I am working with XYPLOTS, where x-axis is a DateTime variable and y-axis contains a numeric variable. Due to huge number of DateTime (for 3 days and every 15 mins there is a data point).
The graph looks good but the xaxis labels are merged together.
If i reduce the tick numbers, the labels can be seen clearly. I dont know how to change the tick numbers or limit the intervals for a DATETIME variable.
DateTime looks like this : 2014-04-08 17:00:00, 2014-04-08 17:15:00, ... etc.
Code I use right now:
xyplot(upper + lower + New1 ~ DateTime,data = a1,type = "l",lty = c(2, 2, 1),lwd = c(1, 1, 3),col.line = c(rep("black",2), "red"), scales=list(x=list(rot=45)))
This dataset can be a good example except that, x is datetime not just year:
df <- data.frame(x=paste0(rep(1960:1999, each=4), paste0("Q", 1:4)), y=1:160)
How can I handle this!
I fixed the same with ggplot. I used library(ggplot2) and library(scales) to handle the issue. It came out very well.
Related
I would like to create an interactive histogram with dates on the x-axis.
I have used ggplot+ggplotly.
I've read I need to use to pass the proper information using the "text=as.character(mydates)" option and sometimes "tooltips=mytext".
This trick works for other kinds of plots but there is a problem with the histograms, instead of getting a single bar with a single value I get many sub-bars stacked.
I guess the reason is passing "text=as.character(fechas)" produces many values instead of just the class value defining that bar.
How can I solve this problem?
I have tried filtering myself the data but I don't know how to make this the parameters match the parameters used by the histogram, such as where the dates start for each bar.
library(lubridate)
library(ggplot2)
library(ggplotly)
Ejemplo <- data.frame(fechas = dmy("1-1-20")+sample(1:100,100, replace=T),
valores=runif(100))
dibujo <- ggplot(Ejemplo, aes(x=fechas, text=as.character(fechas))) +
theme_bw() + geom_histogram(binwidth=7, fill="darkblue",color="black") +
labs(x="Fecha", y="Nº casos") +
theme(axis.text.x=element_text(angle=60, hjust=1)) +
scale_x_date(date_breaks = "weeks", date_labels = "%d-%m-%Y",
limits=c(dmy("1-1-20"), dmy("1-4-20")))
ggplotly(dibujo)
ggplotly(dibujo, tooltip = "text")
As you can see, the bars are not regular histogram bars but something complex.
Using just ggplot instead of ggplotly shows the same problem, though then you woulnd't need to use the extra "text" parameter.
Presently, feeding as.character(fechas) to the text = ... argument inside of aes() will display the relative counts of distinct dates within each bin. Note the height of the first bar is simply a count of the total number of dates between 6th of January and the 13th of January.
After a thorough reading of your question, it appears you want the maximum date within each weekly interval. In other words, one date should hover over each bar. If you're partial to converting ggplot objects into plotly objects, then I would advise pre-processing the data frame before feeding it to the ggplot() function. First, group by week. Second, pull the desired date by each weekly interval to show as text (i.e., end date). Next, feed this new data frame to ggplot(), but now layer on geom_col(). This will achieve similar output since you're grouping by weekly intervals.
library(dplyr)
library(lubridate)
library(ggplot2)
library(plotly)
set.seed(13)
Ejemplo <- data.frame(fechas = dmy("1-1-20") + sample(1:100, 100, replace = T),
valores = runif(100))
Ejemplo_stat <- Ejemplo %>%
arrange(fechas) %>%
filter(fechas >= ymd("2020-01-01"), fechas <= ymd("2020-04-01")) %>% # specify the limits manually
mutate(week = week(fechas)) %>% # create a week variable
group_by(week) %>% # group by week
summarize(total_days = n(), # total number of distinct days
last_date = max(fechas)) # pull the maximum date within each weekly interval
dibujo <- ggplot(Ejemplo_stat, aes(x = factor(week), y = total_days, text = as.character(last_date))) +
geom_col(fill = "darkblue", color = "black") +
labs(x = "Fecha", y = "Nº casos") +
theme_bw() +
theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
scale_x_discrete(label = function(x) paste("Week", x))
ggplotly(dibujo) # add more text (e.g., week id, total unique dates, and end date)
ggplotly(dibujo, tooltip = "text") # only the end date is revealed
The "end date" is displayed once you hover over each bar, as requested. Note, the value "2020-01-12" is not the last day of the second week. It is the last date observed in the second weekly interval.
The benefit of the preprocessing approach is your ability to modify your grouped data frame, as needed. For example, feel free to limit the date range to a smaller (or larger) subset of weeks, or start your weeks on a different day of the week (e.g., Sunday). Furthermore, if you want more textual options to display, you could also display your total number of unique dates next to each bar, or even display the date ranges for each week.
I have a dataframe with two columns, Global Active Power is a numeric column and DateTime is a datetime type column. When I execute the command plot(DateTime,Global Active Power), I automatically get the days of the week as ticks on the x axis.
1. Can someone explain how this is happening?
2. Also, when I run plot(as.factor(weekdays(DateTime)),Global Active Power), I do not get the same plot, instead I get a boxplot.
Your DateTime column has its dates and times all within a 48-hour period, so R chooses the day of the week as the most appropriate x axis labels for you. You can change this formatting to whatever you like.
Since your example did not include any data, I've had to create some dummy data to show how this works:
set.seed(69)
x <- (as.POSIXct("2020-05-29 10:30:00") + 1:(24 * 60) * 300)[1:1000]
y <- rpois(1000, 50 * sin(seq(0, 12, length.out = 1000))^2) / 10
df <- data.frame(DateTime = x, `Global Active Power` = y)
So plotting this data, we get a similar layout to the plot in your question:
plot(df$DateTime, df$Global.Active.Power, type ="l", xlab ="Date", ylab ="Power")
Now, if I want to format with, say, the date, then I would draw the plot without an x axis then add a formatted axis like this:
plot(df$DateTime, df$Global.Active.Power,
type = "l", xaxt = "n", xlab = "Date", ylab = "Power")
axis.POSIXct(1, df$DateTime, format = "%d %b")
As for why your plot changes to a boxplot when you change the x axis to a factor variable according to the day of the week, you have transformed your time variable from a continuous to a discrete variable. There are only two weekdays in your data, so you will only have two points on your x axis where data can appear. R chooses a boxplot here because otherwise your plot would just be a mess, as you can see if I change the date-times to just dates:
plot(as.Date(df$DateTime),df$Global.Active.Power)
Created on 2020-05-29 by the reprex package (v0.3.0)
I have a basic dataframe with 3 columns: (i) a date (when a sample was taken); (ii) a site location and (iii) a binary variable indicating what the condition was when sampling (e.g. wet versus dry).
Some reproducible data:
df <- data.frame(Date = rep(seq(as.Date("2010-01-01"), as.Date("2010-12-01"), by="months"),times=2))
df$Site <- c(rep("Site.A",times = 12),rep("Site.B",times = 12))
df$Condition<- as.factor(c(0,0,0,0,1,1,1,1,0,0,0,0,
0,0,0,0,0,1,1,0,0,0,0,0))
What I would like to do is use ggplot to create a bar chart indicating the condition of each site (y axis) over time (x axis) - the condition indicated by a different colour. I am guessing some kind of flipped barplot would be the way to do this, but I cannot figure out how to tell ggplot2 to recognise the values chronologically, rather than summed for each condition. This is my attempt so far which clearly doesn't do what I need it to.
ggplot(df) +
geom_bar(aes(x=Site,y=Date,fill=Condition),stat='identity')+coord_flip()
So I have 2 questions. Firstly, how do I tell ggplot to recognise changes in condition over time and not just group each condition in a traditional stacked bar chart?
Secondly, it seems ggplot converts the date to a numerical value, how would I reformat the x-axis to show a time period, e.g. in a month-year format? I have tried doing this via the scale_x_date function, but get an error message.
labDates <- seq(from = (head(df$Date, 1)),
to = (tail(df$Date, 1)), by = "1 months")
Datelabels <-format(labDates,"%b %y")
ggplot(df) +
geom_bar(aes(x=Site,y=Date,fill=Condition),stat='identity')+coord_flip()+
scale_x_date(labels = Datelabels, breaks=labDates)
I have also tried converting sampling times to factors and displaying these instead. Below I have done this by changing each sampling period to a letter (in my own code, the factor levels are in a month-year format - I put letters here for simplicity). But I cannot format the axis to place each level of the factor as a tick mark. Either a date or factor solution for this second question would be great!
df$Factor <- as.factor(unique(df$Date))
levels(df$Factor) <- list(A = "2010-01-01", B = "2010-02-01",
C = "2010-03-01", D = "2010-04-01", E = "2010-05-01",
`F` = "2010-06-01", G = "2010-07-01", H = "2010-08-01",
I = "2010-09-01", J = "2010-10-01", K= "2010-11-01", L = "2010-12-01")
ggplot(df) +
geom_bar(aes(x=Site,y=Date,fill=Condition),stat='identity')+coord_flip()+
scale_y_discrete(breaks=as.numeric(unique(df$Date)),
labels=levels(df$Factor))
Thank you in advance!
It doesn't really make sense to use geom_bar() considering you do not want to summarise the data and require the visualisation over "time"
I would rather use geom_line() and increase the line thickness if you want to portray a bar chart.
library(tidyr)
library(dplyr)
library(ggplot2)
library(scales)
library(lubridate)
df <- data.frame(Date = rep(seq.Date(as.Date("2010-01-01"), as.Date("2010-12-01"), by="months"),times=2))
df$Site <- c(rep("Site.A",times = 12),rep("Site.B",times = 12))
df$Condition<- as.factor(c(0,0,0,0,1,1,1,1,0,0,0,0,
0,0,0,0,0,1,1,0,0,0,0,0))
df$Date <- ymd(df$Date)
ggplot(df) +
geom_line(aes(y=Site,x=Date,color=Condition),size=10)+
scale_x_date(labels = date_format("%b-%y"))
Note using coord_flip() also does not work, I think this causes the Date issue, see below threads:
how to use coord_carteisan and coord_flip together in ggplot2
In ggplot2, coord_flip and free scales don't work together
R could be amazingly powerful and frustrating at the same time. This makes teaching R to non-statisticians (business students in my case) rather challenging. Let me illustrate this with a simple task.
Let's say you are working with a monthly time series dataset. Most business data are usually plotted as monthly time series. We would like to plot the data such that the x-axis depicts a combination of month and year. For instance, January 2017 could be depicted as 2017-01. It should be straightforward with the plot command. Not true.
Data Generation
Let's illustrate this with an example. I'll generate a random time series of monthly data for 120 observations representing 10 years of information starting in January 2007 and ending in December 2017. Here's the code.
set.seed(1234)
x <- rnorm(120)
d <-.07
y <- cumsum(x+d)*-1
Since we have not declared the data as time series, plotting it with the plot command would not return the intended labels for the x-axis. See the code and the chart below.
plot(y, type="l")
Now there should be an option in the plot or the plot.ts command to display the time series specific x-axis. I couldn't find one. So here's the workaround.
Declare the data set to be time series.
Use tsp and seq to generate the required x-axis labels.
Plot the chart but suppress x-axis.
Use the axis command to add the custom x-axis labels.
Add an extra step to draw a vertical line at 2012.
Here's the code.
my.ts <- ts(y, start=c(2007, 1), end=c(2017, 12), frequency=12)
tsp = attributes(my.ts)$tsp
dates = seq(as.Date("2007-01-01"), by = "month", along = my.ts)
plot(my.ts, xaxt = "n", main= "Plotting outcome over time",
ylab="outcome", xlab="time")
axis(1, at = seq(tsp[1], tsp[2], along = my.ts), labels = format(dates, "%Y-%m"))
abline(v=2012, col="blue", lty=2, lwd=2)
The result is charted below.
This is a workable solution for most data scientists. But if your audience comprises business students or professionals there are too many lines of code to write.
Question: Is it possible to plot a time series variable (object) using the plot command with the format option controlling how the x-axis will be displayed?
--
ggplot2 package has the scale_x_date function for plotting time series in desired scales, labels, breaks and limits (day, month, year formats).
All you need is date class object and values y. For eg.
dates = seq(as.Date("01-01-2007", format = "%d-%m-%Y"), length.out = 120, by = "month")
df <- data.frame(dates, y)
# use the format you need in your plot using scale_x_date
library(ggplot2)
ggplot(df, aes(dates, y)) + geom_line() + scale_x_date(date_labels = "%b-%Y") +
geom_vline(xintercept = as.Date("01-01-2012", format = "%d-%m-%Y"), linetype = 'dotted', color = 'blue')
I think the question boils down to wanting a pre-written function for the custom axis you have in mind. Note that plot(my.ts) does give a plot with ticks every month and labels every year which to me looks better than the plot shown in the question but if you want a custom axis since R is a programming language you can certainly write a simple function for that and from then on it's just a matter of calling that function.
For example, to get you started here is a function that accepts a frequency 12 ts object. It draws an X axis with ticks for each month labelling the years and each every'th month where the every argument can be a divisor of 12. The default is 3 so a label for every third month is shown (except Jan which is shown as the year). len is the number of letters of the month shown and can be 1, 2 or 3. 1 means show Jul as J, 2 means Ju and 3 means Jul. The default is 1.
xaxis12 <- function(ser, every = 3, len = 1) {
tt <- time(ser)
axis(side = 1, at = tt, labels = FALSE)
is.every <- cycle(ser) %in% seq(1, 12, every)[-1]
month.labs <- substr(month.abb[cycle(ser)][is.every], 1, len)
axis(side = 1, at = tt[is.every], labels = month.labs,
cex.axis = 0.7, tcl = -0.75)
is.jan <- cycle(ser) == 1
year.labs <- sprintf("'%02d", as.integer(tt)[is.jan] %% 100)
axis(side = 1, at = tt[is.jan], labels = year.labs,
cex.axis = 0.7, tcl = -1)
}
# test
plot(my.ts, xaxt = "n")
xaxis12(my.ts)
Gabor is spot-on. It really just depends on what you want, and what you are willing to dig up or alter. Here is a simple alternative using a newer and less-well-known package that is excellent for plotting xts types:
## alternative
library(rtsplot) # load the plotting package
library(xts) # load the xts time-series container package
xx <- as.xts(my.ts) # create an xts object
rtsplot(xx, main= "Plotting outcome over time")
rtsplot.x.highlight(xx, which(index(xx)=="Jan 2012"), 1)
As you can see, the plotting then is two calls -- rtsplot has lots of nice defaults. Below is a screenshot as I am lazy, the plot window does of course not have a title bar...
I have 1417 days of sale data from 2012-01-01 to present (2015-11-20). I can't figure out how to have a single-year (Jan 1 - Dec 31) axis and each year's sales on the same, one year-long window, even when using ggplot's color = as.factor(Year) option.
Total sales are type int
head(df$Total.Sales)
[1] 495 699 911 846 824 949
and I have used the lubridate package to pull Year out of the original Day variable.
df$Day <- as.Date(as.numeric(df$Day), origin="1899-12-30")
df$Year <- year(df$Day)
But because Day contains the year information
sample(df$Day, 1)
[1] "2012-05-05"
ggplot is still graphing three years instead of synchronizing them to the same period of time (one, full year):
g <- ggplot(df, aes(x = Day, y = Total.Sales, color = as.factor(Year))) +
geom_line()
I create some sample data as follows
set.seed(1234)
dates <- seq(as.Date("2012-01-01"), as.Date("2015-11-20"), by = "1 day")
values <- sample(1:6000, size = length(dates))
data <- data.frame(date = dates, value = values)
Providing something of the sort is, by the way, what is meant by a reproducible example.
Then I prepare some additional columns
library(lubridate)
data$year <- year(data$date)
data$day_of_year <- as.Date(paste("2012",
month(data$date),mday(data$date), sep = "-"))
The last line is almost certainly what Roland meant in his comment. And he was right to choose the leap year, because it contains all possible dates. A normal year would miss February 29th.
Now the plot is generated by
library(ggplot2)
library(scales)
g <- ggplot(data, aes(x = day_of_year, y = value, color = as.factor(year))) +
geom_line() + scale_x_date(labels = date_format("%m/%d"))
I call scale_x_date to define x-axis labels without the year. This relies on the function date_format from the package scales. The string "%m/%d" defines the date format. If you want to know more about these format strings, use ?strptime.
The figure looks as follows:
You can see immediately what might be the trouble with this representation. It is hard to distinguish anything on this plot. But of course this is also related to the fact that my sample data is wildly varying. Your data might look different. Otherwise, consider using faceting (see ?facet_grid or ?facet_wrap).