This is part of series of article in which I demonstrate that R is quite easy to learn as it has tips, tricks, shortcuts and graphical user interfaces that can easily cut down your time to learn it, and adding great value to your resume and your analytical capabilities.
Let’s assume you are given a dataset -and you just want to a simple analysis on it.
Well here is the R code for it.
Let the dataset name by Ajay (note R is syntax sensitive unlike the SAS language!!). So ajay is not the same as Ajay.
Ajay <- read.table(“C:/Users/KUSHU/Desktop/A.csv”, header=TRUE, sep=”,”,
+ na.strings=”NA”, dec=”.”, strip.white=TRUE)
Note here the path if input data is C:/Users/KUSHU/Desktop/A.csv
We are assuming header=true , that means variable names are in first row
Sep=”,” refers to separator between two consecutive data elements (which is a comma here since we are reading data from a comma separated value)
dec=”.” means we use “.” for seperating decimal points
strip.white=TRUE (how you treat blank spaces)
This looks so intimidating to a new R user to learn. Instead you can just use the Graphical user interface R commander, like this
and then you can simply click your way into the menu. The code is automatically generated thus helping you learn
DESCRIBE DATA STRUCTURE
Now that we have inputted the data we need to see data quality
We get just the variable names using, simply the command names
and we get the data structure using simply the command str
The first five observations in a dataset can be given from
The last five observations in a dataset can be given from
DESCRIBE VARIABLE STRUCTURE
We can get summary statistics on the entire data using
But if we want only to refer to one variable say Names and save time,
we refer it to
Similarly we can plot the dataset using a simple command plot
I personally like the describe command, but for that I need to LOAD a new package
library(Hmisc) loads the package.
Suppose I want to add comments to my coding so it looks clean and legible, I just use the # sign and anything after # looks commented out
Finally, I want to save all my results. Welcome, I can export them using menus in the GUI, or using the menu within R, or modify the read. table statement to simply write. table and it saves the dataset.
R is easier than you think and doing simple analysis in R is much faster due to the sheer efficiency of the syntax
Enjoy your R coding.