I am using the following data frame in R
TIME PRICE
2013-01-01 23:55:03 446.6167
2013-01-01 23:55:02 441.0114
2013-01-01 23:54:59 446.7600
I am using function ggplot to plot the data points and label fixed intervals using scale_x_datetime.
library(ggplot2) # including necessary libraries
lims <- as.POSIXct(strptime(c("2013-01-01 00:00:00","2013-01-01 23:59:00"), format = "%Y-%m-%d %H:%M:%S"))
ggplot(open,aes(x=TIME,y=PRICE))
+ geom_point(size = 1.0, color="navy")
+ xlab("Time")
+ ylab("Price")
+ ggtitle("time vs Price ")
+ scale_x_datetime(breaks = date_breaks("200 min"), minor_breaks=date_breaks("15 min"), labels=date_format("%H:%M:%S"),limits=lims)
Despite specifying the limits, the x axis labels are not in order as shown beneath:
You need to put as.POSIXct(TIME) in you aes like so. I had to change your code a bit to fit your limited 3-point data example.
open <- read.table(text="TIME PRICE
'2013-01-01 23:55:03' 446.6167
'2013-01-01 23:55:02' 441.0114
'2013-01-01 23:54:59' 446.7600",header=TRUE, stringsAsFactors=FALSE)
lims <- as.POSIXct(strptime(c("2013-01-01 00:00:00","2013-01-01 23:59:00"),
format = "%Y-%m-%d %H:%M:%S"))
ggplot(open,aes(x=as.POSIXct(TIME),y=PRICE)) +
geom_point(size = 3.0, color="red")+
xlab("Time")+
ylab("Price")+
ggtitle("time vs Price ")+
scale_x_datetime(breaks = date_breaks("360 min"),
minor_breaks=date_breaks("15 min"),
labels=date_format("%H:%M:%S"),limits=lims)
EDIT
You should try to use xts to transform your data in a time series object. This will order the time correctly. Then, you fortify it to use with ggplot2.
library(xts)
open <- read.table(text="TIME PRICE
'2013-01-01 23:55:03' 446.6167
'2013-01-01 23:55:02' 441.0114
'2013-01-01 23:54:59' 446.7600",header=TRUE, stringsAsFactors=FALSE)
open <- xts(open$PRICE,as.POSIXct(open$TIME))
open <- fortify(open)
lims <- as.POSIXct(strptime(c("2013-01-01 00:00:00","2013-01-01 23:59:00"),
format = "%Y-%m-%d %H:%M:%S"))
ggplot(open,aes(x=Index,y=open)) +
geom_point(size = 3.0, color="green")+
xlab("Time")+
ylab("Price")+
ggtitle("time vs Price ")+
scale_x_datetime(breaks = date_breaks("360 min"),
minor_breaks=date_breaks("15 min"),
labels=date_format("%H:%M:%S"),limits=lims)
I would use lubridate:
library(data.table)
library(lubridate)
library(ggplot2)
time<-seq(ymd_hms('2013-01-01 00:00:01'),ymd_hms('2013-01-02 23:59:59'), by = '1 min')
price<-sample(length(time))
dt<-data.table(time,price)
ggplot(dt,aes(x=time,y=price)) + geom_point(size = 1.0, color="navy") + xlab("Time") + ylab("Price") + ggtitle("time vs Price ")
resulting in:
Related
I have measurement data from the past months:
Variables
x <- df$DatoTid
y <- df$Partikler
color <- df$Opgave
I'm trying to plot my data based on the timestamp, so that I have the hours of the day in the x-axis, instead of the specific POSIXct datetime.
I would like the labels and ticks of the x-axis to be fx "00:00", "01:00",..."24:00".
So that noon is in the middle of the x-axis.
So far I tried to convert the datetime values into characters.
Doesn't look good yet (as you can see the axis ticks and labels are gone. Possibly other things are wrong as well).
Can someone help me?
And please let me know how to upload the data for you. I don't know how to add a huge .csv-file....
# Rounding up to nearest 10 min:
head(df)
df$Tid2 <- format(strptime("1970-01-01", "%Y-%m-%d", tz="CET") +
round(as.numeric(df$DatoTid)/300)*300 + 3600, "%Y-%m-%d %H:%M:%S")
head(df)
df$Tid2 <- as.character(df$Tid2)
str(df)
x <- df$Tid2
y <- df$Partikler
color <- df$Opgave
plot2 <- ggplot(data = df, aes(x = x, y = y, color = color)) +
geom_point(shape=16, alpha=0.6, size=1.8) +
scale_y_continuous(labels=function(x) format(x, big.mark = ".", decimal.mark = ",", scientific = FALSE)) +
scale_x_discrete(breaks=c("00:00:00", "06:00:00", "09:00:00", "12:00:00", "18:00:00", "21:00:00")) +
scale_color_discrete(name = "Case") +
xlab(" ") +
ylab(expression(paste("Partikelkoncentration [pt/cc]"))) +
myTheme +
theme(legend.text=element_text(size=8), legend.title=element_text(size=8))
plot2
I would approach this by making a new time stamp that uses a single day, but the hours/minutes/seconds of your existing time stamp.
First, here's a made-up version of your data, here using a linear trend in Partikler:
library(tidyverse); library(lubridate)
df <- data_frame(Tid2 = seq.POSIXt(from = ymd_h(2019010100),
to = ymd_h(2019011500), by = 60*60),
Partikler = seq(from = 0, to = 2.5E5, along.with = Tid2),
Opgave = as.factor(floor_date(Tid2, "3 days")))
# Here's a plot that's structurally similar to yours:
ggplot(df, aes(Tid2, Partikler, col = Opgave)) +
geom_point() +
scale_color_discrete(name = "Case")
Now, if we change the timestamps to be in the same day, we can control them like usual in ggplot, but with them collapsed into a single day of timing. We can also change the x axis so it doesn't mention the date component of the time stamp:
df2 <- df %>%
mutate(Tid2_sameday = ymd_hms(paste(Sys.Date(),
hour(Tid2), minute(Tid2), second(Tid2))))
ggplot(df2, aes(Tid2_sameday, Partikler, col = Opgave)) +
geom_point() +
scale_color_discrete(name = "Case") +
scale_x_datetime(date_labels = "%H:%M")
I want to display difftime data with ggplot2 and I want the tick format to be hh:mm.
library(ggplot2)
a= as.difftime(c("0:01", "4:00"), "%H:%M", unit="mins")
b= as.difftime(c('0:01', "2:47"), "%H:%M", unit="mins")
ggplot(data=NULL, aes(x=b, y=a)) + geom_point(shape=1) +
scale_x_time(labels = date_format("%H:%M"),
breaks = "1 hour")
But I get the following warning:
Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.
Warning message:
In structure(as.numeric(x), names = names(x)) : NAs introduced by coercion
and this as a graph:
Update:
my example was too minimal, I also need to be able to display negative differences, so this would be better data:
a= as.difftime(c(-60, -4*60), unit="mins")
b= as.difftime(c(-60, 2*60+47), unit="mins")
ggplot(data=NULL, aes(x=b, y=a)) + geom_point(shape=1)
The answer has two parts.
Plotting difftime objects
According to help("scale_x_time"), ggplot2 supports three date/time classes: scale_*_date for dates (class Date), scale_*_datetime for datetimes (class POSIXct), and scale_*_time for times (class hms). The last one is what we need here.
Class hms is a custom class for difftime vectors. as.hms() has a method for difftime. So. difftime objects can be plotted with ggplot2 by coercing to class hms:
a <- as.difftime(c(-60, -4 * 60), unit = "mins")
b <- as.difftime(c(-60, 2 * 60 + 47), unit = "mins")
library(ggplot2)
ggplot(data = NULL, aes(x = hms::as.hms(b), y = hms::as.hms(a))) +
geom_point(shape = 1)
Please, note that negative time differences are shown as well.
Formatting the tick labels
The OP has requested that tick marks should be labeled in hh:mm format. Apparently, the default formatting is hh:mm:ss. This can be modified by specifying a function that takes the breaks as input and returns labels as output to the labels parameter of the scale_x_time() and scale_y_time() functions:
format_hm <- function(sec) stringr::str_sub(format(sec), end = -4L)
ggplot(data = NULL, aes(x = hms::as.hms(b), y = hms::as.hms(a))) +
geom_point(shape = 1) +
scale_x_time(name = "b", labels = format_hm) +
scale_y_time(name = "a", labels = format_hm)
The format_hm() function truncates the :ss part from the default format. In addition, the axis are labeled nicely.
Depending on your constraints, you might consider translating the difftimes to distinct datetimes, which ggplot can handle just fine:
library(lubridate)
a_date_times <- floor_date(Sys.time(), "1 day") + a
b_date_times <- floor_date(Sys.time(), "1 day") + b
ggplot(data=NULL, aes(x=a_date_times, y=b_date_times)) +
geom_point(shape=1)
My best approach so far is:
library(ggplot2)
library(lubridate)
a= as.difftime(c(-60, -4*60), unit="mins")
b= as.difftime(c(-60, 2*60+47), unit="mins")
xbreaks = seq(ceiling(min(b)/60), floor(max(b)/60)) * 60
ybreaks = seq(ceiling(min(a)/60), floor(max(a)/60)) * 60
ggplot(data=NULL, aes(x=b, y=a)) + geom_point(shape=1) +
scale_x_continuous(labels = f, breaks = xbreaks) +
scale_y_continuous(labels = f, breaks = ybreaks)
f <- function(x){
t = seconds_to_period(abs(x)*60)
r = sprintf("% 2i:%02i", sign(x)*hour(t), minute(t))
return(r)
}
Currently using ggplot2 and scales doing this but would be ideal to show a date range +/- 1 Year (for example). I shouldn't really be hardcoding these dates as it's not very efficient.
library(scales) #date time scales
library(ggplot2) # Visualization
ggplot(dataset,aes(x=datetime_start, y=dataset$Product, color=Stage, order = - as.numeric(Stage))) +
geom_segment(aes(x=From,xend=To,yend=dataset$Product), size=10) +
scale_x_datetime(
breaks = date_breaks("1 month"),
labels=date_format("%b%y"),
limits = c(
as.POSIXct("2016-03-01"),
as.POSIXct("2018-02-01")
)
) +
Expand the scale:
library(ggplot2)
df <- data.frame(x = seq(Sys.Date()-lubridate::years(2), Sys.Date(), by="3 month"))
df$y <- 1:nrow(df)
p <- ggplot(df, aes(x, y)) + geom_line()
p + scale_x_date(expand = c(0, 365))
I want to set bounds for the x-axis for a plot of time-series data which features only time (no dates). My limits are:
lims <- strptime(c("03:00","16:00"), format = "%H:%M")
And my ggplot prints fine, but when I add this to scale_x_datetime
scale_x_datetime(limits = lims)
I get Error: Invalid input: time_trans works with objects of class POSIXct only
Fully reproducible example courtesy of How to create a time scatterplot with R?
dates <- as.POSIXct(as.Date("2011/01/01") + sample(0:365, 100, replace=TRUE))
times <- as.POSIXct(runif(100, 0, 24*60*60), origin="2011/01/01")
df <- data.frame(
dates = dates,
times = times
)
lims <- strptime(c("04:00","16:00"), format = "%H:%M")
library(scales)
library(ggplot2)
ggplot(df, aes(x=dates, y=times)) +
geom_point() +
scale_y_datetime(limits = lims, breaks=date_breaks("4 hour"), labels=date_format("%H:%M")) +
theme(axis.text.x=element_text(angle=90))
the error message says that you should use as.POSIXct on lims.
You also need to add the date (year, month and day) in lims, because by default it will be `2015, which is off limits.
lims <- as.POSIXct(strptime(c("2011-01-01 03:00","2011-01-01 16:00"), format = "%Y-%m-%d %H:%M"))
ggplot(df, aes(x=dates, y=times)) +
geom_point() +
scale_y_datetime(limits =lims, breaks=date_breaks("4 hour"), labels=date_format("%H:%M"))+
theme(axis.text.x=element_text(angle=90))
I have a data.frame like this:
library(ggplot2)
library(reshape2)
tasks <- c("Review literature", "Mung data")
dfr <- data.frame(
name = factor(tasks, levels = tasks),
start.date = c("24/08/2010 01:00:01", "24/08/2010 01:00:10", "01/11/2010 01:30:00", "01/11/2010 02:00:00"),
end.date = c("24/08/2010 02:00:00", "24/08/2010 03:00:00", "01/11/2010 02:00:00", "01/11/2010 04:00:00")
)
mdfr <- melt(dfr, measure.vars = c("start.date", "end.date"))
I would like to plot this data using ggplot2 so that different dates are on different facets and only time portion is show on x-axis? I tried something like:
ggplot(mdfr, aes(as.Date(value, "%H/%M/%S"), name)) +
geom_line(size = 6) +
xlab("") + ylab("") +
theme_bw() + facet_wrap(~as.Date(value, "%d/%m/%Y"))
Error in layout_base(data, vars, drop = drop) :
At least one layer must contain all variables used for facetting
Added to your melted data frame two new columns value2 and date. value2 is POSIXct class of your times and date column contains just date part of your original value and converted to factor to use for faceting.
mdfr$value2<-as.POSIXct(strptime(mdfr$value, "%d/%m/%Y %H:%M:%S"))
mdfr$date<-as.factor(as.Date(strptime(mdfr$value, "%d/%m/%Y %H:%M:%S")))
Now you can use new value2 as x and date for facetting. I used facet_grid() with scales="free_x" and space="free_x" to get evenly spaced time intervals in both facets.
ggplot(mdfr, aes(value2, name)) +
geom_line(size = 6) +
xlab("") + ylab("") +
theme_bw() + facet_grid(~date,scales="free_x",space="free_x")