I am trying to set up a bar chart to compare control and experimental samples taken of specific compounds. The data set is known as 'hydrocarbon3' and contains the following information:
Exp. Contr.
c12 89 49
c17 79 30
c26 78 35
c42 63 3
pris 0.5 0.8
phy 0.5 0.9
nap 87 48
nap1 83 44
nap2 78 44
nap3 73 20
acen1 81 50
acen2 86 46
fluor 83 11
fluor1 68 13
fluor2 79 17
dibe 65 7
dibe1 67 6
dibe2 56 10
phen 82 13
phen1 70 12
phen2 65 15
phen3 53 14
fluro 62 9
pyren 48 11
pyren1 34 10
pyren2 19 8
chrys 22 3
chrys1 21 3
chrys2 21 3
When I create a bar chart with the formula:
barplot(as.matrix(hydrocarbon3),
main=c("Fig 1. Change in concentrations of different hydrocarbon compounds\nin sediments with and without the presence of bacteria after 21 days"),
beside=TRUE,
xlab="Oiled sediment samples collected at 21 days",
space=c(0,2),
ylab="% loss in concentration relative to day 0")
I receive this diagram, however I need the control and experimental samples of each chemical be next to each other allow a more accurate comparison, rather than the experimental samples bunched on the left and control samples bunched on the right: Is there a way to correct this on R?
Try transposing your matrix:
barplot(t(as.matrix(hydrocarbon3)), beside=T)
Basically, barplot will plot things in the order they show up in the matrix, which, since a matrix is just a vector wrapped colwise, means barplot will plot all the values of the first column, then all those of the second column, etc.
Check this question out: Barplot with 2 variables side by side
It uses ggplot2, so you'll have to use the following code before running it:
intall.packages("ggplot2")
library(ggplot2)
Hopefully this works for you. Plus it looks a little nicer with ggplot2!
> df
row exp con
1 a 1 2
2 b 2 3
3 c 3 4
> barplot(rbind(df$exp,df$con),
+ beside = TRUE,names.arg=df$row)
produces:
Related
I am using facet wrap to plot Weight Gain versus Caloric Intake for four different diets. Diet is a four-level factor, Weight Gain and Caloric Intake are numeric. I am adding a regression line to each plot facet. What I want to do is add a horizontal line for the group mean weight gain for each diet in the plot (4 different mean values). The problem is when I use the geom_hline function it puts the global mean on all of the plots, which is not what I want.
I tried using stat_summary(fun.y=mean,geom="line"), but it gives me line segments joining each of the points in every plot.
Below is the code I am using that is giving me the single global mean on all plots. Also the data set I am using. I've included the labeller code for completeness but I really just need help with drawing the group mean lines.
Thanks in advance for any help.
# Calculate slopes and means to use for facet labels
#
wgSlope<-rep(NA,nlevels(vitaminData$Diet))
dietMeans<-rep(NA,nlevels(vitaminData$Diet))
for (i in 1:nlevels(vitaminData$Diet)){
dietMeans[i]<-mean(filter(vitaminData,Diet==i)$WeightGain)
#
# Get regression lines and coefficients for each facet
#
lm<-lm(WeightGain~CaloricIntake,data=filter(vitaminData,Diet==i))
wgSlope[i]<-lm$coefficients[2]
}
#
# Build facet labels
#
dietLabel<-c(`1`=
paste("Diet 1, Slope=",round(wgSlope[1],2),", Mean=",round(dietMeans[1],1)),
`2`=paste("Diet 2, Slope=",round(wgSlope[2],2),", Mean=",round(dietMeans[2],1)),
`3`=paste("Diet 3, Slope =",round(wgSlope[3],2),", Mean=",round(dietMeans[3],1)),
`4`=paste("Diet 4, Slope =",round(wgSlope[4],2),", Mean=",round(dietMeans[4],1)))
#
# Draw the plots
#
ggplot(data=vitaminData,
aes(y=WeightGain,x=CaloricIntake,color=Diet))+
theme_bw()+
geom_point(aes(color=Diet,fill=Diet,shape=Diet))+
geom_smooth(method="lm",se=FALSE,linetype=2,alpha=0.5)+
labs(x="Caloric Intake",y="Weight Gain")+
scale_color_manual(values=c("red","blue","orange","darkgreen"))+
geom_hline(yintercept=mean(vitaminData$WeightGain))+
facet_wrap(~Diet,labeller=labeller(Diet=dietLabel))+
theme(legend.position="none")
Diet WeightGain CaloricIntake
<fct> <dbl> <dbl>
1 1 48 35
2 1 67 44
3 1 78 44
4 1 69 51
5 1 53 47
6 2 65 40
7 2 49 45
8 2 37 37
9 2 73 53
10 2 63 42
11 3 79 51
12 3 52 41
13 3 63 47
14 3 65 47
15 3 67 48
16 4 59 53
17 4 50 52
18 4 59 52
19 4 42 45
20 4 34 38
Here's an approach using dplyr. (Add library(dplyr) or library(tidyverse) if not already loaded.)
geom_hline(data = vitaminData %>%
group_by(Diet) %>%
summarize(mean = mean(WeightGain)),
aes(yintercept = mean)) +
I have a data frame 'heat' demonstrating people's performance across time.
'Var1' represents the code of persons.
'Var2' represents a time line (measured by number of days from the starting point).
'Variable' is the score they get at a given time point.
Var1 Var2 value
1 1 36 -0.6941826
2 2 36 -0.5585414
3 3 36 0.8032384
4 4 36 0.7973031
5 5 36 0.7536959
6 6 36 -0.5942059
....
54 10 73 0.7063218
55 11 73 -0.6949616
56 12 73 -0.6641516
57 13 73 0.6890433
58 14 73 0.6310124
59 15 73 -0.6305091
60 16 73 0.6809655
61 17 73 0.8957870
....
101 13 110 0.6495796
102 14 110 0.5990869
103 15 110 -0.6210600
104 16 110 0.6441960
105 17 110 0.7838654
....
Now I want to cluster their performance and reflect it on a heatmap. So I used the function dist() and hclust() to clustered the data frame and plotted it with ggplot2:
ggplot(data = heat) + geom_tile(aes(x = Var2, y = Var1 %>% as.character(),
fill = value)) +
scale_fill_gradient(low = "yellow",high = "red") +
geom_vline(xintercept = c(746, 2142, 2917))
It looks like this:
However, I am more interested in what happened around day 746, day 2142 and day 2917 (the black lines). I would like the scores around these days bearing more weight in the clustering. I want people demonstrating similar performance around these days to have more priority to be clustered together. Is there a way of doing this?
As long as your weights are integer, you supposedly can just replicate those days artificially.
If you want more control, just compute the distance matrix yourself, with whatever weighted distance you want to use.
Morning Community,
I wanted to ask a quick question regarding rCharts graph outputs compared to native R.
Question 1: Why are graphs from rCharts displayed in my browser rather than the viewer in R?
Question 2: How can I force (or choose to use) the graphing function in native R instead?
See these two screen shots:
Code for native R:
# Simple Scatterplot
attach(mtcars)
plot(wt, mpg, main="Scatterplot Example",
xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19)
Code for rChart:
library(rCharts)
myData
plot<-Highcharts$new()
plot$chart(polar = TRUE, type = "line",height=NULL)
plot$xAxis(categories=myData$Subject.ID, tickmarkPlacement= 'on', lineWidth=1)
plot$yAxis(gridLineInterpolation= 'circle', lineWidth=1, min=NULL,max=NULL,endOnTick=T,tickInterval=10)
plot$series(data = myData[,"A"],name = "A", pointPlacement="on")
plot
rChart Data used
Subject.ID A B C
1 1 65 29 60
2 2 87 67 59
3 3 98 54 24
4 4 67 44 23
5 5 54 50 4
6 6 83 60 54
7 7 82 55 27
8 8 80 48 32
9 9 88 56 44
10 10 68 68 56
11 11 90 76 69
12 12 41 47 45
13 13 NA 82 NA
14 14 NA 55 NA
Ps: As an aside, I understand that I am graphing two different functions, a scatterplot vs radar plot. My goal is to understand whether or not native R can display (or perhaps another word) the graph output from rCharts - Even if I lose interactivity.
I have reached out to the developer for rCharts and he has replied back to me:
"The native viewer that comes with the R GUI is NOT capable of displaying html. So, the only way to view html output like what rCharts generates is to use the browser. The RStudio viewer on the other hand is capable of displaying html and so rCharts takes advantage of that."
I am building a streambed hydrology calculator in R using multiple tables from an Access database. I am having trouble automating and calculating the same set of indices for multiple sites. The following sample dataset describes my data structure:
> Thalweg
StationID AB0 AB1 AB2 AB3 AB4 AB5 BC1 BC2 BC3 BC4 Xdep_Vdep
1 1AAUA017.60 47 45 44 55 54 6 15 39 15 11 18.29
2 1AXKR000.77 30 27 24 19 20 18 9 12 21 13 6.46
3 2-BGU005.95 52 67 62 42 28 25 23 26 11 19 20.18
4 2-BLG011.41 66 85 77 83 63 35 10 70 95 90 67.64
5 2-CSR003.94 29 35 46 14 19 14 13 13 21 48 6.74
where each column represents certain field-measured parameters (i.e. depth of a reach section) and each row represents a different site.
I have successfully used the apply functions to simultaneously calculate simple functions on multiple rows:
> Xdepth <- apply(Thalweg[, 2:11], 1, mean) # Mean Depth
> Xdepth
1 2 3 4 5
33.1 19.3 35.5 67.4 25.2
and appending the results back to the proper station in a dataframe.
However, I am struggling when I want to calculate and save variables that are subsequently used for further calculations. I cannot seem to loop or apply the same function to multiple columns on a single row and complete the same calculations over the next row without mixing variables and data.
I want to do:
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + other_variables), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + other_variables), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + other_variables), Thalweg$AB3)
# etc.
Depth_AB0 <- (Thalweg$AB0 - Residual_AB0)
Depth_AB1 <- (Thalweg$AB1 - Residual_AB1)
Depth_AB2 <- (Thalweg$AB2 - Residual_AB2)
# etc.
I have tried and subsequently failed at for loops such as:
for (i in nrow(Thalweg)){
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + Stacks_Equation), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + Stacks_Equation), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + Stacks_Equation), Thalweg$AB3)
Residuals <- data.frame(Thalweg$StationID, Residual_AB0, Residual_AB1, Residual_AB2, Residual_AB3)
}
Is there a better way to approach looping through multiple lines of data when I need unique variables saved for each specific row that I am currently calculating? Thank you for any suggestions.
your exact problem is still a mistery to me...
but it looks like you want a double for loop
for(i in 1:nrow(thalweg)){
residual=thalweg[i,"Xdep_Vdep"]
for(j in 2:11){
residual=min(residual,thalweg[i,j])
}
}
I'm previously a SAS user - since I don't have SAS anymore I need to learn to use R for work.
The dataset has the following column:
market date sitename impression clicks
I want to transpose it into:
market date sitename-impression sitename-clicks
I think in SAS I used to do:
Proc Transpose
by market date;
id sitename;
var impression clicks;
run;
I do have a book on R and googled a lot, but couldn't find the solution that works...
Would really appreciate if anyone can help.
Thanks in advance!!!
Let me start by saying welcome to stackoverflow. Glad to have anew user. When you ask a question it's helpful and encouraged for you to provide the code you're using and a reproducible data set that looks like the original. This is called a minimal reproducible example. To get a data set into here you can use several options, here are two: use dput() around the object name and cut and paste what is displayed in the console or just post the dataframe directly. For the code provide all the code necessary to replicate your problem. I hope you find this helpful for future questions you'll ask.
I may not fully understand but I think you want to transform, not transpose, the data.
dat <- data.frame(market=rnorm(10), date=rnorm(10), #let's create a data set
sitename=rnorm(10), impression=rnorm(10), clicks=rnorm(10))
dat #look at it (I pasted it below)
# > dat
# market date sitename impression clicks
# 1 -0.9593797 -0.08411994 1.6079129 -0.5204772 -0.31633966
# 2 -0.5088689 1.78799500 -0.2469315 1.3476964 -0.04344779
# 3 -0.1527465 0.81673996 1.7824969 -1.5531260 -1.28304384
# 4 -0.7026194 0.52072913 -0.1174356 0.5722210 -1.20474443
# 5 -0.4537490 -0.69139062 1.1124277 -0.2452974 -0.33025320
# 6 0.7466588 0.36318337 -0.4623319 -0.9036768 -0.65754302
# 7 0.8007612 2.59588554 0.1820732 0.4318629 -0.36308748
# 8 1.0781715 -1.01512734 0.2297475 0.9219439 -1.15687902
# 9 0.3731450 -0.19004572 0.5190749 -1.4020371 -0.97370295
# 10 0.7724259 1.76528303 0.5781786 -0.5490849 -0.83819036
#now to create the new columns (I think this is what you want)
#the easiest way is to use transform. ?tranform for more
dat.new <- transform(dat, sitename.clicks=sitename-clicks,
impression.clicks=impression-clicks)
dat.new #here's the new data set. Notice it has the new and old columns.
#To get rid of the old columns you can use indexing and specify the columns you want.
dat.new[, c(1:2, 6:7)]
#We could have also done:
dat.new[, c(1,2,6,7)]
#or said the columns not wanted with negative indexing:
dat.new[, -c(3:5)]
EDIT In looking at Brian's comments and the variables I would think that a long to wide transformation is what the poster desires. I would likely approach it using Wickham's reshape2 package as well, as this method is easier for me to work with and I imagine it would be easier for an R beginner as well. However, here is a base way to do the long to wide format using the same data set Brian provided:
wide <- reshape(DF, v.names=c("impression", "clicks"), idvar=c("market", "date"),
timevar="sitename", direction="wide")
reshape(wide)
The reshape function is very flexible but takes some getting used to to use appropriately. I'm leaving my previous response up as well to keep the history of this post though I now believe this is not the posters intent. It serves as a reminder that a reproducible example is very helpful in providing clarity to your query.
Example data, as Tyler said, is important. I interpreted your question differently because I thought your data was different. I didn't take the - as a literal subtraction of numerics, but a combination of variables.
DF <- expand.grid(market = LETTERS[1:5],
date = Sys.Date()+(0:5),
sitename = letters[1:2])
n <- nrow(DF)
DF$impression <- sample(100, n, replace=TRUE)
DF$clicks <- sample(100, n, replace=TRUE)
I find the reshape2 package useful for these sort of transpositions/transformations/rearrangements.
library("reshape2")
dcast(melt(DF, id.vars=c("market","date","sitename")),
market+date~sitename+variable)
gives
market date a_impression a_clicks b_impression b_clicks
1 A 2012-02-28 74 97 11 71
2 A 2012-02-29 34 30 88 35
3 A 2012-03-01 40 85 40 49
4 A 2012-03-02 46 12 99 20
5 A 2012-03-03 6 95 85 56
6 A 2012-03-04 61 61 42 64
7 B 2012-02-28 4 53 74 9
8 B 2012-02-29 43 27 92 59
9 B 2012-03-01 34 26 86 43
10 B 2012-03-02 81 47 84 35
11 B 2012-03-03 3 5 91 48
12 B 2012-03-04 19 26 99 21
13 C 2012-02-28 22 31 100 53
14 C 2012-02-29 40 83 95 27
15 C 2012-03-01 78 89 81 29
16 C 2012-03-02 57 55 79 87
17 C 2012-03-03 37 61 3 97
18 C 2012-03-04 83 61 41 77
19 D 2012-02-28 81 18 47 3
20 D 2012-02-29 90 100 17 83
21 D 2012-03-01 12 40 35 93
22 D 2012-03-02 85 14 63 67
23 D 2012-03-03 63 53 29 58
24 D 2012-03-04 40 79 56 70
25 E 2012-02-28 97 62 68 31
26 E 2012-02-29 24 84 17 63
27 E 2012-03-01 94 93 32 2
28 E 2012-03-02 6 26 86 26
29 E 2012-03-03 100 34 37 80
30 E 2012-03-04 89 87 72 11
The column names have a _ between them rather than a -, but you can change that if you want. I wouldn't recommend it, though, because then you will have problems later referencing the column since the - will be taken as subtraction (you would need to quote the name).