PlantGrowth

library(tidyverse)

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library(ggplot2)

#load in the data
{data(PlantGrowth)}

#this is a dataset of plant weight values, with a group factor
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

#there are 30 rows and 3 levels to the group factor. There is a control
#and two treatment groups
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 ...

#there are 10 values per group and roughly normal distribution of weights
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

hist(PlantGrowth$weight)

#quickly view differences between group distributions
plot(PlantGrowth$weight~PlantGrowth$group)

#create a data.frame object with mean and sd
averages <- PlantGrowth %>%
  group_by(group) %>%
  summarise(mean = mean(weight), sd = sd(weight))

#plot the basic bar graph with the means and standard deviation error bars
#here the highest average weight is in treatment 2 plants
ggplot(data = averages, aes(x = group, y = mean)) +
  geom_col() +
  geom_errorbar(ymin = averages$mean-averages$sd, ymax = averages$mean+averages$sd) +
  ylim(0, 6) +
  ylab("mean weight")