ggTimeSeries

https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/
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ggTimeSeries is an R package offering novel time series visualizations based on ggplot2, including...
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### Install ggTimeSeries Package

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Provides the command to install the ggTimeSeries package from CRAN.

```R
install.packages('ggTimeSeries')
```

--------------------------------

### Example Usage of stat_steamgraph

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_steamgraph

This example demonstrates how to use stat_steamgraph with ggplot2 to create a steamgraph from sample data.

```APIDOC
## Examples
```R
# NOT RUN {
{
library(ggplot2)
set.seed(10)
df = 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)))))),
Variable = c(rep('a', 1000), rep('b', 1000), rep('c',
    1000), rep('d', 1000)),
VariableLabel = c(rep('Class A', 1000), rep('Class B',
    1000), rep('Class C', 1000), rep('Class D', 1000))
)

ggplot(df, aes(x = Time, y = Signal, group = Variable, fill = VariableLabel)) +
  stat_steamgraph()}
# }
```
```

--------------------------------

### Marimekko Plot Example

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_marimekko

Demonstrates how to create a Marimekko plot using stat_marimekko with sample data. Ensure ggplot2 is loaded. The fill aesthetic should be mapped to a categorical variable, and weights can be specified.

```R
library(ggplot2)
ggplot(
   data.frame(
      x1 = round(3 * runif(10000), 0),
      y1 = pmax(pmin(round(3 * rnorm(10000), 0), 3), -3),
      weight = 1:10000
   )
) +
   stat_marimekko(
      aes(
         xbucket = x1,
         ybucket = y1,
         fill = factor(y1),
         weight = weight
      ),
      xlabelyposition = 1.1,
      color = 'black'
)
```

--------------------------------

### Generate a steamgraph from time series data

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_steamgraph

This example demonstrates how to create a steamgraph using stat_steamgraph. It requires ggplot2 and generates sample time series data with multiple variables and labels, then plots them with Time on the x-axis, Signal on the y-axis, grouped by Variable, and filled by VariableLabel.

```R
library(ggplot2)
set.seed(10)
df = 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)))),
Variable = c(rep('a', 1000), rep('b', 1000), rep('c',
    1000), rep('d', 1000)),
VariableLabel = c(rep('Class A', 1000), rep('Class B',
    1000), rep('Class C', 1000), rep('Class D', 1000))
)

ggplot(df, aes(x = Time, y = Signal, group = Variable, fill = VariableLabel)) +
  stat_steamgraph()
```

--------------------------------

### Example of stat_occurrence

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_occurrence

Demonstrates how to use stat_occurrence with ggplot2 to plot a time series as a dot plot. Requires data.table and ggplot2 libraries.

```r
library(data.table)
library(ggplot2)
set.seed(1)
dfData = data.table(x = 1:100, y = floor(4 * abs(rnorm(100, 0 , 0.4))))
ggplot(dfData, aes(x =x, y = y) )+
   stat_occurrence()+
   coord_fixed()
```

--------------------------------

### Horizon Plot with Random Data and stat_horizon

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_horizon

Generates a horizon plot using randomly generated cumulative sum data. This example showcases the flexibility of stat_horizon with different data distributions. Ensure reproducibility by setting the seed.

```R
set.seed(10)
ggplot(data.frame(x = 1:1000, y = cumsum(rnorm(1000))), aes(x =x, y = y, fill = y) )+
   stat_horizon() +
   scale_fill_continuous(low = 'white', high = 'red')
```

--------------------------------

### Basic ggplot_waterfall Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_waterfall

Generates a waterfall plot using the ggplot_waterfall function. Requires the ggplot2 library and a data frame with x and y columns. The example is not run by default.

```R
library(ggplot2)
set.seed(1)
dfData = data.frame(x = 1:100, y = cumsum(rnorm(100)))
ggplot_waterfall(
   dtData = dfData,
   'x',
   'y'
)
```

--------------------------------

### Basic Horizon Plot with stat_horizon

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_horizon

Demonstrates how to create a basic horizon plot using stat_horizon. Requires ggplot2 and ggtimeseries. The fill aesthetic is mapped to the y value for coloring.

```R
library(ggplot2)
ggplot(data.frame(x = 1:89, y = as.numeric(unlist(austres))), aes(x =x, y = y, fill = y) )+
   stat_horizon() +
   scale_fill_continuous(low = 'white', high = 'red')
```

--------------------------------

### Create and Format a Waterfall Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Demonstrates how to create a basic waterfall plot using ggplot_waterfall and then apply custom labels.

```R
# creating some data
set.seed(1)
dfData = data.frame(x = 1:100, y = cumsum(rnorm(100)))

# base plot
p1 = ggplot_waterfall(
   dtData = dfData,
   'x',
   'y'
)

# adding some formatting
p1 +
   xlab(NULL) +
   ylab(NULL)
```

--------------------------------

### stat_horizon Function

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_horizon

This snippet details the usage, arguments, aesthetics, and cosmetic tips for the stat_horizon function.

```APIDOC
## stat_horizon: Plot a time series as a horizon plot

### Description
Plot a time series as a horizon plot

### Usage
```R
stat_horizon(mapping = NULL, data = NULL, show.legend = NA,
  inherit.aes = TRUE, na.rm = T, bandwidth = NULL, ...)
```

### Arguments
* `mapping`: mapping
* `data`: dataframe
* `show.legend`: logical
* `inherit.aes`: logical
* `na.rm`: logical
* `bandwidth`: bandwith
* `...`: other functions

A horizon plot breaks the Y dimension down using colours. This is useful when visualising y values spanning a vast range and / or trying to highlight outliers without losing context of the rest of the data. Horizon plots are best viewed in an apsect ratio of very low vertical length.

### Aesthetics
x, y, fill. Fill argument is overridden internally but is required for ggplot to assign a colour / fill scale.

### Other parameters
bandwidth, to dictate the span of a band.

### Cosmetic Tips
The minimalist look can be achieved by appending the following chunk of code to the output object: ` + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme(    axis.text = element_blank(),    axis.ticks = element_blank(),    legend.position = 'none',    strip.background = element_blank(),    # strip.text = element_blank(), # useful if only one year of data    plot.background = element_blank(),    panel.border = element_blank(),    panel.background  = element_blank(),    panel.grid = element_blank(),    panel.border = element_blank() ) `

### Also See
`ggplot_horizon`, a more polished but less flexible alternative.

### Examples
```R
# NOT RUN {
{
library(ggplot2)
ggplot(data.frame(x = 1:89, y = as.numeric(unlist(austres))), aes(x =x, y = y, fill = y) )+
   stat_horizon() +
   scale_fill_continuous(low = 'white', high = 'red')

set.seed(10)
ggplot(data.frame(x = 1:1000, y = cumsum(rnorm(1000))), aes(x =x, y = y, fill = y) )+
   stat_horizon() +
   scale_fill_continuous(low = 'white', high = 'red')}
# }
```
```

--------------------------------

### Create Calendar Heatmap with Continuous Values

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Generates a calendar heatmap to visualize daily data with continuous values. It helps in detecting weekly, monthly, or seasonal patterns. Requires data with a date column and a value column.

```R
# creating some data
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') +
   facet_wrap(~Year, ncol = 1)
```

--------------------------------

### Create and Format a Marimekko Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Illustrates the creation of a Marimekko plot using stat_marimekko, including data preparation and axis formatting for percentage display.

```R
# 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)
```

--------------------------------

### Create Basic Horizon Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Generates a basic horizon plot, useful for visualizing y values spanning a vast range with a skewed distribution. It highlights outliers without losing context of the data's variation.

```R
# 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.")
```

--------------------------------

### Create Calendar Heatmap with Categorical Values

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Generates a calendar heatmap to visualize daily data with categorical values. It helps in detecting weekly, monthly, or seasonal patterns. Requires data with a date column and a categorical column.

```R
# 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)
```

--------------------------------

### Create Basic Steamgraph

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Generates a basic steamgraph, an aesthetically appealing version of a stacked area chart. It highlights changes in data by placing groups with the most variance on the edges.

```R
# creating some data
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)))
```

--------------------------------

### Create and Format an Occurrence Dot Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Shows how to generate an occurrence dot plot with stat_occurrence and customize axis labels and aspect ratio.

```R
# creating some data
set.seed(1)
dfData = data.table(x = 1:100, y = floor(4 * abs(rnorm(100, 0 , 0.4))))

# base plot
p1 = ggplot(dfData, aes(x =x, y = y) )+
   stat_occurrence()

# adding some formatting
p1 +
   xlab(NULL) +
   ylab(NULL) +
   coord_fixed(ylim = c(0,1 + max(dfData$y)))
```

--------------------------------

### Basic ggplot_horizon Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_horizon

Generates a basic horizon plot using the ggplot_horizon function. Ensure the ggplot2 library is loaded and data is prepared in a data frame with specified x and y columns.

```R
library(ggplot2)
set.seed(1)
dfData = data.frame(x = 1:1000, y = cumsum(rnorm(1000)))
p1 = ggplot_horizon(dfData, 'x', 'y')
p1
```

--------------------------------

### Basic Calendar Heatmap Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_calendar_heatmap

Generates a basic calendar heatmap using sample data. Ensure 'data.table' and 'ggplot2' libraries are loaded. The 'DateCol' and 'ValueCol' arguments specify the date and value columns respectively.

```R
library(data.table)
library(ggplot2)
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)
   )
# you could also try categorical data with
# ValueCol = sample(c('a','b','c'), 730, replace = T)
p1 = ggplot_calendar_heatmap(
   dtData,
   'DateCol',
   'ValueCol'
)
p1
```

--------------------------------

### Cosmetic Tips for Horizon Plots

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_horizon

Applies a set of cosmetic adjustments to a horizon plot for a minimalist aesthetic. This includes removing axis labels, ticks, and legends, and adjusting the plot's coordinate system.

```R
xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = 'none',
    strip.background = element_blank(),
    # strip.text = element_blank(), # useful if only one year of data
    plot.background = element_blank(),
    panel.border = element_blank(),
    panel.background  = element_blank(),
    panel.grid = element_blank(),
    panel.border = element_blank()
) + coord_fixed( 0.5 * diff(range(dfData$x)) / diff(range(dfData$y)))
```

--------------------------------

### Cosmetic Tips for stat_occurrence

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_occurrence

Provides code to achieve a minimalist look for the stat_occurrence plot, including axis and theme adjustments.

```r
+    xlab(NULL) +    ylab(NULL) +    scale_fill_continuous(low = 'green', high = 'red') +    theme(
       axis.text.y = element_blank(),
       axis.ticks.y = element_blank(),
       legend.position = 'none',
       strip.background = element_blank(),
       panel.background = element_blank(),
       panel.border = element_blank(),
       panel.grid = element_blank(),
       panel.border = element_blank()
    ) +    coord_fixed(ylim = c(0,1 + max(dfData$y)))
```

--------------------------------

### Marimekkofy Function

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/Marimekkofy

This function transforms or prepares data for Marimekko-style visualizations. It takes a dataframe and specifies columns for x-axis buckets, y-axis buckets, and optional weights.

```APIDOC
## Marimekkofy

### Description
Marimekkofy

### Usage
```R
Marimekkofy(data, xbucket = "xbucket", ybucket = "ybucket",
  weight = NULL)
```

### Arguments
* `data` (dataframe): The input dataframe.
* `xbucket` (string): The column name for the x-axis buckets. Defaults to "xbucket".
* `ybucket` (string): The column name for the y-axis buckets. Defaults to "ybucket".
* `weight` (NULL or numeric): Optional column name for weights. Defaults to NULL.

### Value
* `df` (dataframe): The transformed dataframe.
```

--------------------------------

### Load dtClimate Data

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/dtClimate

Load the dtClimate dataset into your R environment. This dataset is a data.table with 23628 rows and 5 columns.

```r
data(dtClimate)
```

--------------------------------

### Cosmetic Tips for Minimalist Waterfall Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_waterfall

Applies cosmetic tips to remove axis labels, ticks, and background elements for a minimalist look. This is appended to an existing ggplot object.

```R
+ xlab(NULL) + ylab(NULL) + theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = 'none',
    strip.background = element_blank(),
    plot.background = element_blank(),
    panel.background = element_blank(),
    panel.border = element_blank(),
    panel.grid = element_blank(),
    panel.border = element_blank()
  )
```

--------------------------------

### stat_marimekko Function

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_marimekko

This snippet details the usage and arguments of the stat_marimekko function, which is used to create marimekko plots. It also outlines the required aesthetics and provides cosmetic tips for styling the plot.

```APIDOC
## stat_marimekko: Plot two categorical variables as marimekko

### Description
Plot two categorical variables as marimekko.

### Usage
```R
stat_marimekko(mapping = NULL, data = NULL, show.legend = NA, inherit.aes = TRUE, na.rm = T, xlabelyposition = NULL, ...)
```

### Arguments
* `mapping`: mapping
* `data`: data
* `show.legend`: logical
* `inherit.aes`: logical
* `na.rm`: logical
* `xlabelyposition`: position
* `...`: other functions

A marimekko plot, or a mosaic plot, visualises the co-occurrence of two categorical / ordinal variables. In a time series, it could be used to visualise the transitions from one state to another by considering each state to be a category and plotting current category vs. next category.

### Aesthetics
* `xbucket`: xbucket
* `ybucket`: ybucket
* `fill`: The fill argument needs to be assigned to ybucket, or some other column which is a one to one mapping of ybucket.

### Cosmetic Tips
The minimalist look can be achieved by appending the following chunk of code to the output object: ` + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme(    axis.text = element_blank(),    axis.ticks = element_blank(),    legend.position = 'none',    strip.background = element_blank(),    # strip.text = element_blank(), # useful if only one year of data    plot.background = element_blank(),    panel.border = element_blank(),    panel.background  = element_blank(),    panel.grid = element_blank(),    panel.border = element_blank() ) `

### Examples
```R
# NOT RUN {
library(ggplot2)
ggplot(
   data.frame(
      x1 = round(3 * runif(10000), 0),
      y1 = pmax(pmin(round(3 * rnorm(10000), 0), 3), -3),
      weight = 1:10000
   )
) +
   stat_marimekko(
      aes(
         xbucket = x1,
         ybucket = y1,
         fill = factor(y1),
         weight = weight
      ),
      xlabelyposition = 1.1,
      color = 'black'
)}
# }
```
```

--------------------------------

### StatMarimekko

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/StatMarimekko

Transforms data for the tiles of the heatmap.

```APIDOC
## StatMarimekko

### Description
Transforms data for the tiles of the heatmap.

### Usage
```
StatMarimekko
```

### Arguments
This function does not take any explicit arguments in its usage signature.

### Format
An object of class `StatMarimekko` (inherits from `Stat`, `ggproto`, `gg`) of length 4.
```

--------------------------------

### Minimalist Calendar Heatmap Styling

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_calendar_heatmap

Applies cosmetic adjustments to a calendar heatmap for a minimalist aesthetic, removing axis labels, ticks, and legends.

```R
+ xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = 'none',
    strip.background = element_blank(),
    panel.background = element_blank(),
    panel.border = element_blank()
  )
```

--------------------------------

### stat_calendar_heatmap Function

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_calendar_heatmap

This snippet details the usage and arguments of the stat_calendar_heatmap function.

```APIDOC
## stat_calendar_heatmap

### Description
A calendar heatmap provides context for weeks, and day of week and is a better way to visualise daily data than line charts.

### Usage
```R
stat_calendar_heatmap(mapping = NULL, data = NULL, show.legend = NA, inherit.aes = TRUE, na.rm = T, bandwidth = NULL, ...)
```

### Arguments
* `mapping`: mapping
* `data`: df
* `show.legend`: logical
* `inherit.aes`: logical
* `na.rm`: logical
* `bandwidth`: bandwidth
* `...`: more functions

### Aesthetics
* `date`: The date aesthetic maps to a date column.
* `fill`: The fill aesthetic maps to the values to be visualized in the heatmap.

### Data Tips
`strftime` can help extract the value of the year, week of year, and day of week from the date column. You might need to extract the year to facet multiple years as demonstrated in the example. This stat uses the following transformation to obtain the x and y coordinate to be used in the heatmap - `data$x = 1 + as.integer(strftime(data$date, "%W")) data$y = as.integer(strftime(data$date, "%w")) data$y[data$y == 0L] = 7 data$y = 8 - data$y `

### Cosmetic Tips
The minimalist look can be achieved by appending the following chunk of code to the output object: ` + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme( axis.text = element_blank(), axis.ticks = element_blank(), legend.position = 'none', strip.background = element_blank(), panel.background = element_blank(), panel.border = element_blank() ) `

### Also See
`ggplot_calendar_heatmap`, a polished but less flexible alternative.

### Examples
```R
# NOT RUN {
library(ggplot2)
DailyData = data.frame(
      DateCol = seq(
         as.Date("1/01/2014", "%d/%m/%Y"),
         as.Date("31/12/2015", "%d/%m/%Y"),
         "days"
      ),
      ValueCol = runif(730)
   )
   DailyData$Year = strftime(DailyData$DateCol, "%Y")
ggplot(
   DailyData,
   aes(
      date = DateCol,
      fill = ValueCol
   )
) + 
   stat_calendar_heatmap() + 
   facet_wrap(~Year, ncol = 1)
}
# }
```
```

--------------------------------

### Calendar Heatmap Plotting

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_calendar_heatmap

Use this stat to create a calendar heatmap visualization for daily data. It requires a date column and a fill aesthetic. Faceting by year is demonstrated for multi-year data.

```R
library(ggplot2)
DailyData = data.frame(
      DateCol = seq(
         as.Date("1/01/2014", "%d/%m/%Y"),
         as.Date("31/12/2015", "%d/%m/%Y"),
         "days"
      ),
      ValueCol = runif(730)
   )
   DailyData$Year = strftime(DailyData$DateCol, "%Y")
ggplot(
   DailyData,
   aes(
      date = DateCol,
      fill = ValueCol
   )
) + 
   stat_calendar_heatmap() + 
   facet_wrap(~Year, ncol = 1)
```

--------------------------------

### StatSteamgraph Function Usage

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/StatSteamgraph

This snippet shows the basic usage of the StatSteamgraph function. It is used to transform data for steam graphs.

```r
StatSteamgraph
```

--------------------------------

### Set Minimal Theme for ggTimeSeries

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Sets a minimal theme for ggplot2 plots, removing elements like axis ticks, legend, and panel borders. This is often a prerequisite for using ggTimeSeries visualizations.

```R
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()
)
```

--------------------------------

### Customizing Calendar Heatmap with Additional Geoms

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_calendar_heatmap

Demonstrates how to add custom geoms and scales to a ggplot_calendar_heatmap object. This allows for further customization beyond the default plot.

```R
p1 +
geom_text(label = '!!!') +
scale_colour_continuous(low = 'red', high = 'green')
```

--------------------------------

### stat_steamgraph function signature

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_steamgraph

This is the function signature for stat_steamgraph. It defines the default arguments for mapping, data, legend display, aesthetic inheritance, and handling of missing values.

```R
stat_steamgraph(mapping = NULL, data = NULL, show.legend = NA,
  inherit.aes = TRUE, na.rm = T, ...)
```

--------------------------------

### ggplot_horizon Function Signature

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_horizon

This snippet shows the signature of the ggplot_horizon function, detailing its parameters for creating horizon plots.

```APIDOC
## ggplot_horizon

### Description
A horizon plot breaks the Y dimension down using colours. This is useful when visualising y values spanning a vast range and / or trying to highlight outliers without losing context of the rest of the data. Horizon plots are best viewed in an apsect ratio of very low vertical length.

### Usage
```R
ggplot_horizon(dtData, cXColumnName, cYColumnName, bandwidth = NULL,
  vcGroupingColumnNames = NULL)
```

### Arguments
*   `dtData`: Data set which may include other columns apart from date and values.
*   `cXColumnName`: Column name of dates.
*   `cYColumnName`: Column name of values.
*   `bandwidth`: the width of one band of Y values. easier to differentiate between the bands.
*   `vcGroupingColumnNames`: The set of columns which together define the group for the chart to operate within If you plan to facet your plot, you should specify the same column names to this argument.

### Value
Returns a gpplot friendly object which means the user can use ggplot scales, etc. to modify the look.

### Cosmetic Tips
The minimalist look can be achieved by appending the following chunk of code to the example output object: ` + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme(    axis.text = element_blank(),    axis.ticks = element_blank(),    legend.position = 'none',    strip.background = element_blank(),    # strip.text = element_blank(), # useful if only one year of data    plot.background = element_blank(),    panel.border = element_blank(),    panel.background  = element_blank(),    panel.grid = element_blank(),    panel.border = element_blank() ) + coord_fixed( 0.5 * diff(range(dfData$x)) / diff(range(dfData$y))) `

### Also See
`stat_horizon`, a less polished but more flexible alternative.

### Examples
```R
# NOT RUN {
{
library(ggplot2)
set.seed(1)
dfData = data.frame(x = 1:1000, y = cumsum(rnorm(1000)))
p1 = ggplot_horizon(dfData, 'x', 'y')
p1
# add new geoms or colours
p1 +
geom_text(label = '!!!') +
scale_colour_continuous(low = 'red', high = 'green')
}
# }
```
```

--------------------------------

### StatMarimekko Usage

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/StatMarimekko

This snippet shows the basic usage of the StatMarimekko object. It is used to transform data for Marimekko plots.

```R
StatMarimekko
```

--------------------------------

### Marimekkofy Function Definition

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/Marimekkofy

Defines the Marimekkofy function signature. Use this function to prepare dataframes for Marimekko chart visualizations.

```R
Marimekkofy(data, xbucket = "xbucket", ybucket = "ybucket",
  weight = NULL)
```

--------------------------------

### Basic Waterfall Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_waterfall

Generates a basic waterfall plot from a data frame. Ensure ggplot2 is loaded and data is prepared with x and y aesthetics.

```R
library(ggplot2)
set.seed(1)
dfData = data.frame(x = 1:20, y = cumsum(rnorm(20)))
ggplot(dfData, aes(x =x, y = y) )+
   stat_waterfall()
```

--------------------------------

### StatCalendarMonthBreaks Usage

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/StatCalendarMonthBreaks

This snippet shows the basic usage of StatCalendarMonthBreaks. It is used to transform data for month breaks in calendar heatmaps.

```r
StatCalendarMonthBreaks
```

--------------------------------

### stat_occurrence Usage

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_occurrence

Defines the usage of the stat_occurrence function, including its arguments and default values.

```r
stat_occurrence(mapping = NULL, data = NULL, show.legend = NA,
  inherit.aes = TRUE, na.rm = T, bandwidth = NULL, ...)
```

--------------------------------

### ggplot_calendar_heatmap Function Signature

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_calendar_heatmap

This is the function signature for ggplot_calendar_heatmap. It outlines the required and optional arguments for creating a calendar heatmap.

```R
ggplot_calendar_heatmap(dtDateValue, cDateColumnName = "",
  cValueColumnName = "", vcGroupingColumnNames = "Year",
  dayBorderSize = 0.25, dayBorderColour = "black",
  monthBorderSize = 2, monthBorderColour = "black",
  monthBorderLineEnd = "round")
```

--------------------------------

### ggplot_calendar_heatmap

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_calendar_heatmap

Plots a calendar heatmap. A calendar heatmap provides context for weeks, and day of week which makes it a better way to visualise daily data than line charts. Largely uses Codoremifa's code from stackoverflow.com/questions/22815688/calendar-time-series-with-r.

```APIDOC
## Function: ggplot_calendar_heatmap

### Description
Plots a calendar heatmap. This visualization is beneficial for daily data as it provides context for weeks and days of the week, offering a better alternative to line charts for such data. The implementation is largely based on code by Codoremifa from stackoverflow.com/questions/22815688/calendar-time-series-with-r.

### Usage
```R
ggplot_calendar_heatmap(dtDateValue, cDateColumnName = "",
  cValueColumnName = "", vcGroupingColumnNames = "Year",
  dayBorderSize = 0.25, dayBorderColour = "black",
  monthBorderSize = 2, monthBorderColour = "black",
  monthBorderLineEnd = "round")
```

### Arguments
* **dtDateValue** (data.frame/data.table) - The dataset containing the dates and values, potentially with other columns.
* **cDateColumnName** (character) - The name of the column that contains the dates.
* **cValueColumnName** (character) - The name of the column that contains the data values.
* **vcGroupingColumnNames** (character or character vector) - Column names that define the grouping for the chart. If faceting is used, these should match the facetting columns. The function automatically adds a year variable for faceting.
* **dayBorderSize** (numeric) - The size of the border around each individual day cell.
* **dayBorderColour** (character) - The color of the border around each individual day cell.
* **monthBorderSize** (numeric) - The size of the border around each month.
* **monthBorderColour** (character) - The color of the border around each month.
* **monthBorderLineEnd** (character) - Specifies the line end style for the border around each month (e.g., 'round', 'butt', 'square').

### Value
Returns a ggplot object that can be further customized using ggplot scales, additional geoms, and other ggplot functionalities.

### Cosmetic Tips
To achieve a minimalist look, append the following code chunk to the output object:
```R
+ xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = 'none',
    strip.background = element_blank(),
    # strip.text = element_blank(), # useful if only one year of data
    plot.background = element_blank(),
    panel.border = element_blank(),
    panel.background  = element_blank(),
    panel.grid = element_blank(),
    panel.border = element_blank()
)
```

### Examples
```R
# NOT RUN {
library(data.table)
library(ggplot2)
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)
   )
# Example with categorical data:
# ValueCol = sample(c('a','b','c'), 730, replace = T)

p1 = ggplot_calendar_heatmap(
   dtData,
   'DateCol',
   'ValueCol'
)
p1

# Adding new geoms to the plot:
p1 +
geom_text(label = '!!!') +
scale_colour_continuous(low = 'red', high = 'green')
}
# }
```
```

--------------------------------

### Format Horizon Plot

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2

Adds formatting to a horizon plot, including continuous color scaling and fixed aspect ratio. The fixed coordinate system helps maintain the visual integrity of the plot.

```R
# 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)))
```

--------------------------------

### StatWaterfall R Object

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/StatWaterfall

This represents the StatWaterfall object in R, used for data transformation in horizon plots. It inherits from Stat, ggproto, and gg.

```r
StatWaterfall
```

--------------------------------

### stat_occurrence

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_occurrence

Plots a time series as a dot plot. Useful for encoding the count of rare events directly in the chart.

```APIDOC
## stat_occurrence

### Description
Plots a time series as a dot plot.

### Usage
```R
stat_occurrence(mapping = NULL, data = NULL, show.legend = NA, inherit.aes = TRUE, na.rm = T, bandwidth = NULL, ...)
```

### Arguments
- `mapping`: mapping
- `data`: df
- `show.legend`: logical
- `inherit.aes`: logical
- `na.rm`: logical
- `bandwidth`: bandwidth
- `...`: more functions

### Aesthetics
- `x`
- `y`

### Examples
```R
# NOT RUN {
{
library(data.table)
library(ggplot2)
set.seed(1)
dfData = data.table(x = 1:100, y = floor(4 * abs(rnorm(100, 0 , 0.4))))
ggplot(dfData, aes(x =x, y = y) )+
   stat_occurrence()+
   coord_fixed()}
# }
```
```

--------------------------------

### stat_steamgraph Function

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_steamgraph

This snippet documents the stat_steamgraph function, detailing its arguments and purpose. It's used within ggplot2 to generate steamgraphs.

```APIDOC
## stat_steamgraph

### Description
Plot multiple time series as a steamgraph.

### Usage
```R
stat_steamgraph(mapping = NULL, data = NULL, show.legend = NA, inherit.aes = TRUE, na.rm = T, ...)
```

### Arguments
* `mapping`: mapping
* `data`: data
* `show.legend`: logical
* `inherit.aes`: logical
* `na.rm`: logical
* `...`: other functions

### Details
Plots geom_ribbon for each time series and stacks them one on top of the other. It's a more aesthetically appealing version of a stacked area chart. The groups with the most variance are placed on the outside, and the groups with the least variance are placed on the inside.

### Aesthetics
`geom_steamgraph` needs x, y, group, fill.

### Cosmetic Tips
The minimalist look can be achieved by appending the following chunk of code to the example output object: ` + xlab(NULL) + ylab(NULL) + theme( axis.text = element_blank(), axis.ticks = element_blank(), legend.position = 'none', strip.background = element_blank(), # strip.text = element_blank(), # useful if only one year of data plot.background = element_blank(), panel.background = element_blank(), panel.border = element_blank(), panel.grid = element_blank(), panel.border = element_blank() ) + coord_fixed( 0.2 * diff(range(df$Time)) / diff(range(df$Signal))) `
```

--------------------------------

### stat_waterfall Function

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/stat_waterfall

Plots a time series as a waterfall plot, highlighting the change in the time series rather than its value. It requires 'x' and 'y' aesthetics.

```APIDOC
## stat_waterfall

### Description
Plot a time series as a waterfall plot.

### Usage
```R
stat_waterfall(mapping = NULL, data = NULL, show.legend = NA, inherit.aes = TRUE, na.rm = T, bandwidth = NULL, ...)
```

### Arguments
- **mapping**: mapping
- **data**: data
- **show.legend**: legend
- **inherit.aes**: logical
- **na.rm**: logical
- **bandwidth**: bandwidth
- **...**: more functions

### Aesthetics
`stat_waterfall` needs x, y

### Cosmetic Tips
The minimalist look can be achieved by appending the following chunk of code to the output object: ` + xlab(NULL) + ylab(NULL) + theme( axis.text = element_blank(), axis.ticks = element_blank(), legend.position = 'none', strip.background = element_blank(), plot.background = element_blank(), panel.background = element_blank(), panel.border = element_blank(), panel.grid = element_blank(), panel.border = element_blank() ) `

### Also See
`ggplot_waterfall`, a flexible but less polished alternative.

### Examples
```R
# NOT RUN {
{
library(ggplot2)
set.seed(1)
dfData = data.frame(x = 1:20, y = cumsum(rnorm(20)))
ggplot(dfData, aes(x =x, y = y) )+ stat_waterfall()}
# }
```
```

--------------------------------

### ggplot_waterfall Function

Source: https://www.rdocumentation.org/packages/ggTimeSeries/versions/1.0.2/topics/ggplot_waterfall

Plots a waterfall plot highlighting the change in the time series rather than the value of the time series itself.

```APIDOC
## ggplot_waterfall

### Description
A waterfall plot highlights the change in the time series rather than the value of the time series itself.

### Usage
```R
ggplot_waterfall(dtData, cXColumnName, cYColumnName, nArrowSize = 0.25,
  vcGroupingColumnNames = NULL)
```

### Arguments
*   `dtData` (data.frame): Data set which may include other columns apart from the columns mapped to x and y.
*   `cXColumnName` (string): Column name of the x mapping.
*   `cYColumnName` (string): Column name of the y mapping.
*   `nArrowSize` (numeric): The size of the arrow head on the plot in cm. Defaults to 0.25.
*   `vcGroupingColumnNames` (vector of strings, optional): The set of columns which together define the group for the chart to operate between. If you plan to facet your plot, you should specify the same column names to this argument.

### Value
Returns a ggplot friendly object which means the user can use ggplot scales to modify the look, add more geoms, etc.

### Cosmetic Tips
The minimalist look can be achieved by appending the following chunk of code to the output object: ` + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme(    axis.text = element_blank(),    axis.ticks = element_blank(),    legend.position = 'none',    strip.background = element_blank(),    # strip.text = element_blank(), # useful if only one year of data    plot.background = element_blank(),    panel.background = element_blank(),    panel.border = element_blank(),    panel.grid = element_blank(),    panel.border = element_blank() ) `

### Also See
`stat_waterfall`, a flexible but less polished alternative.

### Examples
```R
# NOT RUN {
library(ggplot2)
set.seed(1)
dfData = data.frame(x = 1:100, y = cumsum(rnorm(100)))
ggplot_waterfall(
   dtData = dfData,
   'x',
   'y'
)
}
# 
```
```
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