Group 3 (PlantGrowth)

Load the ‘PlantGrowth’ data set using this code:

{data(PlantGrowth)}

1. Describe and summarize your assigned data set.

library(dplyr)
library(ggplot2)
{data(PlantGrowth)}

head(PlantGrowth)
##   weight group
## 1   4.17  ctrl
## 2   5.58  ctrl
## 3   5.18  ctrl
## 4   6.11  ctrl
## 5   4.50  ctrl
## 6   4.61  ctrl
str(PlantGrowth)
## 'data.frame':    30 obs. of  2 variables:
##  $ weight: num  4.17 5.58 5.18 6.11 4.5 4.61 5.17 4.53 5.33 5.14 ...
##  $ group : Factor w/ 3 levels "ctrl","trt1",..: 1 1 1 1 1 1 1 1 1 1 ...
summary(PlantGrowth)
##      weight       group   
##  Min.   :3.590   ctrl:10  
##  1st Qu.:4.550   trt1:10  
##  Median :5.155   trt2:10  
##  Mean   :5.073            
##  3rd Qu.:5.530            
##  Max.   :6.310
PlantGrowth %>% ggplot() +
                # Set aesthetics (x, y, fill)
                geom_histogram(aes(x = weight,
                                   fill = group),
                               alpha = 0.5, bins=7) +
                # Split the graph by species
                facet_grid(rows = vars(group)) +
                # Format axis labels
                labs(x = "Weight (g)", y = "Count") +
                # Add colours manually
                scale_fill_manual(values = c("darkorange",
                                             "purple",
                                             "cyan4")) +
                # Set plot theme
                theme_minimal() +
                # Remove legend
                theme(legend.position = "none")

2. Do different treatments affect plant weight? Graph your data.

ggplot(PlantGrowth, aes(x = group, y = weight)) +
  geom_boxplot(aes(x = group)) +
  labs(x = "Treatment group", y = "Weight (g)") +
  ggtitle("Plant weight by treatment") +
  theme_minimal()

3. Which treatment has the highest average growth?

# Add mean and sd 
plantgrowth_summary <- 
  PlantGrowth %>% 
     group_by(group) %>%
     summarise(count = n(),
               mean = mean(weight, na.rm = TRUE),
               ssd = sd(weight, na.rm = TRUE)) %>%
     mutate(se = ssd / sqrt(count),
            lower_ci = mean - qt(1 - (0.05 / 2), count - 1) * se,
            upper_ci = mean + qt(1 - (0.05 / 2), count - 1) * se)

# Nice table output
knitr::kable(plantgrowth_summary)
group count mean ssd se lower_ci upper_ci
ctrl 10 5.032 0.5830914 0.1843897 4.614882 5.449118
trt1 10 4.661 0.7936757 0.2509823 4.093239 5.228761
trt2 10 5.526 0.4425733 0.1399540 5.209402 5.842598 
# Plot
plantgrowth_summary %>% ggplot() +
  geom_point(aes(x = group, y = mean,
                 colour = group),
             size = 3) +
  geom_errorbar(aes(x = group,
                    ymin = lower_ci,
                    ymax = upper_ci,
                    colour = group),
                width = 0.2, size = 1) +
  labs(x = "Treatment Type", y = "Weight (g)",) +
  scale_colour_manual(values = c("darkorange","purple","cyan4")) + 
  ggtitle("Weight of plants by treatment type")

                theme_minimal() +
                theme(legend.position = "none")
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From the final plot, tr2 seems to give the highest average growth because the mean point of tr2 is higher than the rest of the treatments.