Examples
Here you’ll find a series of example of calls to
yf_get(). Most arguments are self-explanatory, but you can
find more details at the help files.
The steps of the algorithm are:
- check cache files for existing data
- if not in cache, fetch stock prices from YF and clean up the raw data
- write cache file if not available
- calculate all returns
- build diagnostics
- return the data to the user
Fetching a single stock price
library(yfR)
# set options for algorithm
my_ticker <- 'GM'
first_date <- Sys.Date() - 30
last_date <- Sys.Date()
# fetch data
df_yf <- yf_get(tickers = my_ticker,
first_date = first_date,
last_date = last_date)
# output is a tibble with data
head(df_yf)## # A tibble: 6 × 11
## ticker ref_date price_open price_high price_low price_close volume
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GM 2026-03-06 74.7 75.2 73.3 75.2 8190700
## 2 GM 2026-03-09 73.4 74.7 71.9 74.7 8829600
## 3 GM 2026-03-10 74.8 77.2 74.8 74.9 8915600
## 4 GM 2026-03-11 75.5 76.6 74.4 74.8 5636100
## 5 GM 2026-03-12 73.2 74.0 73.1 73.4 7341000
## 6 GM 2026-03-13 73.6 73.9 72.1 72.4 7229700
## # ℹ 4 more variables: price_adjusted <dbl>, ret_adjusted_prices <dbl>,
## # ret_closing_prices <dbl>, cumret_adjusted_prices <dbl>Fetching many stock prices
library(yfR)
library(ggplot2)
my_ticker <- c('TSLA', 'GM', 'MMM')
first_date <- Sys.Date() - 100
last_date <- Sys.Date()
df_yf_multiple <- yf_get(tickers = my_ticker,
first_date = first_date,
last_date = last_date)
p <- ggplot(df_yf_multiple, aes(x = ref_date, y = price_adjusted,
color = ticker)) +
geom_line()
p
Fetching daily/weekly/monthly/yearly price data
library(yfR)
library(ggplot2)
library(dplyr)
my_ticker <- 'GE'
first_date <- '2005-01-01'
last_date <- Sys.Date()
df_dailly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'daily') %>%
mutate(freq = 'daily')
df_weekly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'weekly') %>%
mutate(freq = 'weekly')
df_monthly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'monthly') %>%
mutate(freq = 'monthly')
df_yearly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'yearly') %>%
mutate(freq = 'yearly')
# bind it all together for plotting
df_allfreq <- bind_rows(
list(df_dailly, df_weekly, df_monthly, df_yearly)
) %>%
mutate(freq = factor(freq,
levels = c('daily',
'weekly',
'monthly',
'yearly'))) # make sure the order in plot is right
p <- ggplot(df_allfreq, aes(x = ref_date, y = price_adjusted)) +
geom_line() +
facet_grid(freq ~ ticker) +
theme_minimal() +
labs(x = '', y = 'Adjusted Prices')
print(p)
Changing format to wide
library(yfR)
library(ggplot2)
my_ticker <- c('TSLA', 'GM', 'MMM')
first_date <- Sys.Date() - 100
last_date <- Sys.Date()
df_yf_multiple <- yf_get(tickers = my_ticker,
first_date = first_date,
last_date = last_date)
print(df_yf_multiple)## # A tibble: 201 × 11
## ticker ref_date price_open price_high price_low price_close volume
## * <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GM 2025-12-26 82.8 83.1 82.6 83.1 3411600
## 2 GM 2025-12-29 83.1 83.4 82.5 82.9 4759400
## 3 GM 2025-12-30 83.2 83.2 82.2 82.3 3240500
## 4 GM 2025-12-31 82.2 82.3 81.2 81.3 4483700
## 5 GM 2026-01-02 81.4 81.5 79.6 81.0 7468900
## 6 GM 2026-01-05 80.5 83.4 80.0 83.2 10468400
## 7 GM 2026-01-06 82.7 82.8 81.2 82.2 7972900
## 8 GM 2026-01-07 82.1 82.7 81.6 81.9 6362000
## 9 GM 2026-01-08 82.8 85.2 82.4 85.1 11643400
## 10 GM 2026-01-09 83.5 84.4 81 82.9 12142900
## # ℹ 191 more rows
## # ℹ 4 more variables: price_adjusted <dbl>, ret_adjusted_prices <dbl>,
## # ret_closing_prices <dbl>, cumret_adjusted_prices <dbl>
l_wide <- yf_convert_to_wide(df_yf_multiple)
names(l_wide)## [1] "price_open" "price_high" "price_low"
## [4] "price_close" "volume" "price_adjusted"
## [7] "ret_adjusted_prices" "ret_closing_prices" "cumret_adjusted_prices"
prices_wide <- l_wide$price_adjusted
head(prices_wide)## # A tibble: 6 × 4
## ref_date GM MMM TSLA
## <date> <dbl> <dbl> <dbl>
## 1 2025-12-26 82.9 161. 475.
## 2 2025-12-29 82.7 161. 460.
## 3 2025-12-30 82.1 160. 454.
## 4 2025-12-31 81.1 159. 450.
## 5 2026-01-02 80.8 161. 438.
## 6 2026-01-05 83.0 163. 452.