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I want to produce a figure that will have several scatterplots. Each scatterplot should have its own axis limits, but I want the aspect ratio of each plot to be 1 so that the 1:1 line goes through the plot at a 45 degree angle.
Here's an example of what I have going on:
met <- structure(list(datetime = structure(c(946756800, 946756800, 946756800,946756800, 946756800, 946756800, 946756800, 960465600, 960465600,960465600), class = c("POSIXct", "POSIXt"), tzone = "UTC"), Met_Parameter = c("1/MO l","albedo", "Bowen r", "conv mix h", "mech mix h", "MO l", "sens h flux","wind s", "wstar", "z0"), `Original Met` = c(0.0746268656716418,0.15, 0.8, -999, 24, 13.4, -0.7, 4, 1.2, 1), `Converted Met` = c(-0.0105263157894737,0.15, 0.8, 600, 367, -95, 21.6, 4, 1.2, 1)), row.names = c(1L,2L, 3L, 4L, 5L, 6L, 7L, 1054L, 1055L, 1056L), class = "data.frame")
pOUT <-
ggplot2::ggplot(data = met,
aes(x = `Original Met`,
y = `Converted Met`)) +
ggplot2::geom_point()+
ggplot2::geom_abline(intercept = 0,
slope = 1
) +
ggplot2::facet_wrap("Met_Parameter",
scales = "free") +
ggplot2::ggtitle("Comparison of Original Meteorology Versus Up Over Down Meterology") +
ggplot2::theme_bw()
What I am missing still is how to make the x and y axes fixed within the plot.
Thanks!
I am working on an exercise asking me "Plot the residuals against Y_hat, each predictor variable, and each two-factor interaction term on separate graphs." Here is a snippet of the data set I am using:
> dput(head(Commercial_Properties, 10))
structure(list(Rental_Rates = c(13.5, 12, 10.5, 15, 14, 10.5,
14, 16.5, 17.5, 16.5), Age = c(1, 14, 16, 4, 11, 15, 2, 1, 1,
8), Op_Expense_Tax = c(5.02, 8.19, 3, 10.7, 8.97, 9.45, 8, 6.62,
6.2, 11.78), Vacancy_Rate = c(0.14, 0.27, 0, 0.05, 0.07, 0.24,
0.19, 0.6, 0, 0.03), Total_Sq_Ft = c(123000, 104079, 39998, 57112,
60000, 101385, 31300, 248172, 215000, 251015), residuals = c(`1` = -1.03567244005944,
`2` = -1.51380641405037, `3` = -0.591053402133659, `4` = -0.133568082335235,
`5` = 0.313283765150399, `6` = -3.18718522392237, `7` = -0.538356748944345,
`8` = 0.236302385996349, `9` = 1.98922037248654, `10` = 0.105829602747806
)), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
))
From here I created the proper linear model that includes two factor interaction terms:
commercial_properties_lm_two_degree_interaction <-
lm(data=Commercial_Properties,
formula=Rental_Rates ~ (Age + Op_Expense_Tax + Vacancy_Rate + Total_Sq_Ft)^2)
Next what I was hoping to accomplish was to plot the residuals not just of the linear terms, but also of the interaction terms. I attempted to do this using the residualPlots() function in the car package
library(car)
residualPlots(model=commercial_properties_lm_two_degree_interaction,
terms=~ (Age + Op_Expense_Tax + Vacancy_Rate + Total_Sq_Ft)^2)
When applied in this way the output only produced the residual plots against the linear terms, it didn't plot any interactions. So I then attempted to do it manually, but I got an error:
residualPlots(model=commercial_properties_lm_two_degree_interaction,
terms=~ Age + Op_Expense_Tax + Vacancy_Rate + Tota_Sq_Ft +
Age:Op_Expense_Tax + Age:Vacancy_Rate)
Error in termsToMf(model, terms) : argument 'terms' not interpretable.
Now if I were to do things completely manually I was able to get an interaction plot for example:
with(data=Commercial_Properties, plot(x=Op_Expense_Tax * Vacancy_Rate, y=residuals))
plotted successfully. My issue is that sure I can do this completely manually for a reasonably small amount of variables, but it will get extremely tedious once the amount of variables begins to get larger.
So my question is if there is a way to use an already created function in R to make residual plots of the interaction terms or would I be left to doing it completely manually or most likely having to write some sort of loop ?
Note, I'm not asking about partial residuals. I haven't gotten to that point in my text I'm using. Just plain interaction terms against residuals.
You could do an eval(parse()) approach using the 'term.labels' attribute.
With gsub(':', '*', a[grep(':', a)]) pull out the interaction terms and replace : with * so it can be evaluated.
a <- attr(terms(commercial_properties_lm_two_degree_interaction), 'term.labels')
op <- par(mfrow=c(2, 3))
with(Commercial_Properties,
lapply(gsub(':', '*', a[grep(':', a)]), function(x)
plot(eval(parse(text=x)), residuals, xlab=x)))
par(op)
Edit
This is how we would do this with a for loop in R (but see comments below):
as <- gsub(':', '*', a[grep(':', a)])
op <- par(mfrow=c(2, 3))
for (x in as) {
with(Commercial_Properties,
plot(eval(parse(text=x)), residuals, xlab=x)
)
}
par(op)
I have an issue with plotting continuous distributions without sharp edges in ggplot2. I need to show two of them on one plot. Also, it does not have to be ggplot2 to achieve this result.
I am aware, that the number of data points directly influences the smoothness of the line, but it was not the case here. Below you can see some sample data (from dput)
sample.data<-list(beta.data = structure(list(cluster = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), beta.density = c(0, 3.42273368363934e-43, 8.42987148403021e-29,
2.04764468657484e-20, 1.69485562831516e-14, 6.07999638837842e-10, 2.88180370232676e-06, 0.00314064636750876, 0.954118897015866, 0, 0, 3.80101893822358e-36, 6.43342582657081e-22, 6.82956252277493e-14, 1.75291058931833e-08, 0.000131874335695378, 0.0750918340641428, 3.72532418759802, 5.05242078519544, 0), pr = c(0, 0.111111111111111, 0.222222222222222, 0.333333333333333, 0.444444444444444, 0.555555555555556, 0.666666666666667, 0.777777777777778, 0.888888888888889, 1, 0, 0.111111111111111, 0.222222222222222, 0.333333333333333, 0.444444444444444, 0.555555555555556, 0.666666666666667, 0.777777777777778, 0.888888888888889, 1)), row.names = c(NA, -20L), class = "data.frame"), beta.params = structure(list(cluster = 1:2, a = c(49, 50), b = c(2, 10), ni.beta = c(0.961,0.833), sd.beta = c(0.00072, 0.00228)), row.names = c(NA,-2L), class = "data.frame"))
Before I was using geom_col, but it discretizes values. I went with geom_area:
ggplot(sample.data$beta.data, aes(x = pr, y = beta.density)) +
geom_area(stat = "function",
fun = dbeta,
args = list(shape1 = sample.data$beta.params[1,"a"], shape2 = sample.data$beta.params[1,"b"]),
fill = "#F1C40F",
colour = "black",
alpha = 0.7) +
geom_area(stat = "function",
fun=dbeta,
args = list(shape1 = sample.data$beta.params[2,"a"], shape2 = sample.data$beta.params[2,"b"]),
fill = "#3498DB",
colour = "black",
alpha = 0.7)
I presented you the data with 10 points, but 1000 points look almost the same. It is not the case here, where even 100 points looks ok:
p = seq(0,1, length=100)
plot(p, dbeta(p, 50, 10), ylab="Density", type ="l", col=4, , lwd = 2)
Here I am attaching code to simulate the data. Oh, and these troublesome beta parameters were a = 49 and b = 2.
len <- 100
p <- seq(0,1, length.out = len)
df <- data.frame(rbind(cbind("cl" = rep(1, times = length(p)), "beta" = dbeta(p, 50, 10),"p"= p),
cbind("cl" = rep(1, times = length(p)), "beta" = dbeta(p, 40, 2),"p"= p)))
Do you have any ideas?
EDIT: The pdfs stands here for probability density functions. That is why I have not put "pdf" as a tag. My apologies for the confusion!
Anyway, when I tried to print graphic to PDF file, the result was poor as well (sharp edges). But it the end, it shouldn't matter. I want to see smooth lines whatever I do (reasonably).
EDIT2 It is possible to achieve because:
library(mosaic)
theme_set(theme_bw())
xpbeta(c(0.7, 0.90), shape1 = 49, shape2 = 2)
It produces nice, smoothed beta dist with parameters (49, 2). But then again, I need to show two dists in one chart.
I have found the answer. It still needs some editing (like transparency/alpha which I couldn't figure out), but in general, this is what I meant. Code:
library(mosaic)
plotDist('beta', params=list(49,2), kind='density', type = "h", col = "#3498DB", xlim = c(0,1))
plotDist('beta', params=list(50, 10), kind='density', , type = "h", col = "#F1C40F", add = TRUE)
plotDist('beta', params=list(49,2), kind='density', add = TRUE, col = "black")
plotDist('beta', params=list(50, 10), kind='density', add = TRUE, col = "black")
Result:
We can add as many distributions as we want, using "add" parameter.
Parameter type = "h", is used to draw filled distribution. Without it, the only line is visible. In my answer, I draw the two lines and two filled dists. I would be really happy if someone could show a better answer, though.
EDIT:
I think I found my perfect answer!
Here is the code:
library(ggformula)
theme_set(theme_bw())
gf_dist("beta", shape1 = 49, shape2 = 2, geom = "area", alpha = 0.5, fill = "#F1C40F") %>%
gf_dist("beta", shape1 = 49, shape2 = 2) %>%
gf_dist("beta", shape1 = 50, shape2 = 10, geom = "area", alpha = 0.5, fill = "#3498DB") %>%
gf_dist("beta", shape1 = 50, shape2 = 10)
It is much faster than the previous code, parameter alpha is obvious and it is relatively easy to combine many plots! Because of transparency, you can nicely see the overlap of both distributions.
I have this data called mydf where I have hybrid sample comparison for efficiency. There are seven different efficiency columns for the intermixing of sampleA and sampleB. I want to see the plot for these seven efficiencies to see at which efficiency level will they significantly drop compared to the first few columns.
mydf<-structure(list(sample_A = structure(c(1L, 2L, 2L, 2L, 3L, 4L), .Label = c("2568",
"2669", "2670", "2671", "2946", "LPH-001-10_AK1", "LPH-001-12_AK2",
"LPH-001-9"), class = "factor"), sample_B = structure(c(1L, 2L,
3L, 4L, 3L, 4L), .Label = c("2568", "2669", "2670", "2671", "2946",
"LPH-001-10_AK1", "LPH-001-12_AK2", "LPH-001-9"), class = "factor"),
efficiency = c(1.02, 0.964, 0.415, 0.422, 0.98, 0.986), efficiency2 = c(1,
0.944, 0.395, 0.402, 0.96, 0.966), efficiency3 = c(0.9, 0.844,
0.295, 0.302, 0.86, 0.866), efficiency4 = c(0.32, 0.264,
-0.285, -0.278, 0.28, 0.286), efficiency5 = c(0.02, -0.0360000000000001,
-0.585, -0.578, -0.0200000000000001, -0.0140000000000001),
efficiency6 = c(0.12, 0.0639999999999999, -0.485, -0.478,
0.08, 0.086), efficiency7 = c(0.02, -0.036, -0.585, -0.578,
-0.02, -0.014)), .Names = c("sample_A", "sample_B", "efficiency",
"efficiency2", "efficiency3", "efficiency4", "efficiency5", "efficiency6",
"efficiency7"), row.names = c(NA, 6L), class = "data.frame")
Here's one way to plot your data:
mydf <- structure(list(sample_A=structure(c(1L,2L,2L,2L,3L,4L),.Label=c('2568','2669','2670','2671','2946','LPH-001-10_AK1','LPH-001-12_AK2','LPH-001-9'),class='factor'),sample_B=structure(c(1L,2L,3L,4L,3L,4L),.Label=c('2568','2669','2670','2671','2946','LPH-001-10_AK1','LPH-001-12_AK2','LPH-001-9'),class='factor'),efficiency=c(1.02,0.964,0.415,0.422,0.98,0.986),efficiency2=c(1,0.944,0.395,0.402,0.96,0.966),efficiency3=c(0.9,0.844,0.295,0.302,0.86,0.866),efficiency4=c(0.32,0.264,-0.285,-0.278,0.28,0.286),efficiency5=c(0.02,-0.0360000000000001,-0.585,-0.578,-0.0200000000000001,-0.0140000000000001),efficiency6=c(0.12,0.0639999999999999,-0.485,-0.478,0.08,0.086),efficiency7=c(0.02,-0.036,-0.585,-0.578,-0.02,-0.014)),.Names=c('sample_A','sample_B','efficiency','efficiency2','efficiency3','efficiency4','efficiency5','efficiency6','efficiency7'),row.names=c(NA,6L),class='data.frame');
effCis <- grep('^efficiency',names(mydf));
xlim <- c(1,length(effCis));
ylim <- range(mydf[,effCis],na.rm=T);
ylim[1L] <- floor(ylim[1L]/0.1)*0.1;
ylim[2L] <- ceiling(ylim[2L]/0.1)*0.1;
xticks <- seq_along(effCis);
yticks <- seq(ylim[1L],ylim[2L],0.1);
plot(NA,xlim=xlim,ylim=ylim,xlab='measurement',ylab='efficiency',xaxs='i',yaxs='i',axes=F);
abline(v=xticks,col='lightgrey');
abline(h=yticks,col='lightgrey');
abline(h=0,lwd=2);
axis(1L,xticks,xticks,font=2L,cex.axis=0.7);
axis(2L,yticks,sprintf('%.1f',yticks),las=1L,font=2L,cex.axis=0.7);
hybrid.col <- data.frame(hybrid=seq_len(nrow(mydf)),col=c('red','green','blue','gold','cyan','magenta'),stringsAsFactors=F);
splineN <- 200L;
for (ri in seq_len(nrow(hybrid.col))) {
hybrid <- hybrid.col$hybrid[ri];
col <- hybrid.col$col[ri];
x <- xticks;
y <- c(as.matrix(mydf[hybrid,effCis]));
points(x,y,pch=16L,col=col,xpd=NA);
with(spline(x,y,splineN),{
lines(x,y,col=col,lwd=2,xpd=NA);
localwin <- which(x>2 & x<3);
tp <- which.min(abs(diff(y[localwin])));
if (length(tp)>0L) points(x[localwin[tp]],y[localwin[tp]],col=col,pch=4L);
localwin <- which(x>2 & x<5);
tp <- which.min(diff(y[localwin]));
if (length(tp)>0L) {
m <- diff(y[localwin[seq(tp,len=2L)]])/diff(x[localwin[seq(tp,len=2L)]]);
if (is.finite(m)) abline(y[localwin[tp]]-m*x[localwin[tp]],m,col=col,lty=2L);
};
});
};
legend(5.5,0.95,paste0(mydf$sample_A,' / ',mydf$sample_B),fill=hybrid.col$col,cex=0.7,title='hybrid');
I wasn't 100% sure what you meant by the asymptote. I initially thought maybe you wanted the local maxima of the curves just prior to where they begin to drop, which is why I marked the local maxima with points (symbol X, i.e. pch=4L). But then I realized maybe you meant the tangent line along the drop, so I added lines tangent to the points of steepest slope.
This is the definition of asymptote:
a straight line approached by a given curve as one of the variables in the equation of the curve approaches infinity.
I don't think that's applicable here; plotting this data does not involve taking anything to infinity. I think you want either the local maxima or tangent lines.
I am trying to create a barplot with multiple errorbars. Something like this: http://flyordie.sin.khk.be/r/histogram%20error%20bars.PNG
I have the following dataset:
http://flyordie.sin.khk.be/r/output.csv
I have tried using ggplot2 and lattice graphics, but haven't found anything that suits my needs.
My current code for showing the barchart is this:
data <- read.csv("c:/output.csv")
data
par(las=3)
barplot(data$PlateId,
height=data$HC.Maximum,
names.arg=data$PlateId,
col="lightblue")
And to show the highest errorbars i use this code
library(ggplot2)
limits <- aes(ymax = qc$HC.Maximum, ymin = qc$HC.Minimum)
p <- ggplot(qc, aes(colour=HC.Median,x=PlateId))
p + geom_bar(position="dodge")+ geom_errorbar(limits,position="dodge")
But I have no clue on how to put them on the same graphic (like in my example)
The data:
qc <- structure(list(row = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Row", class = "factor"),
ID = 1:14, PlateId = c(35276L, 35279L, 35280L, 35281L, 35282L,
35290L, 35291L, 35292L, 35293L, 35294L, 35295L, 35296L, 35297L,
35298L), LC.Median = c(439688.495, 509376.055, 475218.99,
497368.215, 481801.9, 468603.43, 494713.175, 459047.385,
482819.47, 495162.31, 449592.51, 460564.95, 478715.915, 452293.465
), LC.Stdev = c(52290.12229, 49648.49436, 55743.10306, 62002.53552,
46908.66149, 52489.615, 48016.94019, 52082.23899, 47934.37133,
58977.84845, 45827.62648, 53514.21095, 49638.98286, 139686.144
), LC.Minimum = c(279610.16, 423651.45, 356422.31, 411639.77,
397362.84, 345178.07, 406073.72, 352834.86, 339035.77, 369554.11,
348688.39, 357341.56, 370463.11, 210367.91), LC.Maximum = c(498195.9,
630648.53, 614625.78, 686737.35, 621372.36, 576491.41, 579708.95,
580633.28, 580125.9, 622108.73, 530234.87, 563616.65, 614936.33,
730272.63), HC.Median = c(507356.465, 553226.525, 447067.77,
452223.76, 453439.37, 422491.755, 447438.8, 435034.635, 446148.105,
438089.69, 466748.63, 440005.81, 454927.74, 483599.71), HC.Stdev = c(65355.46121,
72762.07338, 80118.37641, 43653.99318, 73389.12355, 62590.47601,
46421.36678, 62822.88532, 61175.4241, 64418.56174, 63101.2232,
68166.51814, 61256.74139, 87354.9441), HC.Minimum = c(381552.05,
391124.94, 280614.72, 395454.12, 291433.84, 252579.15, 331661.03,
296223.64, 240262.37, 299431.98, 375224.27, 278780.87, 310275.66,
213170.04), HC.Maximum = c(626483.6, 635111.41, 555357.3,
528822.8, 534172.42, 514927.42, 538385.26, 533024.74, 524973.99,
544335.94, 564954.87, 572206.98, 547489.1, 565338.09), zPrime = c(-3.96,
-23.73, -7.88, -5.81, -5.32, -5.54, -4.48, -7.98, -6.99,
-5.63, -22.54, -33.83, -11.92, -17.44), Sb = c(1.17, 1.03,
0.91, 0.91, 0.89, 0.89, 0.9, 0.92, 0.92, 0.89, 1.04, 0.98,
0.95, 1.09), Sn = c(1.37, 0.3, -0.83, -0.76, -1.22, -1.01,
-1.08, -0.74, -0.86, -0.95, 0.31, -0.2, -0.52, 0.27)), .Names = c("row",
"ID", "PlateId", "LC.Median", "LC.Stdev", "LC.Minimum", "LC.Maximum",
"HC.Median", "HC.Stdev", "HC.Minimum", "HC.Maximum", "zPrime",
"Sb", "Sn"), class = "data.frame", row.names = c(NA, -14L))
When creating plots with multiple layers, I approach it as follows:
Define common aesthetics in the initial call to ggplot
Define additional aesthetics in each additional layer
Note that I have modified your code, since I couldn't get your example to work:
Provide an explicit binwidth=1 to geom_bar to remove the warnings
Remove the position=dodge since this is the default and redundant
Supply an explicit stat=identity to geom_bar
The code:
ggplot(qc, aes(x=PlateId)) +
geom_bar(aes(y=HC.Median), binwidth=1, stat="identity", fill="cyan") +
geom_errorbar(aes(ymin=HC.Minimum, ymax=LC.Minimum), colour="red") +
geom_errorbar(aes(ymin=LC.Maximum, ymax=HC.Maximum), colour="purple")
Just add another geom_errorbar.
#Rename limits to limits_hi
limits_hi <- aes(ymax = qc$HC.Maximum, ymin = qc$HC.Minimum)
#Define the other error bar
limits_lo <- aes(ymax = qc$LC.Maximum, ymin = LC.Minimum)
#I'm not quite sure what you want in the bars; see if this looks right
p <- ggplot(qc, aes(factor(PlateId), HC.Median))
p +
geom_bar(position="dodge") +
geom_errorbar(limits_hi, position="dodge", colour = "red") +
geom_errorbar(limits_lo, position="dodge", colour = "blue") +
opts(axis.text.x = theme_text(angle = 30))