, RF, and Cubist. These 4 machine learning algorithms are used to predict the lung cancer incidence rate in a country. There are several independent variables considered as input of the model.