# Plotting time series data using dygraph# install.packages(c("quantmod", "tidyverse","dygraphs"))# lapply(c("quantmod", "tidyverse","dygraphs"), require, character.only = TRUE)library(dygraphs)par(family="Palatino")quantmod::getSymbols("TWTR", src="yahoo")
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
Warning: TWTR contains missing values. Some functions will not work if objects
contain missing values in the middle of the series. Consider using na.omit(),
na.approx(), na.fill(), etc to remove or replace them.
[1] "TWTR"
class(TWTR)
[1] "xts" "zoo"
m =tail(TWTR, n=30)m =m[,1:(ncol(m)-2)] # drop last two columns names(m)<-c('Open', 'High', 'Low', 'Close') # rename columns for plottingpath <-getwd()setwd("~/quarto/eliannaevans.github.io/") # place dygraph.css into the same directorydygraph(m, main ="Twitter Stock Prices (Candlestick Chart)") |>dyCandlestickGroup(c('Open', 'High', 'Low', 'Close')) |>dyCandlestick() |>dyLegend(show ="always", hideOnMouseOut = T) |>dyCSS("dygraph.css")
tsstudio1.R
# Plotting time series data using TSstudio# install.packages(c("quantmod", "tidyverse","TSstudio"))# lapply(c("quantmod", "tidyverse","TSstudio"), require, character.only = TRUE)library(TSstudio)quantmod::getSymbols("AAPL", src="yahoo")
[1] "AAPL"
ts_plot(AAPL$AAPL.Adjusted, title ="Apple Stock prices",Ytitle ="")
class(AAPL) # What class is this object?
[1] "xts" "zoo"
# Some sample dataset from TSstudiots_seasonal(USgas, type ="box") # month-year matrix data
# What class is USgas?# Sample chartsts_heatmap(USgas)
ts_cor(USgas) # ACF and PACF
ts_lags(USgas, margin = .01)
usgas=data.frame(USgas)
Analyzing Time Series Data
quantmod::getSymbols("AAPL", src="yahoo")
[1] "AAPL"
quantmod::getSymbols("TWTR", src="yahoo")
Warning: TWTR contains missing values. Some functions will not work if objects
contain missing values in the middle of the series. Consider using na.omit(),
na.approx(), na.fill(), etc to remove or replace them.
[1] "TWTR"
summary(AAPL)
Index AAPL.Open AAPL.High AAPL.Low
Min. :2007-01-03 Min. : 2.835 Min. : 2.929 Min. : 2.793
1st Qu.:2011-01-05 1st Qu.: 11.635 1st Qu.: 11.732 1st Qu.: 11.540
Median :2015-01-13 Median : 24.860 Median : 25.142 Median : 24.657
Mean :2015-01-12 Mean : 42.936 Mean : 43.421 Mean : 42.456
3rd Qu.:2019-01-17 3rd Qu.: 48.664 3rd Qu.: 49.087 3rd Qu.: 48.169
Max. :2023-01-24 Max. :182.630 Max. :182.940 Max. :179.120
AAPL.Close AAPL.Volume AAPL.Adjusted
Min. : 2.793 Min. :3.520e+07 Min. : 2.381
1st Qu.: 11.618 1st Qu.:1.111e+08 1st Qu.: 9.903
Median : 24.954 Median :2.260e+08 Median : 22.622
Mean : 42.957 Mean :3.749e+08 Mean : 41.321
3rd Qu.: 48.545 3rd Qu.:5.082e+08 3rd Qu.: 47.099
Max. :182.010 Max. :3.373e+09 Max. :180.960
summary(TWTR)
Index TWTR.Open TWTR.High TWTR.Low
Min. :2013-11-07 Min. :13.95 Min. :14.22 Min. :13.72
1st Qu.:2016-02-29 1st Qu.:25.55 1st Qu.:26.21 1st Qu.:24.91
Median :2018-06-16 Median :35.42 Median :36.10 Median :34.82
Mean :2018-06-16 Mean :36.02 Mean :36.70 Mean :35.34
3rd Qu.:2020-10-04 3rd Qu.:44.20 3rd Qu.:45.02 3rd Qu.:43.33
Max. :2023-01-24 Max. :78.36 Max. :80.75 Max. :76.05
NA's :59 NA's :59 NA's :59
TWTR.Close TWTR.Volume TWTR.Adjusted
Min. :14.01 Min. : 0 Min. :14.01
1st Qu.:25.41 1st Qu.: 12335302 1st Qu.:25.41
Median :35.49 Median : 16913051 Median :35.49
Mean :36.00 Mean : 21751861 Mean :36.00
3rd Qu.:44.13 3rd Qu.: 24280822 3rd Qu.:44.13
Max. :77.63 Max. :269213085 Max. :77.63
NA's :59 NA's :59 NA's :59