# initial setup knitr::opts_chunk$set(cache = F, fig.width = 12, fig.height = 5, warning = F, message = F, tidy = F, prompt = T, strip.white = T, comment = NA) stopifnot(length(params$filename) > 0) stopifnot(length(params$projectid) > 0) stopifnot(length(params$subjectid) > 0) ############################## EXAMPLE CALL #################################### # rmarkdown::render("status_report.Rmd", # params=list( # filename = dir(".", c(".csv"), recursive = T), # output_file = paste("status_", # format(Sys.time(), '%Y%m%d'), ".pdf", sep="") # ) ############################## EXAMPLE CALL ####################################
\hfill
This report is intended to give a summary of analysis result of project
r params$projectid
. The result can be reproduced using the identical data
files and identical version of rmarkdown file stated in the table below.
Name Value
.Rmd version v 0.90.0
Project name r params$projectid
Subject id r params$subjectid
Data file r params$filename
\pagebreak
Project process status plot.
df <- read.csv(filename) statusplot(df)
In total r length(params$subjectid
subjects' data files have been anaylzed in this project.
And the individual parameters are:
x <- table(df$var) knitr::kable(matrix(x, byrow=T, nrow=1), col.names=names(x))
Statistical summary plots with dashed line representing location of mean.
# Boxplot par(mar=c(3,1,1,1)) p <- ggplot(df, aes(factor(var), var2)) p + geom_boxplot() + geom_hline(aes(yintercept=mean(var2)), linetype="dashed", color="gray22", size=1) + coord_cartesian(ylim=c(0, 24)) + scale_y_continuous(breaks=seq(0, 24, 6)) + xlab("Var1") + ylab("var2") + ggtitle("Boxplot of Daily Variable") par(mar=c(1,1,10,1)) # Histogram p <- ggplot(df, aes(x=var2, fill=var)) p + geom_bar() + geom_vline(aes(xintercept=mean(var2)), linetype="dashed", color="gray22", size=1)
\pagebreak
Time series plot, with smoothed conditional mean plotted (when samples < 1000, use method of local polynomial regression fitting; otherwise, use method of generalized additive models with integrated smoothness estimation)
# Time Series Plot p <- ggplot(df, aes(var1, var2)) p + geom_line(aes(color=factor(var1)), size=1) + geom_smooth(color="gray45", size=1, linetype="dashed") + facet_grid(var1 ~ .) + theme(legend.position="none") + coord_cartesian(ylim=c(0, 24)) + scale_y_continuous(breaks=seq(0, 24, 6)) + xlab("Time") + ylab("Variable") + ggtitle("Time Series Plot")
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