绘制 2x2x2 时间序列的原始值和预测值

Question

This is the sample of my data

library(tidyr)
library(dplyr)
library(ggplot2)

resource <- c("good","good","bad","bad","good","good","bad","bad","good","good","bad","bad","good","good","bad","bad")

fertilizer <- c("none", "nitrogen","none","nitrogen","none", "nitrogen","none","nitrogen","none", "nitrogen","none","nitrogen","none", "nitrogen","none","nitrogen")

t0 <-  sample(1:20, 16)
t1 <-  sample(1:20, 16) 
t2 <-  sample(1:20, 16)
t3 <-  sample(1:20, 16)
t4 <-  sample(1:20, 16)
t5 <-  sample(1:20, 16)
t6 <-  sample(10:100, 16)
t7 <-  sample(10:100, 16)
t8 <-  sample(10:100, 16)
t9 <-  sample(10:100, 16)
t10 <-  sample(10:100, 16)

replicates <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16)

data <- data.frame(resource, fertilizer,replicates, t0,t1,t2,t3,t4,t5,t6,t7,t8,t9,t10)

data$resource <- as.factor(data$resource)
data$fertilizer <- as.factor(data$fertilizer)

data.melt <- data %>% ungroup %>% gather(time, value, -replicates, -resource, -fertilizer)

data.melt$predict <- sample(1:200, 176)

Where, there are 2 factors for resources and fertilizer, so there are effectively 4 treatments and 4 x 4 = 16 replicates. Time is a factor with 10 levels. I ran a model, and predicted values which is in the predict column.

Now I want to plot a time-series with time on the x-axis and mean of the fitted value (predict) on and the raw values (value) on the y-axis, for each type of resource and fertilizer (4 treatments) [That is 4 plots]. I also want to add a confidence interval for the algal growth at each time point. Here is my attempt at the code.

ggplot(df, aes(x=time, y=predicted)) + geom_point(size=3)+ stat_summary(geom = "point", fun.y = "mean") + facet_grid(resource + fertilizer ~.) 

With this simple code, I still get only 2 graphs and not 4. Also, the means of the predict function are not plotted. I don't know how to plot the value and predicted together, and the corresponding confidence intervals.

It would be helpful if anyone could also show how all four treatments can be on a single plot, and if I can get it to facet (like above)

Answer 1

My proposed solution is to create a second data.frame containing all summary statistics such as mean predicted value. I show one way to do this with group_by and summarize from the dplyr package. The summary data needs to have columns resource , fertilizer and time that match the main data. The summary data also has columns with additional y values.

Then, the main data and the summary data need to be provided separately to the appropriate ggplot functions, but not in the main ggplot() call. facet_grid can be used to split the data into four plots.

# Convert time to factor, specifying correct order of time points.
data.melt$time = factor(data.melt$time, levels=paste("t", seq(0, 10), sep=""))

# Create an auxilliary data.frame containing summary data.
# I've used standard deviation as place-holder for confidence intervals;
# I'll let you calculate those on your own.
summary_dat = data.melt %>%
              group_by(resource, fertilizer, time) %>%
              summarise(mean_predicted=mean(predict),
                        upper_ci=mean(predict) + sd(predict),
                        lower_ci=mean(predict) - sd(predict))

p = ggplot() + 
    theme_bw() +
    geom_errorbar(data=summary_dat, aes(x=time, ymax=upper_ci, ymin=lower_ci),
                  width=0.3, size=0.7, colour="tomato") + 
    geom_point(data=data.melt, aes(x=time, y=value),
               size=1.6, colour="grey20", alpha=0.5) +
    geom_point(data=summary_dat, aes(x=time, y=mean_predicted),
               size=3, shape=21, fill="tomato", colour="grey20") +
    facet_grid(resource ~ fertilizer)

ggsave("plot.png", plot=p, height=4, width=6.5, units="in", dpi=150)