- 19th Jan 2022
- 23:50 pm

summary_statistics_B <- function(list_of_vectors, na.rm){ n = length(list_of_vectors) len = c() median_vec = c() mean_vec = c() correlation_mat = matrix(1, nrow = length(list_of_vectors), ncol = length(list_of_vectors)) for (i in 1:n) { len[i] = length(list_of_vectors[[i]]) if(na.rm == T){ list_of_vectors[[i]] = list_of_vectors[[i]][!is.na(list_of_vectors[[i]])] } mean_vec[i] = sum(list_of_vectors[[i]])/len[i] list_of_vectors[[i]] = sort(list_of_vectors[[i]]) median_vec[i] = ifelse(len[i]%%2 == 0, (list_of_vectors[[i]][len/2+1]+list_of_vectors[[i]][len/2])/2, list_of_vectors[[i]][(len+1)/2]) } corr <- function(vec1, vec2){ ((sum(vec1*vec2) - sum(vec1)*sum(vec2)/length(vec1))/(length(vec1)-1))/(sqrt(((sum(vec1^2) - sum(vec1)^2/length(vec1))/(length(vec1)-1))*((sum(vec2^2) - sum(vec2)^2/length(vec2))/(length(vec2)-1)))) } for (i in 1:length(list_of_vectors)) { for (j in 1:length(list_of_vectors)) { correlation_mat[i, j] = corr(list_of_vectors[[i]], list_of_vectors[[j]]) } } return(list(length_of_vectors = len, median_of_vectors = median_vec, mean_of_vectors = mean_vec, correlation_matrix = correlation_mat)) } # HEre in part B we need to give the list of vectors as input # Suppose if we have a matrix or a dataframe then we will call the function as summary_statistics_B(as.list(mtcars), na.rm = T)

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