Why is data visualization important?
Data Visualization is important because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner – and you can experiment with different scenarios by making slight adjustments.Preparing your organization for data visualization technology requires that you first:
- Understand the data you’re trying to visualize, including its size and cardinality (the uniqueness of data values in a column).
- Determine what you’re trying to visualize and what kind of information you want to communicate.
- Know your audience and understand how it processes visual information.
- Use a visual that conveys the information in the best and simplest form for your audience.
Basic scatterplot -Main arguments x and y are vectors indicatingthe x and y coordinates of the data points (in this case, 10).
Code-plot(x = 1:10, y = 1:10, xlab = "My-XAxis", ylab = "My=YAxis", main = "Graph Title")
Using Transparent Colors in plots - Example ofbasic plotting with color using "yarrr" package transparent color -Most plotting functions have a color argument(usually
col) that allows you to specify the color of whatever your plotting.Code-plot(x = pirates$height, y = pirates$weight, col = yarrr::transparent("blue", trans.val = .9), pch = 16, main = "col = yarrr::transparent('blue', .9)")
Using default R Colors in plots(usually
col) that allows you to specify the color of whatever your plotting.Codeplot(x = pirates$height, y = pirates$weight, col = "blue", pch = 16, main = "col ='blue'")
Plotting scatterplot with arguments- Example of plottingplot()function makes a scatterplot from two vectors x and y, where the x vector indicates the x (horizontal) values of the points, and the y vector indicates the y (vertical) values.Codeplot(x = 1:10, # x-coordinates y = 1:10, # y-coordinates type = "p", # Just draw points (no lines) main = "My First Plot", xlab = "This is the x-axis label", ylab = "This is the y-axis label", xlim = c(0, 11), # Min and max values for x-axis ylim = c(0, 11), # Min and max values for y-axis col = "blue", # Color of the points pch = 16, # Type of symbol (16 means Filled circle) cex = 1) # Size of the symbols
Histograms are the most common way to plot a vector of numeric data.
Code -hist(x = ChickWeight$weight, main = "Chicken Weights", xlab = "Weight", xlim = c(0, 500))
Barplot typically shows summary statistics for different groups.The primary argument to a barplot isheight: a vector of numeric values which will generate the height of each bar.To add names below the bars, use thenames.argargument.Codebarplot(height = 1:5, # A vector of heights names.arg = c("G1", "G2", "G3", "G4", "G5"), # A vector of names main = "Example Barplot", xlab = "Group", ylab = "Height")
Pirateplot is a plot contained in the "yarrrpackage"written specifically by, and for R pirates The pirateplot is an easy-to-use function that, unlike barplots and boxplots, can easily show raw data, descriptive statistics, and inferential statistics in one plot.
Code - yarrr::pirateplot(formula = weight ~ Time, # dv is weight, iv is Diet
data = ChickWeight,
main = "Pirateplot of chicken weights",
xlab = "Diet",
ylab = "Weight")
Finally, we can save these graphs as pdf file using pdf function of RCode pdf(file = "D:\MyPlot.pdf", # The directory you want to save the file in
width = 4, # The width of the plot in inches
height = 4) # The height of the plot in inches
# Step 2: Create the plot with R code
plot(x = 1:10,
y = 1:10)
abline(v = 0) # Additional low-level plotting commands
text(x = 0, y = 1, labels = "Random text")
# Step 3: Run dev.off() to create the file!
dev.off() 
