I'm trying to create a frequency plot of number of appearances of a graph type by year.
I have played around with ggplot2 for a while, but I think this is over my head (I'm just getting started with R)
I attached a schematic of what I would like the result to look like. One of the other issues I'm running into is that there are many years that the graph types don't appear. Is there a way to exclude the graph type if it does not appear that year?
e.g. in 1940 there is no "sociogram" I don't want to have a bunch of lines at 0...
year <- c("1940","1940","1940","1940","1940","1940","1940","1940","1940","1940","1940","1941","1941","1941","1941","1941","1941","1941","1941","1941","1941","1941","1941","1941","1941")
type <- c("Line","Column", "Stacked Column", "Scatter with line", "Scatter with line", "Scatter with line", "Scatter with line", "Map with distribution","Line","Line","Line","Bar","Bar","Stacked bar","Column","Column","Sociogram","Sociogram","Column","Column","Column","Line","Line","Line","Line")
ytmatrix <- cbind(as.Date(as.character(year), "%Y", type))
Please let me know if something doesn't make sense. StackOverflow is quickly becoming one of my favorite sites!
Thank,
Jon
Here's what I have so far...
Thank you again for all your help!
And here's how I did it (I can't share the data file yet, since it's something we're hoping to use it for a publication, but the ggplot area is probably the more interesting, though I didn't really do anything new/that wasn't discussed in the post):
AJS = read.csv(data) #read in file
Type = AJS[,17] #select and name "Type" column from csv
Year = AJS[,13] #select and name "Year" column from csv
Year = substr(Year,9,12) #get rid of junk from year column
Year = as.Date(Year, "%Y") #convert the year character to a date
Year = format(Year, "%Y") #get rid of the dummy month and day
Type = as.data.frame(Type) #create data frame
yt <- cbind(Year,Type) #bind the year and type together
library(ggplot2)
trial <- ggplot(yt, aes(Year,..count.., group= Type)) + #plot the data followed by aes(x- axis, y-axis, group the lines)
geom_density(alpha = 0.25, aes(fill=Type)) +
opts(axis.text.x = theme_text(angle = 90, hjust = 0)) + #adjust the x axis ticks to horizontal
opts(title = expression("Trends in the Use of Visualizations in The American Journal of Sociology")) + #Add title
scale_y_continuous('Appearances (10 or more)') #change Y-axis label
trial
This might be a more interesting dataframe to experiment with:
df1 <- data.frame(date = as.Date(10*365*rbeta(100, .5, .1)),group="a")
df2 <- data.frame(date = as.Date(10*365*rbeta(50, .1, .5)),group="b")
df3 <- data.frame(date = as.Date(10*365*rbeta(25, 3,3)),group="c")
dfrm <- rbind(df1,df2,df3)
I thought working with an example in the help(stat_density) page would work, but it does not:
m <- ggplot(dfrm, aes(x=date), group=group)
m+ geom_histogram(aes(y=..density..)) + geom_density(fill=NA, colour="black")
However an example I found in a search of hte archives found a posting by #Hadley Wickham that does work:
m+ geom_density(aes(fill=group), colour="black")
Related
i have a dataset given with:
Country Time Value
1 USA 1999-Q1 292929
2 USA 1999-Q2 392023
3. USA 1999-Q3 9392992
4
.... and so on. Now I would like to plot this dataframe with Time being on the x-axis and y being the Value. But the problem I face is I dont know how to plot the Time. Because it is not given in month/date/year. If that would be the case I would just code as.Date( format = "%m%d%y"). I am not allowed to change the quarterly name. So when I plot it, it should stay that way. How can I do this?
Thank you in advance!
Assuming DF shown in the Note at the end, convert the Time column to yearqtr class which directly represents year and quarter (as opposed to using Date class) and use scale_x_yearqtr. See ?scale_x_yearqtr for more information.
library(ggplot2)
library(zoo)
fmt <- "%Y-Q%q"
DF$Time <- as.yearqtr(DF$Time, format = fmt)
ggplot(DF, aes(Time, Value, col = Country)) +
geom_point() +
geom_line() +
scale_x_yearqtr(format = fmt)
(continued after graphics)
It would also be possible to convert it to a wide form zoo object with one column per country and then use autoplot. Using DF from the Note below:
fmt <- "%Y-Q%q"
z <- read.zoo(DF, split = "Country", index = "Time",
FUN = as.yearqtr, format = fmt)
autoplot(z) + scale_x_yearqtr(format = fmt)
Note
Lines <- "
Country Time Value
1 USA 1999-Q1 292929
2 USA 1999-Q2 392023
3 USA 1999-Q3 9392992"
DF <- read.table(text = Lines)
Using ggplot2:
library(ggplot2)
ggplot(df, aes(Time, Value, fill = Country)) + geom_col()
I know other people have already answered, but I think this more general answer should also be here.
When you do as.Date(), you can only do the beginning. I tried it on your data frame (I called it df), and it worked:
> as.Date(df$Time, format = "%Y")
[1] "1999-11-28" "1999-11-28" "1999-11-28"
Now, I don't know if you want to use plot(), ggplot(), the ggplot2 library... I don't know that, and it doesn't matter. However you want to specify the y axis, you can do it this way.
I have a basic dataframe with 3 columns: (i) a date (when a sample was taken); (ii) a site location and (iii) a binary variable indicating what the condition was when sampling (e.g. wet versus dry).
Some reproducible data:
df <- data.frame(Date = rep(seq(as.Date("2010-01-01"), as.Date("2010-12-01"), by="months"),times=2))
df$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
I create a dummy timeseries xts object with missing data on date 2-09-2015 as:
library(xts)
library(ggplot2)
library(scales)
set.seed(123)
seq <- seq(as.POSIXct("2015-09-01"),as.POSIXct("2015-09-02"), by = "1 hour")
ob1 <- xts(rnorm(length(seq),150,5),seq)
seq2 <- seq(as.POSIXct("2015-09-03"),as.POSIXct("2015-09-05"), by = "1 hour")
ob2 <- xts(rnorm(length(seq2),170,5),seq2)
final_ob <- rbind(ob1,ob2)
plot(final_ob)
# with ggplot
df <- data.frame(time = index(final_ob), val = coredata(final_ob) )
ggplot(df, aes(time, val)) + geom_line()+ scale_x_datetime(labels = date_format("%Y-%m-%d"))
After plotting my data looks like this:
The red coloured rectangular portion represents the date on which data is missing. How should I show that data was missing on this day in the main plot?
I think I should show this missing data with a different colour. But, I don't know how should I process data to reflect the missing data behaviour in the main plot.
Thanks for the great reproducible example.
I think you are best off to omit that line in your "missing" portion. If you have a straight line (even in a different colour) it suggests that data was gathered in that interval, that happened to fall on that straight line. If you omit the line in that interval then it is clear that there is no data there.
The problem is that you want the hourly data to be connected by lines, and then no lines in the "missing data section" - so you need some way to detect that missing data section.
You have not given a criteria for this in your question, so based on your example I will say that each line on the plot should consist of data at hourly intervals; if there's a break of more than an hour then there should be a new line. You will have to adjust this criteria to your specific problem. All we're doing is splitting up your dataframe into bits that get plotted by the same line.
So first create a variable that says which "group" (ie line) each data is in:
df$grp <- factor(c(0, cumsum(diff(df$time) > 1)))
Then you can use the group= aesthetic which geom_line uses to split up lines:
ggplot(df, aes(time, val)) + geom_line(aes(group=grp)) + # <-- only change
scale_x_datetime(labels = date_format("%Y-%m-%d"))
I have about 20 years of daily data in a time series. It has columns Date, rainfall and other data.
I am trying plot rainfall vs Time. I want to get 20 line plots with different colours and legend is generated that show the years in one graph. I tried the following codes but it is not giving me the desired results. Any suggestion to fix my issue would be most welcome
library(ggplot2)
library(seas)
data(mscdata)
p<-ggplot(data=mscdata,aes(x=date,y=precip,group=year,color=year))
p+geom_line()+scale_x_date(labels=date_format("%m"),breaks=date_breaks("1 months"))
It doesnt look great but here's a method. We first coerce the data into dates in the same year:
mscdata$dayofyear <- as.Date(format(mscdata$date, "%j"), format = "%j")
Then we plot:
library(ggplot2)
library(scales)
p <- ggplot(data = mscdata, aes(x = dayofyear, y = precip, group = year, color = year))
p + geom_line() +
scale_x_date(labels = date_format("%m"), breaks = date_breaks("1 months"))
While I agree with #Jaap that this may not be the best way to depict these data, try to following:
mscdata$doy <- as.numeric(strftime(mscdata$date, format="%j"))
ggplot(data=mscdata,aes(x=doy,y=precip,group=year)) +
geom_line(aes(color=year))
Although the given answers are good answers to your questions as it stands, i don't think it will solve your problem. I think you should be looking at a different way to present the data. #Jaap already suggested using facets. Take for example this approach:
#first add a month column to your dataframe
mscdata$month <- format(mscdata$date, "%m")
#then plot it using boxplot with year on the X-axis and month as facet.
p1 <- ggplot(data = mscdata, aes(x = year, y = precip, group=year))
p1 + geom_boxplot(outlier.shape = 3) + facet_wrap(~month)
This will give you a graph per month, showing the rainfall per year next to one each other. Because i use boxplot, the peaks in rainfall show up as dots ('normal' rain events are inside box).
Another possible approach would be to use stat_summary.
I am wondering how to dynamically set the x axis limits of a time series plot containing two time series with different dates. I have developed the following code to provide a reproducible example of my problem.
#Dummy Data
Data1 <- data.frame(Date = c("4/24/1995","6/23/1995","2/12/1996","4/14/1997","9/13/1998"), Area_2D = c(20,11,5,25,50))
Data2 <- data.frame(Date = c("6/23/1995","4/14/1996","11/3/1997","11/6/1997","4/15/1998"), Area_2D = c(13,15,18,25,19))
Data3 <- data.frame(Date = c("4/24/1995","6/23/1995","2/12/1996","4/14/1996","9/13/1998"), Area_2D = c(20,25,28,30,35))
Data4 <- data.frame(Date = c("6/23/1995","4/14/1996","11/3/1997","11/6/1997","4/15/1998"), Area_2D = c(13,15,18,25,19))
#Convert date column as date
Data1$Date <- as.Date(Data1$Date,"%m/%d/%Y")
Data2$Date <- as.Date(Data2$Date,"%m/%d/%Y")
Data3$Date <- as.Date(Data3$Date,"%m/%d/%Y")
Data4$Date <- as.Date(Data4$Date,"%m/%d/%Y")
#PLOT THE DATA
max_y1 <- max(Data1$Area_2D)
# Define colors to be used for cars, trucks, suvs
plot_colors <- c("blue","red")
plot(Data1$Date,Data1$Area_2D, col=plot_colors[1],
ylim=c(0,max_y1), xlim=c(min_x1,max_x1),pch=16, xlab="Date",ylab="Area", type="o")
par(new=T)
plot(Data2$Date,Data2$Area_2D, col=plot_colors[2],
ylim=c(0,max_y1), xlim=c(min_x1,max_x1),pch=16, xlab="Date",ylab="Area", type="o")
The main problem I see with the code above is there are two different x axis on the plot, one for Data1 and another for Data2. I want to have a single x axis spanning the date range determined by the dates in Data1 and Data2.
My questions is:
How do i dynamically create an x axis for both series? (i.e select the minimum and maximum date from the data frames 'Data1' and 'Data2')
The solution is to combine the data into one data.frame, and base the x-axis on that. This approach works very well with the ggplot2 plotting package. First we merge the data and add an ID column, which specifies to which dataset it belongs. I use letters here:
Data1$ID = 'A'
Data2$ID = 'B'
merged_data = rbind(Data1, Data2)
And then create the plot using ggplot2, where the color denotes which dataset it belongs to (can easily be changed to different colors):
library(ggplot2)
ggplot(merged_data, aes(x = Date, y = Area_2D, color = ID)) +
geom_point() + geom_line()
Note that you get one uniform x-axis here. In this case this is fine, but if the timeseries do not overlap, this might be problematic. In that case we can use multiple sub-plots, known as facets in ggplot2:
ggplot(merged_data, aes(x = Date, y = Area_2D)) +
geom_point() + geom_line() + facet_wrap(~ ID, scales = 'free_x')
Now each facet has it's own x-axis, i.e. one for each sub-dataset. What approach is most valid depends on the specific situation.