I am fairly new to R and ggplot2 and am having some trouble plotting multiple variables in the same histogram plot.
My data is already grouped and just needs to be plotted. The data is by week and I need to plot the number for each category (A, B, C and D).
Date A B C D
01-01-2011 11 0 11 1
08-01-2011 12 0 3 3
15-01-2011 9 0 2 6
I want the Dates as the x axis and the counts plotted as different colors according to a generic y axis.
I am able to plot just one of the categories at a time, but am not able to find an example like mine.
This is what I use to plot one category. I am pretty sure I need to use position="dodge" to plot multiple as I don't want it to be stacked.
ggplot(df, aes(x=Date, y=A)) + geom_histogram(stat="identity") +
labs(title = "Number in Category A") +
ylab("Number") +
xlab("Date") +
theme(axis.text.x = element_text(angle = 90))
Also, this gives me a histogram with spaces in between the bars. Is there any way to remove this? I tried spaces=0 as you would do when plotting bar graphs, but it didn't seem to work.
I read some previous questions similar to mine, but the data was in a different format and I couldn't adapt it to fit my data.
This is some of the help I looked at:
Creating a histogram with multiple data series using multhist in R
http://www.cookbook-r.com/Graphs/Plotting_distributions_%28ggplot2%29/
I'm also not quite sure what the bin width is. I think it is how the data should be spaced or grouped, which doesn't apply to my question since it is already grouped. Please advise me if I am wrong about this.
Any help would be appreciated.
Thanks in advance!
You're not really plotting histograms, you're just plotting a bar chart that looks kind of like a histogram. I personally think this is a good case for faceting:
library(ggplot2)
library(reshape2) # for melt()
melt_df <- melt(df)
head(melt_df) # so you can see it
ggplot(melt_df, aes(Date,value,fill=Date)) +
geom_bar() +
facet_wrap(~ variable)
However, I think in general, that changes over time are much better represented by a line chart:
ggplot(melt_df,aes(Date,value,group=variable,color=variable)) + geom_line()
Related
I tried to create a relatively simple boxplot plot in R's ggplot2: One value on the x axis and several variables on the y axis. I'm using a code similar to this one:
ggplot() +
# Boxplot 1
geom_boxplot(df[which(df$Xvalue=="Boxplot1"),],
mapping = aes(X, "Y")) +
# Boxplot 2
geom_boxplot(df[which(df$Xvalue=="Boxplot2"),],
mapping = aes(X, "Y")) +
# Boxplot 3
geom_boxplot(df[which(df$Xvalue=="Boxplot3"),],
mapping = aes(X, "Y")) +
The boxplots in my real code are ordered alphabetically, however, I need them to be in a customized, categorial order.
I'm aware I could restructure my data frame so that I don't use a subset and a new geom_boxplot command for each boxplot, but I've structured the data that way for other reasons and that's not the solution I'm looking for right now.
Maybe there is an easy way using the scale_Y_manual or else? Any help is appreciated!
I would like to visualize a data frame much like the following in a plot:
grade number
A 2
B 6
C 1
D 0
E 1
The idea is to have the grades on the x-axis as categories and the number of pupils who received the respective grade on the y-axis.
My task is to display them not as points like in a line chart, but as thickness above the category like in a violin plot. This is really about the pure visuals of it.
I tried ggplot2's violin, but It always takes the values of the number column for the y-axis. But the y-axis is supposed to have just one single dimension: the level around which the density-plot is rotated.
I'd be very happy If someone had a hint at how I should maybe restructure my data or maybe if I am completely mistaken with my approach.
Ah, yes: on top I'd like to display the grade-point-average as a small bar.
Thank you very much in advance for taking your time. I'm sure the solution is very obvious, but I just don't see it.
As #Gregor mentioned, a smoothed density estimate (which is what a violin plot is) with just five ordinal values isn't really appropriate here. Even if you had plus/minus grades, you'd still probably be better off with bars or lines. See below for a few options:
library(ggplot2)
# Fake data
dat = data.frame(grades=LETTERS[c(1:4,6)],
count=c(5,12,11,5,3), stringsAsFactors=FALSE)
# Reusable plot elements
thm = list(theme_bw(),
scale_y_continuous(limits=c(0,max(dat$count)), breaks=seq(0,20,2)),
labs(x="Grade", y="Count"))
ggplot(dat, aes(grades, count)) +
geom_bar(stat="identity", fill=hcl(240,100,50)) +
geom_text(aes(y=0.5*count, label=paste0(count, " (", sprintf("%1.1f", count/sum(count)*100),"%)")),
colour="white", size=3) +
thm
ggplot(dat, aes(grades, count)) +
geom_line(aes(group=1),alpha=0.4) +
geom_point() +
thm
ggplot(dat, aes(x=as.numeric(factor(grades)))) +
geom_ribbon(aes(ymin=0, ymax=count), fill="grey80") +
geom_text(aes(y=count, label=paste0(sprintf("%1.1f", count/sum(count)*100),"%")), size=3) +
scale_x_continuous(labels=LETTERS[c(1:4,6)]) +
thm
I want to create a scatterplot in ggplot where there are multiple y values for each x value. I want to add these y values and plot the sum against the x value.
>df
a b
1 2
1 2
2 1
2 4
3 1
3 5
I want a plot that plots the sums of the b values for each a
a b
1 4
2 5
3 6
I can do this for a barplot by making a stacked barplot:
ggplot(data=df, aes(x=df$a, y=df$b)) + geom_bar(stat="identity")
but if I do this with geom_point ggplot just plots each value of y without stacking.
I could use ddply for this, but that would require a number of more steps. If there is a more expedient way I'd appreciate it.
I searched the site for other answers. While there were plenty about "stacked scatterplots" they were all about overlaid plots.
I don't see anything stacked about your bar chart example. If you just want to summarize the values to a single pont, you can use stat_summary
ggplot(data=df, aes(x=a, y=b)) + stat_summary(fun.y=sum, geom="point")
There are many ways to achieve this effect - of a 'histogram' but without bars, whose height is the sum of all values at the same X.
This type of graph is called a Cleveland Dot Plot, and is used because the conspicuous bars of a histogram can a distraction or at worse be misleading. (see works by Cleveland, Tufte etc).
One way to achieve this is to pre-process the data to do the sum, using functions such as table or hist or tapply or xtabs...
Note that base R has the function dotchart for the production of this type of graph.
dotchart(xtabs(rev(df)))
... but since we are discussing ggplot, which has powerful ways to summarise the data while plotting it, let's stick to MrFlick's theme of how to do it directly ggplot operators (i.e. not preprocessed).
Using a weighted bin summary statistic:
ggplot(data=df, aes(x=factor(a),weight=b)) + geom_point(stat="bin")
you may want to adjust the lower y limit to 0 here.
By stacking the height of the points:
ggplot(data=df, aes(x=factor(a),y=b)) + geom_point(position="stack")
the additional dots visible on this plot are probably superfluous and definitely ambiguous, but highlight the fact of multiplicity in the source data.
Building a dotplot
This one is popular in newspapers, but usually has dollar bills instead of giant black holes:
ggplot(data=df, aes(x=factor(a),weight=b)) + geom_dotplot(method="histodot")
It's probably not what you are looking for, but it's worth being aware of.
You should also be aware that scales are difficult to get correct in this mode, so it's best used in a hand-tuned mode, with the y scale numbering turned off.
The data has 4 columns and roughly 600 rows. The data is twitter data collected using the twitteR package, and then summarized into a data frame. The summary is based on how many words from these libraries each tweet has, the tweets are given a score and then the summary is the number of tweets which get specific scores. So the columns are the two types of scores, the dates, and then the number of tweets with those scores.
Score1 Score2 Date Number
0 0 01/10/2015 50
0 1 01/10/2015 34
1 0 01/10/2015 10
...and so on
With dates and data that extend over a month, and the scores either way can go +/- 10 or so.
I'm trying to plot that kind of data using a bubble plots, score1 on the x axis and score2 on the y axis with the size of the bubble dependant on the number (how many tweets of with those scores there were per day).
My problem is that I only know how to use ggplot.
g <- ggplot(
twitterdata,
aes(x=score1, y=score2, size=number, label=""), guide=FALSE) +
geom_point(colour="black", fill="red", shape=21) +
scale_size_area(max_size = 30) +
scale_x_continuous(name="score1", limits=c(0, 10)) +
scale_y_continuous(name="score2", limits=c(-10, 10)) +
geom_text(size=4) +
theme_bw()
and that just gives me the plot for all dates, and what I need is a good way to see how that data changes over time. I've looked into using sliders and selectors but I really have no idea what would be the best tool to use. I've tried subsetting the data based on date, which works nicely but ideally I could make some kind of interactive graph.
I really need some way select certain days out of that data to plot so it doesn't pile up all on itself, but do it interactively so it can be presented.
Any help would be greatly appreciated, thank you.
It sounds like this won't completely satisfy your use case, but an extremely low-overhead way to add some interactivity to your plot would be to install.packages('plotly') and add the following line to your code:
# your original code
g <- ggplot(
twitterdata,
aes(x=score1, y=score2, size=number, label=""),
guide=FALSE)+
geom_point(colour="black", fill="red", shape=21) +
scale_size_area(max_size = 30) +
scale_x_continuous(name="score1", limits=c(0,10)) +
scale_y_continuous(name="score2", limits=c(-10,10)) +
geom_text(size=4) +
theme_bw()
# add this line
gg <- ggplotly(g)
Details and demos: https://plot.ly/ggplot2/
As Eric suggested, if you want sliders and such you should check out shiny. Here's a demo combining shiny with plotly: https://plot.ly/r/shiny-tutorial/
I am trying to plot two vectors with different values, but equal length on the same graph as follows:
a<-23.33:52.33
b<-33.33:62.33
days<-1:30
df<-data.frame(x,y,days)
a b days
1 23.33 33.33 1
2 24.33 34.33 2
3 25.33 35.33 3
4 26.33 36.33 4
5 27.33 37.33 5
etc..
I am trying to use ggplot2 to plot x and y on the x-axis and the days on the y-axis. However, I can't figure out how to do it. I am able to plot them individually and combine the graphs, but I want just one graph with both a and b vectors (different colors) on x-axis and number of days on y-axis.
What I have so far:
X<-ggplot(df, aes(x=a,y=days)) + geom_line(color="red")
Y<-ggplot(df, aes(x=b,y=days)) + geom_line(color="blue")
Is there any way to define the x-axis for both a and b vectors? I have also tried using the melt long function, but got stuck afterwards.
Any help is much appreciated. Thank you
I think the best way to do it is via a the approach of melting the data (as you have mentioned). Especially if you are going to add more vectors. This is the code
library(reshape2)
library(ggplot2)
a<-23:52
b<-33:62
days<-1:30
df<-data.frame(x=a,y=b,days)
df_molten=melt(df,id.vars="days")
ggplot(df_molten) + geom_line(aes(x=value,y=days,color=variable))
You can also change the colors manually via scale_color_manual.
A simpler solution is to use only ggplot. The following code will work in your case
a<-23.33:52.33
b<-33.33:62.33
days<-1:30
df<-data.frame(a,b,days)
ggplot(data = df)+
geom_line(aes(x = df$days,y = df$a), color = "blue")+
geom_line(aes(x = df$days,y = df$b), color = "red")
I added the colors, you might want to use them to differentiate between your variables.