Using ggplot function, it is possible to group/color the column of interest and plot the data based on that as follows:
ggplot(inputDataFrame, aes(as.numeric(interestingColumn) , group = AnotherColumn)) +
coord_cartesian(xlim = c(0,400)) + geom_line(stat='ecdf')
How can I also add the curve/plot regarding the whole data in "interestingColumn" regardless of the "group" criteria. So that I can compare the whole data and its subdivision groups in one plot.
For instance, running the above code, I will get the figure as follows and I will get the cumulative values for each product separately. How can I add a plot to the following plot which shows the whole products consumption regardless of the product group.
Thanks.
You can add a geom_line without the color aesthetics and a geom_line with the color aesthetics. Also see below how to create a reproducible example.
# create your reproducible example...
set.seed(1)
inputDataFrame <- data.frame(interestingColumn = rnorm(100, 200, 80),
AnotherColumn = factor(rbinom(100, 4, .3)))
# plotting
ggplot(inputDataFrame, aes(as.numeric(interestingColumn))) +
coord_cartesian(xlim = c(0,400)) +
geom_line(stat='ecdf') +
geom_line(aes(color=AnotherColumn), stat='ecdf')
Related
I did everything in ggplot, and it was everything working well. Now I need it to show data when I point a datapoint. In this example, the model (to identify point), and the disp and wt ( data in axis).
For this I added the shape (same shape, I do not actually want different shapes) to model data. and asked ggplot not to show shape in legend. Then I convert to plotly. I succeeded in showing the data when I point the circles, but now I am having problems with the legend showing colors and shapes separated with a comma...
I did not wanted to make it again from scrach in plotly as I have no experience in plotly, and this is part of a much larger shiny project, where the chart adjust automatically the axis scales and adds trend lines the the chart among other things (I did not include for simplicity) that I do not know how to do it in plotly.
Many thanks in advance. I have tried a million ways for a couple of days now, and did not succeed.
# choose mtcars data and add rowname as column as I want to link it to shapes in ggplot
data1 <- mtcars
data1$model <- rownames(mtcars)
# I turn cyl data to character as when charting it showed (Error: Continuous value supplied to discrete scale)
data1$cyl <- as.character(data1$cyl)
# linking colors with cylinders and shapes with models
ccolor <- c("#E57373","purple","green")
cylin <- c(6,4,8)
# I actually do not want shapes to be different, only want to show data of model when I point the data point.
models <- data1$model
sshapes <- rep(16,length(models))
# I am going to chart, do not want legend to show shape
graff <- ggplot(data1,aes(x=disp, y=wt,shape=model,col=cyl)) +
geom_point(size = 1) +
ylab ("eje y") + xlab('eje x') +
scale_color_manual(values= ccolor, breaks= cylin)+
scale_shape_manual(values = sshapes, breaks = models)+
guides(shape='none') # do not want shapes to show in legend
graff
chart is fine, but when converting to ggplotly, I am having trouble with the legend
# chart is fine, but when converting to ggplotly, I am having trouble with the legend
graffPP <- ggplotly(graff)
graffPP
legend is not the same as it was in ggplot
I succeeded in showing the model and data from axis when I point a datapoint in the chart... but now I am having problems with the legend....
To the best of my knowledge there is no easy out-of-the box solution to achieve your desired result.
Using pure plotly you could achieve your result by assigning legendgroups which TBMK is not available using ggplotly. However, you could assign the legend groups manually by manipulating the plotly object returned by ggplotly.
Adapting my answer on this post to your case you could achieve your desired result like so:
library(plotly)
p <- ggplot(data1, aes(x = disp, y = wt, shape = model, col = cyl)) +
geom_point(size = 1) +
ylab("eje y") +
xlab("eje x") +
scale_color_manual(values = ccolor, breaks = cylin) +
scale_shape_manual(values = sshapes, breaks = models) +
guides(shape = "none")
gp <- ggplotly(p = p)
# Get the names of the legend entries
df <- data.frame(id = seq_along(gp$x$data), legend_entries = unlist(lapply(gp$x$data, `[[`, "name")))
# Extract the group identifier, i.e. the number of cylinders from the legend entries
df$legend_group <- gsub("^\\((\\d+).*?\\)", "\\1", df$legend_entries)
# Add an indicator for the first entry per group
df$is_first <- !duplicated(df$legend_group)
for (i in df$id) {
# Is the layer the first entry of the group?
is_first <- df$is_first[[i]]
# Assign the group identifier to the name and legendgroup arguments
gp$x$data[[i]]$name <- df$legend_group[[i]]
gp$x$data[[i]]$legendgroup <- gp$x$data[[i]]$name
# Show the legend only for the first layer of the group
if (!is_first) gp$x$data[[i]]$showlegend <- FALSE
}
gp
Using a dataset, I have created the following plot:
I'm trying to create the following plot:
Specifically, I am trying to incorporate Twitter names over the first image. To do this, I have a dataset with each name in and a value that corresponds to a point on the axes. A snippet looks something like:
Name Score
#tedcruz 0.108
#RealBenCarson 0.119
Does anyone know how I can plot this data (from one CSV file) over my original graph (which is constructed from data in a different CSV file)? The reason that I am confused is because in ggplot2, you specify the data you want to use at the start, so I am not sure how to incorporate other data.
Thank you.
The question you ask about ggplot combining source of data to plot different element is answered in this post here
Now, I don't know for sure how this is going to apply to your specific data. Here I want to show you an example that might help you to go forward.
Imagine we have two data.frames (see bellow) and we want to obtain a plot similar to the one you presented.
data1 <- data.frame(list(
x=seq(-4, 4, 0.1),
y=dnorm(x = seq(-4, 4, 0.1))))
data2 <- data.frame(list(
"name"=c("name1", "name2"),
"Score" = c(-1, 1)))
The first step is to find the "y" coordinates of the names in the second data.frame (data2). To do this I added a y column to data2. y is defined here as a range of points from the may value of y to the min value of y with some space for aesthetics.
range_y = max(data1$y) - min(data1$y)
space_y = range_y * 0.05
data2$y <- seq(from = max(data1$y)-space, to = min(data1$y)+space, length.out = nrow(data2))
Then we can use ggplot() to plot data1 and data2 following some plot designs. For the current example I did this:
library(ggplot2)
p <- ggplot(data=data1, aes(x=x, y=y)) +
geom_point() + # for the data1 just plot the points
geom_pointrange(data=data2, aes(x=Score, y=y, xmin=Score-0.5, xmax=Score+0.5)) +
geom_text(data = data2, aes(x = Score, y = y+(range_y*0.05), label=name))
p
which gave this following plot:
I am using quantile regression in R with the qgam package and visualising them using the mgcViz package, but I am struggling to understand how to control the appearance of the plots. The package effectively turns gams (in my case mqgams) into ggplots.
Simple reprex:
egfit <- mqgam(data = iris,
Sepal.Length ~ s(Petal.Length),
qu = c(0.25,0.5,0.75))
plot.mgamViz(getViz(egfit))
I am able to control things that can be added, for example the axis labels and theme of the plot, but I'm struggling to effect things that would normally be addressed in the aes() or geom_x() functions.
How would I control the thickness of the line? If this were a normal geom_smooth() or geom_line() I'd simply put size = 1 inside of the geoms, but I cannot see how I'd do so here.
How can I control the linetype of these lines? The "id" is continuous and one cannot supply a linetype to a continuous scale. If this were a nomral plot I would convert "id" to a character, but I can't see a way of doing so with the plot.mgamViz function.
How can I supply a new colour scale? It seems as though if I provide it with a new colour scale it invents new ID values to put on the legend that don't correlate to the actual "id" values, e.g.
plot.mgamViz(getViz(egfit)) + scale_colour_viridis_c()
I fully expect this to be relatively simple and I'm missing something obvious, and imagine the answer to all three of these subquestions are very similar to one another. Thanks in advance.
You need to extract your ggplot element using this:
p1 <- plot.mgamViz(getViz(egfit))
p <- p1$plots [[1]]$ggObj
Then, id should be as.factor:
p$data$id <- as.factor(p$data$id)
Now you can play with ggplot elements as you prefer:
library(mgcViz)
egfit <- mqgam(data = iris,
Sepal.Length ~ s(Petal.Length),
qu = c(0.25,0.5,0.75))
p1 <- plot.mgamViz(getViz(egfit))
# Taking gg infos and convert id to factor
p <- p1$plots [[1]]$ggObj
p$data$id <- as.factor(p$data$id)
# Changing ggplot attributes
p <- p +
geom_line(linetype = 3, size = 1)+
scale_color_brewer(palette = "Set1")+
labs(x="Petal Length", y="s(Petal Length)", color = "My ID labels:")+
theme_classic(14)+
theme(legend.position = "bottom")
p
Here the generated plot:
Hope it is useful!
I have a time series dataset in which the x-axis is a list of events in reverse chronological order such that an observation will have an x value that looks like "n-1" or "n-2" all the way down to 1.
I'd like to make a line graph using ggplot that creates a smooth, continuous line that connects all of the points, but it seems when I try to input my data, the x-axis is extremely wonky.
The code I am currently using is
library(ggplot2)
theoretical = data.frame(PA = c("n-1", "n-2", "n-3"),
predictive_value = c(100, 99, 98));
p = ggplot(data=theoretical, aes(x=PA, y=predictive_value)) + geom_line();
p = p + scale_x_discrete(labels=paste("n-", 1:3, sep=""));
The fitted line and grid partitions that would normally appear using ggplot are replaced by no line and wayyy too many partitions.
When you use geom_line() with a factor on at least one axis, you need to specify a group aesthetic, in this case a constant.
p = ggplot(data=theoretical, aes(x=PA, y=predictive_value, group = 1)) + geom_line()
p = p + scale_x_discrete(labels=paste("n-", 1:3, sep=""))
p
If you want to get rid of the minor grid lines you can add
theme(panel.grid.minor = element_blank())
to your graph.
Note that it can be a little risky, scale-wise, to use factors on one axis like this. It may work better to use a typical continuous scale, and just relabel the points 1, 2, and 3 with "n-1", "n-2", and "n-3".
I'm an undergrad researcher and I've been teaching myself R over the past few months. I just started trying ggplot, and have run into some trouble. I've made a series of boxplots looking at the depth of fish at different acoustic receiver stations. I'd like to add a scatterplot that shows the depths of the receiver stations. This is what I have so far:
data <- read.csv(".....MPS.csv", header=TRUE)
df <- data.frame(f1=factor(data$Tagging.location), #$
f2=factor(data$Station),data$Detection.depth)
df2 <- data.frame(f2=factor(data$Station), data$depth)
df$f1f2 <- interaction(df$f1, df$f2) #$
plot1 <- ggplot(aes(y = data$Detection.depth, x = f2, fill = f1), data = df) + #$
geom_boxplot() + stat_summary(fun.data = give.n, geom = "text",
position = position_dodge(height = 0, width = 0.75), size = 3)
plot1+xlab("MPS Station") + ylab("Depth(m)") +
theme(legend.title=element_blank()) + scale_y_reverse() +
coord_cartesian(ylim=c(150, -10))
plot2 <- ggplot(aes(y=data$depth, x=f2), data=df2) + geom_point()
plot2+scale_y_reverse() + coord_cartesian(ylim=c(150,-10)) +
xlab("MPS Station") + ylab("Depth (m)")
Unfortunately, since I'm a new user in this forum, I'm not allowed to upload images of these two plots. My x-axis is "Stations" (which has 12 options) and my y-axis is "Depth" (0-150 m). The boxplots are colour-coded by tagging site (which has 2 options). The depths are coming from two different columns in my spreadsheet, and they cannot be combined into one.
My goal is to to combine those two plots, by adding "plot2" (Station depth scatterplot) to "plot1" boxplots (Detection depths). They are both looking at the same variables (depth and station), and must be the same y-axis scale.
I think I could figure out a messy workaround if I were using the R base program, but I would like to learn ggplot properly, if possible. Any help is greatly appreciated!
Update: I was confused by the language used in the original post, and wrote a slightly more complicated answer than necessary. Here is the cleaned up version.
Step 1: Setting up. Here, we make sure the depth values in both data frames have the same variable name (for readability).
df <- data.frame(f1=factor(data$Tagging.location), f2=factor(data$Station), depth=data$Detection.depth)
df2 <- data.frame(f2=factor(data$Station), depth=data$depth)
Step 2: Now you can plot this with the 'ggplot' function and split the data by using the `col=f1`` argument. We'll plot the detection data separately, since that requires a boxplot, and then we'll plot the depths of the stations with colored points (assuming each station only has one depth). We specify the two different plots by referencing the data from within the 'geom' functions, instead of specifying the data inside the main 'ggplot' function. It should look something like this:
ggplot()+geom_boxplot(data=df, aes(x=f2, y=depth, col=f1)) + geom_point(data=df2, aes(x=f2, y=depth), colour="blue") + scale_y_reverse()
In this plot example, we use boxplots to represent the detection data and color those boxplots by the site label. The stations, however, we plot separately using a specific color of points, so we will be able to see them clearly in relation to the boxplots.
You should be able to adjust the plot from here to suit your needs.
I've created some dummy data and loaded into the chart to show you what it would look like. Keep in mind that this is purely random data and doesn't really make sense.