One-Way-ANOVA.R

data("PlantGrowth")
dim(PlantGrowth)

## [1] 30  2

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
## 7    5.17  ctrl
## 8    4.53  ctrl
## 9    5.33  ctrl
## 10   5.14  ctrl
## 11   4.81  trt1
## 12   4.17  trt1
## 13   4.41  trt1
## 14   3.59  trt1
## 15   5.87  trt1
## 16   3.83  trt1
## 17   6.03  trt1
## 18   4.89  trt1
## 19   4.32  trt1
## 20   4.69  trt1
## 21   6.31  trt2
## 22   5.12  trt2
## 23   5.54  trt2
## 24   5.50  trt2
## 25   5.37  trt2
## 26   5.29  trt2
## 27   4.92  trt2
## 28   6.15  trt2
## 29   5.80  trt2
## 30   5.26  trt2

class(PlantGrowth)

## [1] "data.frame"

oneway.test(weight ~ group, data = PlantGrowth, var.equal = T)

## 
##  One-way analysis of means
## 
## data:  weight and group
## F = 4.8461, num df = 2, denom df = 27, p-value = 0.01591

################
unique(PlantGrowth$group)

## [1] ctrl trt1 trt2
## Levels: ctrl trt1 trt2

t.test(PlantGrowth$weight[PlantGrowth$group == "ctrl"], PlantGrowth$weight[PlantGrowth$group == "trt1"])

## 
##  Welch Two Sample t-test
## 
## data:  PlantGrowth$weight[PlantGrowth$group == "ctrl"] and PlantGrowth$weight[PlantGrowth$group == "trt1"]
## t = 1.1913, df = 16.524, p-value = 0.2504
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.2875162  1.0295162
## sample estimates:
## mean of x mean of y 
##     5.032     4.661

data_1 <- aov(weight ~ group, data = PlantGrowth)
summary(data_1)

##             Df Sum Sq Mean Sq F value Pr(>F)  
## group        2  3.766  1.8832   4.846 0.0159 *
## Residuals   27 10.492  0.3886                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#################################___ttest == oneway.test(when var-equal = F)
data_2 <- PlantGrowth[PlantGrowth$group == "ctrl" | PlantGrowth$group == "trt1", ]
t.test(PlantGrowth$weight[PlantGrowth$group == "ctrl"], PlantGrowth$weight[PlantGrowth$group == "trt1"])

## 
##  Welch Two Sample t-test
## 
## data:  PlantGrowth$weight[PlantGrowth$group == "ctrl"] and PlantGrowth$weight[PlantGrowth$group == "trt1"]
## t = 1.1913, df = 16.524, p-value = 0.2504
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.2875162  1.0295162
## sample estimates:
## mean of x mean of y 
##     5.032     4.661

oneway.test(weight ~ group, data = data_2, var.equal = F)

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  weight and group
## F = 1.4191, num df = 1.000, denom df = 16.524, p-value = 0.2504