Compare R with Other Languages on Data Manipulation

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Here I compare R with other languages such as C/C++, Java, MATLAB, Python and so on data manipulation. I list a few advantages and disadvantages of R on data manipulation compared with other programming languages.

Generally speaking, it is very convenient to manipulate data in R. Using function read.table (and other derived functions), you can read numerical and character data into a data frame from a text file at the same time. Though other programming languages can do this too, most of them are not as simple and convenient as R. For example, you can read in a table in MATLAB using function dlmread, but the data has to be of the same type. Function xlsread in MATLAB can read numerical and character values at the same type, but as its name indicates, it relies on the Microsoft Excel software. What is more, xlsread read data into a cell which does not have row names and column names attributes. The first row of the data (usually stands for the headers) is read into the cell as the first row of data. This is inconvenient. A matrix and data frame in R can have both column names and row names. They make extracting and recording data very convenient in R. MATLAB is close to R in the sense that its matrix data structure is comparable to the matrix data structure in R, and its structure array data structure is comparable to the data frame data structure in R. However, you can only extract rows from a structure array but not columns. The cell data structure in MATLAB is another data structure that is comparable to the data frame data structre in R, however, as I mentioned before it doesn't have row names and column names attributes. All these things make it a hessle to work on data with different types at the same time in MATLAB.

Python has some libraries (e.g., pandas and pydataframe) that offer similar object as data frame in R and similar functions as read.table and write.table (and their derived functions) in R. One advatange of Python over R is that it has better support of object oriented programming than R. Working on vector alike object in python is more convenient than in R many times. For example, you can easily insert an element into a list in Python with the insert method while in R you have to extract parts of a vector/list and then recombine them with the elements you want to insert.

Support of string in C awful. Java has a better support of string, however, I don't know whether there are libraries offering convenient ways to read and write data like read.table and write.table in R. Honestly speaking, I doubt. What's more, compiled langugaes are not as convenient as script languages to explore data.

Given these things discussed above, Python and R are good choices if you have to deal with both numerical and character data at the same time.