Load packages needed for this session.
library(kableExtra)
library(DT)
library(ggplot2)
library(dplyr)
library(highcharter)
library(plotly)
library(ggiraph)
library(dygraphs)
library(networkD3)
library(leaflet)
library(crosstalk)
All formatting covered under markdown is also applicable here.
Code can be executed during document render. Code can be rendered in inline (within a sentence) or in code chunks (code blocks).
R code can be executed inline line this `r Sys.Date()`
producing 2021-04-28.
R code can be executed inside code chunks like this
```{r}
Sys.Date()
```
which shows the code and output.
Sys.Date()
## [1] "2021-04-28"
Arguments/Parameters to the code chunks are specified inside the {}
. The first argument is the engine used to evaluate the code chunk. Here we mostly use r
. Other options such as python
, bash
etc can be used. Code chunks can take many more optional arguments.
The code and results can be hidden by ```{r,echo=FALSE,results='hide'}`
.
Here is another example of executed R code with input and output.
data(iris)
head(iris[,1:2])
## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9
All chunks options are listed here.
The default code when executed shows input code and output results.
```{r}
Sys.Date()
```
Sys.Date()
## [1] "2021-04-28"
Input code can be hidden.
```{r,echo=FALSE}
Sys.Date()
```
## [1] "2021-04-28"
Output results can be hidden
```{r,results='hide'}
Sys.Date()
```
Sys.Date()
The code can be displayed with code highlighting but not executed.
```{r,eval=FALSE}
Sys.Date()
```
Sys.Date()
R chunks in RMarkdown can be used to control image display size using the arguemnt out.width
.
This image below is displayed at a size of 300 pixels.
```{r,out.width=300}
knitr::include_graphics('assets/cover.jpg')
```
This image below is displayed at a size of 75 pixels.
```{r,out.width=75}
knitr::include_graphics('assets/cover.jpg')
```
Some examples of rendering equations.
$e^{i\pi} + 1 = 0$
\(e^{i\pi} + 1 = 0\)
$$\frac{E \times X^2 \prod I}{2+7} = 432$$
\[\frac{E \times X^2 \prod I}{2+7} = 432\]
$$\sum_{i=1}^n X_i$$
\[\sum_{i=1}^n X_i\]
$$\int_0^{2\pi} \sin x~dx$$
\[\int_0^{2\pi} \sin x~dx\]
$\left( \sum_{i=1}^{n}{i} \right)^2 = \left( \frac{n(n-1)}{2}\right)^2 = \frac{n^2(n-1)^2}{4}$
\(\left( \sum_{i=1}^{n}{i} \right)^2 = \left( \frac{n(n-1)}{2}\right)^2 = \frac{n^2(n-1)^2}{4}\)
$\begin{eqnarray} X & \sim & \mathrm{N}(0,1)\\ Y & \sim & \chi^2_{n-p}\\ R & \equiv & X/Y \sim t_{n-p} \end{eqnarray}$
\(\begin{eqnarray} X & \sim & \mathrm{N}(0,1)\\ Y & \sim & \chi^2_{n-p}\\ R & \equiv & X/Y \sim t_{n-p} \end{eqnarray}\)
$\begin{eqnarray} P(|X-\mu| > k) & = & P(|X-\mu|^2 > k^2)\\ & \leq & \frac{\mathbb{E}\left[|X-\mu|^2\right]}{k^2}\\ & \leq & \frac{\mathrm{Var}[X]}{k^2} \end{eqnarray}$
\(\begin{eqnarray} P(|X-\mu| > k) & = & P(|X-\mu|^2 > k^2)\\ & \leq & \frac{\mathbb{E}\left[|X-\mu|^2\right]}{k^2}\\ & \leq & \frac{\mathrm{Var}[X]}{k^2} \end{eqnarray}\)
View of the data using paged tables. This is the default output for RMarkdown.
Tab. 1: Table using paged tibble.
iris[1:15,]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
The most simple table using kable
from R package knitr
.
knitr::kable(head(iris), 'html')
Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
---|---|---|---|---|
5.1 | 3.5 | 1.4 | 0.2 | setosa |
4.9 | 3.0 | 1.4 | 0.2 | setosa |
4.7 | 3.2 | 1.3 | 0.2 | setosa |
4.6 | 3.1 | 1.5 | 0.2 | setosa |
5.0 | 3.6 | 1.4 | 0.2 | setosa |
5.4 | 3.9 | 1.7 | 0.4 | setosa |
More advanced table using kableExtra
and formattable
.
Tab. 2: Table using kableextra.
iris[c(1:4,51:53,105:108),] %>%
mutate(Sepal.Length=color_bar("lightsteelblue")(Sepal.Length)) %>%
mutate(Sepal.Width=color_tile("white","orange")(Sepal.Width)) %>%
mutate(Species=cell_spec(Species,"html",color="white",bold=T,
background=c("#8dd3c7","#fb8072","#bebada")[factor(.$Species)])) %>%
kable("html",escape=F) %>%
kable_styling(bootstrap_options=c("striped","hover","responsive"),
full_width=F,position="left") %>%
column_spec(5,width="3cm")
Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | |
---|---|---|---|---|---|
1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
51 | 7.0 | 3.2 | 4.7 | 1.4 | versicolor |
52 | 6.4 | 3.2 | 4.5 | 1.5 | versicolor |
53 | 6.9 | 3.1 | 4.9 | 1.5 | versicolor |
105 | 6.5 | 3.0 | 5.8 | 2.2 | virginica |
106 | 7.6 | 3.0 | 6.6 | 2.1 | virginica |
107 | 4.9 | 2.5 | 4.5 | 1.7 | virginica |
108 | 7.3 | 2.9 | 6.3 | 1.8 | virginica |
Interactive table using R package DT
.
Tab. 3: Table using datatable.
iris %>%
slice(1:15) %>%
datatable(options=list(pageLength=7))
{plot(x=iris$Sepal.Length,y=iris$Sepal.Width,
col=c("coral","steelblue","forestgreen")[iris$Species],
xlab="Sepal Length",ylab="Sepal Width",pch=19)
legend(x=7,y=4.47,legend=c("setosa","versicolor","virginica"),
col=c("coral","steelblue","forestgreen"),pch=19)}
Fig. 1: Static plot using base plot.
R package ggplot2
is one of the most versatile and complete plotting solutions.
iris %>%
ggplot(aes(x=Sepal.Length,y=Sepal.Width,col=Species))+
geom_point(size=2)+
labs(x="Sepal Length",y="Sepal Width")
Fig. 2: Static plot using ggplot2.
R package highcharter
is a wrapper around javascript library highcharts
.
h <- iris %>%
hchart(.,"scatter",hcaes(x="Sepal.Length",y="Sepal.Width",group="Species")) %>%
hc_xAxis(title=list(text="Sepal Length"),crosshair=TRUE) %>%
hc_yAxis(title=list(text="Sepal Width"),crosshair=TRUE) %>%
hc_chart(zoomType="xy",inverted=FALSE) %>%
hc_legend(verticalAlign="top",align="right") %>%
hc_size(height=400)
htmltools::tagList(list(h))
Fig. 3: Interactive scatterplot using highcharter.
R package plotly
provides R binding around javascript plotting library plotly
.
p <- iris %>%
plot_ly(x=~Sepal.Length,y=~Sepal.Width,color=~Species,width=500,height=400) %>%
add_markers()
p
Fig. 4: Interactive scatterplot using plotly.
plotly
also has a function called ggplotly
which converts a static ggplot2 object into an interactive plot.
p <- iris %>%
ggplot(aes(x=Sepal.Length,y=Sepal.Width,col=Species))+
geom_point()+
labs(x="Sepal Length",y="Sepal Width")+
theme_bw(base_size=12)
ggplotly(p,width=500,height=400)
Fig. 5: Interactive scatterplot using ggplotly.
ggiraph
is also an R package that can be used to convert a static ggplot2 object into an interactive plot.
p <- ggplot(iris,aes(x=Sepal.Length,y=Petal.Length,colour=Species))+
geom_point_interactive(aes(tooltip=paste0("<b>Petal Length:</b> ",Petal.Length,"\n<b>Sepal Length: </b>",Sepal.Length,"\n<b>Species: </b>",Species)),size=2)+
theme_bw()
tooltip_css <- "background-color:#e7eef3;font-family:Roboto;padding:10px;border-style:solid;border-width:2px;border-color:#125687;border-radius:5px;"
ggiraph(code=print(p),hover_css="cursor:pointer;stroke:black;fill-opacity:0.3",zoom_max=5,tooltip_extra_css=tooltip_css,tooltip_opacity=0.9)
Fig. 6: Interactive scatterplot using ggiraph.
R package dygraphs
provides R bindings for javascript library dygraphs for time series data.
lungDeaths <- cbind(ldeaths, mdeaths, fdeaths)
dygraph(lungDeaths,main="Deaths from Lung Disease (UK)") %>%
dyOptions(colors=c("#66C2A5","#FC8D62","#8DA0CB"))
Fig. 7: Interactive time series plot using dygraph.
R package networkD3
allows the use of interactive network graphs from the D3.js javascript library.
data(MisLinks,MisNodes)
forceNetwork(Links=MisLinks,Nodes=MisNodes,Source="source",
Target="target",Value="value",NodeID="name",
Group="group",opacity=0.4)
Fig. 8: Interactive network plot.
R package leaflet
provides R bindings for javascript mapping library; leafletjs.
leaflet(height=500,width=700) %>%
addTiles(urlTemplate='http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png') %>%
#addProviderTiles(providers$Esri.NatGeoWorldMap) %>%
addMarkers(lat=57.639327,lng=18.288534,popup="RaukR") %>%
setView(lat=57.639327,lng=18.288534,zoom=15)
Fig. 9: Interactive map using leaflet.
R package crosstalk
allows crosstalk
enabled plotting libraries to be linked. Through the shared ‘key’ variable, data points can be manipulated simultaneously on two independent plots.
shared_quakes <- SharedData$new(quakes[sample(nrow(quakes), 100),])
lf <- leaflet(shared_quakes,height=300) %>%
addTiles(urlTemplate='http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png') %>%
addMarkers()
py <- plot_ly(shared_quakes,x=~depth,y=~mag,size=~stations,height=300) %>%
add_markers()
htmltools::div(lf,py)
Fig. 10: Linking independent plots using crosstalk.