ggtimeseries-->ggplot2 extensions
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This document describes ggTimeSeries, an R package that provides extensions to ggplot2 for creating time series plots. It includes examples of using functions from ggTimeSeries to create calendar heatmaps, horizon graphs, steam graphs, and marimekko plots from time series data. The examples demonstrate how to generate sample time series data, create basic plots, and add formatting customizations.
![Reference: https://github.com/Ather-Energy/ggTimeSeries
ggplot2 extensions-
ggTimeSeries
prepared by Volkan OBAN
Examples:
devtools::install_github('Ather-Energy/ggTimeSeries')
minimalTheme = theme_set(theme_bw(12))
minimalTheme = theme_update(
axis.ticks = element_blank(),
legend.position = 'none',
strip.background = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
panel.grid = element_blank(),
panel.border = element_blank()
)
set.seed(1)
dtData = data.table(
DateCol = seq(
as.Date("1/01/2014", "%d/%m/%Y"),
as.Date("31/12/2015", "%d/%m/%Y"),
"days"
),
ValueCol = runif(730)
)
dtData[, ValueCol := ValueCol + (strftime(DateCol,"%u") %in% c(6,7) * runif(1) *
0.75), .I]
dtData[, ValueCol := ValueCol + (abs(as.numeric(strftime(DateCol,"%m")) - 6.5)) *
runif(1) * 0.75, .I]
# base plot
p1 = ggplot_calendar_heatmap(
dtData,
'DateCol',
'ValueCol'
)
# adding some formatting
p1 +
xlab(NULL) +
ylab(NULL) +
scale_fill_continuous(low = 'green', high = 'red') +](https://image.slidesharecdn.com/ggtimeseries-160919203032/85/ggtimeseries-ggplot2-extensions-1-320.jpg)
![facet_wrap(~Year, ncol = 1)
# creating some categorical data
dtData[, CategCol := letters[1 + round(ValueCol * 7)]]
# base plot
p2 = ggplot_calendar_heatmap(
dtData,
'DateCol',
'CategCol'
)
# adding some formatting
p2 +
xlab(NULL) +
ylab(NULL) +
facet_wrap(~Year, ncol = 1)](https://image.slidesharecdn.com/ggtimeseries-160919203032/85/ggtimeseries-ggplot2-extensions-2-320.jpg)
![# creating some data
set.seed(1)
dfData = data.frame(x = 1:1000, y = cumsum(rnorm(1000)))
# base plot
p1 = ggplot_horizon(dfData, 'x', 'y')
print("If you're seeing any vertical white stripes, it's a display thing.")
## [1] "If you're seeing any vertical white stripes, it's a display thing."
# adding some formatting
p1 +
xlab(NULL) +
ylab(NULL) +
scale_fill_continuous(low = 'green', high = 'red') +
coord_fixed( 0.5 * diff(range(dfData$x)) / diff(range(dfData$y)))](https://image.slidesharecdn.com/ggtimeseries-160919203032/85/ggtimeseries-ggplot2-extensions-3-320.jpg)
ggtimeseries-->ggplot2 extensions
- 1. Reference: https://github.com/Ather-Energy/ggTimeSeries ggplot2 extensions- ggTimeSeries prepared by Volkan OBAN Examples: devtools::install_github('Ather-Energy/ggTimeSeries') minimalTheme = theme_set(theme_bw(12)) minimalTheme = theme_update( axis.ticks = element_blank(), legend.position = 'none', strip.background = element_blank(), panel.border = element_blank(), panel.background = element_blank(), panel.grid = element_blank(), panel.border = element_blank() ) set.seed(1) dtData = data.table( DateCol = seq( as.Date("1/01/2014", "%d/%m/%Y"), as.Date("31/12/2015", "%d/%m/%Y"), "days" ), ValueCol = runif(730) ) dtData[, ValueCol := ValueCol + (strftime(DateCol,"%u") %in% c(6,7) * runif(1) * 0.75), .I] dtData[, ValueCol := ValueCol + (abs(as.numeric(strftime(DateCol,"%m")) - 6.5)) * runif(1) * 0.75, .I] # base plot p1 = ggplot_calendar_heatmap( dtData, 'DateCol', 'ValueCol' ) # adding some formatting p1 + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') +
- 2. facet_wrap(~Year, ncol = 1) # creating some categorical data dtData[, CategCol := letters[1 + round(ValueCol * 7)]] # base plot p2 = ggplot_calendar_heatmap( dtData, 'DateCol', 'CategCol' ) # adding some formatting p2 + xlab(NULL) + ylab(NULL) + facet_wrap(~Year, ncol = 1)
- 3. # creating some data set.seed(1) dfData = data.frame(x = 1:1000, y = cumsum(rnorm(1000))) # base plot p1 = ggplot_horizon(dfData, 'x', 'y') print("If you're seeing any vertical white stripes, it's a display thing.") ## [1] "If you're seeing any vertical white stripes, it's a display thing." # adding some formatting p1 + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + coord_fixed( 0.5 * diff(range(dfData$x)) / diff(range(dfData$y)))
- 4. set.seed(10) dfData = data.frame( Time = 1:1000, Signal = abs( c( cumsum(rnorm(1000, 0, 3)), cumsum(rnorm(1000, 0, 4)), cumsum(rnorm(1000, 0, 1)), cumsum(rnorm(1000, 0, 2)) ) ), VariableLabel = c(rep('Class A', 1000), rep('Class B', 1000), rep('Class C', 1000), rep('Class D', 1000)) ) # base plot p1 = ggplot(dfData, aes(x = Time, y = Signal, group = VariableLabel, fill = VariableLabel)) + stat_steamgraph() # adding some formatting p1 + xlab(NULL) + ylab(NULL) + coord_fixed( 0.2 * diff(range(dfData$Time)) / diff(range(dfData$Signal)))
- 5. Example: # creating some data set.seed(1) dfData = data.frame(Signal = pmax(pmin(rnorm(10000), 3), -3)) dfData2 = data.frame( Signal = round(head(dfData$Signal, -1),0), NextSignal = round(tail(dfData$Signal, -1),0), Weight = 1 ) # base plot p1 = ggplot(dfData2, aes(xbucket = Signal, ybucket = NextSignal, fill = NextSignal, weight = Weight) )+ stat_marimekko(color = 'black', xlabelyposition = -0.1) # adding some formatting p1 + xlab('Signal occurrence %') + ylab('Signal | Next signal occurrence %') + scale_x_continuous(breaks = 0:10/10) + scale_y_continuous(breaks = 0:10/10)