A pre-loaded example dataset in R

Main page: https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/npk

npk
##    block N P K yield
## 1      1 0 1 1  49.5
## 2      1 1 1 0  62.8
## 3      1 0 0 0  46.8
## 4      1 1 0 1  57.0
## 5      2 1 0 0  59.8
## 6      2 1 1 1  58.5
## 7      2 0 0 1  55.5
## 8      2 0 1 0  56.0
## 9      3 0 1 0  62.8
## 10     3 1 1 1  55.8
## 11     3 1 0 0  69.5
## 12     3 0 0 1  55.0
## 13     4 1 0 0  62.0
## 14     4 1 1 1  48.8
## 15     4 0 0 1  45.5
## 16     4 0 1 0  44.2
## 17     5 1 1 0  52.0
## 18     5 0 0 0  51.5
## 19     5 1 0 1  49.8
## 20     5 0 1 1  48.8
## 21     6 1 0 1  57.2
## 22     6 1 1 0  59.0
## 23     6 0 1 1  53.2
## 24     6 0 0 0  56.0
options(contrasts = c("contr.sum", "contr.poly"))
npk.aov <- aov(yield ~ block + N*P*K, npk)
npk.aov
## Call:
##    aov(formula = yield ~ block + N * P * K, data = npk)
## 
## Terms:
##                    block        N        P        K      N:P      N:K      P:K
## Sum of Squares  343.2950 189.2817   8.4017  95.2017  21.2817  33.1350   0.4817
## Deg. of Freedom        5        1        1        1        1        1        1
##                 Residuals
## Sum of Squares   185.2867
## Deg. of Freedom        12
## 
## Residual standard error: 3.929447
## 1 out of 13 effects not estimable
## Estimated effects may be unbalanced
summary(npk.aov)
##             Df Sum Sq Mean Sq F value  Pr(>F)   
## block        5  343.3   68.66   4.447 0.01594 * 
## N            1  189.3  189.28  12.259 0.00437 **
## P            1    8.4    8.40   0.544 0.47490   
## K            1   95.2   95.20   6.166 0.02880 * 
## N:P          1   21.3   21.28   1.378 0.26317   
## N:K          1   33.1   33.14   2.146 0.16865   
## P:K          1    0.5    0.48   0.031 0.86275   
## Residuals   12  185.3   15.44                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
coef(npk.aov)
## (Intercept)      block1      block2      block3      block4      block5 
##  54.8750000  -0.8500000   2.5750000   5.9000000  -4.7500000  -4.3500000 
##          N1          P1          K1       N1:P1       N1:K1       P1:K1 
##  -2.8083333   0.5916667   1.9916667  -0.9416667  -1.1750000   0.1416667
options(contrasts = c("contr.treatment", "contr.poly"))
npk.aov1 <- aov(yield ~ block + N + K, data = npk)
summary.lm(npk.aov1)
## 
## Call:
## aov(formula = yield ~ block + N + K, data = npk)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4083 -2.1438  0.2042  2.3292  7.0750 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   53.208      2.276  23.381  8.5e-14 ***
## block2         3.425      2.787   1.229  0.23690    
## block3         6.750      2.787   2.422  0.02769 *  
## block4        -3.900      2.787  -1.399  0.18082    
## block5        -3.500      2.787  -1.256  0.22723    
## block6         2.325      2.787   0.834  0.41646    
## N1             5.617      1.609   3.490  0.00302 ** 
## K1            -3.983      1.609  -2.475  0.02487 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.942 on 16 degrees of freedom
## Multiple R-squared:  0.7163, Adjusted R-squared:  0.5922 
## F-statistic: 5.772 on 7 and 16 DF,  p-value: 0.001805
se.contrast(npk.aov1, list(N=="0", N=="1"), data = npk)
## [1] 1.609175
model.tables(npk.aov1, type = "means", se = TRUE)
## Tables of means
## Grand mean
##        
## 54.875 
## 
##  block 
## block
##     1     2     3     4     5     6 
## 54.03 57.45 60.77 50.12 50.52 56.35 
## 
##  N 
## N
##     0     1 
## 52.07 57.68 
## 
##  K 
## K
##     0     1 
## 56.87 52.88 
## 
## Standard errors for differences of means
##         block     N     K
##         2.787 1.609 1.609
## replic.     4    12    12