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Need to use google co - lab for this question, and use the R compiler, not Python. Given data: horse _ data = c (

Need to use google co-lab for this question, and use the R compiler, not Python. Given data: horse_data=c(0,2,2,1,0,0,1,1,0,3,0,2,1,0,0,1,0,1,0,1,
0,0,0,2,0,3,0,2,0,0,0,1,1,1,0,2,0,3,1,0,0,
0,0,2,0,2,0,0,1,1,0,0,2,1,1,0,0,2,0,0,0,0,
0,1,1,1,2,0,2,0,0,0,1,0,1,2,1,0,0,0,0,1,0,
1,1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,
2,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0,0,1,0,2,
0,0,1,2,0,1,1,3,1,1,1,0,3,0,0,1,0,1,0,0,0,
1,0,1,1,0,0,2,0,0,2,1,0,2,0,1,0,0,0,1,0,0,
1,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,2,1,1,
1,0,2,1,1,0,1,2,0,1,0,0,0,0,1,1,0,1,0,2,0,
2,0,0,0,0,2,1,3,0,1,1,0,0,0,0,2,4,0,1,3,0,
1,1,1,1,2,1,3,1,3,1,1,1,2,1,1,3,0,4,0,1,0,
3,2,1,0,2,1,1,0,0,0,1,0,0,0,0,0,1,0,1,1,0,
0,0,2,2,0,0,0,0) a) Plot the frequencies of the counts present in the data.
b) Calculate \lambda 1 and \lambda 2(from problem 2, given in imagehat()1=1ni=1nxi,hat()2=1ni=1n(xi-(x))2) for the data.
c) Plot the p.m.f. of the Poisson distribution with respect to \lambda 1 and \lambda 2 on top of the frequency plot (give the two p.m.f.s different colors in order to tell which one is which) Which estimate fits the data better?
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