I'm fairly new to R and making plots, so sorry about that. I have a dataset of the voting for counties and I want to make a barplot showing how many mandates each county voted for.
What I've done so far is to extract one row, which includes the name of the county and the number of mandates it voted for the different parties (which are headers).
Fylker AP FRP H KrF SP
Ostlandet 3 2 2 0 1
Sorry for the bad display of code, whenever I paste the code, it looks really weird, despite indenting.
The data is called "Ostlandet" and is only 1 row. So as I tried to explain above, I want to make some sort of barplot out of this. The idea is to have the different parties on the x-axis and number of votes on y. I've tried this so far
ggplot(Ostfold, aes(x = Ostfold[1,])) +
geom_histogram(binwidth = 20)
Which just gave me tons of errors.
I've also tried using barplot, but I just can't seem to figure this out.
Sorry, this is probably super easy, but I'm just getting into coding.
You have a few issues. First, there's no need for extracting rows. Second, the data are in "wide" format (mandates in columns) instead of "long format" (a column named "mandate" with values). Third, you want to plot counts so geom_col() is better than geom_histogram().
The gather() function from the tidyr package will get your data from wide into long:
library(tidyr)
library(ggplot2)
Ostfold %>%
gather(Mandate, Votes, -Fylker)
That should generate something like this:
Fylker Mandate Votes
1 Ostlandet AP 3
2 Ostlandet FRP 2
3 Ostlandet H 2
4 Ostlandet KrF 0
5 Ostlandet SP 1
You can pass that to ggplot:
Ostfold %>%
gather(Mandate, Votes, -Fylker) %>%
ggplot(aes(Mandate, Votes)) + geom_col()
Result for your one row:
For a dataset with multiple counties, you might want to add + facet_wrap(~Fylker) to facet the plot by county, depending on how many there are.
Related
So I don't think this has been asked before, but SO search might just be getting confused by combinations of 'ratio' and 'faceting'. I'm trying to calculate a productivity ratio; number of widgets produced for number of workers on a given day or period. I've got my data structured in a single data frame, with each widget produced each day by each worker in it's own record, and other workers that worked that day but didn't produce a widget also in their own record, along with various metadata.
Something like this:
widget_ind
employee_active_ind
employee_id
day
product_type
employee_bu
1
1
123
6/1/2021
pc
americas
0
1
234
6/1/2021
mac
emea
0
1
345
6/1/2021
mac
apac
1
1
444
6/1/2021
mac
americas
1
1
333
6/1/2021
pc
emea
0
1
356
6/1/2021
pc
americas
I'm trying to find the ratio of widget_inds to employee_active_inds, over time, while retaining the metadata, so that i can filter or facet within the ggplot2 code, something like:
plot <- ggplot(data = df[df$employee_bu == 'americas',],aes(y = (widget_ind/employee_active_ind), x = day)) +
geom_bar(stat = 'identity', position = 'stack') +
facet_wrap(product_type ~ ., scales = 'fixed') + #change these to look at different cuts of metadata
print(plot)
Retaining the metadata is appealing rather than making individual dataframes summarizing by the various combinations, but the results with no faceting aren't even correct (e.g. the ggplot is showing a barchart with a height of ~18 widgets per person; creating a summarized dataframe with no faceting is showing a ratio of less than 1 widget per person).
I'm currently getting this error when I run the ggplot code:
Warning message:
Removed 9865 rows containing missing values (geom_bar).
Which doesn't make sense since in my data frame both widget_ind and employee_active_ind have no NA values, so calculating the ratio of the two should always work?
Edit 1: Clarifying employee_active_ind: I should not have any employee_active_ind = 0, but my current joins produce them (and it passes the reality sniff test; the process we are trying to model allows you to do work on day 1 that results in a widget on day 2, where you may not do any work, so wouldn't be counted as active on that day). I think I need to re-think my data structure. Even so, I'm assuming here that ggplot2 is acting like it would for a given bar chart; it's taking the number in each widget_ind record, for a given day (along with any facets and filters), and is then summing that set and displaying the result. The wrinkle I'm adding is dividing by the number of active employees on that day, and while you can have some one out on a given day, you'd never have everyone out. But that isn't what ggplot is doing is it?
I agree with MrFlick - especially the question concerning employee_active_ind of 0. If you have them, this could create NA values where something is divided by 0.
I am a beginner with R so I don't have much experience. I ran into a problem when trying to split my scatterplot in groups based on infection status. My dataset consists of log transformed antibody levels logapfhap2 in this example. Infection status any Pf inf is coded as Yes or No and gives information on if someone has been infected during the follow-up period. I am plotting timepoints (x) against antibody levels (y). For time point 1 and 14 I would like to make 2 groups based on infection status.
This is the main part of the code I use to plot the data without splitting in groups:
ggplot() +
geom_jitter(data=data2, aes(x='1', y=logapfhap2, colour='PfHAP2A')) +
geom_jitter(data=data2,aes(x='14', y=logbpfhap2, colour='PfHAP2B')) +
geom_jitter(data=TRC, aes(x='C', y=PfHAP2, colour='PfHAP2C'))
which results in this graph:
Then I tried to split it (I only show the first time point here) which returns an error.
ggplot() +
geom_jitter(data=data2[data2$any_Pf_inf=='Yes'],
aes(x='1inf', y=logapfhap2[data2$any_Pf_inf=='Yes'],
colour='PfHAP2A')) +
geom_jitter(data=data2[data2$any_Pf_inf=='No'],
aes(x='1un', y=logapfhap2[data2$any_Pf_inf=='No'],
colour='PfHAP2B'))
I wanted to create this graph but I get this error:
Error: Length of logical index vector must be 1 or 55, got: 482
Hope this is clear! Could anyone help me with this problem? Thanks!
EDIT
Not sure if this makes it clearer, but this is what my data looks like:
I just tried some other things and I have solved it now!
ggplot()+
geom_jitter(data=data2[data2$any_Pf_inf=='Yes',],
aes(x='1inf', y=logapfhap2,
colour='PfHAP2A')) +
geom_jitter(data=data2[data2$any_Pf_inf=='No',],
aes(x='1un', y=logbpfhap2,
colour='PfHAP2B'))
Apparently you have to add a comma after [data2$any_Pf_inf=='Yes',] to extract rows instead of columns.
I'm relatively new to R and a complete beginner with ggplot, but I haven't managed to find an answer to the seemingly simple problem I have. Using ggplot, I would like to make a bar chart in which two of three or more graphed factor levels are stacked.
Essentially, this is the type of data I am looking at:
df <- data.frame(Answer=c("good","good","kinda good","kinda good",
"kinda good","good","bad","good","bad"))
This provides me with a factor with three levels, two of which are very similar:
Answer
1 good
2 good
3 kinda good
4 kinda good
5 kinda good
6 good
7 bad
8 good
9 bad
If I let ggplot go over these data for me now,
c <- ggplot(df, aes(df$Answer))
c + geom_bar()
I will get a bar chart with three columns. However, I would like to end up with two columns, one of which should be a stack of the two factor levels "good" and "kinda good", still visibly separated.
I am working with 100 columns of input (study on orthography), which I will need to go through manually, so I would like to make the code as easily adjustable as possible. Some of them have more than ten levels, and I would need to sort them into three columns. Therefore, in most cases my data would more likely look like this:
df <- data.frame(Answer=c("good","goood","goo0d","good",
"I don't know","Bad","bad","baaad","really bad"))
I would consequently group this into three categories. In approximately half of the cases, I could probably still filter using pattern matching because I will be looking at the use of spaces. The other half, however, is looking at capitalization, which would get a little messy, or at least very tedious.
I have thought of two different approaches to solve this issue more efficiently:
Simply rewriting the factor levels, but this would result in a loss of information (and I would like to keep the two levels separate). I would like to keep the original levels names because I think I need them to graph the ratio within that stacked column and to label the column properly.
I could split the respective column/factor into two separate columns/factors and graph them next to each other, and thus create a "fake" third dimension. This is looking to be the most promising approach, but before I work through 100 columns of data with this - is there a more elegant approach, maybe within the ggplot2 package, where I could just point/group the level names instead of changing/reordering the data frame behind it?
Thanks!
You can try the following for a more automated approach in grouping the answers.
We select some keywords based on your data and loop over them to see which answers may contain each keyword
groups <- c('good','bad','ugly','know')
df <- data.frame(Answer=c("good","medium good","kinda good","still good",
"I don't know","good","bad","good","really bad"))
idx <- sapply(groups, function(x) grepl(x, df$Answer, ignore.case = TRUE))
df$group <- rep(colnames(idx), nrow(idx))[t(idx)]
df
# Answer group
# 1 good good
# 2 medium good good
# 3 kinda good good
# 4 still good good
# 5 I don't know know
# 6 good good
# 7 bad bad
# 8 good good
# 9 really bad bad
library('ggplot2')
ggplot(df, aes(group, fill = Answer)) + geom_bar()
I have a data frame that looks like this (though thousands of times larger).
df<-data.frame(sample(1:100,10,replace=F),sample(1:100,10,replace=F),runif(10,0,1),runif(10,0,1),runif(10,0,1), rep(c("none","summer","winter","sping","allyear"),2))
names(df)<-c("Mother","ID","Wavelength1","Wavelength2","Wavelength3","WaterTreatment")
df
Mother ID Wavelength1 Wavelength2 Wavelength3 WaterTreatment
1 2 34 0.9143670 0.03077356 0.82859497 none
2 24 75 0.6173382 0.05958151 0.66552338 summer
3 62 77 0.2655572 0.63731302 0.30267893 winter
4 30 98 0.9823510 0.45690437 0.40818031 sping
5 4 11 0.7503750 0.93737900 0.24909228 allyear
6 55 76 0.6451885 0.60138475 0.86044856 none
7 97 21 0.5711019 0.99732068 0.04706894 summer
8 87 14 0.7699293 0.81617911 0.18940531 winter
9 92 30 0.5855559 0.70152698 0.73375917 sping
10 93 44 0.1040359 0.85259166 0.37882469 allyear
I want to plot wavelength values on the y axis, and wavelength on the x. I have two ways of doing this:
First method which works, but uses base plot and requires more code than should be necessary:
colors=c("red","blue","green","orange","yellow")
plot(0,0,xlim=c(1,3),ylim=c(0,1),type="l")
for (i in 1:10) {
if (df$WaterTreatment[i]=="none"){
a<-1
} else if (df$WaterTreatment[i]=="allyear") {
a<-2
}else if (df$WaterTreatment[i]=="summer") {
a<-3
}else if (df$WaterTreatment[i]=="winter") {
a<-4
}else if (df$WaterTreatment[i]=="spring") {
a<-5
}
lines(seq(1,3,1),df[i,3:5],type="l",col=colors[a])
}
Second method: I attempt to melt the data to put it in long form, then use ggplot2. The plot it produces is not correct because there is a line for each water treatment, rather than a line for each "Mother" "ID" (the unique identifier, what were the rows in the original data frame).
require(reshape2)
require(data.table)
df_m<-melt(df,id.var=c("Mother","ID","WaterTreatment"))
df_m$variable<-as.numeric(df_m$variable) #sets wavelengths to numeric
qplot(x=df_m$variable,y=df_m$value,data=df_m,color=df_m$WaterTreatment,geom = 'line')
There is probably something simple I'm missing about ggplot2 that would fix the plotting of the lines. I'm a newbie with ggplot, but am working to get more familiar with it and would like to use it in this application.
But more broadly, is there an efficient way to plot this type of wide form data in ggplot2? The time it takes to transform/melt the data is enormous and I'm wondering if it is worth it, or if there is some kind of work around that can eliminate the redundant cells created when melting.
Thanks for your help, if you need more clarity on this question please let me know and I can edit.
I'd like to point out that you are basically re-inventing an existing base plotting function, namely matplot. This could replace your plot and for-loop:
matplot(1:3, t( df[ ,3:5] ), type="l",col=colors[ as.numeric(df$WaterTreatment)] )
With that in mind you might want to search SO for: [r] matplot ggplot2, as I did, and see if this see if this or any of the other hits are effective.
It looks like you want a separate line for each ID, but you want the lines colored based on the value of WaterTreatment. If so, you can do it like this in ggplot:
ggplot(df_m, aes(x=variable, y=value, group=ID, colour=WaterTreatment)) +
geom_line() + geom_point()
You can also use faceting to make it easier to see the different levels of WaterTreatment
ggplot(df_m, aes(x=variable, y=value, group=ID, colour=WaterTreatment)) +
geom_line() + geom_point() +
facet_grid(WaterTreatment ~ .)
To answer your general question: ggplot is set up to work most easily and powerfully with a "long" (i.e., melted) data frame. I guess you could work with a "wide" data frame and plot separate layers for each combination of factors you want to plot. But that would be a lot of extra work compared to a single melt command to get your data into the right format.
My data looks like this example:
dataExample<-data.frame(Time=seq(1:10),
Data1=runif(10,5.3,7.5),
Data2=runif(10,4.3,6.5),
Application=c("Substance1","Substance1","Substance1",
"Substance1","Substance2","Substance2","Substance2",
"Substance2","Substance1","Substance1"))
dataExample
Time Data1 Data2 Application
1 1 6.511573 5.385265 Substance1
2 2 5.870173 4.512775 Substance1
3 3 6.822132 5.109790 Substance1
4 4 5.940528 6.281412 Substance1
5 5 7.269394 4.680380 Substance2
6 6 6.122454 6.015899 Substance2
7 7 5.660429 6.113362 Substance2
8 8 6.649749 4.344978 Substance2
9 9 7.252656 4.764667 Substance1
10 10 7.204440 5.835590 Substance1
I would like to indicate at which time any Substance was applied that is different from dataExample$Application[1].
Here I show you the way I get this ploted, but I assume that there is a much easier way to do it with ggplot.
library(reshape2)
library(ggplot)
plotDataExample<-function(DataFrame){
longDF<-melt(DataFrame,id.vars=c("Time","Application"))
p=ggplot(longDF,aes(Time,value,color=variable))+geom_line()
maxValue=max(longDF$value)
minValue=min(longDF$value)
yAppLine=maxValue+((maxValue-minValue)/20)
xAppLine1=min(longDF$Time[which(longDF$Application!=longDF$Application[1])])
xAppLine2=max(longDF$Time[which(longDF$Application!=longDF$Application[1])])
lineData=data.frame(x=c(xAppLine1,xAppLine2),y=c(yAppLine,yAppLine))
xAppText=xAppLine1+(xAppLine2-xAppLine1)/2
yAppText=yAppLine+((maxValue-minValue)/20)
appText=longDF$Application[which(longDF$Application!=longDF$Application[1])[1]]
textData=data.frame(x=xAppText,y=yAppText,appText=appText)
p=p+geom_line(data=lineData,aes(x=x, y=y),color="black")
p=p+geom_text(data=textData,aes(x=x,y=y,label = appText),color="black")
return(p)
}
plotDataExample(dataExample)
Question:
Do you know a better way to get a similar result so that I could possibly indicate more than one factor (e.g. Substance3, Substance4 ...).
First, made new sample data to have more than 2 levels and twice repeated Substance2.
dataExample<-data.frame(Time=seq(1:10),
Data1=runif(10,5.3,7.5),
Data2=runif(10,4.3,6.5),
Application=c("Substance1","Substance1","Substance2",
"Substance2","Substance1","Substance1","Substance2",
"Substance2","Substance3","Substance3"))
Didn't make this as function to show each step.
Add new column groups to original data frame - this contains identifier for grouping of Applications - if substance changes then new group is formed.
dataExample$groups<-c(cumsum(c(1,tail(dataExample$Application,n=-1)!=head(dataExample$Application,n=-1))))
Convert to long format data for lines of data.
longDF<-melt(dataExample,id.vars=c("Time","Application","groups"))
Calculate positions for Substance identifiers. Used function ddply() from library plyr. For calculation only data that differs from first Application value are used (that's subset()). Then Application and groups are used for grouping of data. Calculated starting, middle and ending positions on x axis and y value taken as maximal value +0.3.
library(plyr)
lineData<-ddply(subset(dataExample,Application != dataExample$Application[1]),
.(Application,groups),
summarise,minT=min(Time),maxT=max(Time),
meanT=mean(Time),ypos=max(longDF$value)+0.3)
Now plot longDF data with ggplot() and geom_line() and add segments above plot with geom_segment() and text with annotate() using new data frame lineData.
ggplot(longDF,aes(Time,value,color=variable))+geom_line()+
geom_segment(data=lineData,aes(x=minT,xend=maxT,y=ypos,yend=ypos),inherit.aes=FALSE)+
annotate("text",x=lineData$meanT,y=lineData$ypos+0.1,label=lineData$Application)