Data Visualization/Plotting

.title[
# Data Visualization/Plotting
]
.author[
### Zulquar Nain
]
.institute[
### AMU
]
.date[
### 2024-04-30
]

---

---

background-image: url("images/data-visualization.jpg")
background-size: cover
# Plotting

[Image:BoldBI](https://www.boldbi.com/blog/data-visualization-importance-and-benefits)

---

# Visualization

**Data visualization** is the process to transform the information (data) into a visual presentation for example graph.

Why `visualisation/Plotting`?

An image speaks louder than words

Data visualizations make data easier for the human brain to understand

visualization also makes it easier to detect patterns, trends, and outliers in groups of data

Good data .color[visualizations] should place meaning into complicated datasets so that their message is clear and concise.

<ru-blockquote>According to Tableau, “[data visualization is] one of the most useful professional skills to develop. The better you can convey your points visually, the better you can leverage that information.”</ru-blockquote>

---

# <span style ="color:red">Visualization/Ploting in `R`</spand>

Visualisation / Plotting is one of greatest strength of `R`

Limited scope of this course

Basic plot function in `R` is `plot()`

Each ***graph*** function has - number of options

***grpahical parameters*** `par()`

---

# Ingredients for plotting

Materials to visualise that is data. No data no visualisation!

Mapping depends on what YOU want to show!

---
# Data

Import

We have learned in previous lectures

## Mapping

We will learn!

--
A basic graph

```r
plot(cars$speed, cars$dist, pch = 19, col = 'red', 
     las = 1, xlab="speed", ylab="Distance", main = "Speed Vs Distance")
```

![](Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-3-1.svg)

---

# Plotting- Setting

### We will use inbuild data sets in `R`

### To view available datasets in `R` Type `data()` and execute

### We will primarily use `data(cars)`

### Most used function for plotting in `R` is `plot()`

---

# Data-Cars

```r
data(cars)
```
--

### Examining data

Do you remember? `head()` ; `tail()` ; `nrow()`

```r
head(cars, 2)
```

```
##   speed dist
## 1     4    2
## 2     4   10
```

```r
tail(cars, 2) 
```

```
##    speed dist
## 49    24  120
## 50    25   85
```
]

```r
ncol(cars)
```

```
## [1] 2
```

```r
str(cars)
```

```
## 'data.frame':	50 obs. of  2 variables:
##  $ speed: num  4 4 7 7 8 9 10 10 10 11 ...
##  $ dist : num  2 10 4 22 16 10 18 26 34 17 ...
```
]

---
# Let's start- `Plot()`

`data(cars)` contains two variables `speed` and `distance`

### First plot

Plotting speed and distance

--
.pull-left[

```r
plot(cars$speed,cars$dist)
```

<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-7-1.png" style="display: block; margin: auto;" />
]

--
.pull-right[

* Here, `cars$speed` is for `x-axis` and `cars$dist` is for `y-axis`

* In `cars$speed`, `cars` is name of the data file and `speed` is variable name

* `plot()` is command to plot

]

---

# Let's start- `Plot()`

### Second plot

```r
 # output-location: fragment
x <- seq(-pi,pi,0.1)
plot(x, sin(x))
```

* Here `x` is for `x-axis` (a generated data using `seq` command )

* `sin(x)` is for `y-axis`

---

# Let's start- `Plot()`

### Adding label and Title

```r
plot(cars$speed, cars$dist,
*    xlab = "Speed", ylab = "Distance", main = "Speed Vs Distance" )
```
<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-10-1.png" style="display: block; margin: auto;" />

* Here to add the label, we have added the highlighted codes.

* Names of the label should always be in `""`

---

# Let's start- `Plot()`

### Changing Color and Plot Type

* We can change the plot type with the argument `type`

```r
"p" - points
"l" - lines
"b" - both points and lines
"c" - empty points joined by lines
"o" - overplotted points and lines
"s" and "S" - stair steps
"h" - histogram-like vertical lines
"n" - does not produce any points or lines
```

]

```r
plot(x, sin(x),
main="The Sine Function",
ylab="sin(x)",
*type="l" )
```

]

---
# Let's start- `Plot()`

### Changing Color and Plot Type

* Similarly, we can define the colors using `col="color name"`

```r
plot(cars$speed, cars$dist,
     xlab = "Speed", ylab = "Distance", main = "Speed Vs Distance",
*    col="red" )
```

<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-14-1.png" style="display: block; margin: auto;" />
--

* See the highlighted part of the code

---

# Let's start- `Plot()`

### Overlaying Plots

* Do YOU remember the function/command? `seq` -- we will use HERE to generate the variables `X, Y, t`

* plotting

```r
plot(t, X,
     main="Overlaying Graphs",
     ylab="",
     type="l",
     col="blue")
*lines(t, Y, col="red", type = "b")
*legend("left",
*      c("X","Y"),
*      fill=c("blue","red")
)
```
]

.pull-right[
 
<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-17-1.png" style="display: block; margin: auto;" />
--

* Checkout `?plot()` and `?legend`

]

---

# Some Baisc Graphs

### R Bar Plot

* Let's assume `AR` contains data of .red[average rainfall] in a day of a week.

```r
AR <- c( 12, 15, 11, 16, 18, 15, 14 )
```

```r
barplot(AR)
```

* There are many other parameters can be added to `barplot()`

* Use `?barplot()` to explore

---

# Some Baisc Graphs

### R Bar Plot
--

* Some of the parameters are added here.

```r
barplot(AR,
*main = "Average rainfall in a Day",
*xlab = "Centimeters (cm)",
*ylab = "Day",
*names.arg = c("Mon", "Tues", "Wed", "Thu", "Fri", "Sat", "Sun"),
*border="blue",
*col="red",
*density=20,
*horiz = TRUE,
*cex.names = .8)#To change the size of label
```

--
* See the highlighted codes

* Output in .red[next slide]

---

# Some Baisc Graphs

### R Bar Plot
--

* Some of the parameters are added here.

---

# Some Baisc Graphs

### Bar Plot of Categorical Data

* For example marks out of 20 of ten students in Math is in vector `MM`

```
##  [1] 17 16 18 17 18 19 18 16 18 18
```

* Simple bar plot

<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-24-1.png" style="display: block; margin: auto;" />
* Does it serve pupose?

* No

---

# Some Baisc Graphs

### Bar Plot of Categorical Data

* Check out `?table()`
]

```r
table(MM)
```

```
## MM
## 16 17 18 19 
##  2  2  5  1
```
]

--
.pull-left[

```r
*barplot(table(MM),
main="Marks of 10 Students",
xlab="Marks",
ylab="Count",
border="blue",
col="red",
density=10
)
```

]

--
.pull-right[

<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-27-1.png" style="display: block; margin: auto;" />
]

---

# Some Baisc Graphs

### Bar Plot of Categorical Data

#### Some more Bar plot

```r
print(titanic_surv)
```

```
##               train.Pclass
## train.Survived   1   2   3
##              0  80  97 372
##              1 136  87 119
```
]

* Here, 1, 2, and 3 represents 1st, 2nd and 3rd class in the train

* 0 and 1 is for the passenger did not survived and survived respectively in the Titanic mishap

]

```r
*barplot(titanic_surv,
        main = "Survival of Each Class",
        xlab = "Class",
        ylab = "No of Passenger",
        col = c("red","green")
)
legend("topleft",
       c("Not survived","Survived"),
       fill = c("red","green")
)
```

]

---

# Some Baisc Graphs

### Bar Plot of Categorical Data

#### Some more Bar plot

---
# Some Baisc Graphs

### Histogram-`hist()`

--
.font60[
*  Histogram is a visual representation of the distribution of a dataset

* We will use the `data(AirPassengers)` in built in `R`

* Explore the function `hist()` using `?hist()`

* A Basic Histogram
]

* put the name of your dataset in between the parentheses like `hist(AirPassengers)`

* Histogram for a specific variable can be drawn as `hist(datasetName$VariableName)`

]
]

```r
hist(AirPassengers)
```

<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-33-1.png" style="display: block; margin: auto;" />
]
]

---

# Some Baisc Graphs

### Histogram-`hist()`

* Other parameters of `hist()`

```r
hist(AirPassengers, 
     main="Histogram for Air Passengers", 
     xlab="Passengers", 
     border="blue", 
     col="green",
*    xlim=c(100,700),
*    las=1,
*    breaks=5)
```
]

]
]

* `xlim=c()` & `ylim=c()` fixes the range of `X` and `Y` axes

* Inside `c()` sets starting and ending points

* `las=1` rotates the label of `Y-axis` Checkout `?las`

* `breaks` is for the size/width of `Histogram BINS` Chechout `?breaks`

]

---

# Some Baisc Graphs

### Pie Chart-`Pie Chart`

* Pie chart is drawn using the `pie()` function in `R` programming

* This function takes in a vector of .red[non-negative] numbers.

* Basic Syntax of is `pie(x, labels, radius, main, col, clockwise)`

* .red[x] is a vector containing the numeric values used in the pie chart.

* .red[labels] is used to give description to the slices.

* .red[radius] indicates the radius of the circle of the pie chart.(value between −1 and +1).

* .red[main] indicates the title of the chart.

* .red[col] indicates the color palette.

* .red[clockwise] is a logical value indicating if the slices are drawn clockwise or anti clockwise.
]
]

* Explore `?pie()`

---

# Some Baisc Graphs

### Pie Chart-`Pie Chart`

--
.font70[

*  In `pie()`, `scores$Obt.Marks` is the vector of positive numbers for which `pie-chart` is drawn

* `scores$Subjects` is the labels

* Note: `scores$Subjects` shows that `Subjects` variable has been selected from`scores` dataset
]

* `Pie Chart`

```r
pie(scores$Obt.Marks, scores$Subjects)
```

]
]

* Data

```r
print(scores)
```

```
##   Subjects Obt.Marks
## 1     Math        70
## 2      Eng        80
## 3     Urdu        60
## 4       Sc        80
## 5     Soc.        90
```

]
]

---

### Pie Chart-`Pie Chart`

### Other parameters 
]

```r
*piepercent<- round(100*(scores$Obt.Marks)/sum((scores$Obt.Marks)), 1) # %age calculation
*pie(scores$Obt.Marks, labels = piepercent, # Labels
*   main = "Scores pie chart", # Title of chart
*   col = rainbow(length(scores$Obt.Marks))) # Color of chart
*legend("topright", # legend position
*      scores$Subjects, # legend labels
*      cex = 0.8, # size of legend texts
*  fill = rainbow(length(scores$Obt.Marks))) # legend color
```

]

---

# Some Baisc Graphs

## Scatterplot Matrix

* In case of more than two variables and to find the correlation between one variable versus the remaining ones

* we use scatterplot matrix. `pairs()` function creates matrices of scatterplots.

* `pairs(formula, data)`

* We will use `data(mtcars)` available within `R`; explor `?mtcars`

```
##               mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4      21   6  160 110  3.9 2.620 16.46  0  1    4    4
## Mazda RX4 Wag  21   6  160 110  3.9 2.875 17.02  0  1    4    4
```

```r
pairs(~wt+mpg+disp+cyl,data = mtcars,
   main = "Scatterplot Matrix")
```

* Plot in the next slide

---

# Some Baisc Graphs

## Scatterplot Matrix

* Plot in the next slide

---

# Multiple Plots

## R Function `par()`

* For drawing multiple graphs in a single plot- use `par()`

* Checkout `?par()`
]
--
.font80[
### Let's take an example 
]
--
.font80[
* For drawing two graphs in one plot

```r
par(mfrow=c(1,2))    # set the plotting area into a 1*2 array (1 Row and 2 Col) 
*barplot(scores$Obt.Marks, names.arg = scores$Subjects, main="Barplot", las=2) # Bar plot
*pie(scores$Obt.Marks, scores$Subjects, main="Piechart", radius=1) # Pie Chart
```
* See the graph in next slide
]
--

* Here parameter `mfrow` used to specify the number of subplot we need.

* It takes in a vector of form `c(m, n)` which divides the given plot into `m*n` array of subplots.

--
 
* For the above example, to plot the two graphs side by side, we have `m=1` and `n=2`.

---

# Multiple Plots

## R Function `par()`- Explore it for more control parameters

---

# Saving / Exporting Graph

* All types of graphs `(bar plot, pie chart, histogram)` etc. can be saved.

* Graphs can be saved as bitmap image( i.e. .png, jpeg, tiff etc) which are fixed size

* Graphs can be also saved as vector image (.pdf, .eps) which are easily resizable

* We will use the `temperature` column of built-in dataset `airquality`

---

# Saving / Exporting Graph

## Saving as `.jpeg` 
.code110[
.pull-left[

```r
jpeg(file="saving_plot1.jpeg") 
# File name
hist(Temp, col="darkgreen")
dev.off() # TO call off
```

]

]

* Image will be saved in `working/default directory`

* we need to call the function `dev.off()` after all the plotting, to save the file and return control to the screen

* The resolution of the image by default will be `$480\times480$` pixel.

---

# Saving / Exporting Graph

## Saving as `.png`

```r
png(file= "I:/My Drive/AMU Class 2021-22(june-Dec)/ECM2073/ECM2073-2021-22 Class/saving_plot2.png",
*width=600, height=350)
hist(Temp, col="gold")
dev.off()
```

]

]]

* You can specify the full path tp save the image at desired plcae (as above)

* You can also specify the resolution at desired level using arguments `width` and `height`

---

# Saving / Exporting Graph

## Saving as `.bmp`

* Size of the plot can be specified in different units such as in inch, cm or mm with the argument `units` and ppi with `res`.

* The following code saves a bmp file of size `6x4 inch` and `100 ppi`.

```r
bmp(file="saving_plot3.bmp",
width=6, height=4, units="in", res=100)
hist(Temp, col="steelblue")
dev.off()
```
]

]

]]

---

# Saving / Exporting Graph

## Saving as `.pdf`

```r
bmp(file="saving_plot4.pdf",
width=6, height=4, units="in", res=100)
hist(Temp, col="violet")
dev.off()
```

]

---

# Plotting in `R`

* This presentation is not exhaustive.

* Adopt learning by doing approach

* Make use of `Google` and `R Documentation`

* It was about basic `R` plotting

* Plotting has become more exciting and easy using `package-ggplot2` in `R`

---

# Some More Basic Grpahs

## Histogram and Density Plot

* Simple Histogram

```r
hist(mtcars$mpg,
*    breaks = 10,col="red")
```
]

* The option breaks= controls the number of bins.

]

]

---

# Some More Basic Grpahs

## Histogram and Density Plot

* Histograms can be a poor method for determining the shape of a distribution because it is so strongly affected by the number of bins used.

* Kernal density plots are usually a much more effective way to view the distribution of a variable

```r
d <- density(mtcars$mpg) # returns the density data
plot(d) # plots the results
```
]

]

]

---

# Some More Basic Grpahs

## Dot Plots

```r
dotchart(mtcars$mpg,
         labels=row.names(mtcars),
         cex=.7,
   main="Gas Milage 4 Car Models",
   xlab="Miles Per Gallon")
```
]]

]

---

# Some More Basic Grpahs

## Dot Plots

* Dotplot: Grouped Sorted and Colored

* Sort by mpg, group and color by cylinder

```r
*x <- mtcars[order(mtcars$mpg),] # sort by mpg
*x$cyl <- factor(x$cyl) # it must be a factor
x$color[x$cyl==4] <- "red"
x$color[x$cyl==6] <- "blue"
x$color[x$cyl==8] <- "darkgreen"
dotchart(x$mpg,labels=row.names(x),cex=.7,groups= x$cyl,
   main="Gas Milage for Car Models\ngrouped by cylinder",
   xlab="Miles Per Gallon", gcolor="black", color=x$color)
```
]]

]

---

# Line Plot

## A basic Line Plot

```r
x <- 1:10      # Create example data
y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9)
plot(x, y1, type = "l",         
     main = "This is my Line Plot",
     xlab = "My X-Values",
     ylab = "My Y-Values")
```
]]

]

---

# Line Plot

## Changing width of line

```r
x <- 1:10      # Create example data
y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9)
plot(x, y1, type = "l", 
*    lwd= 5,
     main = "This is my Line Plot",
     xlab = "My X-Values",
     ylab = "My Y-Values")
```
]]

]

---

# Line Plot

## Multiple Line Plot to one graph

```r
x <- 1:10      # Create example data
y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9)
y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8) 
y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7)
plot(x, y1, type = "l", 
*    lwd= 5,
     main = "This is my Line Plot",
     xlab = "My X-Values",
     ylab = "My Y-Values")
*lines(x, y2, type = "l", col = "red")
*lines(x, y3, type = "l", col = "green")
legend("topleft",         
       legend = c("Line y1", "Line y2", "Line y3"),
       col = c("black", "red", "green"),
       lty = 1)
```
]]

<img src="Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-74-1.png" style="display: block; margin: auto;" />
]

---

# Box Plot

* box plot gives us a visual representation of the quartiles within numeric data

* box plot shows the median (second quartile), first and third quartile, minimum, and maximum

---

# Box Plot

## A Box plot

```r
boxplot(mpg~cyl,data=mtcars, 
        main="Car Milage Data",
   xlab="Number of Cylinders",
   ylab="Miles Per Gallon")
```

]

![](Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-76-1.png)

]

---

# Box Plot

## Box Plot: Adding color

```r
boxplot(mpg~cyl,data=mtcars, 
*       col= c("cyan","blue","steelblue"),
        main="Car Milage Data",
   xlab="Number of Cylinders",
   ylab="Miles Per Gallon")
```

]

![](Lecture13-R-Base-Plot_files/figure-html/unnamed-chunk-78-1.png)

]

---
class: inverse, middle, center

background-size: cover

# .red[THANKS]