HW2

library(datasets)
library(knitr)
dat <- as.data.frame(Loblolly)
attach(dat)
head(dat)

   height age Seed
1    4.51   3  301
15  10.89   5  301
29  28.72  10  301
43  41.74  15  301
57  52.70  20  301
71  60.92  25  301

dat$Seed <- as.factor(dat$Seed)

Data Set

Using the Datasets package in R, the Loblolly dataset was loaded. The data contained in this dataset tracks the height and age of several Loblolly Pine trees.

Summary Tables

The following table shows the average height of all Loblolly pine trees across each age group.

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

dat2 <- dat %>%
  group_by(age) %>%
  summarize(Mean_Height = mean(height))

`summarise()` ungrouping output (override with `.groups` argument)

dat2

# A tibble: 6 x 2
    age Mean_Height
  <dbl>       <dbl>
1     3        4.24
2     5       10.2 
3    10       27.4 
4    15       40.5 
5    20       51.5 
6    25       60.3 

Creating a dynamic plot

In this plot, the goal is to show how the height of each Loblolly tree changes as the tree ages. Hovering over the data points provides the height and age of the given tree. The hovering ability allows the user to easily see the raw data behind the points and to easily distinguish between lines.

library(ggplot2)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

cols <- c("301" = "red", 
          "303"  = "blue",
          "305" = "darkgreen",
          "307" = "green",
          "309" = "orange",
          "311" = "black",
          "313" = "brown",
          "315" = "grey",
          "317" = "purple",
          "319" = "pink",
          "321" = "lightgreen",
          "323" = "cadetblue",
          "325" = "darkgoldenrod",
          "327" = "darkseagreen",
          "329" = "lavender",
          "331" = "lightsalmon"
  
)
p <- ggplot(data = dat, aes(x = age, y = height, color = Seed)) + geom_point() + geom_line() + 
  theme_bw() + ggtitle("Loblolly Pine Growth") + 
  xlab("Age (years)") + ylab("Height (feet)") + labs( color = "Seed") + scale_color_manual(values = cols)

p <- ggplotly(p, tooltip = c("Seed", "age", "height")) %>%
layout(legend = list(x=100, y=0, 
                     orientation = 'h')) %>%
  rangeslider(start = 0, end = 30)


p

As we can see in the chart, the Loblolly pine tree seeds were likely treated similarly as all of the trees had very similar growth profiles. The plot below shows a comparison of 25 year tree height. Seed 329 was the shortest at 56.43 feet while seed 305 was the tallest at 64.10 feet. I added the tooltip to easily see the raw data values for each tree.

q <- ggplot(data = dat[dat[,2]==25,], aes(x = as.character(Seed), y = height, fill = as.factor(Seed))) + geom_col() + 
  theme_bw() + ggtitle("Loblolly Pine Growth") + 
  xlab("Seed Number") + ylab("Height (feet)") + 
  theme(legend.position = 'none') + scale_fill_manual(values = cols)

q <- ggplotly(q, tooltip = "height") 


q