Before delving deeper into the analysis of bank data using R, let’s have a quick brush-up of R skills.
As you know, R is a well-structured functional suite of software for data estimation, manipulation and graphical representation.
Continue reading “Classifying Bank Customer Data Using R? Use K-means Clustering”
Quite interetsing. Juggling with sensor data is starkly different from economics data, document processing or social networking, but very worthwhile. In this blog, we will take a practical approach to analyze smartphone sensor data with R. We are going to use the accelerometer smartphone data that Datarella presented in its Data Fiction competition. The dataset signifies the stimulation along the three axes of the smartphone:
x – for sideways stimulation
y – for forward and backward stimulation
z – for upward and downward stimulation
The trickier part lies in its interpretation – on one hand where there are device, manufacturer and sensor specific mutations and artifacts, the other reflects all acceleration is calculated relative to the sensor orientation of the device. For example, taking out the cell phone out of your pocket and reading a tweet can be presented in the following way:
y acceleration – the phone was in the pocket top down but now has been taken out
z and y acceleration – tossing the phone so that it becomes horizontal
x acceleration – moving the smartphone from the left to the middle of your body
z acceleration – bringing up the phone so that you can read the tweet clearly
And thirdly, the gravity influences all the movements.
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Knowing exactly what to do with your smartphone can be quite intimidating – let us introduce an application of the Twitter BreakoutDetection Open Source library (see Github), which is used extensively for Behavioral Change Point analysis.
setwd("~/Documents/Datarella")
accel <- read.csv("SensorAccelerometer.csv", stringsAsFactors=F)
head(accel)
user_id x y z updated_at type
1 88 -0.06703765 0.05746084 9.615114 2014-05-09 17:56:21.552521 Probe::Accelerometer
2 88 -0.05746084 0.10534488 9.576807 2014-05-09 17:56:22.139066 Probe::Accelerometer
3 88 -0.04788403 0.03830723 9.605537 2014-05-09 17:56:22.754616 Probe::Accelerometer
4 88 -0.01915361 0.04788403 9.567230 2014-05-09 17:56:23.372244 Probe::Accelerometer
5 88 -0.06703765 0.08619126 9.615114 2014-05-09 17:56:23.977817 Probe::Accelerometer
6 88 -0.04788403 0.07661445 9.595961 2014-05-09 17:56:24.53004 Probe::Accelerometer
accel$day <- substr(accel$updated_at, 1, 10)
df <- accel[accel$day == '2014-05-12' & accel$user_id == 88,]
df$timestamp <- as.POSIXlt(df$updated_at) # Transform to POSIX datetime
library(ggplot2)
ggplot(df) + geom_line(aes(timestamp, x, color="x")) +
geom_line(aes(timestamp, y, color="y")) +
geom_line(aes(timestamp, z, color="z")) +
scale_x_datetime() + xlab("Time") + ylab("acceleration")
ggplot(df[df$timestamp >= '2014-05-12 12:32:00' & df$timestamp < '2014-05-12 13:00:00',]) +
geom_line(aes(timestamp, x, color="x")) +
geom_line(aes(timestamp, y, color="y")) +
geom_line(aes(timestamp, z, color="z")) +
scale_x_datetime() + xlab("Time") + ylab("acceleration")
install.packages("devtools") devtools::install_github("twitter/BreakoutDetection") library(BreakoutDetection) bo <- breakout(df$x[df$timestamp >= '2014-05-12 12:32:00' & df$timestamp < '2014-05-12 12:35:00'], min.size=10, method='multi', beta=.001, degree=1, plot=TRUE) bo$plot
The rapid analysis of the acceleration in the x direction presents us with 4 change points, in which the stimulation suddenly starts to change. At the start, the smartphone normally lies flat on a horizontal surface – the sensor reading revolves around value of 9.8 in a positive direction – which means the gravitational force only triggers this axis and not the x or y axes. Therefore, the phone is lying flat. However, things change and after a couple of movements or changing directions, the last observation reveals the phone has been on a position where the x axis has 9.6 acceleration, meaning the phone is being positioned in a landscape orientation facing the right.
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This post originally appeared on – www.r-bloggers.com/how-to-analyze-smartphone-sensor-data-with-r-and-the-breakoutdetection-package
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Unleashed in the market in 1972, Pong is one of the first computer games ever developed. Loosely inspired by tennis, Pong captured the worldwide gaming market soon after its launch. Instantaneously, it became a trending fad. Gaming enthusiasts became intrigued, they desired to delve deeper into the computer coding and system mechanisms mostly to understand the essence of arcade game development.
Today, R-Programming is extensively used to develop numerous board games. But the question to ponder on is – can we create traditional arcade games with R programming?
Continue reading “Using R Programming to Simulate the Incredible Pong Arcade Game”
In this tutorial, we will learn to make a repeat loop with the use of R programming.
A repeat loop is used to iterate over a block of code over several number of times.
In case of a repeat loop, there is no condition to check in for exiting repeat loop.
Hence, we must ourselves put a condition explicitly within a repeat loop body and make use of the break statement to exit the loop. Failing to do so will result into an infinite loop.
repeat {
statement
}
When in the statement block, we must use the statement ‘break’ to exit the loop.
x <- 1
repeat {
print(x)
x = x+1
if (x == 6){
break
}
}[1] 1 [1] 2 [1] 3 [1] 4 [1] 5
Note that in the example above, we have only made use of a condition to check and exit the loop when x equals the value of 6.
That is why we see in our output that only values from 1 to 5 get printed.
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This post originally appeared on – www.datamentor.io/r-programming/repeat-loop
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Pipe, written as “%>%“ is basically an efficient operator, supplied by magrittr R package. The pipe operator is notably famous due to its wide range of use in dplyr and by the proficient dplyr users. The usage of pipe operator allows one to write “sin(5)” as “5 %>% sin“, which is inspired by F#‘s pipe-forward operator “|>” and is further characterised by: Continue reading “Debugging Magrittr Pipelines in R with Bizarro Pipe and Eager Assignment”
In this post, we will discuss how to use the package nmle in R programming, which includes the dataset MathArchieve. To install the package and load it into your R programming environment, use the code mentioned below:
Continue reading “How To Visualize Multivariate Relationships in Large Datasets in R Programming:”
While many firms are stating that they leverage their data to gain valuable insights and translate them into profit. But the basic question remains whether data is an asset or a liability. This is the mind-numbing question that haunts all IT managers and must be given ample concentration on what is collected in terms of data and how can it be managed efficiently.
There can be two approaches to answer this question, the first being that data could be an asset if used ethically and correctly. But when no actionable insight can be gathered from data, it is a liability in the same lines as an old non-performing loan. Optimum use of data is elemental to the operations of any data driven initiative. The main reason behind this data-drive remains to be to obtain faster and better decision making abilities with more accuracy. Nowadays organizations across the board leverage their data to achieve their goals. Currently sales organizations are the frontrunners who mine their data to get the best results and maximize their revenue from already customers. Also crediting companies use their data to evaluate the risks associated with different individual debtors and then act accordingly when setting rates and fees for their loans that seem to be fair based on this information. In these scenarios the companies use real information to make decisions.
To put it simply Business Intelligence is the action of extracting and to derive information that may be of use from the available data. As might be evident the process is a broad one where the quality and the source of the data structure is variable. Transformations like this might in technical terms be described as ETL or extract, transform and load in addition to the presentation of information that is of use.
Some R Programming Experts hold that R is fully able to take on the role of the engine for processes related to BI. Here we will focus only on the BI function of R i.e. to extract, transform load and present information and data. The following packages correspond to indicated processes in Business Intelligence.
Extract
Extraction
In addition to these, there are several other packages that support data in a variety of formats.
Transform
Load
Prsentation
Presenting data is a wholly different ball game than the previously mentioned process of ETL. Never fear, it may be outsourced with ease to tools of BI dashboard with ease by populating the structure of data according to the expectations of the particular data tool. R is able to create a dashboard of a web app directly from within itself through packages like:
These packages let you play host to interactive web apps. They have the ability to query the data in an interactive manner and generate interactive plots. The basis for all of these is an R session engine and is able to execute all functions of R and may leverage the capabilities of statistics of all packages in R.
Extras
For more details on R Programming, get yourself enrolled in superior R programming courses in Pune. R programming certification in Pune by DexLab Analytics is extremely popular.
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One of the fundamental advantages of the ecosystem related to R and the primary reason that lie behind the phenomenal growth of R is the practice and facility to contribute new packages to R. When this is added to the highly stable CRAN which happens to be the primary repository of packages of R,gives it a great advantage. The effectiveness of CRAN is further enhanced by the ability of people with sufficient technical expertise and to contribute packages through a proper system of submission.
It is only with sufficient effort and time that one realizes the system of packages submitted through proper procedures can yield integrated software of high quality.Even those who are relatively new to R Programming the process of discovering the packages that serves as the bedrock of R language growth. Such packages add value to the language in a reliable way.
Cloud computing is and will continue to be of great interest to all data scientists. The AzureML provides Python and R Programmers a rich environment for machine learning. If you are yet to be initiated to Azure as a user this package will go long ways in helping you get started. It provides functions that let you push R code from your local system to the Azure cloud in addition to publishing models and functions as web services.
Using distributed computing when dealing with large sets of data is invariable an irksome problem. This is truer in cases where sharing data amongst collaborators is difficult or simply not possible. The distcomp package implements a crafty partial likelihood algorithm which lets users build statistical models of complexity and sophistication on data sets that are not aggregated.
If there is any primary ensemble method that performs well on diverse sets of data on a constant basis is the forests algorithm. This particular variety performs principal analysis of components on subsets taken at random in the feature space and holds great promise.
In case there is a matrix that forms a superposition of a component that is lowly ranked along with a sparse component, rcpa calls in a robust PCA method that recovers all of these components. The algorithm was publicized by the data scientists at Netflix.
One of the primary machine learning algorithm happens to be the support vector machine. SwarmSVM has for its basis an approach that may be said to be as a clustering approach and makes provisions for 3 different ensemble methods that train support vector machines. A practical introduction to this particular method is also attached with the vignette that comes with the package.
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