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ch1_b.qmd
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---
title-block-banner: true
bibliography: references.bib
---
## Plotting with base R
If you need to quickly visualize your data, base `R` has some functions that will help you do this in a pinch. In this section we'll look at some basics of visualizing univariate and multivariate data.
### Overview
```{r data frame and list, message = FALSE, warning = FALSE}
# Create 50 random numbers between 0 and 100
data=runif(50, 0, 100) #runif stands for random numbers from a uniform distribution
# Let's plot the data
plot(data) # The "plot" function initializes the plot.
plot(data, type="l") # The "type" argument changes the plot type. "l" calls up a line plot
plot(data, type="b") # Buffered points joined by lines
# Try options type = "o" and type = "c" as well.
# We can also quickly visualize boxplots, histograms, and density plots using the same procedure
boxplot(data) # Box-and-whisker plot
hist(data) # Histogram points
plot(density(data)) # Plot with density distribution
```
### Plotting univariate data
Let's dig deeper into the plot function. Here, we will look at how to adjust the colors, shapes, and sizes for markers, axis labels and titles, and the plot title.
```{r Univariate data, message = FALSE, warning = FALSE}
# Line plots
plot(data,type="o", col="red",
xlab="x-axis title",ylab ="y-axis title",
main="My plot", # Name of axis labels and title
cex.axis=2, cex.main=2,cex.lab=2, # Size of axes, title and label
pch=23, # Change marker style
bg="red", # Change color of markers
lty=5, # Change line style
lwd=2 # Selecting line width
)
# Adding legend
legend(1, 100, legend=c("Data 1"),
col=c("red"), lty=2, cex=1.2)
# Histograms
hist(data,col="red",
xlab="Number",ylab ="Value", main="My plot", # Name of axis labels and title
border="blue"
)
# Try adjusting the parameters:
# hist(data,col="red",
# xlab="Number",ylab ="Value", main="My plot", # Name of axis labels and title
# cex.axis=2, cex.main=2,cex.lab=2, # Size of axes, title and label
# border="blue",
# xlim=c(0,100), # Control the limits of the x-axis
# las=0, # Try different values of las: 0,1,2,3 to rotate labels
# breaks=5 # Try using 5,20,50, 100
# ) # Using more options and controls
```
### Plotting multivariate data
Here, we introduce you to data frames: equivalent of tables in `R`. A data frame is a table with a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.
```{r Multivariate data frame, message = FALSE, warning = FALSE}
plot_data=data.frame(x=runif(50,0,10),
y=runif(50,20,30),
z=runif(50,30,40))
plot(plot_data$x, plot_data$y) # Scatter plot of x and y data
# Mandatory beautification
plot(plot_data$x,plot_data$y, xlab="Data X", ylab="Data Y", main="X vs Y plot",
col="darkred",pch=20,cex=1.5) # Scatter plot of x and y data
# Multiple lines on one axis
matplot(plot_data, type = c("b"),pch=16,col = 1:4)
matplot(plot_data, type = c("b","l","o"),pch=16,col = 1:4) # Try this now. Any difference?
legend("topleft", legend = 1:4, col=1:4, pch=1) # Add legend to a top left
legend("top", legend = 1:4, col=1:4, pch=1) # Add legend to at top center
legend("bottomright", legend = 1:4, col=1:4, pch=1) # Add legend at the bottom right
```
### Time series data
Working with time series data can be tricky at first, but here's a quick look at how to quickly generate a time series using the as.Date function.
```{r time series, message = FALSE, warning = FALSE}
date=seq(as.Date('2011-01-01'),as.Date('2011-01-31'),by = 1) # Generate a sequence 31 days
data=runif(31,0,10) # Generate 31 random values between 0 and 10
df=data.frame(Date=date,Value=data) # Combine the data in a data frame
plot(df,type="o")
```
### Combining plots
You can built plots that contain subplots. Using base R, we call start by using the "par" function and then plot as we saw before.
```{r using layout, message = FALSE, warning = FALSE}
par(mfrow=c(2,2)) # Call a plot with 4 quadrants
# Plot 1
matplot(plot_data, type = c("b"),pch=16,col = 1:4)
# Plot 2
plot(plot_data$x,plot_data$y)
# Plot 3
hist(data,col="red",
xlab="Number",ylab ="Value", main="My plot",
border="blue")
# Plot4
plot(data,type="o", col="red",
xlab="Number",ylab ="Value", main="My plot",
cex.axis=2, cex.main=2,cex.lab=2,
pch=23,
bg="red",
lty=5,
lwd=2
)
# Alternatively, we can call up a plot using a matrix
matrix(c(1,1,2,3), 2, 2, byrow = TRUE) # Plot 1 is plotted for first two spots, followed by plot 2 and 3
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE)) # Fixes a layout of the plots we want to make
# Plot 1
matplot(plot_data, type = c("b"),pch=16,col = 1:4)
# Plot2
plot(plot_data$x,plot_data$y)
# Plot 3
hist(data,col="red",
xlab="Number",ylab ="Value", main="My plot",
border="blue"
)
```
### Saving figures to disk
Plots can be saved as image files or a PDF. This is done by specifying the output file type, its size and resolution, then calling the plot.
```{r code for exporting figures, message = FALSE, warning = FALSE}
png("awesome_plot.png", width=4, height=4, units="in", res=400)
#Tells R we will plot image in png of given specification
matplot(plot_data, type = c("b","l","o"),pch=16,col = 1:4)
legend("topleft", legend = 1:4, col=1:4, pch=1)
dev.off() # Very important: this sends the image to disc
# Keep pressing till you get the following:
# Error in dev.off() : cannot shut down device 1 (the null device)
# This ensures that we are no longer plotting.
# It looks like what everything we just plotted was squeezed together to tightly. Let's change the size.
png("awesome_plot.png", width=6, height=4, units="in", res=400) #note change in dimension
#Tells R we will plot image in png of given specification
matplot(plot_data, type = c("b","l","o"),pch=16,col = 1:3)
legend("topleft", legend = 1:3, col=1:3, pch=16)
dev.off()
```
<br>
**Some useful resources**
If you want to plot something a certain way and don't know how to do it, the chances are that someone has asked that question before. Try a Google search for what your are trying to do and check out some of the forums. There is TONS of material online. Here are some additional resources:
1. The R Graph Gallery: <https://www.r-graph-gallery.com/>
2. Graphical parameters: <https://www.statmethods.net/advgraphs/parameters.html>
3. Plotting in R: <https://www.harding.edu/fmccown/r/>
4. Histogram: <https://www.r-bloggers.com/how-to-make-a-histogram-with-basic-r/>