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@H_696_2@代码如下:
library(tIDyversE)
library(tIDyquant)
library(broom)
library(umap)
library(readr)
library(readxl)
library(purrr)
sp_500_prices_tbl <- read_rds("raw_data/sp_500_prices_tbl.rds")
sp_500_prices_tbl
sp_500_index_tbl <- read_rds("raw_data/sp_500_index_tbl.rds")
sp_500_index_tbl
sp_500_daily_returns_tbl <- sp_500_prices_tbl %>%
SELEct(symbol,date,adjusted) %>%
filter(date >= as.Date("2018-01-01")) %>%
mutate(lag_of_one_day = lag(adjusted,1)) %>%
filter(!is.na(lag_of_one_day)) %>%
mutate(diffrence = adjusted - lag_of_one_day) %>%
mutate(pct_return = diffrence/lag_of_one_day) %>%
SELEct(symbol,pct_return)
sp_500_daily_returns_tbl
stock_date_matrix_tbl <- sp_500_daily_returns_tbl %>%
SELEct(symbol,pct_return) %>%
pivot_wIDer(names_from = date,values_from = pct_return,values_fill = 0) %>%
ungroup()
stock_date_matrix_tbl_drop <- stock_date_matrix_tbl
stock_date_matrix_tbl_drop$symbol <- NulL
stock_date_matrix_tbl_drop
km_cluster <- kmeans(na.omit(stock_date_matrix_tbl_drop),centers = 4,nstart = 20)
km_cluster$cluster
broom::tIDy(km_cluster) %>% glimpse()
broom::glance(km_cluster)
kmeans_mapper <- function(center = 3) {
stock_date_matrix_tbl_drop %>%
#SELEct(-symbol) %>%
#kmeans(centers = center,nstart = 20)
kmeans(na.omit(stock_date_matrix_tbl_drop),centers = center,nstart = 20)
} %>% kmeans_mapper() %>% glance()
kmeans_mapped_tbl <- tibble(centers = 1:30) %>%
mutate(k_means = centers %>% map(kmeans_mapper)) %>%
mutate(glance = k_means %>% map(glancE))
@H_696_2@输出应该是这样的:
@H_696_2@以上是大佬教程为你收集整理的总结过程中出错:评估嵌套太深:无限递归/选项(表达式=)?全部内容,希望文章能够帮你解决总结过程中出错:评估嵌套太深:无限递归/选项(表达式=)?所遇到的程序开发问题。
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