One way analysis of variance

d<- read.csv("anova.csv")

summary(d)

##    treatment      values      
##  Control:10   Min.   : 6.868  
##  Treat1 :10   1st Qu.:11.357  
##  Treat2 :10   Median :15.377  
##               Mean   :14.258  
##               3rd Qu.:16.961  
##               Max.   :21.518

tapply(d$value,d$treatment,summary)

## $Control
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   6.868   9.072   9.983  11.002  11.979  17.205 
## 
## $Treat1
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.585  12.366  15.513  15.109  16.731  21.518 
## 
## $Treat2
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.43   15.39   16.80   16.66   17.71   20.53

plot(d)

mod<-lm(values~treatment,data=d)
anova (mod)

## Analysis of Variance Table
## 
## Response: values
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## treatment  2 171.12  85.559  8.6123 0.001279 **
## Residuals 27 268.23   9.935                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod)

## 
## Call:
## lm(formula = values ~ treatment, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5240 -1.9359  0.1353  1.2907  6.4087 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      11.0022     0.9967  11.038 1.64e-11 ***
## treatmentTreat1   4.1070     1.4096   2.914 0.007093 ** 
## treatmentTreat2   5.6614     1.4096   4.016 0.000424 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.152 on 27 degrees of freedom
## Multiple R-squared:  0.3895, Adjusted R-squared:  0.3443 
## F-statistic: 8.612 on 2 and 27 DF,  p-value: 0.001279

TukeyHSD(aov(d$values~d$treatment))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = d$values ~ d$treatment)
## 
## $`d$treatment`
##                    diff        lwr      upr     p adj
## Treat1-Control 4.106977  0.6120417 7.601913 0.0188187
## Treat2-Control 5.661443  2.1665079 9.156379 0.0011951
## Treat2-Treat1  1.554466 -1.9404693 5.049402 0.5206267

g1<-ggplot(d,aes(x=treatment,y=values)) +stat_summary(fun.y=mean,geom="bar",fill="lightgrey",col="black",width=0.7) +stat_summary(fun.data=mean_cl_normal,geom="errorbar",width=0.3)
g1

ylab="My text for the ylabel. Give name of variable and units"
xlab="My text for the xlabel. Give name of variable and units"

g1 + ylab(ylab) + xlab(xlab)

g1<-ggplot(d,aes(x=treatment,y=values)) + stat_summary(fun.y=mean,geom="point")  +stat_summary(fun.data=mean_cl_normal,geom="errorbar")

g1

ylab="My text for the ylabel. Give name of variable and units"
xlab="My text for the xlabel. Give name of variable and units"

g1 + ylab(ylab) + xlab(xlab)