I have nothing to say. Fingers crossed
I’m writing a R markdown file to process bulky RNAseq analysis and generate reports for general lab experiments. To better presents the results in the final output reports, I need to generate dynamic subheadings based on the experimental designs.
Gladly, I found one post online by R | Science Loft.
Basically, the most critical steps is to set “Output text as raw Markdown content” by Set chunk option results="asis".
Meanwhile, with results=”asis”, you could also do much more flexible decorations on your outputs using either html or markdown settings.
Some key points:
- Set chunk option
results="asis" - Create headings using
cat()function in loop - Call
print()on the code to generate desired output (tables, plots, code output, etc) - Wrap the output within appropriate HTML tags
- For
DT::datatableoutputs initiate dependencies previously and usehtmltools::tagList() - For neatly rendered console outputs, wrap the code with
htmltools::pre()andhtmltools::code()
- For
1. Example for creating tables in loop:
The tables printed in loop don’t render in the final document. A solution is to create an initial datatable output is needed to load some magic dependencies. To hide this chunk from the final output though, you can set chunk option include=FALSE:
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```{r, include=FALSE}
library(DT)
datatable(iris)
```
| An example given by R | Science Loft: |
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```{r, results="asis"}
library(htmltools)
list_to_output <- list(data1 = iris,
data2 = mtcars,
data3 = airquality)
for(i in names(list_to_output)){
cat("\n")
cat("##", i, "\n") # Create second level headings with the names.
print(
tagList(
datatable(list_to_output[[i]])
)
)
cat("\n")
}
```
2. Example for creating console output:
We need to wrap the code output in and tags for rendering properly. Without these the console output will render like normal text. This is especially helpful if you are trying to print named vectors where the name and the value will be aligned neatly
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```{r, results="asis"}
for(i in paste("Output", 1:3)){
cat('\n')
cat("###", i, "\n")
vec <- 1:10
pre(# can also use tags manually: <pre><code>BlaBlaBla</code></pre>
code(
print(vec)
)
)
}
```
3.Example for creating plot output
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```{r,fig.width=3, fig.height=3, results="asis"}
for(i in paste("Plot", 1:3)){
cat('\n')
cat("###", i, "\n")
print(
ggplot(iris) +
geom_point(aes(x=Sepal.Length, y=Petal.Length))
)
}
4.Change fig.width and fig.height Dynamically Within an R Markdown Chunk
This part is from Michael J Williams’ blog
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# First,define function
```
figNum=0
subchunkify <- function(g, fig_height=7, fig_width=5,chunkname="plot") {
g_deparsed <- paste0(deparse(
function() {g}
), collapse = '')
if(figNum >= 100000){figNum=0}
figNum = figNum+1
chunkname = paste0(chunkname,".",figNum)
# sub_chunk <- paste0("\n`","``{r sub_chunk_", floor(runif(1) * 10000),
sub_chunk <- paste0("\n`","``{r ",chunkname,
", fig.height=",fig_height,
", fig.width=", fig_width,
", echo=FALSE}",
"\n(",g_deparsed,")()",
"\n`","``\n")
cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
}
```
#Using subchunkify function to define dynamic figsize
```{r echo=FALSE, results='asis'}
g1 <- ggplot(economics, aes(date, unemploy)) +
geom_line()
g2 <- ggplot(economics, aes(date, unemploy)) +
geom_point()
for (i in seq(2, 5)) {
subchunkify(g1, i / 2, i)
}
```
5. Set specific cell colors for DT
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```{r}
library(DT)
df_tmp <- data.frame(a=c("AA","BB"),Color=c("#E41A1C","#377EB8"))
datatable(df_tmp,
options = list(
columnDefs = list(list(className = 'dt-center', targets = "_all"))
)
) %>% DT::formatStyle(columns = "Color", color=styleEqual(df_tmp$Color,df_tmp$Color))#Or backgroundColor = styleEqual(df_tmp$Color,df_tmp$Color)
```
6. Group by specific table columns for DT
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```{r}
#options(DT.options = list(columnDefs = list(
# list(className = 'dt-center', targets = "_all"),
# list(visible=FALSE, targets=c(0))
# )
# )
# )
dt_demographics2[1,1] <- NA
colnames(dt_demographics2)
#[1] "Group2" "Group" "M" "F"
datatable(dt_demographics2,
rownames = FALSE,
extensions = c('RowGroup','Buttons'),
options = list(
ordering=F,
rowGroup = list(dataSrc=c(0),
emptyDataGroup=NULL),#Group by first column, Do not group the first row(with NA values)
columnDefs = list(
list(visible=FALSE, targets=c(0))
), #Set first column not visible
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
)
)
```
