Please use R and post the codes.

Here is the data file: LifeExpectancies_by_Age_Gender.csv

Country	M0	M25	M50	M75	W0	W25	W50	W75
Algeria	63	51	30	13	67	54	34	15
Cameroon	34	29	13	5	38	32	17	6
Madagascar	38	30	17	7	38	3	20	7
Mauritius	59	42	20	6	64	46	25	8
Reunion	56	38	18	7	62	46	25	10
Seychelles	62	44	24	7	69	50	28	14
South_Africa(B)	50	39	20	7	55	43	23	8
South_Africa(W)	65	44	22	7	72	50	27	9
Tunisia	56	46	24	11	63	54	33	19
Canada	69	47	24	8	75	53	29	10
Costa_Rica	65	48	26	9	68	50	27	10
Diminican_Republic	64	50	28	11	66	51	29	11
El_Salvador	56	44	25	10	61	48	27	12
Greenland	60	44	22	6	65	45	25	9
Grenada	61	45	22	8	65	49	27	10
Gautemala	49	40	22	9	51	41	23	8
Honduras	59	42	22	6	61	43	22	7
Jamaica	63	44	23	8	67	48	26	9
Mexico	59	44	24	8	63	46	25	8
Nicaragua	65	48	28	14	68	51	29	13
Panama	65	48	26	9	67	49	27	10
Trinidad(62)	64	63	21	7	68	47	25	9
Trinidad(67)	64	43	21	6	68	47	24	8
United_States(66)	67	45	23	8	74	51	28	10
United_States(NW66)	61	40	21	10	67	46	25	11
United_States(W66)	68	46	23	8	75	52	29	10
United_States(67)	67	45	23	8	74	51	28	10
Argentina	65	46	24	9	71	51	28	10
Chile	59	43	23	10	66	49	27	12
Comumbia	58	44	24	9	62	47	25	10
Ecuador	57	46	28	9	60	49	28	11

The data file LifeExpectancies_by_Age_Gender.csv contains the life expectancy in years by country, age, and gender. These life expectancies were obtained from 1960s data. Variables: MO - Male life expectancies in years at age 0 M25 - Male life expectancies in years at age 25 M50 - Male life expectancies in years at age 50 M75-Male life expectancies in years at age 75 Wo- Female life expectancies in years at age W25 - Female life expectancies in years at age 25 W50 - Female life expectancies in years at age 50 W75 - Female life expectancies in years at age 75 m Conduct the factor analysis using m=3 factor model. Use both principal component and maximum likelihood methods. a) Create tables that include factor loadings, communalities, specific variances, and cumulative proportion of sample variances for unrotated and rotated cases for both methods. b) Interpret the factors using principal component and maximum likelihood methods. Do you have similar interpretations from both methods? Explain. c) Plot the factor scores for factors 1 and 2 for both methods (rotated) and discuss your findings. d) Plot the pairs of factor scores obtained using the principal component and maximum likelihood methods (rotated). Do the loadings agree for corresponding factors for both methods? Discuss your findings. Does your finding suggest that number of factors is too large? The data file LifeExpectancies_by_Age_Gender.csv contains the life expectancy in years by country, age, and gender. These life expectancies were obtained from 1960s data. Variables: MO - Male life expectancies in years at age 0 M25 - Male life expectancies in years at age 25 M50 - Male life expectancies in years at age 50 M75-Male life expectancies in years at age 75 Wo- Female life expectancies in years at age W25 - Female life expectancies in years at age 25 W50 - Female life expectancies in years at age 50 W75 - Female life expectancies in years at age 75 m Conduct the factor analysis using m=3 factor model. Use both principal component and maximum likelihood methods. a) Create tables that include factor loadings, communalities, specific variances, and cumulative proportion of sample variances for unrotated and rotated cases for both methods. b) Interpret the factors using principal component and maximum likelihood methods. Do you have similar interpretations from both methods? Explain. c) Plot the factor scores for factors 1 and 2 for both methods (rotated) and discuss your findings. d) Plot the pairs of factor scores obtained using the principal component and maximum likelihood methods (rotated). Do the loadings agree for corresponding factors for both methods? Discuss your findings. Does your finding suggest that number of factors is too large