Groups two or more column charts exported from FAT2DIC, FAT2DBC, PSUBDIC or PSUBDBC function

bargraph_twofactor(
analysis,
labels = NULL,
ocult.facet = FALSE,
ocult.box = FALSE,
facet.size = 14,
ylab = NULL,
width.bar = 0.3,
sup = NULL
)

## Arguments

analysis

List with DIC, DBC or DQL object

labels

Vector with the name of the facets

ocult.facet

Hide facets

ocult.box

Hide box

facet.size

Font size facets

ylab

Y-axis name

width.bar

Width bar

sup

Number of units above the standard deviation or average bar on the graph

## Value

Returns a column chart grouped by facets

## Examples

library(AgroR)
data(corn)
a=with(corn, FAT2DIC(A, B, Resp, quali=c(TRUE, TRUE),ylab="Heigth (cm)"))
#>
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#>                          Method Statistic   p.value
#>  Shapiro-Wilk normality test(W) 0.9704679 0.6785543
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered normal
#>
#> -----------------------------------------------------------------
#> Homogeneity of Variances
#> -----------------------------------------------------------------
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared)  3.948702 0.5568251
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, the variances can be considered homogeneous
#>
#> -----------------------------------------------------------------
#> Independence from errors
#> -----------------------------------------------------------------
#>                  Method Statistic   p.value
#>  Durbin-Watson test(DW)  2.820109 0.8709071
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered independent
#>
#> -----------------------------------------------------------------
#> -----------------------------------------------------------------
#>
#> CV (%) =  0.49
#> Mean =  159.6208
#> Median =  162.55
#> Possible outliers =  No discrepant point
#>
#> -----------------------------------------------------------------
#> Analysis of Variance
#> -----------------------------------------------------------------
#>               Df   Sum Sq     Mean.Sq   F value        Pr(F)
#> Fator1         2 137.3058  68.6529167  110.0158 8.086134e-11
#> Fator2         1 654.1704 654.1704167 1048.3034 2.080297e-17
#> Fator1:Fator2  2 436.3508 218.1754167  349.6245 3.948414e-15
#> Residuals     18  11.2325   0.6240278
#>
#>
#> -----------------------------------------------------------------
#> Significant interaction: analyzing the interaction
#> -----------------------------------------------------------------
#>
#> -----------------------------------------------------------------
#> Analyzing  F1  inside of each level of  F2
#> -----------------------------------------------------------------
#>
#>                     Df Sum Sq Mean Sq  F value    Pr(>F)
#> Fator2               1 654.17  654.17 1048.303 < 2.2e-16 ***
#> Fator2:Fator1        4 573.66  143.41  229.820 3.492e-15 ***
#>   Fator2:Fator1: 55  2 521.74  260.87  418.046 8.202e-16 ***
#>   Fator2:Fator1: 65  2  51.91   25.96   41.594 1.784e-07 ***
#> Residuals           18  11.23    0.62
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> -----------------------------------------------------------------
#> Analyzing  F2  inside of the level of  F1
#> -----------------------------------------------------------------
#>
#>                     Df  Sum Sq Mean Sq F value    Pr(>F)
#> Fator1               2  137.31   68.65  110.02 8.086e-11 ***
#> Fator1:Fator2        3 1090.52  363.51  582.52 < 2.2e-16 ***
#>   Fator1:Fator2: A1  1  469.71  469.71  752.71 3.876e-16 ***
#>   Fator1:Fator2: A2  1    4.81    4.81    7.70   0.01249 *
#>   Fator1:Fator2: A3  1  616.00  616.00  987.14 < 2.2e-16 ***
#> Residuals           18   11.23    0.62
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> -----------------------------------------------------------------
#> Final table
#> -----------------------------------------------------------------
#>        55     65
#> A1 150 bB 165 bA
#> A2 164 aA 162 cB
#> A3 150 bB 167 aA
#>
#>
#> Averages followed by the same lowercase letter in the column and
#> uppercase in the row do not differ by the tukey (p< 0.05 )

b=with(corn, FAT2DIC(A, B, Resp, mcomp="sk", quali=c(TRUE, TRUE),ylab="Heigth (cm)"))
#>
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#>                          Method Statistic   p.value
#>  Shapiro-Wilk normality test(W) 0.9704679 0.6785543
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered normal
#>
#> -----------------------------------------------------------------
#> Homogeneity of Variances
#> -----------------------------------------------------------------
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared)  3.948702 0.5568251
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, the variances can be considered homogeneous
#>
#> -----------------------------------------------------------------
#> Independence from errors
#> -----------------------------------------------------------------
#>                  Method Statistic   p.value
#>  Durbin-Watson test(DW)  2.820109 0.8709071
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered independent
#>
#> -----------------------------------------------------------------
#> -----------------------------------------------------------------
#>
#> CV (%) =  0.49
#> Mean =  159.6208
#> Median =  162.55
#> Possible outliers =  No discrepant point
#>
#> -----------------------------------------------------------------
#> Analysis of Variance
#> -----------------------------------------------------------------
#>               Df   Sum Sq     Mean.Sq   F value        Pr(F)
#> Fator1         2 137.3058  68.6529167  110.0158 8.086134e-11
#> Fator2         1 654.1704 654.1704167 1048.3034 2.080297e-17
#> Fator1:Fator2  2 436.3508 218.1754167  349.6245 3.948414e-15
#> Residuals     18  11.2325   0.6240278
#>
#>
#> -----------------------------------------------------------------
#> Significant interaction: analyzing the interaction
#> -----------------------------------------------------------------
#>
#> -----------------------------------------------------------------
#> Analyzing  F1  inside of each level of  F2
#> -----------------------------------------------------------------
#>
#>                     Df Sum Sq Mean Sq  F value    Pr(>F)
#> Fator2               1 654.17  654.17 1048.303 < 2.2e-16 ***
#> Fator2:Fator1        4 573.66  143.41  229.820 3.492e-15 ***
#>   Fator2:Fator1: 55  2 521.74  260.87  418.046 8.202e-16 ***
#>   Fator2:Fator1: 65  2  51.91   25.96   41.594 1.784e-07 ***
#> Residuals           18  11.23    0.62
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> -----------------------------------------------------------------
#> Analyzing  F2  inside of the level of  F1
#> -----------------------------------------------------------------
#>
#>                     Df  Sum Sq Mean Sq F value    Pr(>F)
#> Fator1               2  137.31   68.65  110.02 8.086e-11 ***
#> Fator1:Fator2        3 1090.52  363.51  582.52 < 2.2e-16 ***
#>   Fator1:Fator2: A1  1  469.71  469.71  752.71 3.876e-16 ***
#>   Fator1:Fator2: A2  1    4.81    4.81    7.70   0.01249 *
#>   Fator1:Fator2: A3  1  616.00  616.00  987.14 < 2.2e-16 ***
#> Residuals           18   11.23    0.62
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> -----------------------------------------------------------------
#> Final table
#> -----------------------------------------------------------------
#>        55     65
#> A1 150 bB 165 bA
#> A2 164 aA 162 cB
#> A3 150 bB 167 aA
#>
#>
#> Averages followed by the same lowercase letter in the column and
#> uppercase in the row do not differ by the sk (p< 0.05 )

bargraph_twofactor(analysis = list(a,b), labels = c("One","Two"),ocult.box = TRUE)