Yawei Ge, Zhenzhen Chen, Weiquan Luo 3/22/2019
This package ggfun was created as an extension of ggplot2 to add some functions for some specific goals, such as replacing points with images, making it more convenient to deal with map in some cases, generating some curves with given parameters, etc. But it is really an excercise for applying ggplot2 extension functionalities and there is large space to improve.
Here are links to our GitHub repository where the code for the package is stored and to the package website where more information about the package is well shown.
And the shiny app can be found under theinst/shiny-example folder or the system file after you installed the package.
The package can be downloaded from GitHub. You can use the following code to install.
# Install the package from github
devtools::install_github("yaweige/ggfun")You can then load the package in the normal way.
# Load the package
library(ggfun)This function is used to draw scatter plot as geom_point does, but replaced the points with a predefined image (Elephant and donkey).
The usage is the same as geom_image.
library(ggplot2)
library(ggfun)
library(dplyr)
p <- system.file("extdata", "images.jpg", package = "ggfun")
img <- magick::image_read(p)
ggplot() + geom_image(data = mtcars, mapping = aes(x = wt, y = mpg), img = img, size =0.05)
This function applied the algorithm to calculate the distances between the central point (defined by median) and other points, and connect the central point to the farthest point with a line, which can be modified as a regular line object in ggplot2.
madedata <- data.frame(x = rnorm(100, mean = 0, sd = 1),
y = rnorm(100, mean = 0, sd = 1))
ggplot(madedata, aes(x = x, y = y)) +
geom_point() +
stat_star(color = "red")
This function can draw arrow maps for the US. This function requres a specific type of data set, which generated as in the examples, with longutude, latitude and change which usually refers to the changes of party preference in US election for each state.
The required aesthetics are:
x: usually longitudey: usually latitudechange: the changes for each state (can be specified by group aesthetics, as one state by default).
Other features can be modified similar to geom_curve.
usmap <- map_data("state")
madedata <- data.frame(region = unique(usmap$region),
change = (runif(49)-0.5)*2,stringsAsFactors = FALSE)
madedata <- madedata %>%
left_join(usmap,by = "region")madedata %>%
ggplot() +
geom_path(aes(x = long, y = lat, group = group)) +
stat_arrowmap(aes(x = long, y = lat, change = change, group = region),
curvature = 0.3, angle = 60,
size = 1,
arrow = arrow(type = "open",
length = unit(0.1, "inches")))
The function provide a preview of persistant homology for geographic locations. It helps to investigate whether any clustering of the events respect to geographic location. To use the function, a data set with longitude and latitude of multiple geographic locations and a user-defined Geo distance parameter are required.
The required input:
data: The dataframe with longitude and latitudex: longitudey: latituded: the magnitude of real Geo distance (in km) used as the criterion to link two locations
The following example shows the persistant homology of earthquake event around pacific plate.
library(magrittr)
worldmap <- map_data("world2")
p <- ggplot() +
geom_polygon(data=worldmap, aes(x=long, y=lat, group = group),fill="white", colour="#7f7f7f", size=0.5) +
theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank())
data(eqRaw)
eq <- eqRaw %>% filter(LONGITUDE > 110 | LONGITUDE < -45) %>%
mutate(LONGITUDE = ifelse(LONGITUDE < 0, LONGITUDE + 360, LONGITUDE)) %>%
select(YEAR, MONTH,DAY, EQ_MAG_MS, COUNTRY, LOCATION_NAME, LATITUDE, LONGITUDE)
## add layer_PersHomo
fp <- p + layer_PersHomo(data= eq, mapping = aes(x=LONGITUDE, y=LATITUDE), d=450000, colour = "blue"); fp
This function applied the algorithm to calculate the Archimedean spiral. Then we apply ggplot2 to connect each point to draw a spiral.
The required parameters are:
a: rotating the spiralb: distance control between two adjacent curvesn: the number of turns
The following example shows how does this function works based on these three parameters.
ggplot() +
stat_ars(aes(a = 8, b = 20, n = 2), col = "coral")
This function is used to draw regression line for the each level of the categorical variable.
The required aesthetics are:
x: the numerical explanatory variabley: the numerical response variableid: the categorical variable with more than one level
The following example shows two regression line for the given dataset.
ggplot(indexf, aes(x = length, y = width, id = sex)) +
geom_point() +
stat_rl(aes(x = length, y = width, id = sex, colour = sex))