I'm trying to create a plot similar to the ones here:
Basically I want a histogram, where each bin shows how long was spent in that range of cadence (e.g 1 hour in 0-20rpm, 3 hours in 21-40rpm, etc)
library("rjson") # 3rd party library, so: install.packages("rjson")
# Load data from Strava API.
# Ride used for example is http://app.strava.com/rides/13542320
url <- "http://app.strava.com/api/v1/streams/13542320?streams[]=cadence,time"
d <- fromJSON(paste(readLines(url)))
Each value in d$cadence (rpm) is paired with the same index in d$time (the number of seconds from the start).
The values are not necessarily uniform (as can be seen if you compare plot(x=d$time, y=d$cadence, type='l') with plot(d$cadence, type='l') )
If I do the simplest possible thing:
hist(d$cadence)
..this produces something very close, but the Y value is "frequency" instead of time, and ignores the time between each data point (so the 0rpm segment in particular will be underrepresented)
You need to create a new column to account for the time between samples.
I prefer data.frames to lists for this kind of thing, so:
d <- as.data.frame(fromJSON(paste(readLines(url))))
d$sample.time <- 0
d$sample.time[2:nrow(d)] <- d$time[2:nrow(d)]-d$time[1:(nrow(d)-1)]
now that you've got your sample times, you can simply "repeat" the cadence measures for anything with a sample time more than 1, and plot a histogram of that
hist(rep(x=d$cadence, times=d$sample.time),
main="Histogram of Cadence", xlab="Cadence (RPM)",
ylab="Time (presumably seconds)")
There's bound to be a more elegant solution that wouldn't fall apart for non-integer sample times, but this works with your sample data.
EDIT: re: the more elegant, generalized solution, you can deal with non-integer sample times with something like new.d <- aggregate(sample.time~cadence, data=d, FUN=sum), but then the problem becomes plotting a histogram for something that looks like a frequency table, but with non-integer frequencies. After some poking around, I'm coming to the conclusion you'd have to roll-your-own histogram for this case by further aggregating the data into bins, and then displaying them with a barchart.
Related
I'm trying to visualize some data with a ridge plot, but I'm wondering if there's a way I can weight the densities of the ridges.
Basically I have the following:
set.seed(1)
example <- data.frame(matrix(nrow=100,ncol=3))
colnames(example) <- c("year","position","weight")
example$year <- as.character(rep(c(1,2,3,4,5),each=20) )
example$position <- runif(100,1,10)
example$weight <- sample(1:3,100,replace = T)
A sample of position in 5 different years. I want to plot the distribution change over time with a ridge plot, but in the dataset, there is also a column for "weight," meaning that some samples counted more than others. Is there a way to incorporate this into my ridges distribution plot? And also is there a way to make rows with more sample*weight be taller than rows with less? So not normalize every year's height to one?
ggplot(example,aes(x=position,y=year))+
ggridges::geom_density_ridges()+
theme_classic()
I was thinking I could try to pipe the dataset to repeat rows for number of weight value that they have, and so they would get counted more than x number of times (or, "weight" number of times) and change the density. Can't quite figure out how to do that though. Also, in my dataset, the weights aren't integers, so I'm hoping for a better solution.
Or, is there another package/technique that might achieve that?
For this dataset we can repeat the rows based on weight column and then plot:
library(ggplot2)
library(ggridges)
example2 <- example[rep(seq_along(example$weight), example$weight), ]
ggplot(example2,aes(x=position,y=year))+
ggridges::geom_density_ridges()+
theme_classic()
#> Picking joint bandwidth of 1.02
However, if you have wights that are not integer, this would not work. There's this open issue on github that you may want to give it a shot.
Another idea would be normalizing your weights in your original dataset to be integer by rounding them to certain digits and multiplying them by 10 to the power of your desired precision. Then you can utilize previous solution for your actual dataset.
For my thesis i want to create a histogram on standardized earnings. This histogram should ideally have the following properties:
The histogram should be able to have the intervals of the data
(bins) played with.
Since i have my data in a spreadsheet. Is it possible to consider
more than one column?
Also it should have the ability to set the range of the data that is
included in the histogram for example from -50 mio. to 200 mio. (But
i could do this in my input)
Sadly I was not able to perform this task my own.
I have downloaded the data from orbis in spreadsheet (xlsx). Afterwards I cleaned my data of symbols that R can't read, saved everything as a Tab separated .txt and imported it into R-Studio:
setwd("/path")
getwd()
df<- read.table("importFile", header = TRUE)
View(df)
This worked nicely.
Now i tried creating the histogram
library(ggplot2)
myplot=ggplot(df, aes(JuStandartisiert2007))
myplot+ stat_count(width = 1000)
Then i received the following warning:
position_stack requires non-overlapping x intervals
My histogram looks horrible:
This perplexes me, I tried making a histogram on the airquality dataset and it works without problems.
Also note that i have to use stat_count for my histogram in a youtube video i saw, they did it the following way:
myplot+ geom_histogram(binwidth = 10)
My questions are now:
What is wrong with my Data why i have overlapping x Values? To my naked eye my data looks the same than that from R's airquality dataset.
How can I sepparate my x values?
Can i set max and min values for the data that enters my Histogram?
Can I consider more than one column in my dataset.
Here is my Dataset as TAB separated txt file.
https://www.dropbox.com/sh/jbscj6cftpcqaxh/AADglvv_xnG2wWN-o2SIrTwpa?dl=0
I would rather begin with base plotting such as:
hist(df$JuStandartisiert2007,breaks=1000,xlim=c(-2,2))
you can also observe the limits for the x-axis.
In order to have the plot of two columns try :
plot(df$JuStandartisiert2007,df$BilanzsummeAktiva2007,xlim = c(-5,5),ylim=c(-1,1000))
Once again observe the x and y limits represented by: xlim and ylim
I want to combine a time series of in situ values (line) with boxplots of estimated values of special dates. I tried to understand this "Add a line from different result to boxplot graph in ggplot2" question, but my dates make me drive crazy. Sometimes I only have in situ values of a date, sometimes only estimated values and sometimes both together.
I uploaded a sample of my data here:
http://www.file-upload.net/download-9942494/estimated.txt.html
http://www.file-upload.net/download-9942495/insitu.txt.html
How can I create a plot with both data sets that looks like this http://www.file-upload.net/download-9942496/desired_outputplot.png.html
in the end?
I got help and have a solution now:
insitu <- read.table("insitu.txt",header=TRUE,colClasses=c("Date","numeric"))
est <- read.table("estimated.txt",header=TRUE,colClasses=c("Date","numeric"))
insitu.plot <- xyplot(insitu~date_fname,data=insitu,type="l",
panel=function(x,y,...){panel.grid(); panel.xyplot(x,y,...)},xlab=list(label="Date",cex=2))
est.plot <- xyplot(estimated~date,data=est,panel=panel.bwplot,horizontal=FALSE)
both <- insitu.plot+est.plot
update(both,xlim=range(c(est$date,insitu$date_fname))+c(-1,1),ylim=range(c(est$estimated,insitu$insitu)))
So... newbie R user here. I have some observations that I'd like to record using R and be able to add to later.
The items are sorted by weights, and the number at each weight recorded. So far what I have looks like this:
weights <- c(rep(171.5, times=1), rep(171.6, times=2), rep(171.7, times=4), rep(171.8, times=18), rep(171.9, times=39), rep(172.0, times=36), rep(172.1, times=34), rep(172.2, times=25))
There will be a total of 500 items being observed.
I'm going to be taking additional observations over time to (hopefully) see how the distribution of weights changes with use/wear. I'd like to be able plots showing either stacked histograms or boxplots.
What would be the best way to format / store this data to facilitate this kind of use case? A matrix, dataframe, something else?
As other comments have suggest, the most versatile (and perhaps useful) container (structure) for your data would be a data frame - for use with the library(ggplot2) for your future plotting and graphing needs(such as BoxPlot with ggplot and various histograms
Toy example
All the code below does is use your weights vector above, to create a data frame with some dummy IDs and plot a box and whisker plot, and results in the below plot.
library(ggplot2)
IDs<-sample(LETTERS[1:5],length(weights),TRUE) #dummy ID values
df<-data.frame(ID=IDs,Weights=weights) #make data frame with your
#original `weights` vector
ggplot(data=df,aes(factor(ID),Weights))+geom_boxplot() #box-plot
This is for research I am doing for my Masters Program in Public Health
I am graphing data against each other, a standard x,y type deal, over top of that I am plotting a predicted line. I get what I think to be the most funky looking point/boxplot looking thing ever with an x axis that is half filled out and I don't understand why as I do not call a boxplot function. When I call the plot function it is my understanding that only the points will plot.
The data I am plotting looks like this
TOTAL.LACE | DAYS.TO.FAILURE
9 | 15
16 | 7
... | ...
The range of the TOTAL.LACE is from 0 to 19 and DAYS.TO.FAILURE is 0 - 30
My code is as follows, maybe it is something before the plot but I don't think it is:
# To control the type of symbol we use we will use psymbol, it takes
# value 1 and 2
psymbol <- unique(FAILURE + 1)
# Build a test frame that will predict values of the lace score due to
# a patient being in a state of failure
test <- survreg(Surv(time = DAYS.TO.FAILURE, event = FAILURE) ~ TOTAL.LACE,
dist = "logistic")
pred <- predict(test, type="response") <-- produces numbers from about 14 to 23
summary(pred)
ord <- order(TOTAL.LACE)
tl_ord <- TOTAL.LACE[ord]
pred_ord <- pred[ord]
plot(TOTAL.LACE, DAYS.TO.FAILURE, pch=unique(psymbol)) <-- Produces goofy graph
lines(tl_ord, pred_ord) <-- this produces the line not boxplots
Here is the resulting picture
Not to sure how to proceed from here, this is an off shoot of another problem I had with the same data set at this link here I am not understanding why boxplots are being drawn, the reason being is I did not specifically call the boxplot() command so I don't know why they appeared along with point plots. When I issue the following command: plot(DAYS.TO.FAILURE, TOTAL.LACE) I only get points on the resulting plot like I expected, but when I change the order of what is plotted on x and y the boxplots show up, which to me is unexpected.
Here is a link to sample data that will hopefully help in reproducing the problem as pointed out by #Dwin et all Some Sample Data
Thank you,
Since you don't have a reproducible example, it is a little hard to provide an answer that deals with your situation. Here I generate some vaguely similar-looking data:
set.seed(4)
TOTAL.LACE <- rep(1:19, each=1000)
zero.prob <- rbinom(19000, size=1, prob=.01)
DAYS.TO.FAILURE <- rpois(19000, lambda=15)
DAYS.TO.FAILURE <- ifelse(zero.prob==1, DAYS.TO.FAILURE, 0)
And here is the plot:
First, the problem with some of the categories not being printed on the x-axis is because they don't fit. When you have so many categories, to make them all fit you have to display them in a smaller font. The code to do this is to use cex.axis and set the value <1 (you can read more about this here):
boxplot(DAYS.TO.FAILURE~TOTAL.LACE, cex.axis=.8)
As to the question of why your plot is "goofy" or "funky-looking", it is a bit hard to say, because those terms are rather nebulous. My guess is that you need to more clearly understand how boxplots work, and then understand what these plots are telling you about the distribution of your data. In a boxplot, the midline of the box is the 50th percentile of your data, while the bottom and top of the box are the 25th and 75th percentiles. Typically, the 'whiskers' will extend out to the furthest datapoint that is at most 1.5 times the inter-quartile range beyond the ends of the box. In your case, for the first 9 TOTAL.LACEs, more than 75% of your data are 0's, so there is no box and thus no whiskers are possible. Everything beyond the whisker limits is plotted as an individual point. I don't think your plots are "funky" (although I'll admit I have no idea what you mean by that), I think your data may be "funky" and your boxplots are representing the distributions of your data accurately according to the rules by which boxplots are constructed.
In the future (and I mean this politely), it will help you get more useful and faster answers if you can write questions that are more clearly specified, and contain a reproducible example.
Update: Thanks for providing more information. I gather by "funky" you mean that it is a boxplot, rather than a typical scatterplot. The thing to realize is that plot() is a generic function that will call different methods depending on what you pass to it. If you pass simple continuous data, it will produce a scatterplot, but if you pass continuous data and a factor, then it will produce a boxplot, even if you don't call boxplot explicitly. Consider:
plot(TOTAL.LACE, DAYS.TO.FAILURE)
plot(as.factor(TOTAL.LACE), DAYS.TO.FAILURE)
Evidently, you have converted DAYS.TO.FAILURE to a factor without meaning to. Presumably this was done in the pch=unique(psymbol) argument via the code psymbol <- unique(FAILURE + 1) above. Although I haven't had time to try this, I suspect eliminating that line of code and using pch=(FAILURE + 1) will accomplish your goals.