We provide online training for R and Python, click here for more info ! When a data frame is created in R, sometimes the data frame contains dozens of variables and only a subset of them will be used in data analysis. Thus, selecting these variables and saving them into Read more…
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! In this post we show several methods of dropping unwanted variables of a data frame in R. Say we have a data frame ‘grade’ in the current R working Read more…
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! When we have created a data frame in R , for example by reading a csv file from working directory, the first row of the file will be used Read more…
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! String variables in Python are data objects encapsulated in double or single quotes. For example we can create a string variable ‘msg’ and print it out in the following Read more…
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! In R programming, variable type can be numeric or character. Categorical variables are usually called factors. R provides a series of functions working with specific type test and type Read more…
In data analysis it is often needed to set new values to a variable based on one or several conditions, and these kinds of operations are called recode variables. The most frequently applied recoding variables in R may be the setting some values to missing values (NA), and recoding a continuous to the values of a categorical variable.
Categorical variables, including nominal and ordinal variables in R programming language are called factor variables. For example, gender(male/female) is nominal, and survey results (excellent, good, normal, bad) have ordinal values. Categorical variables are useful because many data analysis operations are related to values in different categories, such as contingency tables between two categorical variables for independence analysis, hypothesis testing of homogeneity of variances, just name a few.
read.table() function in R is often used when a delimited ASCII file (e.g. text file or csv file) is to be imported to generate a data frame. The basic syntax is
df <- read.table(input, options)