I'm definitely a neophyte to R for visualizing data, so bear with me.
I'm looking to create side-by-side dot plots of seven categorical samples with many gene expression values corresponding with individual gene names. mydata.csv file looks like the following
B27 B28 B30 B31 LTNP5.IFN.1 LTNP5.IFN.2 LTNP5.IL2.1
1 13800.91 13800.91 13800.91 13800.91 13800.91 13800.91 13800.91
2 6552.52 5488.25 3611.63 6552.52 6552.52 6552.52 6552.52
3 3381.70 1533.46 1917.30 2005.85 3611.63 4267.62 5488.25
4 2985.37 1188.62 1051.96 1362.32 2717.68 2985.37 5016.01
5 1917.30 2862.19 2625.29 2493.26 2428.45 2717.68 4583.02
6 990.69 777.97 1269.05 1017.26 5488.25 5488.25 4267.62
I would like each sample data to be organized in its own dot plot in one graph. Additionally, if I could point out individual data points of interest, that would be great.
Thanks!
You can use base R, but you need to convert to matrix first.
dotchart(as.matrix(df))
or, we can transpose the matrix to arrange it by sample:
dotchart(t(as.matrix(df)))
Considering your [toy] data is stored in a data frame called a:
library(reshape2)
library(ggplot2)
a$trial<-1:dim(a)[1] # also, nrow(a)
b<-melt(data = a,varnames = colnames(a)[1:7],id.vars = "trial")
b$variable<-as.factor(b$variable)
ggplot(b,aes(trial,value))+geom_point()+facet_wrap(~variable)
produces
What we did:
Loaded required libraries (reshape2 to convert from wide to long and ggplot2 to, well, plot); melted the data into long formmat (more difficult to read, easier to process) and then plotted with ggplot.
I introduced trial to point to each "run" each variable was measured, and so I plotted trial vs value at each level of variable. The facet_wrap part puts each plot into a subplot region determined by variable.
Related
Okay, let me be as clear as I can in my problem. I'm new to R, so your patience is appreciated.
I want to create a histogram using two different vectors. The first vector contains a list of models (products). These models are listed as either integers, strings, or NA. I'm not exactly sure how R is storing them (I assume they're kept as strings), or if that is a relevant issue. I also have a vector containing a list of incidents pertaining to that model. So for example, one row in the dataframe might be:
Model Incidents
XXX1991 7
How can I create a histogram where the number of incidents for each model is shown? So the histogram will look like
| =
| =
Frequency of | =
Incidents | = =
| = = =
| = = = = =
- - - - - -
Each different Model
Just to give a general idea.
I also need to be able to map everything out with standard deviation lines, so that it's easy to see which models are the least reliable. But that's not the main question here. I just don't want to do anything that will make me unable to use standard deviation in the future.
So far, all I really understand is how to make a histogram with the frequency marked, but for some reason, the x-axis is marked with numbers, not the models' names.
I don't really care if I have to download new packages to make this work, but I suspect that this already exists in basic R or ggplot2 and I'm just too dumb to figure it out.
Feel free to ask clarfying questions. Thanks.
EDIT: I forgot to mention, there are multiple rows of incidents listed under each model. So to add to my example earlier:
Model Incidents
XXX1991 7
XXX1991 1
XXX1991 19
3
5
XXX1002 9
XXX1002 4
etc . . .
I want to add up all the incidents for a model under one label.
I am assuming that you did not mean to leave the model blank in your example, so I filled in some values.
You can add up the number of incidents by model using aggregate then make the relevant plot using barplot.
## Example Data
data = read.table(text="Model Incidents
XXX1991 7
XXX1991 1
XXX1991 19
XXX1992 3
XXX1992 5
XXX1002 9
XXX1002 4",
header=TRUE)
TAB = aggregate(data$Incidents, list(data$Model), sum)
TAB
Group.1 x
1 XXX1002 13
2 XXX1991 27
3 XXX1992 8
barplot(TAB$x, names.arg=TAB$Group.1 )
I am trying to learn R, and use the corrplot library to draw Y:City and X: Population graph. I wrote the below code:
When you look at the picture above, there are 2 columns City and population. When I run the code I get this error message:
Error in cor(Illere_Gore_Nufus) : 'x' must be numeric.
My excel data:
In general, correlation plot (Scattered plot) can be plotted only when you have two continuous variable. Correlation is a value that tells you how two continuous variables are linearly related. The Correlation value will always fall between -1 and 1, where correlation value of -1 depicts weak linear relationship and correlation value of 1 depicts strong linear relationship between the two variables. Correlation value of 0 says that there is no linear relationship between the two variables, however, there could be curvi-linear relationship between the two variables
For example
Area of the land Vs Price of the land
Here is the Data
The correlation value for this data is 0.896, which means that there is a strong linear correlation between Area of the land and Price of the land (Obviously!).
Scatter plot in R would look like this
Scatter plot
The R code would be
area<-c(650,785,880,990,1100,1250,1350,1800,2200,2800)
price<-c(250,275,280,290,350,340,400,335,420,460)
cor(area,price)
plot(area,price)
In Excel, for the same example, you can select the two columns, go to Insert > Scatter plot (under charts section)
Scatter plot
In your case, the information can be plotted in bar graph with city in y axis and population in x axis or vice versa!
Hope I have answered you query!
Some assumptions
You are asking how to do this in Excel, but your question is tagged R and Power BI (also RStudio, but that has been edited away), so I'm going to show you how to do this with R and Power BI. I'm also going to show you why you got that error message, and also why you would get an error message either way because your dataset is just not sufficient to make a correlation plot.
My answer
I'm assuming you would like to make a correlation plot of the population between the cities in your table. In that table you'd need more information than only one year for each city. I would check your data sources and see if you could come up with population numbers for, let's say, the last 10 years. In lack of the exact numbers for the cities in your table, I'm going to use some semi-made up numbers for the population in the 10 most populous countries (following your datastrutcture):
Country 2017 2016 2015 2014 2013
China 1415045928 1412626453 1414944844 1411445597 1409517397
India 1354051854 1340371473 1339431384 1343418009 1339180127
United States 326766748 324472802 325279622 324521777 324459463
Indonesia 266794980 266244787 266591965 265394107 263991379
Brazil 210867954 210335253 209297939 209860881 209288278
Pakistan 200813818 199761249 200253292 197655630 197015955
Nigeria 195875237 192568158 195757661 191728478 190886311
Bangladesh 166368149 165630262 165936711 166124290 164669751
Russia 143964709 143658415 143146914 143341653 142989754
Mexcio 137590740 137486490 136768870 137177870 136590740
Writing and debugging R code in Power BI is a real pain, so I would recommend installing R studio, write your little R snippets there, and then paste it into Power B.
The reason for your error message is that the function cor() onlyt takes numerical data as arguments. In your code sample the city names are given as arguments. And there are more potential traps in your code sample. You have to make sure that your dataset is numeric. And you have to make sure that your dataset has a shape that the cor() will accept.
Below is an R script that will do just that. Copy the data above, and store it in a file called data.xlsx on your C drive.
The Code
library(corrplot)
library(readxl)
# Read data
setwd("C:/")
data <- read_excel("data.xlsx")
# Set Country names as row index
rownames(data) <- data$Country
# Remove Country from dataframe
data$Country <- NULL
# Transpose data into a readable format for cor()
data <- data.frame(t(data))
# Plot data
corrplot(cor(data))
The plot
Power BI
In Power BI, you need to import the data before you use it in an R visual:
Copy this:
Country,2017,2016,2015,2014,2013
China,1415045928,1412626453,1414944844,1411445597,1409517397
India,1354051854,1340371473,1339431384,1343418009,1339180127
United States,326766748,324472802,325279622,324521777,324459463
Indonesia,266794980,266244787,266591965,265394107,263991379
Brazil,210867954,210335253,209297939,209860881,209288278
Pakistan,200813818,199761249,200253292,197655630,197015955
Nigeria,195875237,192568158,195757661,191728478,190886311
Bangladesh,166368149,165630262,165936711,166124290,164669751
Russia,143964709,143658415,143146914,143341653,142989754
Mexcio,137590740,137486490,136768870,137177870,136590740
Save it as countries.csv in a folder of your choosing, and pick it up in Power BI using
Get Data | Text/CSV, click Edit in the dialog box, and in the Power Query Editor, click Use First Row as headers so that you have this table in your Power Query Editor:
Click Close & Apply and make sure that you've got the data available under VISUALIZATIONS | FIELDS:
Click R under VISUALIZATIONS:
Select all columns under FIELDS | countries so that you get this setup:
Take parts of your R snippet that we prepared above
library(corrplot)
# Set Country names as row index
data <- dataset
rownames(data) <- data$Country
# Remove Country from dataframe
data$Country <- NULL
# Transpose data into a readable format for cor()
data <- data.frame(t(data))
# Plot data
corrplot(cor(data))
And paste it into the Power BI R script Editor:
Click Run R Script:
And you're gonna get this:
That's it!
If you change the procedure to importing data from an Excel file instead of a textfile (using Get Data | Excel , you've successfully combined the powers of Excel, Power BI and R to produce a scatterplot!
I hope this is what you were looking for!
I am a beginner with R so I don't have much experience. I ran into a problem when trying to split my scatterplot in groups based on infection status. My dataset consists of log transformed antibody levels logapfhap2 in this example. Infection status any Pf inf is coded as Yes or No and gives information on if someone has been infected during the follow-up period. I am plotting timepoints (x) against antibody levels (y). For time point 1 and 14 I would like to make 2 groups based on infection status.
This is the main part of the code I use to plot the data without splitting in groups:
ggplot() +
geom_jitter(data=data2, aes(x='1', y=logapfhap2, colour='PfHAP2A')) +
geom_jitter(data=data2,aes(x='14', y=logbpfhap2, colour='PfHAP2B')) +
geom_jitter(data=TRC, aes(x='C', y=PfHAP2, colour='PfHAP2C'))
which results in this graph:
Then I tried to split it (I only show the first time point here) which returns an error.
ggplot() +
geom_jitter(data=data2[data2$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 am completely new to R. I tried reading the reference and a couple of good introductions, but I am still quite confused.
I am hoping to do the following:
I have produced a .txt file that looks like the following:
area,energy
1.41155882174e-05,1.0914586287e-11
1.46893363946e-05,5.25011714434e-11
1.39244046855e-05,1.57904991488e-10
1.64155121046e-05,9.0815757601e-12
1.85202830392e-05,8.3207522281e-11
1.5256036289e-05,4.24756620609e-10
1.82107587343e-05,0.0
I have the following command to read the file in R:
tbl <- read.csv("foo.txt",header=TRUE).
producing:
> tbl
area energy
1 1.411559e-05 1.091459e-11
2 1.468934e-05 5.250117e-11
3 1.392440e-05 1.579050e-10
4 1.641551e-05 9.081576e-12
5 1.852028e-05 8.320752e-11
6 1.525604e-05 4.247566e-10
7 1.821076e-05 0.000000e+00
Now I want to store each column in two different vectors, respectively area and energy.
I tried:
area <- c(tbl$first)
energy <- c(tbl$second)
but it does not seem to work.
I need to different vectors (which must include only the numerical data of each column) in order to do so:
> prob(energy, given = area), i.e. the conditional probability P(energy|area).
And then plot it. Can you help me please?
As #Ananda Mahto alluded to, the problem is in the way you are referring to columns.
To 'get' a column of a data frame in R, you have several options:
DataFrameName$ColumnName
DataFrameName[,ColumnNumber]
DataFrameName[["ColumnName"]]
So to get area, you would do:
tbl$area #or
tbl[,1] #or
tbl[["area"]]
With the first option generally being preferred (from what I've seen).
Incidentally, for your 'end goal', you don't need to do any of this:
with(tbl, prob(energy, given = area))
does the trick.
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)