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)))
Related
I have a big data frame with IDs, date, and test results in it - I want to run a for loop to go through all the IDs and plot a line graph that shows the evolution of the results across time, but also to add some key-indicators as points in the graph. (key indicators come from a different data frame).
Not all the IDs have all the indicators - the problem is that when I plot these points, I use dplyr filtering to filter for ID == i and something like key_ind != 0. My problem is that when there are no key indicators for a certain ID, the filtered data frame has 0 observations and ggplot returns an error.
I want that when there are no points to plot (the test results still get plotted as a line) - they wont be plotted, but the line graph of the results still be plotted. Does that make sense? How can I do that? I have tried using tryCatch() but it didn't work.
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
R has some built in datasets, namely I'm using "lynx" and "LakeHuron" which are of different lengths. "lynx" contains data on annual lynx trappings from 1821-1934, and "LakeHuron" contains annual water level from 1872-1975.
I need to plot LakeHuron data on the y-axis, and lynx data on the x-axis, but only for the years 1875-1934 inclusive. I created two vectors:
lynx.years = c(lynx)
huron.years = c(LakeHuron)
I am stuck at the point of trying to only make a plot for the specified year range. Can someone help me figure out how to plot the data from the two vectors for only the years 1875-1934?
Thank you!
1) The question did not specify what sort of plot was desired so assume it is a two panel plot with one series in each panel with years on the X axis such that only the range of years mentioned is shown. That range of years is the intersection of the years of the two series so:
plot(na.omit(cbind(LakeHuron, lynx)))
Drop the na.omit if you want to plot the entirety of the two series.
2) If what is wanted is to rescale the two series so that their shapes can be shown on a single panel despite vastly different ranges:
ts.plot(scale(na.omit(cbind(LakeHuron, lynx))), col = 1:2)
Again, we could drop the na.omit if the entire series were desired.
3) If what is wanted is to plot one vs. the other then:
plot(unclass(cbind(LakeHuron, lynx)))
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
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.