Using R Language for Statistical Computing for Pesticide Application Calculations Donyo H. Gacnhev, Agricultural University - Plovdiv, Bulgaria, donyo@abv.bg Abstract- 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. I. INTRODUCTION R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team [1]. During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn The advantages of R are: • free and open-source • easy to be learn program language • a lot of specific objects as vectors, dataframes, lists which facilitate the manipulation of the data • different ways of entering the initial data - depending to the personal preferences of the given user or specifications of the data • numerous build-in mathematical and statistical formulas • numerous (over 5000 !) additional packages with function and algorithms created by many people from around the world cover almost every human and scientific areas MAYFEB Journal of Agricultural Science Vol 1 (2016) - Pages 10-26 10