Calculating mean and interquartile range of 'cut' data to plot - r

Question

Apologies I am new to R, I have a dataset with height and canopy density of trees for example:
i_h100 i_cd
2.89 0.0198
2.88 0.0198
17.53 0.658
27.23 0.347
I want to regroup 'h_100' into 2m intervals going from 2m min to 30m max, I then want to calculate the mean i_cd value and interquartile range for each of these intervals so that I can then plot these with a least squares regression. There is something wrong with the code I am using to get the mean. This is what I have so far:
mydata=read.csv("irelandish.csv")
height=mydata$i_h100
breaks=seq(2,30,by=2) #2m intervals
height.cut=cut(height, breaks, right=TRUE)
#attempt at calculating means per group
install.packages("dplyr")
mean=summarise(group_by(cut(height, breaks, right=TRUE),
mean(mydata$i_cd)))
install.packages("reshape2")
dcast(mean)
Thanks in advance for any advice.

Answer 1

Using aggregate() to calculate the groupwise means.
# Some example data
set.seed(1)
i_h100 <- round(runif(100, 2, 30), 2)
i_cd <- rexp(100, 1/i_h100)
mydata <- data.frame(i_cd, i_h100)
# Grouping i_h100
mydata$i_h100_2m <- cut(mydata$i_h100, seq(2, 30, by=2))
head(mydata)
# i_cd i_h100 i_h100_2m
# 1 2.918093 9.43 (8,10]
# 2 13.735728 12.42 (12,14]
# 3 13.966347 18.04 (18,20]
# 4 2.459760 27.43 (26,28]
# 5 8.477551 7.65 (6,8]
# 6 6.713224 27.15 (26,28]
# Calculate groupwise means of i_cd
i_cd_2m_mean <- aggregate(i_cd ~ i_h100_2m, mydata, mean)
# And IQR
i_cd_2m_iqr <- aggregate(i_cd ~ i_h100_2m, mydata, IQR)
upper <- i_cd_2m_mean[,2]+(i_cd_2m_iqr[,2]/2)
lower <- i_cd_2m_mean[,2]-(i_cd_2m_iqr[,2]/2)
# Plotting the result
plot.default(i_cd_2m_mean, xaxt="n", ylim=range(c(upper, lower)),
main="Groupwise means \U00B1 0.5 IQR", type="n")
points(upper, pch=2, col="lightblue", lwd=1.5)
points(lower, pch=6, col="pink", lwd=1.5)
points(i_cd_2m_mean, pch=16)
axis(1, i_cd_2m[,1], as.character(i_cd_2m[,1]), cex.axis=0.6, las=2)

Answer 2

Here is a solution,
library(reshape2)
library(dplyr)
mydata <- data_frame(i_h100=c(2.89,2.88,17.53,27.23),i_cd=c(0.0198,0.0198,0.658,0.347))
height <- mydata$i_h100
breaks <- seq(2,30,by=2) #2m intervals
height.cut <- cut(height, breaks, right=TRUE)
mydata$height.cut <- height.cut
mean_i_h100 <- mydata %>% group_by(height.cut) %>% summarise(mean_i_h100 = mean(i_h100))
A few remarks:
it is better to avoid naming variables with function names, so I changed the mean variable to mean_i_h100
I am using the pipe notation, which makes the code more readable, it avoids repeating the first argument of each function, you can find a more detailed explanation here.
Without the pipe notation, the last line of code would be:
mean_i_h100 <- summarise(group_by(mydata,height.cut),mean_i_h100 = mean(i_h100))
you have to load the two packages you installed with library