I am using the chart library MPAndroidChart in Kotlin
Everything is working correctly, `but I have a problem with the axis scales. In my graph my maximum values are as follows:
X axis: 1947.7872f
Y axis: 861.5f
But when the graph is made the X axis should be larger than the Y axis, but in my graph it almost shows the same. I need the axes to have the scale respecting the real numerical values of both.
My code to make the graph is the following:
private fun setupLineChartData() {
val yVals = ArrayList<Entry>()
yVals.add(Entry(0f, 241.5f, "0"))
yVals.add(Entry(162.3156f, 323.9f, "1"))
yVals.add(Entry(324.6312f, 529.6f, "2"))
yVals.add(Entry(486.9468f, 738.2f, "3"))
yVals.add(Entry(649.2624f, 839.6f, "4"))
yVals.add(Entry(811.578f, 860.8f, "5"))
yVals.add(Entry(973.8936f, 861.5f, "6"))
yVals.add(Entry(1136.2092f, 860.8f, "7"))
yVals.add(Entry(1298.5248f, 839.6f, "8"))
yVals.add(Entry(1460.8404f, 738.2f, "9"))
yVals.add(Entry(1623.156f, 529.6f, "10"))
yVals.add(Entry(1785.4716f, 323.9f, "11"))
yVals.add(Entry(1947.7872f, 241.5f, "12"))
val set1: LineDataSet
set1 = LineDataSet(yVals, "DataSet 1")
set1.color = Color.BLUE
set1.setCircleColor(Color.BLUE)
set1.lineWidth = 1f
set1.circleRadius = 3f
set1.setDrawCircleHole(true)
set1.valueTextSize = 0f
set1.setDrawFilled(true)
val dataSets = ArrayList<ILineDataSet>()
dataSets.add(set1)
val data = LineData(dataSets)
// set data
lineChart.data = data
lineChart.description.isEnabled = false
lineChart.legend.isEnabled = false
lineChart.setPinchZoom(true)
lineChart.xAxis.setLabelCount(13, true)
lineChart.xAxis.labelRotationAngle = -90f
val minXRange = 0f
val maxXRange = 1947.7872f
lineChart.setVisibleXRange(minXRange, maxXRange)
lineChart.xAxis.position = XAxis.XAxisPosition.BOTTOM
}
I tried with lineChart.setScaleMinima (1f, 1f) but it doesn't work.
For more details see image:
Related
In my program, im using mplcursors on a matplotlib graph so I can identify certain points precisely.
mplcursors.cursor(multiple=True).connect("add", lambda sel: sel.annotation.draggable(False))
Now I made a complex graph with multiple axis:
first = 1
offset = 60
for x in range(len(cat_list)):
if "Time" not in cat_list[x]:
if first and not cat_list[x].startswith("EngineSpeed"):
parasites[x] = ParasiteAxes(host, sharex = host)
host.parasites.append(parasites[x])
parasites[x].axis["right"].set_visible(True)
parasites[x].set_ylabel(cat_list[x])
parasites[x].axis["right"].major_ticklabels.set_visible(True)
parasites[x].axis["right"].label.set_visible(True)
p_plot, = parasites[x].plot(t, t_num_list[x], label = cat_list[x])
#parasites[x].axis["right"+str(x+1)].label.set_color(p_plot.get_color())
parasites[x].axis["right"].label.set_color(p_plot.get_color())
first = 0
elif not cat_list[x].startswith("EngineSpeed"):
parasites[x] = ParasiteAxes(host, sharex = host)
host.parasites.append(parasites[x])
parasites[x].set_ylabel(cat_list[x])
new_axisline = parasites[x].get_grid_helper().new_fixed_axis
parasites[x].axis["right"+str(x+1)] = new_axisline(loc = "right",
axes = parasites[x],
offset = (offset, 0))
p_plot, = parasites[x].plot(t, t_num_list[x])
parasites[x].axis["right"+str(x+1)].label.set_color(p_plot.get_color())
offset = offset + 60
host.legend()
fig.add_axes(host)
plt.show()
This code results in the following graph:
https://i.stack.imgur.com/Wl7yC.png
Now I have to somehow be able to select certain points by selecting which axis im using. How do I make a selection menu for choosing an active axis and how do I then use mplcursors to select my points?
Thanks,
Ziga
I'm struggling with getting my plot title to the center using ggsurvplot...
I've seen some posts mentioning something like xxxx$plot + theme(....)
but this solution does not seem to work for me.
Here's my code, maybe you can see what I'm missing:
surv_object_CA19.9 <- Surv(time = data_OS$OS_Days / 30, event = data_OS$Status.Death)
CA19.9_surv_fit <- survfit(surv_object_CA19.9 ~ CA19.9.initial_status, data = data_OS)
CA19.9_OS <- ggsurvplot(CA19.9_surv_fit, data = data_OS, pval = TRUE, xlab = "Time [Months]",
ylab = "Overall survival", risk.table = TRUE, legend.title = "",
risk.table.col. = "strata", risk.table.y.text = FALSE, surv.scale = "percent",
break.x.by = 6, xlim = c(0, 60), legend.labs = c("Pathological", "Normal"),
title = "Overall survival for patients with initially pathological or normal CA19-9 values",
CA19.9_OS$plot + theme(plot.title = element_text(hjust = 0.5)))
Thank you for any help! I'm still new to R and not particularly a friend of it yet, so any tips are highly appreciated!
One relatively easy solution is to define your own custom theme based off of the theme that is used in ggsurvplot(). Looking at the documentation for the function shows us that it is applying via ggtheme= the theme theme_survminer(). We can create a custom function that uses %+replace% to overwrite one of the theme elements of interest from theme_survminer():
custom_theme <- function() {
theme_survminer() %+replace%
theme(
plot.title=element_text(hjust=0.5)
)
}
Then, you can use that theme by association with the ggtheme= argument of ggsurvplot():
library(ggplot2)
library(survminer)
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)
ggsurvplot(fit, data = lung, title='Awesome Plot Title', ggtheme=custom_theme())
#Add parameters to your theme as follows
centr = theme_grey() + theme(plot.title = element_text(hjust = 0.5, face = "bold"))
#Fit the model
fit<- survfit(Surv(time, status) ~ sex, data = lung)
#create survival plot
ggsurvplot(fit, data = lung, title="Your Title Here", ggtheme=centr)
I have created this plot with 18 grids using facet_grid command and two different fitting equations (for Jan - Apr, and May - Jun). I have two things that I need help with:
(may sound obvious, but) I haven't been able to find on the internet working codes extract a curve maximum for a stat_smooth fit. I'd appreciate if someone could show and explain what the codes mean. This is the closest I could find, but I am not sure what it means:
gb <- ggplot_build(p1)
curve_max <- gb$data[[1]]$x[which(diff(sign(diff(gb$data[[1]]$y)))==-2)+1]
How to add a vertical line to indicate max value on each curve?
Data file (rlc2 <- read_excel)
Plot
plot <- ggplot(rlc2, aes(par, etr, color=month, group=site))+
geom_point()+
stat_smooth(data = subset(rlc2, rlc2$month!="May" & rlc2$month!="Jun"),
method = "glm",
formula = y ~ x + log(x),
se = FALSE,
method.args = list(family = gaussian(link = "log"), start=c(a=0, b=0, c=0)))+
stat_smooth(data = subset(rlc2, rlc2$month=="May" | rlc2$month=="Jun"),
method = "nlsLM",
formula = y ~ M*(1 - exp(-(a*x))),
se = FALSE,
method.args = list(start=c(M=0, a=10)))+
facet_grid(rows = vars(month), cols = vars(site))
plot
field_rlc_plot
Any other advice are also welcome. I am educated as programmer so my codes are probably a bit messy. Thank you for helping.
Try this:
First, fit the data and extract the maximum of the fit.
my.fit <- function(month, site, data) {
fit <- glm(formula = etr ~ par + log(par),
data = data,
family=gaussian(link = "log")
)
#arrange the dersired output in a tibble
tibble(max = max(fit$fitted.values),
site = site,
month = month)
}
#Apply a custom function `my.fit` on each subset of data
#according to month and site using the group_by/nest/map method
# the results are rowbinded and returned in a data.frame
my.max<-
rlc2 %>%
dplyr::group_by(month, site) %>%
tidyr::nest() %>%
purrr::pmap_dfr(my.fit)
Next, join the results back on your data and plot a geom_line
rlc2 %>%
dplyr::left_join(my.max) %>%
ggplot(aes(x = par, y = etr))+
geom_point()+
stat_smooth(data = subset(rlc2, rlc2$month!="May" & rlc2$month!="Jun"),
method = "glm",
formula = y ~ x + log(x),
se = FALSE,
method.args = list(family = gaussian(link = "log"), start=c(a=0, b=0, c=0)))+
stat_smooth(data = subset(rlc2, rlc2$month=="May" | rlc2$month=="Jun"),
method = "nlsLM",
formula = y ~ M*(1 - exp(-(a*x))),
se = FALSE,
method.args = list(start=c(M=0, a=10)))+
geom_line(aes(y=max), col="red")+
facet_grid(rows = vars(month), cols = vars(site))
I'm trying to create a depth profile graph with the variables depth, distance and temperature. The data collected is from 9 different points with known distances between them (distance 5m apart, 9 stations, 9 different sets of data). The temperature readings are according to these 9 stations where a sonde was dropped directly down, taking readings of temperature every 2 seconds. Max depth at each of the 9 stations were taken from the boat also.
So the data I have is:
Depth at each of the 9 stations (y axis)
Temperature readings at each of the 9 stations, at around .2m intervals vertical until the bottom was reached (fill area)
distance between the stations, (x axis)
Is it possible to create a depth profile similar to this? (obviously without the greater resolution in this graph)
I've already tried messing around with ggplot2 and raster but I just can't seem to figure out how to do this.
One of the problems I've come across is how to make ggplot2 distinguish between say 5m depth temperature reading at station 1 and 5m temperature reading at station 5 since they have the same depth value.
Even if you can guide me towards another program that would allow me to create a graph like this, that would be great
[ REVISION ]
(Please comment me if you know more suitable interpolation methods, especially not needing to cut under bottoms data.)
ggplot() needs long data form.
library(ggplot2)
# example data
max.depths <- c(1.1, 4, 4.7, 7.7, 8.2, 7.8, 10.7, 12.1, 14.3)
depth.list <- sapply(max.depths, function(x) seq(0, x, 0.2))
temp.list <- list()
set.seed(1); for(i in 1:9) temp.list[[i]] <- sapply(depth.list[[i]], function(x) rnorm(1, 20 - x*0.5, 0.2))
set.seed(1); dist <- c(0, sapply(seq(5, 40, 5), function(x) rnorm(1, x, 1)))
dist.list <- sapply(1:9, function(x) rep(dist[x], length(depth.list[[x]])))
main.df <- data.frame(dist = unlist(dist.list), depth = unlist(depth.list) * -1, temp = unlist(temp.list))
# a raw graph
ggplot(main.df, aes(x = dist, y = depth, z = temp)) +
geom_point(aes(colour = temp), size = 1) +
scale_colour_gradientn(colours = topo.colors(10))
# a relatively raw graph (don't run with this example data)
ggplot(main.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp)) + # geom_contour() +
scale_fill_gradientn(colours = topo.colors(10))
If you want a graph such like you showed, you have to do interpolation. Some packages give you spatial interpolation methods. In this example, I used akima package but you should think seriously that which interpolation methods to use.
I used nx = 300 and ny = 300 in below code but I think it would be better to decide those values carefully. Large nx and ny gives a high resolution graph, but don't foreget real nx and ny (in this example, real nx is only 9 and ny is 101).
library(akima); library(dplyr)
interp.data <- interp(main.df$dist, main.df$depth, main.df$temp, nx = 300, ny = 300)
interp.df <- interp.data %>% interp2xyz() %>% as.data.frame()
names(interp.df) <- c("dist", "depth", "temp")
# draw interp.df
ggplot(interp.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp)) + # geom_contour() +
scale_fill_gradientn(colours = topo.colors(10))
# to think appropriateness of interpolation (raw and interpolation data)
ggplot(interp.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp), alpha = 0.3) + # interpolation
scale_fill_gradientn(colours = topo.colors(10)) +
geom_point(data = main.df, aes(colour = temp), size = 1) + # raw
scale_colour_gradientn(colours = topo.colors(10))
Bottoms don't match !!I found ?interp says "interpolation only within convex hull!", oops... I'm worrid about the interpolation around the problem-area, is it OK ? If no problem, you need only cut the data under the bottoms. If not, ... I can't answer immediately (below is an example code to cut).
bottoms <- max.depths * -1
# calculate bottom values using linear interpolation
approx.bottoms <- approx(dist, bottoms, n = 300) # n must be the same value as interp()'s nx
# change temp values under bottom into NA
library(dplyr)
interp.cut.df <- interp.df %>% cbind(bottoms = approx.bottoms$y) %>%
mutate(temp = ifelse(depth >= bottoms, temp, NA)) %>% select(-bottoms)
ggplot(interp.cut.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp)) +
scale_fill_gradientn(colours = topo.colors(10)) +
geom_point(data = main.df, size = 1)
If you want to use stat_contour
It is harder to use stat_contour than geom_raster because it needs a regular grid form. As far as I see your graph, your data (depth and distance) don't form a regular grid, it means it is much difficult to use stat_contour with your raw data. So I used interp.cut.df to draw a contour plot. And stat_contour have a endemic problem (see How to fill in the contour fully using stat_contour), so you need to expand your data.
library(dplyr)
# 1st: change NA into a temp's out range value (I used 0)
interp.contour.df <- interp.cut.df
interp.contour.df[is.na(interp.contour.df)] <- 0
# 2nd: expand the df (It's a little complex, so please use this function)
contour.support.func <- function(df) {
colname <- names(df)
names(df) <- c("x", "y", "z")
Range <- as.data.frame(sapply(df, range))
Dim <- as.data.frame(t(sapply(df, function(x) length(unique(x)))))
arb_z = Range$z[1] - diff(Range$z)/20
df2 <- rbind(df,
expand.grid(x = c(Range$x[1] - diff(Range$x)/20, Range$x[2] + diff(Range$x)/20),
y = seq(Range$y[1], Range$y[2], length = Dim$y), z = arb_z),
expand.grid(x = seq(Range$x[1], Range$x[2], length = Dim$x),
y = c(Range$y[1] - diff(Range$y)/20, Range$y[2] + diff(Range$y)/20), z = arb_z))
names(df2) <- colname
return(df2)
}
interp.contour.df2 <- contour.support.func(interp.contour.df)
# 3rd: check the temp range (these values are used to define contour's border (breaks))
range(interp.cut.df$temp, na.rm=T) # 12.51622 20.18904
# 4th: draw ... the bottom border is dirty !!
ggplot(interp.contour.df2, aes(x = dist, y = depth, z = temp)) +
stat_contour(geom="polygon", breaks = seq(12.51622, 20.18904, length = 11), aes(fill = ..level..)) +
coord_cartesian(xlim = range(dist), ylim = range(bottoms), expand = F) + # cut expanded area
scale_fill_gradientn(colours = topo.colors(10)) # breaks's length is 11, so 10 colors are needed
# [Note]
# You can define the contour's border values (breaks) and colors.
contour.breaks <- c(12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5)
# = seq(12.5, 20.5, 1) or seq(12.5, 20.5, length = 9)
contour.colors <- c("darkblue", "cyan3", "cyan1", "green3", "green", "yellow2","pink", "darkred")
# breaks's length is 9, so 8 colors are needed.
# 5th: vanish the bottom border by bottom line
approx.df <- data.frame(dist = approx.bottoms$x, depth = approx.bottoms$y, temp = 0) # 0 is dummy value
ggplot(interp.contour.df2, aes(x = dist, y = depth, z = temp)) +
stat_contour(geom="polygon", breaks = contour.breaks, aes(fill = ..level..)) +
coord_cartesian(xlim=range(dist), ylim=range(bottoms), expand = F) +
scale_fill_gradientn(colours = contour.colors) +
geom_line(data = approx.df, lwd=1.5, color="gray50")
bonus: legend technic
library(dplyr)
interp.contour.df3 <- interp.contour.df2 %>% mutate(temp2 = cut(temp, breaks = contour.breaks))
interp.contour.df3$temp2 <- factor(interp.contour.df3$temp2, levels = rev(levels(interp.contour.df3$temp2)))
ggplot(interp.contour.df3, aes(x = dist, y = depth, z = temp)) +
stat_contour(geom="polygon", breaks = contour.breaks, aes(fill = ..level..)) +
coord_cartesian(xlim=range(dist), ylim=range(bottoms), expand = F) +
scale_fill_gradientn(colours = contour.colors, guide = F) + # add guide = F
geom_line(data = approx.df, lwd=1.5, color="gray50") +
geom_point(aes(colour = temp2), pch = 15, alpha = 0) + # add
guides(colour = guide_legend(override.aes = list(colour = rev(contour.colors), alpha = 1, cex = 5))) + # add
labs(colour = "temp") # add
You want to treat this as a 3-D surface with temperature as the z dimension. The given plot is a contour plot and it looks like ggplot2 can do that with stat_contour.
I'm not sure how the contour lines are computed (often it's linear interpolation along a Delaunay triangulation). If you want more control over how to interpolate between your x/y grid points, you can calculate a surface model first and feed those z coordinates into ggplot2.
I asked this question yesterday about storing a plot within an object. I tried implementing the first approach (aware that I did not specify that I was using qplot() in my original question) and noticed that it did not work as expected.
library(ggplot2) # add ggplot2
string = "C:/example.pdf" # Setup pdf
pdf(string,height=6,width=9)
x_range <- range(1,50) # Specify Range
# Create a list to hold the plot objects.
pltList <- list()
pltList[]
for(i in 1 : 16){
# Organise data
y = (1:50) * i * 1000 # Get y col
x = (1:50) # get x col
y = log(y) # Use natural log
# Regression
lm.0 = lm(formula = y ~ x) # make linear model
inter = summary(lm.0)$coefficients[1,1] # Get intercept
slop = summary(lm.0)$coefficients[2,1] # Get slope
# Make plot name
pltName <- paste( 'a', i, sep = '' )
# make plot object
p <- qplot(
x, y,
xlab = "Radius [km]",
ylab = "Services [log]",
xlim = x_range,
main = paste("Sample",i)
) + geom_abline(intercept = inter, slope = slop, colour = "red", size = 1)
print(p)
pltList[[pltName]] = p
}
# close the PDF file
dev.off()
I have used sample numbers in this case so the code runs if it is just copied. I did spend a few hours puzzling over this but I cannot figure out what is going wrong. It writes the first set of pdfs without problem, so I have 16 pdfs with the correct plots.
Then when I use this piece of code:
string = "C:/test_tabloid.pdf"
pdf(string, height = 11, width = 17)
grid.newpage()
pushViewport( viewport( layout = grid.layout(3, 3) ) )
vplayout <- function(x, y){viewport(layout.pos.row = x, layout.pos.col = y)}
counter = 1
# Page 1
for (i in 1:3){
for (j in 1:3){
pltName <- paste( 'a', counter, sep = '' )
print( pltList[[pltName]], vp = vplayout(i,j) )
counter = counter + 1
}
}
dev.off()
the result I get is the last linear model line (abline) on every graph, but the data does not change. When I check my list of plots, it seems that all of them become overwritten by the most recent plot (with the exception of the abline object).
A less important secondary question was how to generate a muli-page pdf with several plots on each page, but the main goal of my code was to store the plots in a list that I could access at a later date.
Ok, so if your plot command is changed to
p <- qplot(data = data.frame(x = x, y = y),
x, y,
xlab = "Radius [km]",
ylab = "Services [log]",
xlim = x_range,
ylim = c(0,10),
main = paste("Sample",i)
) + geom_abline(intercept = inter, slope = slop, colour = "red", size = 1)
then everything works as expected. Here's what I suspect is happening (although Hadley could probably clarify things). When ggplot2 "saves" the data, what it actually does is save a data frame, and the names of the parameters. So for the command as I have given it, you get
> summary(pltList[["a1"]])
data: x, y [50x2]
mapping: x = x, y = y
scales: x, y
faceting: facet_grid(. ~ ., FALSE)
-----------------------------------
geom_point:
stat_identity:
position_identity: (width = NULL, height = NULL)
mapping: group = 1
geom_abline: colour = red, size = 1
stat_abline: intercept = 2.55595281266726, slope = 0.05543539319091
position_identity: (width = NULL, height = NULL)
However, if you don't specify a data parameter in qplot, all the variables get evaluated in the current scope, because there is no attached (read: saved) data frame.
data: [0x0]
mapping: x = x, y = y
scales: x, y
faceting: facet_grid(. ~ ., FALSE)
-----------------------------------
geom_point:
stat_identity:
position_identity: (width = NULL, height = NULL)
mapping: group = 1
geom_abline: colour = red, size = 1
stat_abline: intercept = 2.55595281266726, slope = 0.05543539319091
position_identity: (width = NULL, height = NULL)
So when the plot is generated the second time around, rather than using the original values, it uses the current values of x and y.
I think you should use the data argument in qplot, i.e., store your vectors in a data frame.
See Hadley's book, Section 4.4:
The restriction on the data is simple: it must be a data frame. This is restrictive, and unlike other graphics packages in R. Lattice functions can take an optional data frame or use vectors directly from the global environment. ...
The data is stored in the plot object as a copy, not a reference. This has two
important consequences: if your data changes, the plot will not; and ggplot2 objects are entirely self-contained so that they can be save()d to disk and later load()ed and plotted without needing anything else from that session.
There is a bug in your code concerning list subscripting. It should be
pltList[[pltName]]
not
pltList[pltName]
Note:
class(pltList[1])
[1] "list"
pltList[1] is a list containing the first element of pltList.
class(pltList[[1]])
[1] "ggplot"
pltList[[1]] is the first element of pltList.
For your second question: Multi-page pdfs are easy -- see help(pdf):
onefile: logical: if true (the default) allow multiple figures in one
file. If false, generate a file with name containing the
page number for each page. Defaults to ‘TRUE’.
For your main question, I don't understand if you want to store the plot inputs in a list for later processing, or the plot outputs. If it is the latter, I am not sure that plot() returns an object you can store and retrieve.
Another suggestion regarding your second question would be to use either Sweave or Brew as they will give you complete control over how you display your multi-page pdf.
Have a look at this related question.