Suppose you want to generate a table for a function f(i,j), for 1 ≤ i ≤ N and 1 ≤j ≤ M. This can be accomplished easily using a double for loop. In a functional programming language like R, the emphasis is to use in-built functions that can perform the task, instead of using a for loop. In this article, I will illustrate how the sapply() function can be used to get the job done using few lines of code.
Suppose we want to reproduce a table of Chi-squared values computed for different p-values as shown on this Wikipedia page: https://en.wikipedia.org/wiki/Chi-squared_distribution:
Here is the code in R that does the job:
p_value <-c(0.95,0.90,0.80,0.70,0.50,0.30,0.20,0.10,0.05,0.01,0.001)DegreeFreedom <-seq(1,10)Chisq_values<-sapply(p_value,function(x)sapply(DegreeFreedom, function(y)qchisq(1-x, y))) rownames(Chisq_values)=DegreeFreedom colnames(Chisq_values)=p_value print(round(Chisq_values,3))
Here is the output:
As data scientists, it’s important to always make use of functions and libraries in R and python, that way instead of wasting time writing long codes, the focus should be on getting the specific data science project done in a timely and efficient manner.
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