5.3

{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) Enter this data set, giving it the name “tomatodata”: ```{r} tomatodata <- c( 6.3, 7.2, 6.8, 5.4, 6.5, 5.9, 6.6, 6.5, 7.1, 7.0)

Take the average:
```{r}
mean(tomatodata)

Take the median: ```{r} median(tomatodata)

Find the standard deviation:
```{r}
sd(tomatodata)

Find the largest number: ```{r} max(tomatodata)

Find the smallest number:
```{r}
min(tomatodata)

Find the 30th percentile: ```{r} quantile(tomatodata, 0.30)

Find the 5-number summary (min, 25th percentile, median, 75th percentile, max):
```{r}
quantile(tomatodata)

Find the interquartile range (75th percentile minus the 25th percentile) ```{r} IQR(tomatodata)

Make a histogram:
```{r}
hist(tomatodata)

Make a box plot (display the five number summary): ```{r} boxplot(tomatodata)

Make a horizontal box plot:
```{r}
boxplot(tomatodata, horizontal=TRUE)

Make the output say Tomato data is awesome! {r} print("Tomato data is awesome!") Make the output say The mean tomato size is 6.53 {r} print(paste("The mean tomato size is", mean(tomatodata), sep = "")) Install the tidyverse package in the console below markdown ```{r} install.packages(“tidyverse”)

Load the tidyverse library and diamonds data into the markdown
```{r}
library(tidyverse)

Load the mtcars data ```{r} data(mtcars)

Preview the first 6 rows
```{r}
head(mtcars)

Use tidyverse language to make a histogram of any numeric variable in mtcars: ```{r} ggplot(data = mtcars, aes(x = mpg)) + geom_histogram()

```