“R” as software for Data Analysis

Data analysis is an integral part of a researcher’s life. The organisation of the data by the researcher and the expectation of the software used for data analysis differ often and this becomes a challenge for the research. The sessions on research methodology classes unfortunately focus more on the analysing tools and do not lay much emphasis on organising and shaping of data which is special skill and often researchers are found in a difficult stage at this nascent stage of data analysis.

R as a software has a lot of very useful and contemporary functions that can be used for reshaping of data. Within the reshape package functions like cast and melt are very useful. To add to it a lot of helpful and easy to understand tutorials can be found online that aid in the using of this function. These are free of cost and easy to comprehend. These functions are handy and become quite simpler with each use.

The main drawback in the data reshaping function of R is that it is difficult to use by beginners or without any tutorials. The main reason behind this is that they are the reshaping functions for a general purpose and have been created for the solution of a lot of multiple complexities in data handling of research. It can be said clearly that cast and melt functions are the only two functions in R that take care of reshaping data problems. These two functions provide a lot of scope for data manipulation but with a lot of complexity and that is the reason it takes a lot of practice re reading of the documentation before using the functions.

The long and wide option in R is an advantageous data reshaping option for novice researchers with its own pros and cons. From long to wide transformation is possible in the reshaping function with the use of some special commands. Even if these commands are quite simple they have a very restrictive functionality.

In the educational scenario there may be a problem with the teaching of these functions. It is difficult to understand their functioning in the introductory lessons and the complexities may only be comprehended with a lot of continuous practice. To explain the commands in the class would also need the tutor to first in depth explain the data manipulative process.