#1
thermo<-read.delim("clipboard")
summary(thermo)

t.test(temp~manufacturer, data=thermo)
# 	Welch Two Sample t-test
# 
# data:  temp by manufacturer
# t = 0.11962, df = 11.888, p-value = 0.9068
# alternative hypothesis: true difference in means between group celsimet and group termom is not equal to 0
# 95 percent confidence interval:
#  -1.72338  1.92338
# sample estimates:
# mean in group celsimet   mean in group termom 
#                   18.0                   17.9 

var.test(temp~manufacturer, data=thermo)
# F test to compare two variances
# 
# data:  temp by manufacturer
# F = 6.0674, num df = 9, denom df = 9, p-value = 0.01302
# alternative hypothesis: true ratio of variances is not equal to 1
# 95 percent confidence interval:
#   1.50706 24.42738
# sample estimates:
# ratio of variances 
#           6.067416 

#Computing variances of the two groups
tapply(thermo$temp, thermo$manufacturer, var)
#  celsimet    termom 
# 6.0000000 0.9888889

library(ggplot2)
ggplot(thermo, aes(x=manufacturer, y=temp))+geom_boxplot(fill="grey")+
  theme_classic()

# Dotchart with standard deviation is a btter option here
ggplot(thermo, aes(x=manufacturer, y=temp))+
  stat_summary(fun.data="mean_sdl")

# 2
plants<-read.delim("clipboard")
summary(plants)

aov.1<-aov(Biomass~Fertilizer, data=plants)
summary(aov.1)
#             Df Sum Sq Mean Sq F value  Pr(>F)    
# Fertilizer   4  26740    6685   45.96 3.6e-11 ***
# Residuals   25   3636     145                    
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

plot(aov.1)# ANOVA diagnostic plots

TukeyHSD(aov.1)
#                               diff        lwr        upr     p adj
# Mineral-Control           69.83333  49.383672  90.282994 0.0000000
# MinSlow-Control           86.00000  65.550339 106.449661 0.0000000
# NitrateAmm-Control        47.16667  26.717006  67.616328 0.0000040
# OrganicManure-Control     68.83333  48.383672  89.282994 0.0000000
# MinSlow-Mineral           16.16667  -4.282994  36.616328 0.1713159
# NitrateAmm-Mineral       -22.66667 -43.116328  -2.217006 0.0245596
# OrganicManure-Mineral     -1.00000 -21.449661  19.449661 0.9998971
# NitrateAmm-MinSlow       -38.83333 -59.282994 -18.383672 0.0000769
# OrganicManure-MinSlow    -17.16667 -37.616328   3.282994 0.1311420
# OrganicManure-NitrateAmm  21.66667   1.217006  42.116328 0.0340129


#Plotting
ggplot(plants, aes(x=Fertilizer, y=Biomass))+geom_boxplot(fill="grey")+
  theme_classic()

library(Hmisc)
ggplot(plants, aes(x=Fertilizer, y=Biomass))+
  stat_summary(fun.data="mean_cl_normal")