- Mtcars
- The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973â€“74 models)

Create a bar graph showing the mean MPG for different levels of the â€˜cylâ€™ variable.

`data11 <- mtcars[, 1:2] cyl_means <- data.frame(CYL_Number=c(), MPG_Mean=c()) for (cyln in sort(unique(data11[,2]))){ cyl_means <- rbind(cyl_means, data.frame(CYL_Number=cyln, MPG_Mean=mean(data11[which(data11[,2]==cyln), 1]))) } print(cyl_means)`

`## CYL_Number MPG_Mean ## 1 4 26.66364 ## 2 6 19.74286 ## 3 8 15.10000`

Now the Bar:

`barplot(cyl_means[,2], main="Mean of MPG for Number of Cylinders", xlab="Number or Cylinders", names.arg=c("4", "6", "8"))`

Create a histogram of the â€˜mpgâ€™ variable.

`hist(mtcars$mpg, main="MPG distribution", xlab="MPG")`

For these and all other graphs, create a meaningful title, and label and X and Y axes in a way that communicates that the variables are and what are the levels.

`Done`

- ToothGrowth
- contains the result from an experiment studying the effect of vitamin C on tooth growth in 60 Guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, (orange juice or ascorbic acid (a form of vitamin C and coded as VC).

Here is sample code for creating a bar plot of mean-hp by gears and vs in Mtcars dataset :

```
boxplot(hp~(gear*vs), data=mtcars, main="Horse power by gear",
xlab="gear/engine combinations", ylab="Horse power",
col="gray", border="black", las=1, ylim=c(0,500))
```

Use the same approach to create a graph plotting tooth-length as function of supplement and does in ToothGrow dataset.

```
boxplot(len~(supp*dose), data=ToothGrowth, main="Tooth Lenght by Supplement",
xlab="Supplement/Dose", ylab="Tooth Lenght",
col="pink", border="gray",
las=1, ylim=c(min(ToothGrowth[,1]),max(ToothGrowth[,1])))
```

- USArrests
- This data set contains statistics about violent crime rates by us state.

What are the means of the Murder and Assault variables?

`murder_mean <- mean(USArrests$Murder) assault_mean <- mean(USArrests$Assault) print(paste("Mean of Murder: ", murder_mean), quote = FALSE)`

`## [1] Mean of Murder: 7.788`

`print(paste("Mean of Assault: ", assault_mean), quote = FALSE)`

`## [1] Mean of Assault: 170.76`

What is the standard deviation of the Assault variable?

`sd_assault <- sd(USArrests$Assault) print(paste("Standard Deviation of Assault: ", assault_mean), quote = FALSE)`

`## [1] Standard Deviation of Assault: 170.76`

Create a scatter plot showing the relationship between UrbanPop and Murder. Give it a meaningful title and meaningful X and Y axis labels.

`plot(USArrests$Murder, USArrests$UrbanPop, main="Urban population and Murder Arrest Rate", xlab="Murder Arrests, per 100,000", ylab="Urban Population %")`

Create a box and whiskers plot of the Rape variable.

`OutVals = boxplot(USArrests$Rape, ylab="Rape Arrest Rate", main="Rape Arrest Rate per State")$out`

How many outliers are there? Use

`USArrests[order(USArrests$Rape), ]`

to identify the state(s) that are outliers.`# number of outliers: print(paste("Number of outliers: ", length(OutVals)), quote = FALSE)`

`## [1] Number of outliers: 2`

`#Outlier States: out_st <- c() for (i in OutVals){ out_st <- c(out_st, row.names(USArrests)[which(USArrests$Rape == i)]) } print(out_st)`

`## [1] "Alaska" "Nevada"`

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