Import and cleaning data
library(casabourse)
atw <- daily.data('atw',from = '01-01-2017', to = '19-02-2022')
Ouverture <- atw$Value
Ouverture <- append(Ouverture,0,0)
Ouverture <- Ouverture[-length(Ouverture)]
atw$Ouverture <- Ouverture
atw$Date <- lubridate::dmy(rownames(atw))
atw <- atw[-1,] |> dplyr::relocate(Date, .before = Value)
tibble::tibble(atw)
## # A tibble: 1,243 × 7
## Date Value Minimum Maximum Variation Volume Ouverture
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-02-17 425. 425. 430. -1.14 6148 430
## 2 2017-02-20 425 425 430 -0.02 2650 425.
## 3 2017-02-21 428 426 428 0.71 1635 425
## 4 2017-02-22 414 414 422 -3.27 34164 428
## 5 2017-02-23 409 403 420 -1.21 52513 414
## 6 2017-02-24 415 400 415 1.47 12127 409
## 7 2017-02-27 412 412 424 -0.72 12667 415
## 8 2017-02-28 415 412 420. 0.73 73013 412
## 9 2017-03-01 420. 415. 420. 1.24 32518 415
## 10 2017-03-02 425 421 425 1.15 36172 420.
## # … with 1,233 more rows
Interactive Visualization with {plotly}
library(plotly)
fig <- atw %>% plot_ly(x=~Date, type = "candlestick",
open =~Ouverture, close =~Value,
high =~Maximum, low =~Minimum)
fig <- fig %>% layout(title = 'Chandeliers japonais (ATW)')
fig
Visualization with {ggplot2} and {tidyquant}
