I'm trying to convert some simple data into a form I thought ggplot2 would accept.
I snag some simple stock data and now I just want to plot, later I want to plot say a 10-day moving average or a 30-day historical volatility period to go with it, which is I'm using ggplot.
I thought it would work something like this line of pseudocode
ggplot(maindata)+geom_line(moving average)+geom_line(30dayvol)
library(quantmod)
library(ggplot2)
start = as.Date("2008-01-01")
end = as.Date("2019-02-13")
start
tickers = c("AMD")
getSymbols(tickers, src = 'yahoo', from = start, to = end)
closing_prices = as.data.frame(AMD$AMD.Close)
ggplot(closing_prices, aes(y='AMD.Close'))
But I can't even get this to work. The problem of course appears to be that I don't have an x-axis. How do I tell ggplot to use the index column as a. Can this not work? Do I have to create a new "date" or "day" column?
This line for instance using the Regular R plot function works just fine
plot.ts(closing_prices)
This works without requiring me to enter a hard x-axis, and produces a graph, however I haven't figured out how to layer other lines onto this same graph, evidently ggplot is better so I tried that.
Any advice?
as.Date(rownames(df)) will get you the rownames and parse it as a date. You also need to specify a geom_line()
library(quantmod)
library(ggplot2)
start = as.Date("2008-01-01")
end = as.Date("2019-02-13")
start
tickers = c("AMD")
getSymbols(tickers, src = 'yahoo', from = start, to = end)
closing_prices = as.data.frame(AMD$AMD.Close)
ggplot(closing_prices, aes(x = as.Date(rownames(closing_prices)),y=AMD.Close))+
geom_line()
Edit
Thought it would be easier to explain in the answers as opposed to the comments.
ggplot and dplyr have two methods of evaluation. Standard and non standard evaluation. Which is why in ggplot you have both aes and aes_(). The former being non standard evaluation and the later being standard evaluation. In addition there is also aes_string() which is also standard evaluation.
How are these different?
Its easy to see when we explore all the methods,
#Cleaner to read, define every operation in one step
#Non Standard Evaluation
closing_prices%>%
mutate(dates = as.Date(rownames(.)))%>%
ggplot()+
geom_line(aes(x = dates,y = AMD.Close))
#Standard Evaluation
closing_prices%>%
mutate(dates = as.Date(rownames(.)))%>%
ggplot()+
geom_line(aes_(x = quote(dates),y = quote(AMD.Close)))
closing_prices%>%
mutate(dates = as.Date(rownames(.)))%>%
ggplot()+
geom_line(aes_string(x = "dates",y = "AMD.Close"))
Why are there so many different ways of doing the same thing? In most cases its okay to use non standard evaluation. However if we want to wrap these plots in functions and dynamically change the column to plot based on function parametrs passed as strings. It is helpful to plot using the aes_ and aes_string.
Related
I have applied DBSCAN algorithm on built-in dataset iris in R. But I am getting error when tried to visualise the output using the plot( ).
Following is my code.
library(fpc)
library(dbscan)
data("iris")
head(iris,2)
data1 <- iris[,1:4]
head(data1,2)
set.seed(220)
db <- dbscan(data1,eps = 0.45,minPts = 5)
table(db$cluster,iris$Species)
plot(db,data1,main = 'DBSCAN')
Error: Error in axis(side = side, at = at, labels = labels, ...) :
invalid value specified for graphical parameter "pch"
How to rectify this error?
I have a suggestion below, but first I see two issues:
You're loading two packages, fpc and dbscan, both of which have different functions named dbscan(). This could create tricky bugs later (e.g. if you change the order in which you load the packages, different functions will be run).
It's not clear what you're trying to plot, either what the x- or y-axes should be or the type of plot. The function plot() generally takes a vector of values for the x-axis and another for the y-axis (although not always, consult ?plot), but here you're passing it a data.frame and a dbscan object, and it doesn't know how to handle it.
Here's one way of approaching it, using ggplot() to make a scatterplot, and dplyr for some convenience functions:
# load our packages
# note: only loading dbscacn, not loading fpc since we're not using it
library(dbscan)
library(ggplot2)
library(dplyr)
# run dbscan::dbscan() on the first four columns of iris
db <- dbscan::dbscan(iris[,1:4],eps = 0.45,minPts = 5)
# create a new data frame by binding the derived clusters to the original data
# this keeps our input and output in the same dataframe for ease of reference
data2 <- bind_cols(iris, cluster = factor(db$cluster))
# make a table to confirm it gives the same results as the original code
table(data2$cluster, data2$Species)
# using ggplot, make a point plot with "jitter" so each point is visible
# x-axis is species, y-axis is cluster, also coloured according to cluster
ggplot(data2) +
geom_point(mapping = aes(x=Species, y = cluster, colour = cluster),
position = "jitter") +
labs(title = "DBSCAN")
Here's the image it generates:
If you're looking for something else, please be more specific about what the final plot should look like.
Let's consider data :
library(ggplot2)
library(quantmod)
start <- as.Date("2013-01-01")
end <- as.Date("2016-10-01")
# Apple stock
getSymbols("AAPL", src = "yahoo", from = start, to = end)
And plot :
autoplot(Cl(AAPL))
My question is : is there any way how can I simlpy shorten time period of my plot ? Let's say for example I want to have my plot from '2013-01-01' up to '2014-01-01'. Of course I can do exactly same thing with changing my start and end variables (defined at the very beggining) and redownload data set. However I found this solution inefficient. Is there any simpler way how it can be performed ?
There are two approaches. One is to specify limits to the plotting routine and the other is to subset the data itself. Since the first one is already illustrated by another answer we will focus on the second:
# xts suppports .../... notation
apple <- Cl(AAPL)['2013-01-01/2014-01-01']
# this will extract all rows for 2013
apple <- Cl(AAPL)['2013']
# window function
apple <- window(Cl(AAPL), start = "2013-01-01", end = "2014-01-01")
Having defined apple we can autoplot it.
You can add an xlim = argument to autoplot:
autoplot(Cl(AAPL),
xlim = as.Date(c("2014-01-01","2016-04-01")))
You can also use the + operator if you prefer:
autoplot(Cl(AAPL)) +
xlim(as.Date(c("2014-01-01","2016-04-01")))
See help(autoplot.zoo) for more.
Due to static graph prepared by ggplot, we are shifting our graphs to googleVis with interactive charts. But when it comes to categorization we are facing many problems. Let me give example which will help you understand:
#dataframe
df = data.frame( x = sample(1:100), y = sample(1:100), cat = sample(c('a','b','c'), 100, replace=TRUE) )
ggplot2 provides parameter like alpha, colour, linetype, size which we can use with categories like shown below:
ggplot(df) + geom_line(aes(x = x, y = y, colour = cat))
Not just line chart, but majority of ggplot2 graphs provide categorization based on column values. Now I would like to do the same in googleVis, based on value df$cat I would like parameters to get changed or grouping of line or charts.
Note:
I have already tried dcast to make multiple columns based on category column and use those multiple columns as Y input, but that it not what I would like to do.
Can anyone help me regarding this?
Let me know if you need more information.
vrajs5 you are not alone! We struggled with this issue. In our case we wanted to fill bar charts like in ggplot. This is the solution. You need to add specifically named columns, linked to your variables, to your data table for googleVis to pick up.
In my fill example, these are called roles, but once you see my syntax you can abstract it to annotations and other cool features. Google has them all documented here (check out superheroes example!) but it was not obvious how it applied to r.
#mages has this documented on this webpage, which shows features not in demo(googleVis):
http://cran.r-project.org/web/packages/googleVis/vignettes/Using_Roles_via_googleVis.html
EXAMPLE ADDING NEW DIMENSIONS TO GOOGLEVIS CHARTS
# in this case
# How do we fill a bar chart showing bars depend on another variable?
# We wanted to show C in a different fill to other assets
suppressPackageStartupMessages(library(googleVis))
library(data.table) # You can use data frames if you don't like DT
test.dt = data.table(px = c("A","B","C"), py = c(1,4,9),
"py.style" = c('silver', 'silver', 'gold'))
# Add your modifier to your chart as a new variable e.g. py1.style
test <-gvisBarChart(test.dt,
xvar = "px",
yvar = c("py", "py.style"),
options = list(legend = 'none'))
plot(test)
We have shown py.style deterministically here, but you could code it to be dependent on your categories.
The secret is myvar.googleVis_thing_youneed linking the variable myvar to the googleVis feature.
RESULT BEFORE FILL (yvar = "py")
RESULT AFTER FILL (yvar = c("py", "py.style"))
Take a look at mages examples (code also on Github) and you will have cracked the "categorization based on column values" issue.
I have a table exported in csv from PostgreSQL and I'd like to create a stacked bar graph in R. It's my first project in R.
Here's my data and what I want to do:
It the quality of the feeder bus service for a certain provider in the area. For each user of the train, we assign a service quality based of synchronization between the bus and the train at the train stations and calculate the percentage of user that have a ideal or very good service, a correct service, a deficient service or no service at all (linked to that question in gis.stackexchange)
So, It's like to use my first column as my x-axis labels and my headers as my categories. The data is already normalized to 100% for each row.
In Excel, it's a couple of clicks and I wouldn't mind typing a couple of line of codes since it's the final result of an already quite long plpgsql script... I'd prefer to continue to code instead of moving to Excel (I also have dozens of those to do).
So, I tried to create a stacked bar using the examples in Nathan Yau's "Visualize This" and the book "R in Action" and wasn't quite successful. Normally, their examples use data that they aggregate with R and use that. Mine is already aggregated.
So, I've finally come up with something that works in R:
but I had to transform my data quite a bit:
I had to transpose my table and remove my now-row (ex-column) identifier.
Here's my code:
# load libraries
library(ggplot2)
library(reshape2)
# load data
stl <- read.csv("D:/TEMP/rabat/_stl_rabattement_stats_mtl.csv", sep=";", header=TRUE)
# reshape for plotting
stl_matrix <- as.matrix(stl)
# make a quick plot
barplot(stl_matrix, border=NA, space=0.1, ylim=c(0, 100), xlab="Trains", ylab="%",
main="Qualité du rabattement, STL", las = 3)
Is there any way that I could use my original csv and have the same result?
I'm a little lost here...
Thanks!!!!
Try the ggplot2 and reshape library. You should be able to get the chart you want with
stl$train_order <- as.numeric(rownames(stl))
stl.r <- melt(stl, id.vars = c("train_no", "train_order"))
stl.r$train_no <- factor(
stl.r$train_no,
levels = stl$train_no[order(stl$train_order)])
ggplot(stl.r, aes(x = factor(train_no), y = value, fill = variable)) + geom_bar(stat = 'identity')
It appears that you transposed the matrix manually. This can be done in R with the t() function.
Add the following line after the as.matrix(stl) line:
stl_matrix <- t(stl_matrix)
I have a set of data which contains around 150,000 observations of 800 subjects. Each observation has: subject ID, latitude, longitude, and the time that the subject was at those coordinates. The data covers a 24-hour period.
If I plot all the data at once I just get a blob. Is anyone able to give me some tips as to how I can animate this data so that I can observe the paths of the subjects as a function of time?
I've read the spacetime vignette but I'm not entirely sure it will do what I want. At this point I'm spending a whole lot of time googling but not really coming up with anything that meets my needs.
Any tips and pointers greatly appreciated!
Here my first use of animation package. It was easier than I anticipated and especially the saveHTML is really amazing. Here my scenario(even I think that my R-code will be clearer:)
I generate some data
I plot a basic plot for all persons as a background plot.
I reshape data to get to a wide format in a way I can plot an arrow between present and next position for each person.
I loop over hours , to generate many plots. I put the llop within the powerful saveHTML function.
You get a html file with a nice animation. I show here one intermediate plot.
Here my code:
library(animation)
library(ggplot2)
library(grid)
## creating some data of hours
N.hour <- 24
dat <- data.frame(person=rep(paste0('p',1:3),N.hour),
lat=sample(1:10,3*N.hour,rep=TRUE),
long=sample(1:10,3*N.hour,rep=TRUE),
time=rep(1:N.hour,each=3))
# the base plot with
base <- ggplot() +
geom_point(data=dat,aes(x=lat, y=long,colour = person),
size=5)+ theme(legend.position = "none")
## reshape data to lat and long formats
library(plyr)
dat.segs <- ddply(dat,.(person),function(x){
dd <- do.call(rbind,
lapply(seq(N.hour-1),
function(y)c(y,x[x$time %in% c(y,y+1),]$lat,
x[x$time %in% c(y,y+1),]$long)))
dd
})
colnames(dat.segs) <- c('person','path','x1','x2','y1','y2')
# a function to create the animation
oopt <- ani.options(interval = 0.5)
saveHTML({
print(base)
interval = ani.options("interval")
for(hour in seq(N.hour-1)){
# a segment for each time
tn <- geom_segment(aes(x= x1, y= y1, xend = x2,
yend = y2,colour = person),
arrow = arrow(), inherit.aes = FALSE,
data =subset(dat.segs,path==hour))
print(base <- base + tn)
ani.pause()
}
}, img.name = "plots", imgdir = "plots_dir",
htmlfile = "random.html", autobrowse = FALSE,
title = "Demo of animated lat/long for different persons",
outdir=getwd())
Your question is a bit vague, but I will share how I have done this kind of animation in the past.
Create a function that plots all the subject locations for one time slice:
plot_time = function(dataset, time_id) {
# make a plot with your favorite plotting package (e.g. `ggplot2`)
# Save it as a file on disk (e.g. using `ggsave`), under a regular name,
# frame001.png, frame002.png, see sprintf('frame%03d', time_index)
}
Call this function on each of your timeslices, e.g. using lapply:
lapply(start_time_id:stop_time_id, plot_time)
leading to a set of graphics files on the hard drive called frame001 to framexxx.
Use a tool to render those frames into a movie, e.g. using ffmpeg, see for example.
This is a general workflow, which has been already implemented in the animation package (thanks for reminding me #mdsummer). You can probably leverage that package to get your animation.