I am trying to create a scatterplot in rstudio with my data. I am new to rstudio and having a lot of time understanding. The code I have found says plot().
This is what I used: plot(pa2_wti2$ï..Approving, pa2_wti2$ï..Price)
Even when I tried a single it didn't give me a scatter plot. I have tried ggplot but it says it does not find what I have put as the x.
I have tried:
ggplot(pa2_wti2) +
geom_point(aes(x = Price, y= Approving))
ggplot(pa2_wti2) +
geom_point(aes(x = ï..Price, y= ï..Approving))
Any help is welcomed. Thanks!
Here is some additional information:
wti2 <- read.csv("C:/Users/thomp/OneDrive/Desktop/FQM/Data Project 2/WTI Price Only Take 2.csv")
summary(wti2)
table(wti2)
sd(wti2$ï..Price)
wti2_prices2 = rnorm(wti2$ï..Price, mean=43.92, sd=28.1762)
pa2 <- read.csv("C:/Users/thomp/OneDrive/Desktop/FQM/Data Project 2/PA Approval Only Take 2.csv")
summary(pa2)
table(pa2)
sd(pa2$ï..Approving)
pa2_approve = rnorm(pa2$ï..Approving, mean=50.09, sd=11.46188 )
plot(pa2_wti2$ï..Approving)
plot(plot(pa2_wti2$ï..Approving, pa2_wti2$ï..Price)
Related
Update- this issue was solved, updated code is at the end of the post.
I am trying to create a swimmer plot to visualize individual patient duration of treatment with a drug administered at multiple dose levels (DLs). Each patient will be be assigned to treatment with only one DL, but multiple patients can be assigned to a given DL (e.g. 3 patients at DL1, 3 patients and DL2, etc.). I would like to color code the bars in the swimmer plot according to DL.
I am using the swimplot package for R and have been following the guide located here (https://cran.r-project.org/web/packages/swimplot/vignettes/Introduction.to.swimplot.html).
This guide has been sufficient for most things I have tried, up until I tried to change the colors of the bars in the plot and corresponding legend. Following the section in that guide titled "Modifying Colours and shapes" under "Making the plots more aesthetically pleasing with ggplot manipulations", I was able to change the bar colors in the legend, but not the bars themselves.
Example here
I have been using the following code.
library(ggplot2)
library (swimplot)
library (gdata)
library (readxl)
ClinicalTrial.Arm <- read_excel("Swimmer_Test_Data1.xls")
ClinicalTrial.Arm <- as.data.frame(ClinicalTrial.Arm)
arm_plot <- swimmer_plot(df=ClinicalTrial.Arm,id='id',end='End_trt',width=.85+ scale_fill_manual(name="Arm",values=c("DL1" ="#003f5c", "DL2"="#374c80","DL3"="#7a5195","DL4"="#bc5090","DL5"="#ef5675","DL6"="#ff764a","DL7"="#ffa600"))+ scale_color_manual(name="Arm",values=c("DL1" ="#003f5c", "DL2"="#374c80","DL3"="#7a5195","DL4"="#bc5090","DL5"="#ef5675","DL6"="#ff764a","DL7"="#ffa600"))
arm_plot
I have tried a number of things to fix this, but am quite new to R and don't think I really know enough to troubleshoot effectively. I have tried various syntax changes (e.g. removing quotation marks) and have tried using the geom bar command but wasn't sure how/what to map to X and Y (it also seems like I shouldn't need to do this).
I have also tried using the following code, but get an error.
Colors <- c("DL1" ="#003f5c", "DL2"="#374c80","DL3"="#7a5195","DL4"="#bc5090","DL5"="#ef5675","DL6"="#ff764a","DL7"="#ffa600")
arm_plot <- swimmer_plot(df=ClinicalTrial.Arm,id='id',end='End_trt',width=.85, fill = Colors)+ scale_fill_manual(name="Arm",values=c("DL1" ="#003f5c", "DL2"="#374c80","DL3"="#7a5195","DL4"="#bc5090","DL5"="#ef5675","DL6"="#ff764a","DL7"="#ffa600"))+ scale_color_manual(name="Arm",values=c("DL1" ="#003f5c", "DL2"="#374c80","DL3"="#7a5195","DL4"="#bc5090","DL5"="#ef5675","DL6"="#ff764a","DL7"="#ffa600"))
Error in `check_aesthetics()`:
! Aesthetics must be either length 1 or the same as the data (20): fill
Run `rlang::last_error()` to see where the error occurred.
Any help here would be greatly appreciated.
Solved! Updated, working code
library(ggplot2)
library (swimplot)
library (gdata)
library (readxl)
ClinicalTrial.Arm <- read_excel("Swimmer_Test_Data1.xls")
ClinicalTrial.Arm <- as.data.frame(ClinicalTrial.Arm)
Colors <- c("DL1" ="#003f5c", "DL2"="#374c80","DL3"="#7a5195","DL4"="#bc5090","DL5"="#ef5675","DL6"="#ff764a","DL7"="#ffa600")
arm_plot <- swimmer_plot(df=ClinicalTrial.Arm,id='id',end='End_trt', name_fill = "Arm", width=.85) + scale_fill_manual(name="Arm",values = Colors) +
scale_color_manual(name="Arm",values=Colors)
To make your code work you first have to map a variable on the fill aesthetic which using swimplot could be achieved via the name_fill argument:
Note: As I use the ClinicalTrial.Arm dataset from the swimplot package I adjusted your color palette to make it work with the three categories of the Arm column in this dataset.
library(ggplot2)
library(swimplot)
#pal <- c("DL1" = "#003f5c", "DL2" = "#374c80", "DL3" = "#7a5195", "DL4" = "#bc5090", "DL5" = "#ef5675", "DL6" = "#ff764a", "DL7" = "#ffa600")
pal <- c("Arm A" = "#003f5c", "Arm B" = "#bc5090", "Off Treatment" = "#ffa600")
swimmer_plot(df = ClinicalTrial.Arm, id = "id", end = "End_trt", name_fill = "Arm", width = .85) +
scale_fill_manual(name = "Arm", values = pal)
I'm new to flowCore + R. I would like to mimic a histogram plot after gating that can be manually done in FlowJo software. I got something similar but it doesn't look quite right because it is a "density" plot and is shifted. How can I get the x axis to shift over and look similar to how FlowJo outputs the plot? I tried reading this document but couldn't find a plot similar to the one in FlowJo: howtoflowcore Appreciate any guidance. Thanks.
code snippet:
library(flowCore)
parentpath <- "/parent/path"
subfolder <- "Sample 1"
fcs_files <- list.files(paste0(parentpath, subfolder), pattern = ".fcs")
fs <- read.flowSet(fcs_files)
rect.g <- rectangleGate(filterId = "main",list("FSC-A" = c(1e5, 2e5), "SSC-A" = c(3e4,1e5)))
fs_sub <- Subset(fs, rect.g)
p <- ggcyto(fs_sub[[15]], aes(x= `UV-379-A`)) +
geom_density(fill='black', alpha = 0.4) +
ggcyto_par_set(limits = list(x = c(-1e3, 5e4), y = c(0, 6e-5)))
p
FlowJo output:
R FlowCore output:
The reason that for the "shift" is that the x axis is logarithmic (base 10) in the flowJo graph. To achieve the same result in R, add
+ scale_x_log10()
after the existing code. This might interact weirdly with the axis limits you've set, so bare that in mind.
To make the y-axis "count" rather than density, you can change the first line of your ggcyto() call to:
aes(x= `UV-379-A`, y = after_stat(count))
Let me know if that works - I don't have your data to hand so that's all from memory!
For any purely aesthetic changes, they are relatively easy to look up.
I have an interactions matrix that looks like this:
chr10.117800000 chr10.117801000 chr10.117802000 chr10.117803000 chr10.117804000 chr10.117805000 chr10.117806000
chr10.117800000 0.006484824 0.006451925 0.006422584 0.006386328 0.006292793 0.006277799 0.006231732
chr10.117801000 0.006451925 0.006435975 0.006415112 0.006378994 0.006285668 0.006272825 0.006226796
chr10.117802000 0.006422584 0.006415112 0.006406884 0.006370748 0.006277475 0.006264644 0.006222890
chr10.117803000 0.006386328 0.006378994 0.006370748 0.006346183 0.006307680 0.006294757 0.006254941
chr10.117804000 0.006292793 0.006285668 0.006277475 0.006307680 0.006324919 0.006311969 0.006276300
chr10.117805000 0.006277799 0.006272825 0.006264644 0.006294757 0.006311969 0.006303327 0.006269839
chr10.117806000 0.006231732 0.006226796 0.006222890 0.006254941 0.006276300 0.006269839 0.006244967
chr10.117807000 0.006242481 0.006235449 0.006231538 0.006265652 0.006287100 0.006282769 0.006257918
chr10.117808000 0.006140677 0.006133760 0.006129913 0.006161364 0.006188786 0.006186627 0.006166320
chr10.117809000 0.006098614 0.006091771 0.006087950 0.006119074 0.006146385 0.006146359 0.006130442
I am trying to linearize and label it to prep it for ggplot2, which I have accomplished using this code:
data <- as.vector(data)
data <- cbind(Counts = data, Genotype = "KO")
However, whenever I take my data and plot it using ggplot2 with this command:
blah <- ggplot(data =test, aes(x = Genotype, y = Counts)) + geom_boxplot()
It gives me a weird looking box plot that looks like this:
I have tried to add scale_y_continuous(limits = c(0, 0.002)), but each time that I do that I get an error that I'm trying to add discrete values to a continuous scale. Does anyone know what's going on, or if there is a better way to do this?
I want to compare the fit of different distributions to my data in a single plot. The qqcomp function from the fitdistrplus package pretty much does exactly what I want to do. The only problem I have however, is that it's mostly written using base R plot and all my other plots are written in ggplot2. I basically just want to customize the qqcomp plots to look like they have been made in ggplot2.
From the documentation (https://www.rdocumentation.org/packages/fitdistrplus/versions/1.0-14/topics/graphcomp) I get that this is totally possible by setting plotstyle="ggplot". If I do this however, no points are showing up on the plot, even though it worked perfectly without the plotstyle argument. Here is a little example to visualize my problem:
library(fitdistrplus)
library(ggplot2)
set.seed(42)
vec <- rgamma(100, shape=2)
fit.norm <- fitdist(vec, "norm")
fit.gamma <- fitdist(vec, "gamma")
fit.weibull <- fitdist(vec, "weibull")
model.list <- list(fit.norm, fit.gamma, fit.weibull)
qqcomp(model.list)
This gives the following output:
While this:
qqcomp(model.list, plotstyle="ggplot")
gives the following output:
Why are the points not showing up? Am I doing something wrong here or is this a bug?
EDIT:
So I haven't figured out why this doesn't work, but there is a pretty easy workaround. The function call qqcomp(model.list, plotstyle="ggplot") still returns an ggplot object, which includes the data used to make the plot. Using that data one can easily write an own plot function that does exactly what one wants. It's not very elegant, but until someone finds out why it's not working as expected I will just use this method.
I was able to reproduce your error and indeed, it's really intriguing. Maybe, you should contact developpers of this package to mention this bug.
Otherwise, if you want to reproduce this qqplot using ggplot and stat_qq, passing the corresponding distribution function and the parameters associated (stored in $estimate):
library(ggplot2)
df = data.frame(vec)
ggplot(df, aes(sample = vec))+
stat_qq(distribution = qgamma, dparams = as.list(fit.gamma$estimate), color = "green")+
stat_qq(distribution = qnorm, dparams = as.list(fit.norm$estimate), color = "red")+
stat_qq(distribution = qweibull, dparams = as.list(fit.weibull$estimate), color = "blue")+
geom_abline(slope = 1, color = "black")+
labs(title = "Q-Q Plots", x = "Theoritical quantiles", y = "Empirical quantiles")
Hope it will help you.
I would like to display a plotly plot object in a standalone window that behaves similarly to the window that pops up using the base R plot() function.
Using a basic example from the plotly website:
library(ggplot2)
library(plotly)
d <- diamonds[sample(nrow(diamonds), 1000), ]
p <- ggplot(data = d, aes(x = carat, y = price)) +
geom_point(aes(text = paste("Clarity:", clarity))) +
geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)
p2 <- ggplotly(p)
The p2 object is an htmlwidget object and I get some control over its display using the sizingPolicy element as described here. However, I can't find anything that allows me to set the viewer/browser to something other than my current browser (as a new tab) or within RStudio.
Ideally, I'd like to avoid applications outside of R packages to launch a separate window from within R. However, I would also be happy with figuring out how to granularly control browser output to display p2 as a new window in kiosk or app mode (see the answers to this question for some examples of kiosk/app mode).
Edit: Although I mentioned RStudio when discussing some of the options that I was able to find, I am talking about using R from a simple console. That said, granular display options should hopefully be independent of the user interface.
I have a working solution, but I'll be happy to change the accepted answer if someone has anything better.
I defined a print function that can be used to launch a custom browser command for an htmlwidget object. In this case, I used chromium-browser -app=..., but the overall approach should be general.
print_app <- function(widget) {
# Generate random file name
temp <- paste(tempfile('plotly'), 'html', sep = '.')
# Save. Note, leaving selfcontained=TRUE created files that froze my browser
htmlwidgets::saveWidget(widget, temp, selfcontained = FALSE)
# Launch with desired application
system(sprintf("chromium-browser -app=file://%s", temp))
# Return file name if it's needed for any other purpose
temp
}
Combining with the previous example:
library(ggplot2)
library(plotly)
d <- diamonds[sample(nrow(diamonds), 1000), ]
p <- ggplot(data = d, aes(x = carat, y = price)) +
geom_point(aes(text = paste("Clarity:", clarity))) +
geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)
p2 <- ggplotly(p)
print_app(p2)
It seems like htmlwidgets normally uses the html_print function from htmltools, which in turn selects the browser to use via getOption("viewer", utils::browseURL), which bakes in a lot of the browser selection options -- making it challenging to change.
The idea for saving the html file locally came from this plotly issue: saving plotly plots locally?.
If you are using MacOS, change this line in #ssokolen's answer
# Launch with desired application
system(sprintf("chromium-browser -app=file://%s", temp))
to
system(sprintf("open -a 'google chrome' /%s", temp))
Works in zsh in MacOs Catalina with the Intellij R plugin.