| Title: | Visualizing Changes in Performance Measures and Demographic Affiliations using Animation |
| Version: | 1.0.0 |
| Description: | Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| URL: | https://github.com/KrisanatA/animbook |
| BugReports: | https://github.com/KrisanatA/animbook/issues |
| Depends: | R (≥ 2.10) |
| LazyData: | true |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Imports: | dplyr, gganimate, ggplot2, plotly, purrr, RColorBrewer, rlang, stats, tibble, tidyr, tidyselect |
| NeedsCompilation: | no |
| Packaged: | 2023-12-05 10:12:22 UTC; ottoi |
| Author: | Krisanat Anukarnsakulchularp
 [aut, cre,
cph],
Dianne Cook [aut] |
| Maintainer: | Krisanat Anukarnsakulchularp <krisanat.anu@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-12-05 17:50:07 UTC |
Australian election study data
Description
The aes dataset contains the answers to the surveys that were done in 2019 for the election. This dataset only includes the id, year, party, and gender from the full survey data. The year column comes from the transformations of two different questions to see whether the voter voted for the same party in 2016 and 2019 or not, and if not, who did they vote for before?
Usage
aeles
Format
A data frame with 1,468 rows and 4 variables
- id
The id of the respondent
- year
Year
- party
Party that the respondent votes for in the House of Representatives
- gender
Gender of the respondent
Source
This dataset is from the following; Australian Election Study https://dataverse.ada.edu.au/file.xhtml?fileId=18013&version=3.0
Modified the ggplot object
Description
This function will modify the ggplot object before the user can pass it to the rendering of choice.
Usage
anim_animate(plot)
Arguments
plot |
ggplot object |
Value
A gganimate object if the rendering is gganimate or a ggplot object if the rendering is plotly.
Examples
animbook <- anim_prep(data = osiris, id = ID, values = sales, time = year, group = japan)
plot <- wallaby_plot(animbook)
animate <- anim_animate(plot)
plotly::ggplotly(animate)
Transformed numerical into categorized data
Description
This function transformed the numerical data into the categorized format by grouping data and scaling values.
Usage
anim_prep(
data,
id = NULL,
values = NULL,
time = NULL,
group = NULL,
ncat = 5L,
breaks = NULL,
label = NULL,
group_scaling = NULL,
scaling = "rank"
)
Arguments
data |
A data frame contained the numerical values. |
id |
The column name that represents the identifiers variable. |
values |
The column name contains the numeric values. |
time |
The column name that represents the time variable. |
group |
The column name that represents the distinguished group between the values. |
ncat |
The number of categories to be created for scaling values. |
breaks |
A vector of breaks for creating bins. |
label |
A vector of labels to represent the qtile. |
group_scaling |
The column name that will be used for grouping the variable before scaling. |
scaling |
The scaling method to be used; "rank" or "absolute". |
Details
The function takes the input data and performs several operations to transformed it into categorized format. It is done by grouping data, scales values, and assigned the qtile.
Value
A categorized data.
Examples
# rank scaling
anim_prep(data = osiris, id = ID, values = sales, time = year)
# group_rank scaling
anim_prep(data = osiris, id = ID, values = sales, time = year,
group_scaling = country)
# absolute scaling
anim_prep(data = osiris, id = ID, values = sales, time = year,
scaling = "absolute")
# group_absolute scaling
anim_prep(data = osiris, id = ID, values = sales, time = year,
group_scaling = country, scaling = "absolute")
Transformed category data into a categorized format
Description
This function transformed the category data in the categorized format by ordering the values.
Usage
anim_prep_cat(
data,
id = NULL,
values = NULL,
time = NULL,
group = NULL,
order = NULL,
label = NULL
)
Arguments
data |
A data frame contained the category values. |
id |
The column name that represents the identifiers variable. |
values |
The column name contains the category values. |
time |
The column name that represents the time variable. |
group |
The column name that represents the distinguished group between the values. |
order |
A vector of order for sorting the category values. |
label |
A vector of labels to represent the qtile. |
Details
The function takes the input data, ordering the values, and assigning the variable names.
Value
A categorized data.
Examples
anim_prep_cat(data = aeles, id = id, values = party, time = year)
Simulated data with some change (category)
Description
This data has changed from category A to E between two-time points.
Usage
cat_change
Format
A data frame with 400 rows and 4 variables
- id
The id of the organisation
- time
time
- gp
Either X or Y
- qnt
Quantile group for the two times
Examples
d <- anim_prep_cat(cat_change, id = id, values = qnt,
time = time, group = gp)
d_p <- wallaby_plot(d, height = 1)
d_p_anim <- anim_animate(d_p)
Simulated data with some change (numerical)
Description
This data contained the numerical values for each observation.
Usage
dbl_change
Format
A data frame with 400 rows and 4 variables
- id
The id of the organisation
- time
time
- gp
Either X or Y
- values
Numerical values represent sales
Examples
d <- anim_prep(dbl_change, id = id, values = values,
time = time, group = gp)
d_p <- wallaby_plot(d, height = 1)
d_p_anim <- anim_animate(d_p)
Turn the data into a facetted plot
Description
This function takes in the data which has been prepared by either anim_prep() or anim_prep_cat() and
return the ggplot object. The user can still modify the plot the same as normal using the ggplot2 function.
Usage
funnel_web_plot(data, group_palette = NULL, ...)
Arguments
data |
The animbook object returned from the prep function. |
group_palette |
The vector of the palette used by the function to supply the color to each group. |
... |
Additional arguments for customization. See details for more information. |
Details
This function takes prepared data and generates a ggplot object. The funnel web plot is the plot that shows the line facetted plot showing the pattern between time period. The line jitter can be controlled using additional arguments such as height and width to control the appearance. For the shading area, the alpha argument can be used.
Value
Return a ggplot object.
Examples
animbook <- anim_prep(data = osiris, id = ID, values = sales, time = year, group = japan)
funnel_web_plot(animbook)
Funnel web spider plot data
Description
This function performs data manipulation for facetting.
Usage
funnel_web_spider_data(data)
Arguments
data |
The categorized data. |
Details
The function takes the categorized data and manipulates the data into the format where it can be further facetting.
Value
An object contained the modified data with additional data components.
Kangaroo plot data
Description
This function performs data manipulation and path interpolation of the categorized data. This includes additional data components for labeling and shading.
Usage
kangaroo_data(data, time_dependent = FALSE, height = 0.6, width = 50L)
Arguments
data |
The categorized data. |
time_dependent |
Logical. Should the visualization be time-dependent? The default is FALSE. |
height |
The proportion the point takes in the shaded area. |
width |
The number that controls the runif_max to specify how far apart each point is. |
Details
The function takes the categorized data and interpolates the path for each observation. Additionally, the label and shading data are created.
Value
An object contained the modified data with additional data components.
References
Recreate - Sankey flow chart. (n.d.). Emil Hvitfeldt. Retrieved April 13, 2023, from https://www.emilhvitfeldt.com/post/2018-03-20-recreate-sankey-flow-chart/
Kangaroo path map
Description
This function is used to interpolate the path for the kangaroo plot.
Usage
kangaroo_draw(df)
Arguments
df |
A data frame. |
Value
A mapped data.
Turn the data into a ggplot object for the animate function
Description
This function takes in the data which has been prepared by the anim_prep()
or anim_prep_cat() and return the ggplot object. The user can still modify
the plot as usual.
Usage
kangaroo_plot(
data,
group_palette = NULL,
shade_palette = NULL,
rendering = "gganimate",
time_dependent = FALSE,
...
)
Arguments
data |
The categorized data. |
group_palette |
The vector of the palette used by the function to supply the color of each group. |
shade_palette |
The vector of the palette used by the function to supply the color to the shaded area. |
rendering |
The choice of method used to create and display the plot, either gganimate or plotly. |
time_dependent |
Logical. Should the visualization be time-dependent? The default is FALSE. |
... |
Additional arguments for customization. See details for more information. |
Details
This function takes categorized data and generates a ggplot object. The kangaroo plot is the plot that shows the movement between groups over time. The point position and point size in the shaded area can be controlled using additional arguments such as height, width, and size. For the shading area, the alpha argument can be used.
Value
Return a ggplot object.
Examples
example <- anim_prep(data = dbl_change, id = id, values = values,
time = time, group = gp)
kangaroo_plot(example)
Osiris firm sales data
Description
The Osiris dataset contains information on listed and major unlisted/delisted companies across the world from 2006 to 2018. This dataset only includes the year, ID, country, sales, and japan variables from the full Osiris dataset to give the user an example of the dataset format.
Usage
osiris
Format
A data frame with 10,270 rows and 5 variables
- year
Year
- ID
BvD(Bureau van Dijk) ID
- country
Address of incorp. - Country
- sales
Sales
- japan
Whether the firm is from Japan or not
Source
This dataset is from the following; Bureau van Dijk https://www.bvdinfo.com/en-gb/our-products/data/international/osiris.
Proportional shaded data
Description
This function creates a proportional shaded data using the specified parameters.
Usage
proportional_shade(
initial = NULL,
proportion = NULL,
y = NULL,
position = "left"
)
Arguments
initial |
A numerical of initial values for the proportional shade. |
proportion |
A vector of proportions corresponding to each group. |
y |
A numeric vector specifying the vertical position of the shade diagram. |
position |
A character string specifying the side of the shade diagram. This needs to be either "left" (default) or "right". |
Value
A data frame of the proportional shaded diagram.
Examples
data <- proportional_shade(4, c(0.4, 0.3, 0.2, 0.1), c(4, 3, 2, 1))
ggplot2::ggplot(data = data, ggplot2::aes(x = x, y = y, group = id, fill = as.factor(id))) +
ggplot2::geom_polygon(alpha = 0.1)
Animate sigmoid path
Description
This function creates a path from the starting point (x_start, y_start) to the ending point (x_end, y_end) with the sigmoid shape.
Usage
sigmoid(x_start = 0, x_end = 1, y_start = 0, y_end = 1, scale = 5, n = 100)
Arguments
x_start |
The x-coordinate of the starting point. |
x_end |
The x-coordinate of the ending point. |
y_start |
The y-coordinate of the starting point. |
y_end |
The y-coordinate of the ending point. |
scale |
A numeric value that controls the length of the sigmoid. |
n |
A numeric value that controls the number of points between starting and ending points. |
Value
Return a path in a data frame format
References
Recreate - Sankey flow chart. (n.d.). Emil Hvitfeldt. Retrieved April 13, 2023, from https://www.emilhvitfeldt.com/post/2018-03-20-recreate-sankey-flow-chart/
Examples
path <- sigmoid()
ggplot2::ggplot(data = path, ggplot2::aes(x = x, y = y)) + ggplot2::geom_point()
Animate sine path
Description
This function creates a path from the starting point (x_start, y_start) to the ending point (x_end, y_end) with the sine shape.
Usage
sine(x_start = 0, x_end = 1, y_start = 0, y_end = 1, n = 100)
Arguments
x_start |
The x-coordinate of the starting point. |
x_end |
The x-coordinate of the ending point. |
y_start |
The y-coordinate of the starting point. |
y_end |
The y-coordinate of the ending point. |
n |
A numeric value that controls the number of points between starting and ending points. |
Value
Return a path in a data frame format
References
Brunson JC (2020). “ggalluvial: Layered Grammar for Alluvial Plots.” Journal of Open Source Software, 5(49), 2017. doi:10.21105/joss.02017.
Examples
path <- sine()
ggplot2::ggplot(data = path, ggplot2::aes(x = x, y = y)) + ggplot2::geom_point()
Wallaby plot data
Description
This function performs data manipulation and path interpolation of the categorized data. This includes additional data components for labeling and shading.
Usage
wallaby_data(
data,
subset = "top",
relation = "one_many",
time_dependent = FALSE,
height = 0.6,
width = 50L,
total_point = NULL
)
Arguments
data |
The categorized data. |
subset |
A character string specifying the variable used for subsetting the data. The "top" and "bottom" strings can also be used in this argument. |
relation |
The choice of relationship for the values to display on the plot, either "one_many" or "many_one." |
time_dependent |
Logical. Should the visualizations be time-dependent? The default is FALSE. |
height |
The proportion the point takes in the shaded area. |
width |
The number that controls the runif_max to specify how far apart each point is. |
total_point |
The number of points the users want for the wallaby plot. The default is NULL, where the number of the point is equal to the original number of points. |
Details
The function takes the categorized data and interpolates the path for each observation. Additionally, the label and shading data are created.
Value
An object contained the modified data with additional data components.
References
Recreate - Sankey flow chart. (n.d.). Emil Hvitfeldt. Retrieved April 13, 2023, from https://www.emilhvitfeldt.com/post/2018-03-20-recreate-sankey-flow-chart/
Turn the data into a subset plot for animate function
Description
This function takes in the data which has been prepared by the anim_prep()
or anim_prep_cat() and return the ggplot object. The user can still modify
the plot as usual.
Usage
wallaby_plot(
data,
group_palette = NULL,
shade_palette = NULL,
rendering = "gganimate",
time_dependent = FALSE,
subset = "top",
relation = "one_many",
total_point = NULL,
x_lab = NULL,
...
)
Arguments
data |
The categorized data. |
group_palette |
The vector of the palette used by the function to supply the color of each group. |
shade_palette |
The vector of the palette used by the function to supply the color of each shaded area. |
rendering |
The choice of method used to create and display the plot, either gganimate or plotly. |
time_dependent |
Logical. Should the visualization be time-dependent? The default is FALSE. |
subset |
A character string specifying the variable used for subsetting the data. The "top" and "bottom" strings can also be used in this argument. |
relation |
The choice of relationship for the values to display on the plot, either "one_many" or "many_one." |
total_point |
The number of points the users want for the wallaby plot. The default is NULL, where the number of the point is equal to the original number of points. |
x_lab |
The label for the x-axis. |
... |
Additional arguments for customization. See details for more information. |
Details
This function takes categorized data and generates a ggplot object. The wallaby plot is the plot that shows the movement of the subset data between the start and end of the observable period. The point position and point size in the shaded area can be controlled using additional arguments such as height, width, and size. For the shading area, the alpha argument can be used.
Value
Return a ggplot object.
Examples
animbook <- anim_prep(data = osiris, id = ID, values = sales, time = year, group = japan)
wallaby_plot(animbook)