STA 517 3.0 Programming and Statistical Computing with R

# STA 517 3.0 Programming and Statistical Computing with R
## 🚦Built-in functions in R
### 
### Dr Thiyanga Talagala

---

## Function Anatomy

```r
vec1 <- c(1, 2, 3, 4, 5)
mean(vec1)
```

```
[1] 3
```

```r
vec2 <- c(1, 2, NA, 3, 4, 5)
mean(vec2)
```

```
[1] NA
```

]

## Help

`?mean`

---

## help: `mean`

![](mean.png)

---

## help: `sort`

![](sort.png)

---

![](functionanatomy.PNG)

---

## `mean` with additional inputs

```r
vec1 <- c(1, 2, 3, 4, 5)
mean(vec1)
```

```
[1] 3
```

```r
vec2 <- c(1, 2, NA, 3, 4, 5)
mean(vec2)
```

```
[1] NA
```

]

```r
mean(vec2, na.rm=TRUE)
```

```
[1] 3
```

![](functionanatomy.png)

]
---

![](default.PNG)

---

![](sort.png)

---

```r
vec <- c(10, 1, 2, 4, 100, 15)
sort(vec)
```

```
[1]   1   2   4  10  15 100
```

```r
sort(vec, decreasing = TRUE)
```

```
[1] 100  15  10   4   2   1
```

```r
sort(vec, decreasing = FALSE)
```

```
[1]   1   2   4  10  15 100
```
]

![](default.PNG)

]

---
## `rep`

![](rep.png)

---

# [Vector creation with `rep` function in R!](https://hellor.netlify.app/2021/week1/l12021msc.html#66)

---

## Working with built-in functions in R

- How to call a built-in function

- Arguments matching

- Basic functions

- Test and type conversion functions

- Probability distribution functions

- Reproducibility of scientific results

- Data visualization: `qplot`

---
## Functions in R

👉🏻 Perform a specific task according to a set of instructions.

👉🏻  Some functions we have discussed so far,

> `c`, `matrix`, `array`, `list`, `data.frame`, `str`, `dim`, `length`, `nrow`, `which.max`, `diag`, `summary`

👉🏻 In R, functions are **objects** of **class** *function*.

```r
class(length)
```

```
[1] "function"
```

---

## Functions in R (cont.)

👉🏻 There are basically two types of functions:

> 💻 Built-in functions 
  
Already created or defined in the programming framework to make our work easier.

> 👨 User-defined functions

Sometimes we need to create our own functions for a specific purpose.
      
---

### How to call a built-in function in R

```r
function_name(arg1 = 1, arg2 = 3)

```

**Argument matching**

The following calls to `mean` are all equivalent

```r
mydata <- c(rnorm(20), 100000)
mean(mydata) # matched by position
mean(x = mydata) # matched by name
mean(mydata, na.rm = FALSE)
mean(x = mydata, na.rm = FALSE) 
mean(na.rm = FALSE, x = mydata) 
mean(na.rm = FALSE, mydata)
```

```
[1] 4762.105
```

⚠️ Even though it works, do not change the order of the arguments too much.

---

## Argument matching (cont.)

- some arguments have default values

```r
mean(mydata, trim=0)
```

```
[1] 4762.105
```

```r
mean(mydata) # Default value for trim is 0
```

```
[1] 4762.105
```

]

```r
mean(mydata, trim=0.1)
```

```
[1] 0.2973882
```

```r
mean(mydata, tr=0.1) # Partial Matching
```

```
[1] 0.2973882
```

]

---

background-image: url('helpmean.png')
background-position: center
background-size: contain

## ?mean

---

# Your turn

---

1. Calculate the mean of 1, 2, 3, 8, 10, 20, 56, NA.

2. Arrange the numbers according to the descending order and ascending order.

3. Compute standard error of the above numbers.

---

# Basic maths functions

|  Operator | Description  |
|---|---|
|<img width=400/>|<img width=500/>|
|  abs(x) | absolute value of x   |
| log(x, base = y)  |  logarithm of x with base y; if base is not specified, returns the natural logarithm |
|exp(x)| exponential of x|
|sqrt(x)|square root of x|
|factorial(x)| factorial of x|

---
# Basic statistic functions

|  Operator | Description  |
|---|---|
|<img width=400/>|<img width=400/>|
|  mean(x) | mean of x   |
| median(x)  |  median of x |
|mode(x)| mode of x|
|var(x)|variance of x|
|sd(x)|standard deviation of x|
|scale(x)| z-score of x|
|quantile(x)| quantiles of x|
|summary(x)|summary of x: mean, minimum, maximum, etc.|

---

## Test and Type conversion functions

|  Test | Convert  |
|---|---|
|<img width=400/>|<img width=400/>|
|  is.numeric() | as.numeric()   |
| is.character()  |  as.character() |
|is.vector()| as.vector()|
|is.matrix()|as.matrix()|
|is.data.frame()| as.data.frame()|
|is.factor()| as.factor()|
|is.logical()|as.logical()|
|is.na()||
]

```r
a <- c(1, 2, 3); a
```

```
[1] 1 2 3
```

```r
is.numeric(a)
```

```
[1] TRUE
```

```r
is.vector(a)
```

```
[1] TRUE
```
]

---

## Test and Type conversion functions

```r
b <- as.character(a); b
```

```
[1] "1" "2" "3"
```

```r
is.vector(b)
```

```
[1] TRUE
```

```r
is.character(b)
```

```
[1] TRUE
```
]

---

# Your turn

---

Remove missing values in the following vector

```r
a
```

```
 [1]  0.61940020 -0.93808729  0.95518590 -0.22663938  0.29591186          NA
 [7]  0.36788089  0.71791098  0.71202022  0.22765782          NA          NA
[13] -0.74024324  0.02081516 -0.14979979 -0.22351308  0.98729725          NA
[19]          NA          NA          NA          NA          NA          NA
[25]          NA          NA          NA -1.50016003  0.18682734  0.20808590
[31]  0.70102264 -0.10633074 -1.18460046  0.06475501  0.11568817 -0.04333140
[37] -0.22020064  0.02764713  0.10165760 -0.18234246  1.32914659 -1.29704248
[43]  1.05317749 -0.70109051  0.09798707  0.10457263 -0.21449845
```
---

### Probability distribution functions

- Each probability distribution in R is associated with four functions.

- Naming convention for the four functions:

For each function there is a root name. For example, the **root name** for the normal distribution is `norm`. This root is prefixed by one of the letters `d`, `p`, `q`, `r`.
    
  - **d** prefix for the **distribution** function

- **p** prefix for the **cumulative probability**

- **q** prefix for the **quantile**

- **r** prefix for the **random** number generator

- Example: `dnorm`, `pnorm`, `qnorm`, `rnorm`

---

background-image: url('dis.jpeg')
background-position: center
background-size: contain

---

### Illustration with Standard normal distribution

The general formula for the probability density function of the normal distribution with mean `$\mu$` and variance `$\sigma$` is given by

$$
f_X(x) = \frac{1}{\sigma\sqrt{(2\pi)}} e^{-(x-\mu)^2/2\sigma^2}
$$

If we let the mean `$\mu=0$` and the standard deviation `$\sigma=1$`, we get the probability density function for the standard normal distribution.

$$
f_X(x) = \frac{1}{\sqrt{(2\pi)}} e^{-(x)^2/2}
$$

---

### Standard Normal Distribution

$$
f_X(x) = \frac{1}{\sqrt{(2\pi)}} e^{-(x)^2/2}
$$

```r
dnorm(0)
```

```
[1] 0.3989423
```

]

<div class="figure">
<img src="l32021msc_files/figure-html/unnamed-chunk-18-1.png" alt="Standard normal probability density function: dnorm(0)" width="100%" />
<p class="caption">Standard normal probability density function: dnorm(0)</p>
</div>

]

---

### Standard Normal Distribution

$$
f_X(x) = \frac{1}{\sqrt{(2\pi)}} e^{-(x)^2/2}
$$

```r
pnorm(0)
```

```
[1] 0.5
```

]

<div class="figure">
<img src="l32021msc_files/figure-html/unnamed-chunk-20-1.png" alt="Standard normal probability density function: pnorm(0)" width="100%" />
<p class="caption">Standard normal probability density function: pnorm(0)</p>
</div>

]

---

## Standard Normal Distribution

$$
f_X(x) = \frac{1}{\sqrt{(2\pi)}} e^{-(x)^2/2}
$$

```r
qnorm(0.5)
```

```
[1] 0
```

]

<div class="figure">
<img src="l32021msc_files/figure-html/unnamed-chunk-22-1.png" alt="Standard normal probability density function: qnorm(0.5)" width="100%" />
<p class="caption">Standard normal probability density function: qnorm(0.5)</p>
</div>

]

---

## Normal distribution: `norm`

![](norm.png)

]

```r
pnorm(3)
```

```
[1] 0.9986501
```

```r
pnorm(3, sd=1, mean=0)
```

```
[1] 0.9986501
```

```r
pnorm(3, sd=2, mean=1)
```

```
[1] 0.8413447
```

]

---

### Binomial distribution

```r
dbinom(2, size=10, prob=0.2)
```

```
[1] 0.3019899
```

```r
a <- dbinom(0:10, size=10, prob=0.2)
a
```

```
 [1] 0.1073741824 0.2684354560 0.3019898880 0.2013265920 0.0880803840
 [6] 0.0264241152 0.0055050240 0.0007864320 0.0000737280 0.0000040960
[11] 0.0000001024
```

```r
cumsum(a)
```

```
 [1] 0.1073742 0.3758096 0.6777995 0.8791261 0.9672065 0.9936306 0.9991356
 [8] 0.9999221 0.9999958 0.9999999 1.0000000
```

---

```r
cumsum(a)
```

```
 [1] 0.1073742 0.3758096 0.6777995 0.8791261 0.9672065 0.9936306 0.9991356
 [8] 0.9999221 0.9999958 0.9999999 1.0000000
```

```r
pbinom(0:10, size=10, prob=0.2)
```

```
 [1] 0.1073742 0.3758096 0.6777995 0.8791261 0.9672065 0.9936306 0.9991356
 [8] 0.9999221 0.9999958 0.9999999 1.0000000
```

```r
qbinom(0.4, size=10, prob=0.2)
```

```
[1] 2
```

---

### Standard Normal Distribution: rnorm

```r
set.seed(262020)
random_numbers <- rnorm(5)
random_numbers
```

```
[1] 0.2007818 0.9587335 1.1836906 1.4951375 1.1810922
```

```r
sort(random_numbers) ## sort the numbers then it is easy to map with the graph
```

```
[1] 0.2007818 0.9587335 1.1810922 1.1836906 1.4951375
```

]

]

---
class: inverse, center, middle

# Other distributions in R

---

- **`beta`**: beta distribution

- **`binom`**: binomial distribution

- **`cauchy`**: Cauchy distribution

- **`chisq`**: chi-squared distribution

- **`exp`**: exponential distribution

- **`f`**: F distribution

- **`gamma`**: gamma distribution

- **`geom`**: geometric distribution

- **`hyper`**: hyper-geometric distribution

]

- **`lnorm`**: log-normal distribution

- **`multinom`**: multinomial distribution

- **`nbinom`**: negative binomial distribution

- **`norm`**: normal distribution

- **`pois`**: Poisson distribution

- **`t`**: Student's t distribution

- **`unif`**: uniform distribution

- **`weibull`**: Weibull distribution

]

---

🙋 Getting help with R:  **`?Distributions`**

---

# Your turn

---

**Q1** Suppose `$Z \sim  N(0,1)$`. Calculate the following standard normal probabilities.

- `$P(Z \le 1.25)$`,

- `$P(Z > 1.25)$`,

- `$P(Z \leq -1.25)$`,

- `$P(-.38 \leq Z \leq 1.25)$`.

**Q2** Find the following percentiles for the standard normal distribution.

- 90th, 
     
  - 95th,
     
  - 97.5th,
     
---

**Q3** Determine the `$Z_\alpha$` for the following

- `$\alpha = 0.1$`
      
  - `$\alpha = 0.95$`

**Q4** Suppose `$X \sim N(15, 9)$`. Calculate the following probabilities

- `$P(X \leq 15)$`,
  
  - `$P(X < 15)$`,
  
  - `$P(X \geq 10)$`.
  
<div class="countdown" id="timer_61e370d0" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">02</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
  
  
---

**Q5** A particular mobile phone number is used to receive both voice messages and text messages. Suppose 20% of the messages involve text messages, and consider a sample of 15 messages. What is the probability that

- At most 8 of the messages involve a text message?
    
  - Exactly 8 of the messages involve a text message.
  
<div class="countdown" id="timer_61e37196" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">02</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
  
---

**Q6** Generate 20 random values from a Poisson distribution with mean 10 and calculate the mean. Compare your answer with others.

---

# Reproducibility of scientific results

```r
rnorm(10) # first attempt
```

```
 [1]  1.6582609 -1.8912734 -2.8471112 -2.1617741  0.6401224 -0.4295948
 [7] -0.3122580 -1.0267992  1.4231150  0.8661058
```

```r
rnorm(10) # second attempt
```

```
 [1] -0.91879540 -0.06053766 -0.20263170 -0.26301690  0.97964620 -0.46034817
 [7]  0.81826880 -0.60935778  1.71086661  0.49294451
```

As you can see above you will get different results.

---

# Reproducibility of scientific results (cont.)

```r
set.seed(1)
rnorm(10) # First attempt with set.seed
```

```
 [1] -0.6264538  0.1836433 -0.8356286  1.5952808  0.3295078 -0.8204684
 [7]  0.4874291  0.7383247  0.5757814 -0.3053884
```

```r
set.seed(1)
rnorm(10) # Second attempt with set.seed
```

```
 [1] -0.6264538  0.1836433 -0.8356286  1.5952808  0.3295078 -0.8204684
 [7]  0.4874291  0.7383247  0.5757814 -0.3053884
```
 
---
## R Apply family and its variants

- **`apply()`** function

```r
marks <- data.frame(maths=c(10, 20, 30), chemistry=c(100, NA, 60)); marks
```

```
  maths chemistry
1    10       100
2    20        NA
3    30        60
```

```r
apply(marks, 1, mean)
```

```
[1] 55 NA 45
```

---

```r
apply(marks, 2, mean)
```

```
    maths chemistry 
       20        NA 
```

```r
apply(marks, 1, mean, na.rm=TRUE)
```

```
[1] 55 20 45
```

---

# Your turn

---

Calculate the row and column wise standard deviation of the following matrix

```
     [,1] [,2] [,3] [,4]
[1,]    1    6   11   16
[2,]    2    7   12   17
[3,]    3    8   13   18
[4,]    4    9   14   19
[5,]    5   10   15   20
```

---

# Your turn

---

**Your turn**

Find about the following variants of apply family functions in R **`lapply()`**, **`sapply()`**, **`vapply()`**, **`mapply()`**, **`rapply()`**, and **`tapply()`** functions.

Resourses: You can follow the DataCamp tutorial [here](https://www.datacamp.com/community/tutorials/r-tutorial-apply-family?utm_source=adwords_ppc&utm_campaignid=1658343524&utm_adgroupid=63833882055&utm_device=c&utm_keyword=%2Bapply%20%2Br&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=319558765408&utm_targetid=aud-299261629574:kwd-309818754193&utm_loc_interest_ms=&utm_loc_physical_ms=1009919&gclid=EAIaIQobChMImfeQkOTq5wIVVQwrCh1BngrmEAAYASAAEgLEa_D_BwE).

- You should clearly explain,

- syntax for each function/ Provide your own example for each function

- function inputs
    
    - how each function works?/ The task of the function.
    
    - output of the function.
    
    - differences between the functions (apply vs lapply, apply vs sapply, etc.)

---

## Data Visualization: qplot()

?qplot
--

---
# Installing R Packages

## Method 1

![](inst.png)
---

# Installing R Packages

## Method 2

```r
install.packages("ggplot2")
```

---

## Load package

```r
library(ggplot2)
```

Now search `?qplot`

Note: You shouldn't have to re-install packages each time you open R. However, you do need to load the packages you want to use in that session via `library`.

---

# `install.packages` vs `library`

![](loadpkg.jpeg)

Image credit: [Professor Di Cook](http://dicook.org/)
---

## mozzie dataset

```r
library(mozzie)
data(mozzie)
```

---

## Data Visualization with R

```r
boxplot(mpg ~ cyl, data = mtcars,   
        xlab = "Quantity of Cylinders",  
        ylab = "Miles Per Gallon",   
        main = "Boxplot Example",  
        notch = TRUE,   
        varwidth = TRUE,   
        col = c("green","yellow","red"),  
        names = c("High","Medium","Low")  
)  
```

]

```r
counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution",
   xlab="Number of Gears")
```

]

---

Default R installation: graphics package

```
 [1] "abline"          "arrows"          "assocplot"       "axis"           
 [5] "Axis"            "axis.Date"       "axis.POSIXct"    "axTicks"        
 [9] "barplot"         "barplot.default" "box"             "boxplot"        
[13] "boxplot.default" "boxplot.matrix"  "bxp"             "cdplot"         
[17] "clip"            "close.screen"    "co.intervals"    "contour"        
[21] "contour.default" "coplot"          "curve"           "dotchart"       
[25] "erase.screen"    "filled.contour"  "fourfoldplot"    "frame"          
[29] "grconvertX"      "grconvertY"      "grid"            "hist"           
[33] "hist.default"    "identify"        "image"           "image.default"  
[37] "layout"          "layout.show"     "lcm"             "legend"         
[41] "lines"           "lines.default"   "locator"         "matlines"       
[45] "matplot"         "matpoints"       "mosaicplot"      "mtext"          
[49] "pairs"           "pairs.default"   "panel.smooth"    "par"            
[53] "persp"           "pie"             "plot"            "plot.default"   
[57] "plot.design"     "plot.function"   "plot.new"        "plot.window"    
[61] "plot.xy"         "points"          "points.default"  "polygon"        
[65] "polypath"        "rasterImage"     "rect"            "rug"            
[69] "screen"          "segments"        "smoothScatter"   "spineplot"      
[73] "split.screen"    "stars"           "stem"            "strheight"      
[77] "stripchart"      "strwidth"        "sunflowerplot"   "symbols"        
[81] "text"            "text.default"    "title"           "xinch"          
[85] "xspline"         "xyinch"          "yinch"          
```

---

![](qplot.png)
---

]

]

---

background-image: url('qplottheory.png')
background-position: center
background-size: contain

---

## mozzie

```r
head(mozzie)
```

```
# A tibble: 6 × 28
     ID  Year  Week Colombo Gampaha Kalutara Kandy Matale `Nuwara Eliya` Galle
  <int> <int> <int>   <int>   <int>    <int> <int>  <int>          <int> <int>
1     1  2008    52      15       7        1    11      4              0     0
2     2  2009     1      44      23        5    16     21              2     0
3     3  2009     2      39      19       11    42      9              1     2
4     4  2009     3      57      23       12    28      3              2     1
5     5  2009     4      53      24       19    32     20              2     2
6     6  2009     5      29      17       10    21      6              0     3
# … with 18 more variables: Hambantota <int>, Matara <int>, Jaffna <int>,
#   Kilinochchi <int>, Mannar <int>, Vavuniya <int>, Mulative <int>,
#   Batticalo <int>, Ampara <int>, Trincomalee <int>, Kurunagala <int>,
#   Puttalam <int>, Anuradhapura <int>, Polonnaruwa <int>, Badulla <int>,
#   Monaragala <int>, Ratnapura <int>, Kegalle <int>
```

---
## Data Visualization with `qplot`

## plot vs qplot

```r
plot(mozzie$Colombo, mozzie$Gampaha)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-46-1.png" width="100%" />
]

```r
qplot(Colombo, Gampaha, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-47-1.png" width="100%" />
]
---
## Data Visualization with `qplot`

```r
qplot(Colombo, Gampaha, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-48-1.png" width="100%" />
]

```r
qplot(Colombo, Gampaha, data=mozzie,
      colour=Year)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-49-1.png" width="100%" />
]
---
## Data Visualization with `qplot`

```r
qplot(Colombo, Gampaha, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-50-1.png" width="100%" />
]

```r
qplot(Colombo, Gampaha, data=mozzie, 
      size=Year)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-51-1.png" width="100%" />
]

---
## Data Visualization with `qplot`

```r
qplot(Colombo, Gampaha, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-52-1.png" width="100%" />
]

```r
qplot(Colombo, Gampaha, data=mozzie, 
      size=Year, alpha=0.5)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-53-1.png" width="100%" />
]

---

## Data Visualization with `qplot`

```r
qplot(Colombo, Gampaha, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-54-1.png" width="100%" />
]

```r
qplot(Colombo, Gampaha, data=mozzie,
      geom="point")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-55-1.png" width="100%" />
]

---
## Data Visualization with `qplot`

```r
qplot(ID, Gampaha, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-56-1.png" width="100%" />
]

```r
qplot(ID, Gampaha, data=mozzie, 
      geom="line")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-57-1.png" width="100%" />
]

---
## Data Visualization with `qplot`

```r
qplot(ID, Gampaha, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-58-1.png" width="100%" />
]

```r
qplot(ID, Gampaha, data=mozzie,
      geom="path")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-59-1.png" width="100%" />
]

---

## Data Visualization with `qplot`

```r
qplot(Colombo, Gampaha, data=mozzie,
      geom="line")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-60-1.png" width="100%" />
]

```r
qplot(Colombo, Gampaha, data=mozzie, 
      geom="path")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-61-1.png" width="100%" />
]

---
## Data Visualization with `qplot`

```r
qplot(Colombo, Gampaha, data=mozzie, 
      geom=c("line", "point"))
```

<img src="l32021msc_files/figure-html/unnamed-chunk-62-1.png" width="100%" />
]

```r
qplot(Colombo, Gampaha, data=mozzie,
      geom=c("path", "point"))
```

<img src="l32021msc_files/figure-html/unnamed-chunk-63-1.png" width="100%" />
]
---

## Data Visualization with `qplot`

```r
boxplot(Colombo~Year, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-64-1.png" width="100%" />
]

```r
qplot(factor(Year), Colombo, data=mozzie,
      geom="boxplot")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-65-1.png" width="100%" />
]

---

## Data Visualization with `qplot`

```r
qplot(factor(Year), Colombo, data=mozzie,
      geom="boxplot")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-66-1.png" width="100%" />
]

```r
qplot(factor(Year), Colombo, data=mozzie) # geom="point"-default
```

<img src="l32021msc_files/figure-html/unnamed-chunk-67-1.png" width="100%" />
]

---
## Data Visualization with `qplot`
.pull-left[

```r
qplot(factor(Year), Colombo, data=mozzie, 
      geom="point")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-68-1.png" width="100%" />
]

```r
qplot(factor(Year), Colombo, data=mozzie, 
      geom="jitter") # geom="point"-default
```

<img src="l32021msc_files/figure-html/unnamed-chunk-69-1.png" width="100%" />
]

---

## Data Visualization with `qplot`
.pull-left[

```r
qplot(factor(Year), Colombo, data=mozzie,
      geom="jitter")
```

<img src="l32021msc_files/figure-html/unnamed-chunk-70-1.png" width="100%" />
]

```r
qplot(factor(Year), Colombo, data=mozzie,
      geom=c("jitter", "boxplot")) # geom="point"-default
```

<img src="l32021msc_files/figure-html/unnamed-chunk-71-1.png" width="100%" />
]

---

]

```r
qplot(factor(Year), Colombo, data=mozzie,
      geom=c("jitter", "boxplot")) # geom="point"-default
```

<img src="l32021msc_files/figure-html/unnamed-chunk-74-1.png" width="100%" />
]

---

```r
qplot(factor(Year), Colombo, data=mozzie,
      geom=c("jitter", "boxplot")) # geom="point"-default
```

<img src="l32021msc_files/figure-html/unnamed-chunk-75-1.png" width="100%" />
]

```r
qplot(factor(Year), Colombo, data=mozzie,
      geom=c("jitter", "boxplot"),
      outlier.shape = NA) # geom="point"-default
```

<img src="l32021msc_files/figure-html/unnamed-chunk-76-1.png" width="100%" />
]

---

## Data Visualization with `qplot`
.pull-left[

```r
qplot(Colombo, data=mozzie)
```

<img src="l32021msc_files/figure-html/unnamed-chunk-77-1.png" width="100%" />
]

```r
qplot(Colombo, data=mozzie, geom="density") 
```

<img src="l32021msc_files/figure-html/unnamed-chunk-78-1.png" width="100%" />
]

---

# Your turn

---

Explore `iris` dataset with suitable graphics.

```r
head(iris)
```

```
  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
```

## Thank you!

Slides available at: hellor.netlify.app