R programming language

One of the challenging research problems in the domain of time series analysis and forecasting is making efficient and robust prediction of stock market prices. With rapid development and evolution of sophisticated algorithms and with the availability of extremely fast computing platforms, it has now become possible to effectively extract, store, process and analyze high volume stock market time series data. Complex algorithms for forecasting are now available for speedy execution over parallel architecture leading to fairly accurate results. In this paper, we have used time series data of the two sectors of the Indian economy - Consumer Durables sector and the Small Cap sector for the period January 2010 - December 2015 and proposed a decomposition approach for better understanding of the behavior of each of the time series. Our contention is that various sectors reveal different time series patterns and understanding them is essential for portfolio formation. Further, based on thi...

Alcohol consumption in higher education institutes is not a new problem; the legal drinking age in the India is minimum 18 year, but heavy drinking by underage students and by those who are age 18 or older is dangerous, and disruptive. Excessive drinking among students is associated with a variety of negative consequences that include fatal and nonfatal injuries; alcohol poisoning; blackouts; academic failure; violence, including rape and assault; unintended pregnancy; sexually transmitted diseases, including HIV/AIDS; property damage; and vocational and criminal consequences that could jeopardize future job prospects. Because students vary widely in their drinking rates, it would be inaccurate to characterize all institutions as having an equally urgent drinking problem. But among students who do drink heavily, the problem is serious: the two out of five students who engage in binge drinking risk a wide range of alcohol-related consequences, including grave injuries and death. This...

Tarihçilerin interaktif haritalar yaptığı, dilbilimcilerin metinlerde kullanılan kelimelerin örüntülerini belirlemek için bilgisayar teknolojilerini kullandığı günümüzde, hemen hemen her disiplinden araştırmacıların Internet’i ve Web’i interaktif bir şekilde kullandığı gözlenmektedir. Yakınsama ile birlikte disiplinler arası sınırların belirsizleşmeye başladığı söylenebilir. Dijital İnsanî Bilimler (DİB) de bu gelişmeler neticesinde geleneksel insanî bilimlerde bilgisayar teknolojilerinin kullanılmaya başlanmasıyla ortaya çıkmıştır. Araştırmada DİB’in genel özelliklerini belirlemek amaçlanmıştır. Bu sayede yeni kaynak türlerinin belirlenmesi, yeni yöntem ve araştırma davranışlarının kütüphaneler için oluşturduğu zorlukların tespit edilmesi amaçlanmıştır. Araştırmada DİB alanında yayımlanan yayınlar üzerinden bilimetrik analizler yürütülmüştür. Erişilen yayınlar Keyword Plus (ID), doküman özleri ve doküman başlıklarında kullanılan kavramlar üzerinden incelenmiştir. Kavramların incelenmesinde (anahtar kelime, başlık ya da özler) çoklu uyum analizi (Multiple Correspondence Analysis) kullanılmıştır. DİB’de ortaya çıkan yeni kaynak türleri, kütüphanelerde hangi hizmetleri gerektirmektedir? DİB’de kullanılan yöntemler kütüphane ve bilgi hizmeti sağlayan kuruluşlar için yeni altyapıları gerektirmekte midir? gibi sorulara cevap aranmış, DİB’in kütüphane ve bilgi hizmetleri açısından ortaya çıkardığı meydan okumalara dikkat çekilmiştir. Araştırmada kütüphaneler başta olmak üzere bilgi hizmeti sağlayan kuruluşların, DİB gibi tezahür eden alanlar ile bu alanlara yönelik olarak ortaya çıkan kaynak türleri, ihtiyaçları ve araştırma davranışlarına yönelik tespitlerde bulunulmuştur.

Today while historians producing interactive maps and linguists use computer technology in order to determine the word pattern used in texts, it is observed that researchers from almost every discipline use internet and web interactively. It can be claimed that boundaries among the disciplines have started to become uncertain with convergence. As a result of these developments, digital humanities emerged by the beginning of using computer technologies in traditional humanities. The aim of this study was to determine the general characteristics of Digital Humanities. In this way, it is aimed to identify new types of resources and to identify the challenges of new methods and research behaviors for libraries. In this study, the scientometrics was conducted by considering scientific papers published in the field of digital humanities. Accessed publications were evaluated according to the concepts/terms used in keyword plus, document abstracts and titles. Multiple Correspondence Analysis was used in the analysis of the concepts (keywords, title or abstracts). Scope/field-oriented findings as a result of the analysis were discussed. This study tried to answer the questions such as; which kind of services do new resource types appeared in digital humanities require in libraries? Do methods used in digital humanities require new infrastructures for libraries and organizations providing information services? And points out the new challenges posed by digital humanities for libraries and information services. This study presented findings related to emerging new resource types, needs and research behaviors appeared because of developing digital humanities fields for organizations, especially libraries, providing information services.

R language and environment for statistical computing is used in the agricultural science and practice not only for conducting of the statistical analyses of the data but for performing specific calculations rapidly and securely. Using and applying the pesticides in the agriculture is associated with making a lot of calculations and statistical analyses, which guarantee the right and correct application of these chemical substances. There is many disadvangatages if such kind calulations are performed by hand (in correct computing, time consuming, formulas and algorithms memorizing, using complex and in most cases-expensive statistical software, etc.) and in order to be eliminated this disadvantages, so called computer method is used as alternative which include creating and using specific calculators as freeware or shareware software. Sometimes simple calculators are created as Excel worksheets / macros which approach have a lot of disadvantages. With increasing popularity of the R language more and more scientists and specialists from agricultural area spotted it as viable alternative and way of performing the necessity calculations for the application of the pesticides and statistical manipulation of the data received from scientific trials.

Abstract This study uses a novel approach to describe modern English academic texts in terms of the amount and distribution of syntactic subordination, coordination and phrasal structures. Inconsistent results have been reported in previous scholarship regarding the trajectory of syntactic complexification based on English language backgrounds and linguistic proficiency of writers. For the first time, a combination of two predictive statistical modelling methods of mixed-effects modelling and supervised machine learning modelling with random forest is used to examine the extent to which the type, amount, and distribution of these syntactic structures can be attributed to a text-intrinsic feature (sub-genres or rhetorical sections of academic register) and a text-extrinsic factor (English language background based on academic context) in a corpus of master’s dissertations. To revisit the theories of English L1 vs. L2 texts, strong predictors of syntactic complexity across academic writings of EFL, ESL, and English L1 postgraduate students were identified. The findings show that EFL texts are dominantly subordinate in nature, English L1’s dominantly phrasal, and the ESL dissertations exhibited similar amounts of subordination and phrasal structures. Distinct syntactic structures also characterise rhetorical sections: abstracts are predominantly phrasal, literature reviews distinctly subordinate, and conclusion sections have noticeable amounts of verb phrases.

The focus of this study is to determine the existence and extent of statistical bias towards any of the Doki Doki Literature Club characters with respect to the points distribution of the Poem Minigame. The results of this study show that the bias is towards Sayori (2.180 +/- 0.819 vs. overall mean of 1.962 +/- 0.839), who is the first character in the game to experience a traumatic outcome, suggesting that the statistical component of the Poem Minigame--whether intentional or not--attempts to gear the player towards her to accentuate further the emotional impact of her death in Act 1.

The main purpose of this article is to present the use of R programming language in cartographic visualization demonstrating using machine learning methods in geographic education. Current trends in education technologies are largely influenced by the possibilities of distance-learning, e-learning and self- learning. In view of this, the main tendencies in modern geographic education include active use of open source GIS and publicly available free geospatial datasets that can be used by students for cartographic exercises, data visualization and mapping, both at intermediate and advanced levels. This paper contributes to the development of these methods and is fully based on the datasets and tools available for every student: the R programming language and the free open source datasets. The case study demonstrated in this paper show the examples of both physical geographic mapping (geomorphology) and socio-economic geography (regional mapping) which can be used in the classes and in self-learning. The objective of this research includes geomorphological modelling of the terrain relief in Italy and regional mapping. The data include DEM SRTM90 and datasets on regional borders of Italy embedded in R packages ‘maps’ and ‘mapdata’. Modelling references to the characteristics of slope, aspect, hillshade and elevation, their visualization using R packages: ‘raster’ and ‘tmap’. Regional mapping of Italy was made using main package ‘ggmap’ with the ‘ggplot2’ as a wrapper. The results present five thematic maps (slope, aspect, hillshade, elevation and regions of Italy) created in R language. Traditionally used in statistical analysis, R is less known as a perfect tool in geographic education. This paper contributes to the development of methods in geographic education by presenting new technologies of the machine learning methods of mapping.

Logistic regression analysis of factors contributing to bank deposit subscription and direct marketing success.

View project at:

https://github.com/JackKRasmus-Vorrath/Logistic_Regression_Direct-Marketing_Bank-Term-Deposit-Subscription

This report sets out the main findings and recommendations for using a Bayesian Belief Network (BBN) modelling approach on both qualitative and quantitative data (provided by an anonymous client) with the specification of assessing and managing bacteriological growth risk across a water system.

It is a well-known fact that advertisement plays a major role in increasing the sales of any product introduced in the market. Particularly advertisement that uses the image of a celebrity has huge impact. But as the trends change, the current trend is to use the celebrities from the field of sports. Using sports celebrities in the advertisements in order to reach wider audience has become a trend in the Indian context and moreover huge amount of money is being spent for this purpose. That is the reason why the present research has focused on this particular area. The major focus of the present research is to identify the impact of sports celebrity advertisements on the purchase intention of the consumers. It has included both high involvement product as well as low involvement product. Mixtures of 450 U.G. students and faculties from 10 colleges which are affiliated to Manonmaniam Sundaranar University, Tirunelveli have been selected for analysis. The respondents were chosen using convenience sampling method. the present research has suggested that in the case of Low Involvement Product sports celebrity advertisements can be used to increase the purchase intention of the consumers. But in the case of High Involvement Product, sports celebrity advertisements can be used only while introducing new products and for popularising it.

The dataset has a good mix of categorical independent variables, and a continuous dependent variable (price). This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold from May 2014 to May 2015. It is a useful dataset for evaluating simple regression models. In this dataset, I predict the sales price of houses in King County, Seattle. It includes homes sold between May 2014 and May 2015.  The most significant obstacle I encountered was while cleaning our original dataset. Initially, the data I planned to use was from data.boston.gov. Based on professor recommendation, I replaced the data set. The old dataset needs a large amount of work, not enough variables, and data is old. I chose newly cleaned data-set to work on instead of Boston city data-set. The new data-set for “King County” in Washington state.

Como analisar a participação política em ambientes deliberativos, usando software livre? Este trabalho demonstra, na prática, opções de técnicas de análise de dados sobre a participação política em instituições públicas deliberativas, utilizando a análise de conteúdo como base metodológica, por meio do programa R Project for Statistical Computing. Foram desenvolvidos procedimentos para analisar: a (1) presença no conselho por instituição; (2) presença por grupo setorial; (3) saliência de temas nos debates; (4) situações de fala por grupo setorial e (5) relação entre grupos setoriais e atuação política. Como exemplo foi replicado um trabalho de análise da participação política no Conselho Gestor da Área de Proteção Ambiental Costa dos Corais.

I would like to creat an open-source R package that makes working with Eurostat and OECD symmetric input-output tables very easy. The aim is to make various comparisons, for example in backward linkages or employment multipliers accross regions easy to compute. My 0.2 version works with 7 types of Eurostat input-output tables from 2010. I will soon add Hungarian, Croatian and OECD standard types. At the moment the bulk data can be organized into tidy SIOTs and some multipliers (value added, tax, employment, if data permits) and coefficients can be calculated.

I would like to release the code soon on CRAN and publish the documentation in the Journal of Statistical Software. I am looking for cooperation to use the software or test ie, i.e. to make comparable input-output economics research.  I am mainly interested in creative and cultural industries, but the software works with any industry.  It would be particularly interesting to re-run already published cases and complement them with international comparison.

Please get in touch on [www.linkedin.com/in/antaldaniel/](https://www.linkedin.com/in/antaldaniel/) or in email.

Untuk melihat bentuk distribusi sekumpulan data numeric dalam bentuk grafik pada umumnya langkah pertama yang harus dilakukan adalah membuat tabel disttribusi frekuensi data tersebut. Dalam R untuk membuat tabel disribusi frekuensi dapat dilakukan dengan fungsi table(). Penggunaan fungsi ini mungkin saja tidak cukup baik dan tidak memberikan cukup informasi tentang bentuk distribusi sekumpulan data.

The goal of this Project is to build a linear model for predicting the average amount of tip in dollars a waiter is expected to earn from the restaurant given the predictor variables i.e. total bill paid, day, the gender of the customer (sex) time of the party, smoker, and size of the party. And this was achieved through the use of the Linear Regression method.

The dataset of 200 observations and 7 variables was divided into training and testing sets in a ratio of 8:2 respectively. The model was fitted using the lm() function of R on the train set and tested on the testing set using predict() function. And the model fitness was deeply analyzed to understand how well it fits the data.

Using Lasso regularization approach, the model was improved and this helped to identify the most important predictors in estimating the amount of tip received by the waiter. And also an interaction of size and smoker was included in the final model which greatly improved its data fitness.

Apresentação da Linguagem do "R" na disciplina de "Métodos Quantitativos I" no curso de Ciência Política da Universidade Federal de Pernambuco (UFPE), ministrada pelo prof. Dr. Dalson Figueiredo Filho (DCP/UFPE). Atividade que integra o programa de monitoria da disciplina.

Municipal Solid Waste is one of the demanding environmental challenges faced by many municipalities in Nepal. Current practices and systems in Nepal being unable to deal with the increasing volumes of waste generated by an increasing urban population and its impact on the environment and public health. This paper reviewed the challenges, practices and discusses key policies, legislative and institutional provisions made by the government regarding solid waste management in Nepal. Both published and unpublished documents, reports; articles and books were collected, reviewed, and analyzed. The result shows that an appropriate legal provision including policy and strategic framework needs to be developed for three tiers of government such as federal, provincial, and local. The initiatives of local governments and the public could play a significant role in managing solid wastes. Thus, strengthening the capacity of local governments is indispensable, as primarily they are mandated to provide solid waste management. The public-private partnership offers opportunities for operational efficiency and cost-effectiveness. In summary, for the environment-friendly management of solid wastes, academia could contribute a supportive role in developing and reforming policy, strategy, and guidelines and, strengthening the capacity of concerns at all levels.

This document provides a solution for an R Programming problem about Air Pollution in the United States. The problem is about writing three functions that are meant to interact with a dataset that can be downloaded by following a link provided in the document.

This study provides a model to rate and visualize the bicycle redistribution of Citi Bike, the bikeshare system that operates in New York City.  The share of rebalanced bicycles in proportion to total rides sharply decreased in the spring of 2015, which prompted the question as to what impact, if any, this change in operations had on the availability of bikes and the system’s ability to relay bikes to empty stations.  In terms of public transit, a bikeshare system is only as effective as its ability to respond to commuter supply and demand.  In order to circumvent the absence of data about redistribution routes and times utilized by Citi Bike’s operations team, publicly available trip data was reverse-engineered in order to recreate the rebalancing events over the three years of the bike share’s operation (2013-2015).  Pairwise correlation revealed the stations between which bikes are transferred the most. Data on availability per station, derived from an accumulated JSON feed was integrated in order to derive an hourly score per station. The durations of consecutively empty and full stations were analyzed. Finally, a k-means clustering analysis of availability events was performed in order to visualize the spatial patterns of bicycle supply and demand.  A negative correlation was found between the amount of rebalanced bicycles and the performance of stations based on indicators such as emptiness, fullness, and deliveries per empty instants.

El propósito de este libro es la implementación de métodos numéricos básicos usando el lenguaje de programación (libre) R.  El curso esta orientado a estudiantes con poco conocimiento de programación. Aunque R es un lenguaje y un entorno de programación para análisis estadístico y gráfico, R también puede usarse como herramienta de cálculo numérico, campo en el que puede ser tan eficaz como otras herramientas específicas tales como GNU O CTAVE y su equivalente comercial, MATLAB. En este libro se expone la teoría, a veces con justificaciones teóricas, se presentan varios ejemplos y al final del cada
capítulo, se dedica tiempo a los algoritmos y las implementaciones.

s For the economic development of Bangladesh, export contributes a lot. To keep the pace of development we need to analyze data in higher frequencies The R package " tempdisagg " has been used to disaggregate yearly export into quarterly exports by Denton's additive method, Denton-Cholette additive method, Denton's proportional method and Denton-Cholette proportional method without indicator series; and with quantum index as indicator series. We have tested these methods graphically and found that Denton proportional method and Denton-Cholette proportional with indicator series disaggregated quarterly exports are much closer to the actual quarterly exports than all the Denton's methods with indicator series ae well as without indicator series. This a scholastic work, but the results may be a guideline to temporally disaggregate the annual time series data into quarterly series by using R package " tempdisagg " for other methods of temporal disaggregation namely Chow-Lin. Fernandez and Litterman. The statistical procedures to restore consistencies between variables and within variables are very similar, but the two problems are often treated separately in literature. The first set is problem dependent, in as much as the set of linear restrictions is directly identified from the system of accounts to be reconciled, is generally known as the balancing problem. On the other hand, the restrictions on the time dimension are generally limited to few cases, being related to the nature of the data, where high-frequency variables are usually adjusted such that linear combinations of their values equal known low-frequency series, is called temporal disaggregation (or benchmarking) of time series. Our main focus will be on temporal disaggregation, which is the process of estimating high frequency (disaggregated, e.g., quarterly, monthly) series from available low frequency (aggregated e.g., yearly, quarterly) series and, in case, related high frequency known indicator series. Temporal disaggregation as well as benchmarking deal with the problem to remove the discrepancies between low frequency series (annual benchmarks) and corresponding sums of the high frequency series, but benchmarking problem arises when time series deal with data for the same target variable are measured at different frequencies

The aim of the paper is to compare between two analytics environments in the context of developing a model for dengue fever outbreak prediction using climate, vegetation and dengue data of San Juan, Puerto Rico. For this purpose , R and Python as analytics tool and Multiple Linear Regression (MLR) as analytics technique have been chosen to develop the model. The capabilities, limitations and equation process of MLR is elaborated. Then a software selection framework has been developed and justified to compare between two analytics environments based on it. MLR model is developed using both of R and Python and prediction has been made with a considerable accuracy. Then a critical comparison between these two analytics environments has been made based on the justified analytics environment selection framework in the context of developing the prediction model. Finally, the report suggested the suitable tool in this context and recommended some future improvements.

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In recent years, text mining is become possible through “Data Science” which has started in the field of information technology and also which is candidate for becoming a new science. In this study, a text mining application prepared by R programming language which is very popular in statictics field and provide various text analyses. Also Twitter, which is one of the popular social media tools, was used for having a sample like 'tweets'. These tweets are about Vladimir Putin and Donald Trump and it has written by the people on the Twitter website. Total number of the tweets is 2,000 and these were analyzed by R program for this text mining project.

*Language of the document is Turkish, but you can follow the steps after page 8 clearly. Software lang is international :)*

Direct real estate investment is required large fund before able to invest. Most of the investor in Malaysia has been face the challenge to invest direct real estate nowadays. It is not easy to get expected return like previously that can be more than 6% per annual. Most of the expected return per annual in Malaysia market are below 4%, this create difficulty for them to pay their monthly mortgage loan payment that could 100% cover. An alternative investment related to real estate that have better option which could get expected return more than 5%. That is indirect real estate. For instance, real estate investment trust (REIT), and other similar type of investment in equity structure. When come to decision making for real estate investment in indirect form, there are too many information across. This will create ambiguous for investor to make better decision as they don't know which is the reliable source. Most of the investors in Malaysia were blindly invest with the information that might not valid which they collected in the market. This paper is to create a better way for investor to know how to make better decision with the secondary data and use data science to test the time series for forecasting by (ARIMA) model and predict stock price performance based on dependent variables. Eventually, this could make better decision to reduce risk and improve or optimize investment performance. Data science can cater for big data and easier to reproducible, with the data science technique it will help to continue research from the past research.