Initial try, playing with the original dataset and the ggplot2 command `quickplot`:

```{r, message=FALSE, echo=FALSE}
demo <- read.csv("Donation-Disease.csv", stringsAsFactor=F)
library(ggplot2)
demo$Death <- demo$Death/1000
qplot(demo$Death, demo$MoneyRaised,
      xlab = "Number of Deaths ('000s)",
      ylab = "Amount of Money Raised",
      main = "Where should we be donating our money?")
```

For my next attempt I will be using the dataset `InsectSprays`. 

**Description of Dataset**

The counts of insects in agricultural experimental units treated with different insecticides


Tabulating the data:
```{r, message=FALSE, echo=FALSE}
IS <- InsectSprays
groups <- aggregate(IS$count ~ IS$spray, FUN = "mean")
groups
```

### Plot 1 ###
My first plot with this data doesn't differentiate amongst the different type of sprays:
```{r, message=FALSE, echo=FALSE}
require("RColorBrewer")
color <- brewer.pal(11, "RdYlGn")
ggplot(IS, aes(x=count)) + geom_histogram(binwidth = 3, fill = color) + xlab("Number of bugs killed")
```

### Plot 2 ###
My second plot is a dotplot. Here the dots do discriminate amongst the 6 different types of sprays
```{r, message=FALSE, echo=FALSE}
ggplot(data = IS, aes(x = count, fill = spray)) + geom_dotplot(binwidth = 1, dotsize = 0.9, stackdir = "centerwhole") + xlab("No. of bugs killed") + scale_y_continuous(name="Spray Count", breaks = NULL) + ggtitle("Sprays vs Bugs")
```

### Plot 3 ###
This plot is a variant of Plot 2's data, in boxplot format

```{r, message=FALSE, echo=FALSE}
ggplot(data = IS) + geom_boxplot(aes(y = count, x = spray), fill = brewer.pal(6, "Blues")) + xlab("Spray type") + ylab("No. of bugs killed") + ggtitle("Bug sprays: effectiveness vs consistency")
```