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Create an R script that: Reads in the data as an array from a .txt file (make the txt file name be a variable) • Extract the first row as the set of wavelengths (store as a vector) and remove first row from data. Loop through the rows of the array. Find minima of second derivative as described above Store the minimum values and their indices (n) in two different arrays.

Create an R script that: Reads in the data as an array from a .txt file (make the txt file name be a variable) • Extract the first row as the set of wavelengths (store as a vector) and remove first row from data. Loop through the rows of the array. Find minima of second derivative as described above Store the minimum values and their indices (n) in two different arrays.

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PLEASE DO IN RSTUDIO (R PROGRAMMING) Please show each step in the code. 

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    890.776 890.519 890.263 890.006 889.749 889.493 889.236 888.979 888.722 888.466 888.209 887.952 887.696 887.439 887.182 886.925 886.669 886.412 886.155 885.898 885.641 885.385
-4.25909 -6.9024 9845 9608 9782 9708 9661 9609 9832 9753 9564 9579 9727 9744 9638 9430 9607 9760 9840 9695 9749 9866 9786 9826
-4.25909 -6.4544 9507 9340 9337 9325 9441 9300 9470 9143 9421 9345 9372 9407 9251 9085 9292 9402 9560 9443 9349 9306 9339 9430
-4.25909 -6.0064 9576 9201 9252 9238 9217 9298 9217 9224 9255 9055 9199 9364 9218 9204 9503 9374 9482 9337 9290 9318 9395 9361
-4.25909 -5.5584 9604 9301 9467 9279 9457 9438 9395 9310 9310 9237 9281 9333 9447 9187 9644 9589 9541 9267 9402 9518 9354 9633
ƒ(x−8)−2ƒ(x)+ƒ(x+6) Second derivative of f(x) Computationally, the function is represented as an array f(n), n = 1,2,3,... To numerically locate the minima of the second derivative, look at f(n-1) − 2ƒ(n) + f(n + 1) and find the values of n where it's most negative. Do this as an array function (avoid loops) Second derivative is minimized (most negative) when x = μ. Create a vector that represents f(n-1) − 2f(n) + f(n + 1) and loop through values to find local minima. Those local minima will be the means for our Gaussians. We can also use the value of the second derivative to estimate the standard deviations.
Transcribed Image Text:ƒ(x−8)−2ƒ(x)+ƒ(x+6) Second derivative of f(x) Computationally, the function is represented as an array f(n), n = 1,2,3,... To numerically locate the minima of the second derivative, look at f(n-1) − 2ƒ(n) + f(n + 1) and find the values of n where it's most negative. Do this as an array function (avoid loops) Second derivative is minimized (most negative) when x = μ. Create a vector that represents f(n-1) − 2f(n) + f(n + 1) and loop through values to find local minima. Those local minima will be the means for our Gaussians. We can also use the value of the second derivative to estimate the standard deviations.
Create an R script that: • Reads in the data as an array from a .txt file (make the txt file name be a variable) Extract the first row as the set of wavelengths (store as a vector) and remove first row from data. Loop through the rows of the array. Find minima of second derivative as described above Store the minimum values and their indices (n) in two different arrays.
Transcribed Image Text:Create an R script that: • Reads in the data as an array from a .txt file (make the txt file name be a variable) Extract the first row as the set of wavelengths (store as a vector) and remove first row from data. Loop through the rows of the array. Find minima of second derivative as described above Store the minimum values and their indices (n) in two different arrays.
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