What is the best way to utilize R to calculate the population mean vs the sample mean?

 

*Statistical Analysis Using the R Statistical 

First the population mean is a measure of the center or "average" value in the whole population of a variable measured at the interval or ratio level.

The sample mean is a sample estimate of the population mean. It is the same measure of center, obtained from a sample. The variable in your sample must be measured at the interval or ratio level.

R is a statistical programming language overtaking SPSS and other "traditional" point-and-click software. An important reason is that data analysis rarely consists of simply running a statistical test. Instead, many small steps, such as cleaning and visualizing data, are usually repeated many times, and computers are much faster at doing repetitive tasks.

Great, R is a programming language that is designed and used mainly in the statistics, data science, and scientific communities. R has "become the de-facto standard for writing statistical software among statisticians and has made substantial inroads in the social and behavioural sciences". This means that if we use R, we'll be in good company (and that company will likely be even better and numerous in the future). 

Using R to calculate mean values from population and sample. The mean of an observation variable is a numerical measure of the central location of the data values. It is the sum of its data values divided by data count.

Programming R statistical analyses leads to a flexible, reproducible and time-saving workflow, in comparison to more traditional point-and-click focused applications. R is probably the best programming language around for applied statistics, because it has a large user base and many user-contributed packages that make your life easier. While it may take an hour or so to get acquainted with R, after initial difficulty it is easy to use, and provides a fast and reliable platform for data wrangling, visualization, modeling, and statistical testing.

Step-by-step explanation

Learning to use a programming language for data analysis reduces human labor and saves time that could be better spent doing more important (or fun) things.

To be honest, SPSS is expensive to use: Universities have to pay real money to make it available to students and researchers. R and its supporting applications, on the other hand, are completely free, meaning that both users and developers have easier access to it. R is an open source software package, which means that many cutting edge statistical methods are more quickly implemented in R than SPSS. This is apparent, for example, in the recent uprising of Bayesian methods for data analysis.

As the others have said, R is not difficult to learn because it is a programming language. It is actually very easy to understand and formulate. The difficult thing is the background required for R. You see, R was designed to be used as a statistical tool.

R is a free, open source software program for statistical analysis, based on the S language.

Today, millions of analysts, researchers, and brands such as Facebook, Google, Bing, Accenture, Wipro are using R to solve complex issues. R applications are not limited to just one sector, we can see R programming in banking, e-commerce, finance and many more.

There are still plenty of indications that R is widely used in data science and for statistical analysis, with one recent survey, albeit with a relatively low number of respondents, finding almost half of data scientists still use R on a regular basis.