---

This is my experimental data.

install.packages("readr")
library (readr)
github="https://raw.githubusercontent.com/agronomy4future/r_code/main/corn_grain_yield.csv"
dataA=data.frame(read_csv(url(github),show_col_types = FALSE))

    variety reps nitrogen grain_yield
1       CV1    1       N1       16.59
2       CV1    2       N1       16.59
3       CV1    3       N1       15.91
4       CV1    4       N1       17.57
5       CV1    5       N1       16.40
6       CV1    6       N1       16.20
7       CV1    7       N1       18.45
8       CV1    8       N1       14.35
9       CV1    9       N1       13.27
10      CV1   10       N1       16.15
.
.
.

There are 10 corn varieties, and I want to analyze the effect of nitrogen treatments (N₀, N₁) on grain yield for each variety. This is One-Way ANOVA analysis. Let’s assume that there are no blocks for the replicates. Therefore, the statistical model will be a One-Way ANOVA with no blocks.

ONE_WAY_ANOVA=aov(grain_yield~nitrogen, data=dataA)
summary(ONE_WAY_ANOVA)

             Df    Sum Sq   Mean Sq   F value   Pr(>F)    
nitrogen     1     41.9     41.93     19.02     2.08e-05 ***
Residuals    198   436.4    2.20                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

If we run the above analysis, we can observe the overall effect of nitrogen treatments on grain yield across all varieties, as they are pooled together. However, as I mentioned earlier, what I’m interested in is the effect of nitrogen treatments (N₀, N₁) on grain yield for each individual variety.

In this case, we should divide each varieties in the data like following codes;

ONE_WAY_ANOVA=aov(grain_yield~nitrogen, data=subset(dataA, variety=="CV1"))
ONE_WAY_ANOVA=aov(grain_yield~nitrogen, data=subset(dataA, variety=="CV2"))
ONE_WAY_ANOVA=aov(grain_yield~nitrogen, data=subset(dataA, variety=="CV3"))
.
.
.

However, if we had 100 varieties, analyzing the nitrogen effect for each variety using the subset() function one by one would be impractical. In this case, we need to use a specific code to analyze the nitrogen effect for each variety simultaneously. Today, I will explain how to do this.

---

---

lapply(split(dataA, dataA$variety), function(df) {
  ANOVA=aov(grain_yield~nitrogen, data=df)
  summary(ANOVA)
})

$CV1
            Df Sum Sq Mean Sq F value   Pr(>F)    
nitrogen     1  111.4  111.44   79.28 5.17e-08 ***
Residuals   18   25.3    1.41                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$CV10
            Df Sum Sq Mean Sq F value Pr(>F)  
nitrogen     1  4.334   4.334   4.755 0.0427 *
Residuals   18 16.404   0.911                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$CV2
            Df Sum Sq Mean Sq F value Pr(>F)  
nitrogen     1  2.783  2.7826     3.3  0.086 .
Residuals   18 15.177  0.8431                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$CV3
            Df Sum Sq Mean Sq F value   Pr(>F)    
nitrogen     1  22.22  22.218   39.54 6.28e-06 ***
Residuals   18  10.12   0.562                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$CV4
            Df Sum Sq Mean Sq F value Pr(>F)
nitrogen     1  0.182  0.1824   0.329  0.574
Residuals   18  9.992  0.5551               

$CV5
            Df Sum Sq Mean Sq F value   Pr(>F)    
nitrogen     1  34.72   34.72   49.53 1.45e-06 ***
Residuals   18  12.62    0.70                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$CV6
            Df Sum Sq Mean Sq F value Pr(>F)  
nitrogen     1  2.701   2.701   3.573 0.0749 .
Residuals   18 13.608   0.756                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$CV7
            Df Sum Sq Mean Sq F value Pr(>F)
nitrogen     1  0.173  0.1730   0.283  0.601
Residuals   18 10.992  0.6107               

$CV8
            Df Sum Sq Mean Sq F value Pr(>F)  
nitrogen     1  12.17  12.168   7.709 0.0124 *
Residuals   18  28.41   1.578                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$CV9
            Df Sum Sq Mean Sq F value Pr(>F)  
nitrogen     1  1.625  1.6245   3.199 0.0905 .
Residuals   18  9.142  0.5079                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Source: https://stackoverflow.com/questions/76159449/in-r-how-to-automatically-analyze-statistical-test-i-e-anova-per-treatment

---

---