如果需要在多个环境中存储 data.frames，请使用以下内容：
# Install pacakges if they are not already installed: necessary_packages => vector
necessary_packages <- c("quantmod")
# Create a vector containing the names of any packages needing installation:
# new_pacakges => vector
new_packages <- necessary_packages[!(necessary_packages %in%
installed.packages()[, "Package"])]
# If the vector has more than 0 values, install the new pacakges
# (and their) associated dependencies:
if(length(new_packages) > 0){
install.packages(
new_packages,
dependencies = TRUE
)
}
# Initialise the packages in the session: list of boolean => stdout (console)
lapply(
necessary_packages,
require,
character.only = TRUE
)
# Load ticker data from 2020-01-01 till 2021-02-02
tickers <- c(
"NKLA",
"MPNGF",
"RMO",
"JD",
"MSFT"
)
# Create a new environment: environment => symbolUpdates.env
symbolUpdates.env <- new.env()
# Create a vector of from dates: from_dates => Date Vector
from_dates <- as.Date(
c(
"2020-01-01",
"2020-02-03"
)
)
# Create a vector of to dates:
to_dates <- as.Date(
c(
"2021-02-02",
format(
Sys.Date(),
"%Y-%m-%d"
)
)
)
# Create a vetor environments: env_vec => vector of environments
env_vec <- c(
.GlobalEnv,
symbolUpdates.env
)
# Function to retreive ticker as a data.frame:
# retrieve_ticker_df => function()
retrieve_ticker_df <- function(ticker_vec, from_date, to_date){
# Create a list of size length(tickers):
# df_list => empty list
df_list <- vector(
"list",
length(ticker_vec)
)
# Store each ticker's response as a data.frame in the list:
# df_list => list of data.frames
df_list <- setNames(
lapply(
seq_along(ticker_vec),
function(i){
# Retrieve the data.frame: tmp => data.frame
tmp <- getSymbols.yahoo(
ticker_vec[i],
auto.assign = FALSE,
from = from_date,
to = to_date,
return.class = 'data.frame',
)
# Close all Internet connections as a precaution
# https://stackoverflow.com/a/52758758/2950721
closeAllConnections()
# Create a data.frame and revert index to sequential
# integers: data.frame => env
data.frame(
cbind(
date = as.Date(
row.names(
tmp
)
),
tmp
),
row.names = NULL
)
}
),
ticker_vec
)
# Explicitly define returned object: list of data.frames => env
return(df_list)
}
# Store all the data.frames in a list of data.frames,
# store each list of data.frames in a list:
# ticker_df_list_list => list of list of data.frames
ticker_df_list_list <- lapply(
seq_along(env_vec),
function(i){
retrieve_ticker_df(
tickers,
from_dates[i],
to_dates[i]
)
}
)
# Push each of the lists to the appropriate environment:
# data.frames => env
lapply(
seq_along(ticker_df_list_list),
function(i){
list2env(
ticker_df_list_list[[i]],
envir = env_vec[[i]]
)
}
)
# Initialise an empty list to create some memory
# bound_df_list => empty list
bound_df_list <- vector(
"list",
length(tickers)
)
# Allocate some memory by initialising an
# empty list: ir_list => list
ir_list <- vector(
"list",
length(env_vec) * length(tickers)
)
# Unlist the env_vec, and retrieve the ticker
# data.frames: ir_list => list of data.frames
ir_list <- unlist(
lapply(
env_vec,
function(x){
mget(
tickers,
envir = x
)
}
),
recursive = FALSE
)
# Split-apply-combine based on the
# data.frame names: bound_df_list => list of data.frames
bound_df_list <- lapply(
split(
ir_list,
names(ir_list)
),
function(x){
do.call(
rbind,
x
)
}
)
# Clear up the intermediate objects:
rm(ticker_df_list_list, ir_list, env_vec); gc()
如果不是强制使用多个环境：
# Install pacakges if they are not already installed: necessary_packages => vector
necessary_packages <- c("quantmod")
# Create a vector containing the names of any packages needing installation:
# new_pacakges => vector
new_packages <- necessary_packages[!(necessary_packages %in%
installed.packages()[, "Package"])]
# If the vector has more than 0 values, install the new pacakges
# (and their) associated dependencies:
if(length(new_packages) > 0){
install.packages(
new_packages,
dependencies = TRUE
)
}
# Initialise the packages in the session: list of boolean => stdout (console)
lapply(
necessary_packages,
require,
character.only = TRUE
)
# Load ticker data from 2020-01-01 till 2021-02-02
tickers <- c(
"NKLA",
"MPNGF",
"RMO",
"JD",
"MSFT"
)
# Create a new environment: environment => symbolUpdates.env
symbolUpdates.env <- new.env()
# Create a vector of from dates: from_dates => Date Vector
from_dates <- as.Date(
c(
"2020-01-01",
"2020-02-03"
)
)
# Create a vector of to dates:
to_dates <- as.Date(
c(
"2021-02-02",
format(
Sys.Date(),
"%Y-%m-%d"
)
)
)
# Function to retreive ticker as a data.frame:
# retrieve_ticker_df => function()
retrieve_ticker_df <- function(ticker_vec, from_date, to_date){
# Create a list of size length(tickers):
# df_list => empty list
df_list <- vector(
"list",
length(ticker_vec)
)
# Store each ticker's response as a data.frame in the list:
# df_list => list of data.frames
df_list <- setNames(
lapply(
seq_along(ticker_vec),
function(i){
# Retrieve the data.frame: tmp => data.frame
tmp <- getSymbols.yahoo(
ticker_vec[i],
auto.assign = FALSE,
from = from_date,
to = to_date,
return.class = 'data.frame',
)
# Close all Internet connections as a precaution
# https://stackoverflow.com/a/52758758/2950721
closeAllConnections()
# Create a data.frame and revert index to sequential
# integers: data.frame => env
data.frame(
cbind(
date = as.Date(
row.names(
tmp
)
),
tmp
),
row.names = NULL
)
}
),
ticker_vec
)
# Explicitly define returned object: list of data.frames => env
return(df_list)
}
# Store all the data.frames in a list of data.frames,
# store each list of data.frames in a list:
# ticker_df_list_list => list of list of data.frames
ticker_df_list_list <- lapply(
seq_along(from_dates),
function(i){
retrieve_ticker_df(
tickers,
from_dates[i],
to_dates[i]
)
}
)
# Initialise an empty list to create some memory:
# ir_list => empty list
ir_list <- vector(
"list",
length(tickers) * length(from_dates)
)
# Populate the list with each of the named data.frames:
# ir_list => list of data.frames
ir_list <- unlist(
ticker_df_list_list,
recursive = FALSE
)
# Initialise an empty list to create some memory
# bound_df_list => empty list
bound_df_list <- vector(
"list",
length(tickers)
)
# Split-apply-combine: bound_df_list => list of data.frames
bound_df_list <- lapply(
split(
ir_list,
names(ir_list)
),
function(x){
do.call(
rbind,
x
)
}
)
# Clear up the intermediate objects:
rm(ticker_df_list_list, ir_list); gc()