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# Performing Analysis of a Factor in R Programming – factanal() Function

Factor Analysis also known as Exploratory Factor Analysis is a statistical technique used in R programming to identify the inactive relational structure and further, narrowing down a pool of variables to few variables. The main motive to use this technique is to find out which factor is most responsible for influence in the categorization of weights.

Syntax: factanal(x, factors)

Parameters:
x: represents dataset
factors: specifies number of factors to be fitted

Example:
Let us suppose, there are number of food available in the dataset with their food texture data points such as Oil, Density, Crispy, Fracture, and Hardness.

 # Reading csv file of food texturesfood_textures <- read.csv("https://userpage.fu-berlin.de/soga/300/30100_data_sets/food-texture.csv")  food_textures <- food_textures[, 2:6]  factor_analysis <- factanal(food_textures, factors = 2)  print(factor_analysis)  # Output to be present as PNG file png(file = "factorAnalysisGFG.png")  # Plot factor 1 by factor 2load <- factor_analysis\$loadings[, 1:2]  # Plot graphplot(load, type = "n")text(load, labels = names(food_textures), cex = .9)  # Saving the filedev.off()

Output:

Call:
factanal(x = food_textures, factors = 2)

Uniquenesses:
Oil  Density   Crispy Fracture Hardness
0.334    0.156    0.042    0.256    0.407

Factor1 Factor2
Oil      -0.816
Density   0.919
Crispy   -0.745   0.635
Fracture  0.645  -0.573
Hardness          0.764

Factor1 Factor2