AnĂ¡lise AUDPC - ANOVA e TukeyHSD
library(tidyverse)
library(dplyr)
library(agricolae)df = read.csv("Dataset.csv", stringsAsFactors = FALSE)
#df = read.csv("Database-Labim.csv")
############# SIMPLE CODE
## Separa em 12 dataframes por tipo de tratamento
T1 <- filter(df, str_detect(TRATAMENTO, "T1R"))
T2 <- filter(df, str_detect(TRATAMENTO, "T2R"))
T3 <- filter(df, str_detect(TRATAMENTO, "T3R"))
T4 <- filter(df, str_detect(TRATAMENTO, "T4R"))
T5 <- filter(df, str_detect(TRATAMENTO, "T5R"))
T6 <- filter(df, str_detect(TRATAMENTO, "T6R"))
T7 <- filter(df, str_detect(TRATAMENTO, "T7R"))
T8 <- filter(df, str_detect(TRATAMENTO, "T8R"))
T9 <- filter(df, str_detect(TRATAMENTO, "T9R"))
T10 <- filter(df, str_detect(TRATAMENTO, "T10R"))
T11 <- filter(df, str_detect(TRATAMENTO, "T11R"))
T12 <- filter(df, str_detect(TRATAMENTO, "T12R"))## Separa em outros 12 dataframes com o cĂ¡lculo da AUDPC considerando
## as 4 repetições para cada doença
T1_AUDPC <- T1 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T2_AUDPC <- T2 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T3_AUDPC <- T3 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T4_AUDPC <- T4 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T5_AUDPC <- T5 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T6_AUDPC <- T6 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T7_AUDPC <- T7 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T8_AUDPC <- T8 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T9_AUDPC <- T9 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T10_AUDPC <- T10 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T11_AUDPC <- T11 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
T12_AUDPC <- T12 %>%
group_by(TRATAMENTO) %>%
summarise(TRATAMENTO = unique(TRATAMENTO),
DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
################################################################################################RENAME TREATMENT #######################
#Diseases
T1$TRATAMENTO = 'T1'
T2$TRATAMENTO = 'T2'
T3$TRATAMENTO = 'T3'
T4$TRATAMENTO = 'T4'
T5$TRATAMENTO = 'T5'
T6$TRATAMENTO = 'T6'
T7$TRATAMENTO = 'T7'
T8$TRATAMENTO = 'T8'
T9$TRATAMENTO = 'T9'
T10$TRATAMENTO = 'T10'
T11$TRATAMENTO = 'T11'
T12$TRATAMENTO = 'T12'
#AUDPC
T1_AUDPC$TRATAMENTO = 'T1'
T2_AUDPC$TRATAMENTO = 'T2'
T3_AUDPC$TRATAMENTO = 'T3'
T4_AUDPC$TRATAMENTO = 'T4'
T5_AUDPC$TRATAMENTO = 'T5'
T6_AUDPC$TRATAMENTO = 'T6'
T7_AUDPC$TRATAMENTO = 'T7'
T8_AUDPC$TRATAMENTO = 'T8'
T9_AUDPC$TRATAMENTO = 'T9'
T10_AUDPC$TRATAMENTO = 'T10'
T11_AUDPC$TRATAMENTO = 'T11'
T12_AUDPC$TRATAMENTO = 'T12'
####################################################
########## Create new dataframes in easy style for statistical analysis
df1 <- bind_rows(T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12)
df1_audpc <- bind_rows(T1_AUDPC,T2_AUDPC,T3_AUDPC,T4_AUDPC,T5_AUDPC,T6_AUDPC,T7_AUDPC,T8_AUDPC,T9_AUDPC,T10_AUDPC,T11_AUDPC,T12_AUDPC)
##########################################################################################################################
#### AUDPC for each disease and all 12 treatments
#### Note: Each treatment has 4 replicates
print(df1_audpc)### DIPLOCARPON - incidĂªncia
df1_audpc %>%
ggplot(aes(TRATAMENTO, DIPLOCARPON.In)) +
geom_jitter(size=3, width = .1)+
geom_boxplot(size=1)
#################### ANOVA AUDPC #######################
ANOVA_audpc<-aov(DIPLOCARPON.In~TRATAMENTO,df1_audpc)
res <- ANOVA_audpc$residuals
shapiro.test(res)
Shapiro-Wilk normality test
data: res
W = 0.98382, p-value = 0.7418 summary(ANOVA_audpc) Df Sum Sq Mean Sq F value Pr(>F)
TRATAMENTO 11 8625 784.1 1.25 0.292
Residuals 36 22584 627.3 TukeyHSD(ANOVA_audpc) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = DIPLOCARPON.In ~ TRATAMENTO, data = df1_audpc)
$TRATAMENTO
diff lwr upr p adj
T10-T1 2.375 -59.44013 64.19013 1.0000000
T11-T1 -17.625 -79.44013 44.19013 0.9968689
T12-T1 -23.875 -85.69013 37.94013 0.9661082
T2-T1 14.000 -47.81513 75.81513 0.9996075
T3-T1 8.375 -53.44013 70.19013 0.9999977
T4-T1 -11.375 -73.19013 50.44013 0.9999473
T5-T1 -26.625 -88.44013 35.19013 0.9300292
T6-T1 -11.250 -73.06513 50.56513 0.9999528
T7-T1 -20.375 -82.19013 41.44013 0.9896304
T8-T1 -15.125 -76.94013 46.69013 0.9991956
T9-T1 -28.250 -90.06513 33.56513 0.8997424
T11-T10 -20.000 -81.81513 41.81513 0.9910551
T12-T10 -26.250 -88.06513 35.56513 0.9360388
T2-T10 11.625 -50.19013 73.44013 0.9999347
T3-T10 6.000 -55.81513 67.81513 0.9999999
T4-T10 -13.750 -75.56513 48.06513 0.9996688
T5-T10 -29.000 -90.81513 32.81513 0.8834093
T6-T10 -13.625 -75.44013 48.19013 0.9996963
T7-T10 -22.750 -84.56513 39.06513 0.9760023
T8-T10 -17.500 -79.31513 44.31513 0.9970548
T9-T10 -30.625 -92.44013 31.19013 0.8430194
T12-T11 -6.250 -68.06513 55.56513 0.9999999
T2-T11 31.625 -30.19013 93.44013 0.8149427
T3-T11 26.000 -35.81513 87.81513 0.9398459
T4-T11 6.250 -55.56513 68.06513 0.9999999
T5-T11 -9.000 -70.81513 52.81513 0.9999951
T6-T11 6.375 -55.44013 68.19013 0.9999999
T7-T11 -2.750 -64.56513 59.06513 1.0000000
T8-T11 2.500 -59.31513 64.31513 1.0000000
T9-T11 -10.625 -72.44013 51.19013 0.9999733
T2-T12 37.875 -23.94013 99.69013 0.6005446
T3-T12 32.250 -29.56513 94.06513 0.7962498
T4-T12 12.500 -49.31513 74.31513 0.9998671
T5-T12 -2.750 -64.56513 59.06513 1.0000000
T6-T12 12.625 -49.19013 74.44013 0.9998537
T7-T12 3.500 -58.31513 65.31513 1.0000000
T8-T12 8.750 -53.06513 70.56513 0.9999963
T9-T12 -4.375 -66.19013 57.44013 1.0000000
T3-T2 -5.625 -67.44013 56.19013 1.0000000
T4-T2 -25.375 -87.19013 36.44013 0.9486807
T5-T2 -40.625 -102.44013 21.19013 0.4985829
T6-T2 -25.250 -87.06513 36.56513 0.9503329
T7-T2 -34.375 -96.19013 27.44013 0.7270397
T8-T2 -29.125 -90.94013 32.69013 0.8805431
T9-T2 -42.250 -104.06513 19.56513 0.4403042
T4-T3 -19.750 -81.56513 42.06513 0.9919150
T5-T3 -35.000 -96.81513 26.81513 0.7053119
T6-T3 -19.625 -81.44013 42.19013 0.9923194
T7-T3 -28.750 -90.56513 33.06513 0.8890186
T8-T3 -23.500 -85.31513 38.31513 0.9696890
T9-T3 -36.625 -98.44013 25.19013 0.6467976
T5-T4 -15.250 -77.06513 46.56513 0.9991328
T6-T4 0.125 -61.69013 61.94013 1.0000000
T7-T4 -9.000 -70.81513 52.81513 0.9999951
T8-T4 -3.750 -65.56513 58.06513 1.0000000
T9-T4 -16.875 -78.69013 44.94013 0.9978529
T6-T5 15.375 -46.44013 77.19013 0.9990657
T7-T5 6.250 -55.56513 68.06513 0.9999999
T8-T5 11.500 -50.31513 73.31513 0.9999413
T9-T5 -1.625 -63.44013 60.19013 1.0000000
T7-T6 -9.125 -70.94013 52.69013 0.9999943
T8-T6 -3.875 -65.69013 57.94013 1.0000000
T9-T6 -17.000 -78.81513 44.81513 0.9977097
T8-T7 5.250 -56.56513 67.06513 1.0000000
T9-T7 -7.875 -69.69013 53.94013 0.9999988
T9-T8 -13.125 -74.94013 48.69013 0.9997873##################################################
### DIPLOCARPON - severidade
df1_audpc %>%
ggplot(aes(TRATAMENTO, DIPLOCARPON.Sev)) +
geom_jitter(size=3, width = .1)+
geom_boxplot(size=1)
#################### ANOVA AUDPC #######################
ANOVA_audpc<-aov(DIPLOCARPON.Sev~TRATAMENTO,df1_audpc)
res <- ANOVA_audpc$residuals
shapiro.test(res)
Shapiro-Wilk normality test
data: res
W = 0.97529, p-value = 0.4008 summary(ANOVA_audpc) Df Sum Sq Mean Sq F value Pr(>F)
TRATAMENTO 11 111.8 10.163 1.138 0.362
Residuals 36 321.4 8.927 TukeyHSD(ANOVA_audpc) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = DIPLOCARPON.Sev ~ TRATAMENTO, data = df1_audpc)
$TRATAMENTO
diff lwr upr p adj
T10-T1 1.875 -5.499036 9.249036 0.9988590
T11-T1 0.250 -7.124036 7.624036 1.0000000
T12-T1 0.750 -6.624036 8.124036 0.9999999
T2-T1 2.625 -4.749036 9.999036 0.9812407
T3-T1 2.250 -5.124036 9.624036 0.9944516
T4-T1 -0.125 -7.499036 7.249036 1.0000000
T5-T1 -0.375 -7.749036 6.999036 1.0000000
T6-T1 -1.375 -8.749036 5.999036 0.9999400
T7-T1 -2.000 -9.374036 5.374036 0.9979720
T8-T1 -0.500 -7.874036 6.874036 1.0000000
T9-T1 -2.375 -9.749036 4.999036 0.9913756
T11-T10 -1.625 -8.999036 5.749036 0.9996969
T12-T10 -1.125 -8.499036 6.249036 0.9999920
T2-T10 0.750 -6.624036 8.124036 0.9999999
T3-T10 0.375 -6.999036 7.749036 1.0000000
T4-T10 -2.000 -9.374036 5.374036 0.9979720
T5-T10 -2.250 -9.624036 5.124036 0.9944516
T6-T10 -3.250 -10.624036 4.124036 0.9193098
T7-T10 -3.875 -11.249036 3.499036 0.7890582
T8-T10 -2.375 -9.749036 4.999036 0.9913756
T9-T10 -4.250 -11.624036 3.124036 0.6830366
T12-T11 0.500 -6.874036 7.874036 1.0000000
T2-T11 2.375 -4.999036 9.749036 0.9913756
T3-T11 2.000 -5.374036 9.374036 0.9979720
T4-T11 -0.375 -7.749036 6.999036 1.0000000
T5-T11 -0.625 -7.999036 6.749036 1.0000000
T6-T11 -1.625 -8.999036 5.749036 0.9996969
T7-T11 -2.250 -9.624036 5.124036 0.9944516
T8-T11 -0.750 -8.124036 6.624036 0.9999999
T9-T11 -2.625 -9.999036 4.749036 0.9812407
T2-T12 1.875 -5.499036 9.249036 0.9988590
T3-T12 1.500 -5.874036 8.874036 0.9998593
T4-T12 -0.875 -8.249036 6.499036 0.9999994
T5-T12 -1.125 -8.499036 6.249036 0.9999920
T6-T12 -2.125 -9.499036 5.249036 0.9965703
T7-T12 -2.750 -10.124036 4.624036 0.9735856
T8-T12 -1.250 -8.624036 6.124036 0.9999768
T9-T12 -3.125 -10.499036 4.249036 0.9368702
T3-T2 -0.375 -7.749036 6.999036 1.0000000
T4-T2 -2.750 -10.124036 4.624036 0.9735856
T5-T2 -3.000 -10.374036 4.374036 0.9516477
T6-T2 -4.000 -11.374036 3.374036 0.7554677
T7-T2 -4.625 -11.999036 2.749036 0.5671735
T8-T2 -3.125 -10.499036 4.249036 0.9368702
T9-T2 -5.000 -12.374036 2.374036 0.4521513
T4-T3 -2.375 -9.749036 4.999036 0.9913756
T5-T3 -2.625 -9.999036 4.749036 0.9812407
T6-T3 -3.625 -10.999036 3.749036 0.8493328
T7-T3 -4.250 -11.624036 3.124036 0.6830366
T8-T3 -2.750 -10.124036 4.624036 0.9735856
T9-T3 -4.625 -11.999036 2.749036 0.5671735
T5-T4 -0.250 -7.624036 7.124036 1.0000000
T6-T4 -1.250 -8.624036 6.124036 0.9999768
T7-T4 -1.875 -9.249036 5.499036 0.9988590
T8-T4 -0.375 -7.749036 6.999036 1.0000000
T9-T4 -2.250 -9.624036 5.124036 0.9944516
T6-T5 -1.000 -8.374036 6.374036 0.9999976
T7-T5 -1.625 -8.999036 5.749036 0.9996969
T8-T5 -0.125 -7.499036 7.249036 1.0000000
T9-T5 -2.000 -9.374036 5.374036 0.9979720
T7-T6 -0.625 -7.999036 6.749036 1.0000000
T8-T6 0.875 -6.499036 8.249036 0.9999994
T9-T6 -1.000 -8.374036 6.374036 0.9999976
T8-T7 1.500 -5.874036 8.874036 0.9998593
T9-T7 -0.375 -7.749036 6.999036 1.0000000
T9-T8 -1.875 -9.249036 5.499036 0.9988590##################################################
### MICOSFAERELA - incidĂªncia
df1_audpc %>%
ggplot(aes(TRATAMENTO, MICOSFAERELA.In)) +
geom_jitter(size=3, width = .1)+
geom_boxplot(size=1)
#################### ANOVA AUDPC #######################
ANOVA_audpc<-aov(MICOSFAERELA.In~TRATAMENTO,df1_audpc)
res <- ANOVA_audpc$residuals
shapiro.test(res)
Shapiro-Wilk normality test
data: res
W = 0.9757, p-value = 0.4145 summary(ANOVA_audpc) Df Sum Sq Mean Sq F value Pr(>F)
TRATAMENTO 11 419.3 38.12 1.304 0.262
Residuals 36 1051.9 29.22 TukeyHSD(ANOVA_audpc) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = MICOSFAERELA.In ~ TRATAMENTO, data = df1_audpc)
$TRATAMENTO
diff lwr upr p adj
T10-T1 -3.000 -16.341177 10.341177 0.9996331
T11-T1 4.000 -9.341177 17.341177 0.9952050
T12-T1 1.750 -11.591177 15.091177 0.9999983
T2-T1 1.875 -11.466177 15.216177 0.9999966
T3-T1 3.125 -10.216177 16.466177 0.9994623
T4-T1 -4.125 -17.466177 9.216177 0.9938104
T5-T1 -4.375 -17.716177 8.966177 0.9900417
T6-T1 -2.875 -16.216177 10.466177 0.9997549
T7-T1 -3.625 -16.966177 9.716177 0.9979394
T8-T1 -3.250 -16.591177 10.091177 0.9992275
T9-T1 -3.375 -16.716177 9.966177 0.9989104
T11-T10 7.000 -6.341177 20.341177 0.7905935
T12-T10 4.750 -8.591177 18.091177 0.9812163
T2-T10 4.875 -8.466177 18.216177 0.9772262
T3-T10 6.125 -7.216177 19.466177 0.8970257
T4-T10 -1.125 -14.466177 12.216177 1.0000000
T5-T10 -1.375 -14.716177 11.966177 0.9999999
T6-T10 0.125 -13.216177 13.466177 1.0000000
T7-T10 -0.625 -13.966177 12.716177 1.0000000
T8-T10 -0.250 -13.591177 13.091177 1.0000000
T9-T10 -0.375 -13.716177 12.966177 1.0000000
T12-T11 -2.250 -15.591177 11.091177 0.9999780
T2-T11 -2.125 -15.466177 11.216177 0.9999877
T3-T11 -0.875 -14.216177 12.466177 1.0000000
T4-T11 -8.125 -21.466177 5.216177 0.6090455
T5-T11 -8.375 -21.716177 4.966177 0.5658950
T6-T11 -6.875 -20.216177 6.466177 0.8081712
T7-T11 -7.625 -20.966177 5.716177 0.6936505
T8-T11 -7.250 -20.591177 6.091177 0.7534517
T9-T11 -7.375 -20.716177 5.966177 0.7340008
T2-T12 0.125 -13.216177 13.466177 1.0000000
T3-T12 1.375 -11.966177 14.716177 0.9999999
T4-T12 -5.875 -19.216177 7.466177 0.9197233
T5-T12 -6.125 -19.466177 7.216177 0.8970257
T6-T12 -4.625 -17.966177 8.716177 0.9846482
T7-T12 -5.375 -18.716177 7.966177 0.9547042
T8-T12 -5.000 -18.341177 8.341177 0.9726273
T9-T12 -5.125 -18.466177 8.216177 0.9673710
T3-T2 1.250 -12.091177 14.591177 1.0000000
T4-T2 -6.000 -19.341177 7.341177 0.9088187
T5-T2 -6.250 -19.591177 7.091177 0.8843448
T6-T2 -4.750 -18.091177 8.591177 0.9812163
T7-T2 -5.500 -18.841177 7.841177 0.9472126
T8-T2 -5.125 -18.466177 8.216177 0.9673710
T9-T2 -5.250 -18.591177 8.091177 0.9614109
T4-T3 -7.250 -20.591177 6.091177 0.7534517
T5-T3 -7.500 -20.841177 5.841177 0.7140452
T6-T3 -6.000 -19.341177 7.341177 0.9088187
T7-T3 -6.750 -20.091177 6.591177 0.8250195
T8-T3 -6.375 -19.716177 6.966177 0.8707843
T9-T3 -6.500 -19.841177 6.841177 0.8563599
T5-T4 -0.250 -13.591177 13.091177 1.0000000
T6-T4 1.250 -12.091177 14.591177 1.0000000
T7-T4 0.500 -12.841177 13.841177 1.0000000
T8-T4 0.875 -12.466177 14.216177 1.0000000
T9-T4 0.750 -12.591177 14.091177 1.0000000
T6-T5 1.500 -11.841177 14.841177 0.9999997
T7-T5 0.750 -12.591177 14.091177 1.0000000
T8-T5 1.125 -12.216177 14.466177 1.0000000
T9-T5 1.000 -12.341177 14.341177 1.0000000
T7-T6 -0.750 -14.091177 12.591177 1.0000000
T8-T6 -0.375 -13.716177 12.966177 1.0000000
T9-T6 -0.500 -13.841177 12.841177 1.0000000
T8-T7 0.375 -12.966177 13.716177 1.0000000
T9-T7 0.250 -13.091177 13.591177 1.0000000
T9-T8 -0.125 -13.466177 13.216177 1.0000000##################################################### MICOSFAERELA - severidade
df1_audpc %>%
ggplot(aes(TRATAMENTO, MICOSFAERELA.Sev)) +
geom_jitter(size=3, width = .1)+
geom_boxplot(size=1)
#################### ANOVA AUDPC #######################
ANOVA_audpc<-aov(MICOSFAERELA.Sev~TRATAMENTO,df1_audpc)
res <- ANOVA_audpc$residuals
shapiro.test(res)
Shapiro-Wilk normality test
data: res
W = 0.97746, p-value = 0.4778 summary(ANOVA_audpc) Df Sum Sq Mean Sq F value Pr(>F)
TRATAMENTO 11 142.6 12.964 2.147 0.0417 *
Residuals 36 217.4 6.038
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 TukeyHSD(ANOVA_audpc) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = MICOSFAERELA.Sev ~ TRATAMENTO, data = df1_audpc)
$TRATAMENTO
diff lwr upr p adj
T10-T1 -2.125000e+00 -8.189625 3.939625 0.9833486
T11-T1 2.000000e+00 -4.064625 8.064625 0.9895882
T12-T1 6.250000e-01 -5.439625 6.689625 0.9999999
T2-T1 1.250000e-01 -5.939625 6.189625 1.0000000
T3-T1 2.000000e+00 -4.064625 8.064625 0.9895882
T4-T1 -2.750000e+00 -8.814625 3.314625 0.9042506
T5-T1 -3.000000e+00 -9.064625 3.064625 0.8442750
T6-T1 -2.875000e+00 -8.939625 3.189625 0.8763649
T7-T1 -1.500000e+00 -7.564625 4.564625 0.9991122
T8-T1 -1.625000e+00 -7.689625 4.439625 0.9981779
T9-T1 -1.625000e+00 -7.689625 4.439625 0.9981779
T11-T10 4.125000e+00 -1.939625 10.189625 0.4475179
T12-T10 2.750000e+00 -3.314625 8.814625 0.9042506
T2-T10 2.250000e+00 -3.814625 8.314625 0.9745569
T3-T10 4.125000e+00 -1.939625 10.189625 0.4475179
T4-T10 -6.250000e-01 -6.689625 5.439625 0.9999999
T5-T10 -8.750000e-01 -6.939625 5.189625 0.9999955
T6-T10 -7.500000e-01 -6.814625 5.314625 0.9999991
T7-T10 6.250000e-01 -5.439625 6.689625 0.9999999
T8-T10 5.000000e-01 -5.564625 6.564625 1.0000000
T9-T10 5.000000e-01 -5.564625 6.564625 1.0000000
T12-T11 -1.375000e+00 -7.439625 4.689625 0.9996035
T2-T11 -1.875000e+00 -7.939625 4.189625 0.9938142
T3-T11 4.440892e-15 -6.064625 6.064625 1.0000000
T4-T11 -4.750000e+00 -10.814625 1.314625 0.2511813
T5-T11 -5.000000e+00 -11.064625 1.064625 0.1918354
T6-T11 -4.875000e+00 -10.939625 1.189625 0.2200685
T7-T11 -3.500000e+00 -9.564625 2.564625 0.6813267
T8-T11 -3.625000e+00 -9.689625 2.439625 0.6348898
T9-T11 -3.625000e+00 -9.689625 2.439625 0.6348898
T2-T12 -5.000000e-01 -6.564625 5.564625 1.0000000
T3-T12 1.375000e+00 -4.689625 7.439625 0.9996035
T4-T12 -3.375000e+00 -9.439625 2.689625 0.7261640
T5-T12 -3.625000e+00 -9.689625 2.439625 0.6348898
T6-T12 -3.500000e+00 -9.564625 2.564625 0.6813267
T7-T12 -2.125000e+00 -8.189625 3.939625 0.9833486
T8-T12 -2.250000e+00 -8.314625 3.814625 0.9745569
T9-T12 -2.250000e+00 -8.314625 3.814625 0.9745569
T3-T2 1.875000e+00 -4.189625 7.939625 0.9938142
T4-T2 -2.875000e+00 -8.939625 3.189625 0.8763649
T5-T2 -3.125000e+00 -9.189625 2.939625 0.8082421
T6-T2 -3.000000e+00 -9.064625 3.064625 0.8442750
T7-T2 -1.625000e+00 -7.689625 4.439625 0.9981779
T8-T2 -1.750000e+00 -7.814625 4.314625 0.9965309
T9-T2 -1.750000e+00 -7.814625 4.314625 0.9965309
T4-T3 -4.750000e+00 -10.814625 1.314625 0.2511813
T5-T3 -5.000000e+00 -11.064625 1.064625 0.1918354
T6-T3 -4.875000e+00 -10.939625 1.189625 0.2200685
T7-T3 -3.500000e+00 -9.564625 2.564625 0.6813267
T8-T3 -3.625000e+00 -9.689625 2.439625 0.6348898
T9-T3 -3.625000e+00 -9.689625 2.439625 0.6348898
T5-T4 -2.500000e-01 -6.314625 5.814625 1.0000000
T6-T4 -1.250000e-01 -6.189625 5.939625 1.0000000
T7-T4 1.250000e+00 -4.814625 7.314625 0.9998402
T8-T4 1.125000e+00 -4.939625 7.189625 0.9999430
T9-T4 1.125000e+00 -4.939625 7.189625 0.9999430
T6-T5 1.250000e-01 -5.939625 6.189625 1.0000000
T7-T5 1.500000e+00 -4.564625 7.564625 0.9991122
T8-T5 1.375000e+00 -4.689625 7.439625 0.9996035
T9-T5 1.375000e+00 -4.689625 7.439625 0.9996035
T7-T6 1.375000e+00 -4.689625 7.439625 0.9996035
T8-T6 1.250000e+00 -4.814625 7.314625 0.9998402
T9-T6 1.250000e+00 -4.814625 7.314625 0.9998402
T8-T7 -1.250000e-01 -6.189625 5.939625 1.0000000
T9-T7 -1.250000e-01 -6.189625 5.939625 1.0000000
T9-T8 1.332268e-15 -6.064625 6.064625 1.0000000##################################################### ANTRACNOSE - incidĂªncia
df1_audpc %>%
ggplot(aes(TRATAMENTO, ANTRACNOSE.In)) +
geom_jitter(size=3, width = .1)+
geom_boxplot(size=1)
#################### ANOVA AUDPC #######################
ANOVA_audpc<-aov(ANTRACNOSE.In~TRATAMENTO,df1_audpc)
res <- ANOVA_audpc$residuals
shapiro.test(res)
Shapiro-Wilk normality test
data: res
W = 0.9744, p-value = 0.3719 summary(ANOVA_audpc) Df Sum Sq Mean Sq F value Pr(>F)
TRATAMENTO 11 1678 152.6 1.477 0.183
Residuals 36 3718 103.3 TukeyHSD(ANOVA_audpc) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = ANTRACNOSE.In ~ TRATAMENTO, data = df1_audpc)
$TRATAMENTO
diff lwr upr p adj
T10-T1 -8.250 -33.33047 16.830468 0.9897962
T11-T1 -18.250 -43.33047 6.830468 0.3492378
T12-T1 -15.250 -40.33047 9.830468 0.6112822
T2-T1 -8.125 -33.20547 16.955468 0.9909636
T3-T1 -5.750 -30.83047 19.330468 0.9995599
T4-T1 -19.750 -44.83047 5.330468 0.2445525
T5-T1 -18.125 -43.20547 6.955468 0.3589873
T6-T1 -17.625 -42.70547 7.455468 0.3993867
T7-T1 -13.875 -38.95547 11.205468 0.7331175
T8-T1 -16.750 -41.83047 8.330468 0.4746959
T9-T1 -16.375 -41.45547 8.705468 0.5083225
T11-T10 -10.000 -35.08047 15.080468 0.9577848
T12-T10 -7.000 -32.08047 18.080468 0.9973958
T2-T10 0.125 -24.95547 25.205468 1.0000000
T3-T10 2.500 -22.58047 27.580468 0.9999999
T4-T10 -11.500 -36.58047 13.580468 0.8977825
T5-T10 -9.875 -34.95547 15.205468 0.9612660
T6-T10 -9.375 -34.45547 15.705468 0.9731397
T7-T10 -5.625 -30.70547 19.455468 0.9996420
T8-T10 -8.500 -33.58047 16.580468 0.9871049
T9-T10 -8.125 -33.20547 16.955468 0.9909636
T12-T11 3.000 -22.08047 28.080468 0.9999994
T2-T11 10.125 -14.95547 35.205468 0.9540861
T3-T11 12.500 -12.58047 37.580468 0.8380465
T4-T11 -1.500 -26.58047 23.580468 1.0000000
T5-T11 0.125 -24.95547 25.205468 1.0000000
T6-T11 0.625 -24.45547 25.705468 1.0000000
T7-T11 4.375 -20.70547 29.455468 0.9999691
T8-T11 1.500 -23.58047 26.580468 1.0000000
T9-T11 1.875 -23.20547 26.955468 1.0000000
T2-T12 7.125 -17.95547 32.205468 0.9969656
T3-T12 9.500 -15.58047 34.580468 0.9704658
T4-T12 -4.500 -29.58047 20.580468 0.9999590
T5-T12 -2.875 -27.95547 22.205468 0.9999996
T6-T12 -2.375 -27.45547 22.705468 0.9999999
T7-T12 1.375 -23.70547 26.455468 1.0000000
T8-T12 -1.500 -26.58047 23.580468 1.0000000
T9-T12 -1.125 -26.20547 23.955468 1.0000000
T3-T2 2.375 -22.70547 27.455468 0.9999999
T4-T2 -11.625 -36.70547 13.455468 0.8911765
T5-T2 -10.000 -35.08047 15.080468 0.9577848
T6-T2 -9.500 -34.58047 15.580468 0.9704658
T7-T2 -5.750 -30.83047 19.330468 0.9995599
T8-T2 -8.625 -33.70547 16.455468 0.9855659
T9-T2 -8.250 -33.33047 16.830468 0.9897962
T4-T3 -14.000 -39.08047 11.080468 0.7225496
T5-T3 -12.375 -37.45547 12.705468 0.8463535
T6-T3 -11.875 -36.95547 13.205468 0.8772145
T7-T3 -8.125 -33.20547 16.955468 0.9909636
T8-T3 -11.000 -36.08047 14.080468 0.9216923
T9-T3 -10.625 -35.70547 14.455468 0.9370109
T5-T4 1.625 -23.45547 26.705468 1.0000000
T6-T4 2.125 -22.95547 27.205468 1.0000000
T7-T4 5.875 -19.20547 30.955468 0.9994621
T8-T4 3.000 -22.08047 28.080468 0.9999994
T9-T4 3.375 -21.70547 28.455468 0.9999978
T6-T5 0.500 -24.58047 25.580468 1.0000000
T7-T5 4.250 -20.83047 29.330468 0.9999769
T8-T5 1.375 -23.70547 26.455468 1.0000000
T9-T5 1.750 -23.33047 26.830468 1.0000000
T7-T6 3.750 -21.33047 28.830468 0.9999935
T8-T6 0.875 -24.20547 25.955468 1.0000000
T9-T6 1.250 -23.83047 26.330468 1.0000000
T8-T7 -2.875 -27.95547 22.205468 0.9999996
T9-T7 -2.500 -27.58047 22.580468 0.9999999
T9-T8 0.375 -24.70547 25.455468 1.0000000##################################################### ANTRACNOSE - severidade
df1_audpc %>%
ggplot(aes(TRATAMENTO, ANTRACNOSE.Sev)) +
geom_jitter(size=3, width = .1)+
geom_boxplot(size=1)
#################### ANOVA AUDPC #######################
ANOVA_audpc<-aov(ANTRACNOSE.Sev~TRATAMENTO,df1_audpc)
res <- ANOVA_audpc$residuals
shapiro.test(res)
Shapiro-Wilk normality test
data: res
W = 0.97231, p-value = 0.3109 summary(ANOVA_audpc) Df Sum Sq Mean Sq F value Pr(>F)
TRATAMENTO 11 87.4 7.949 0.382 0.955
Residuals 36 748.9 20.802 TukeyHSD(ANOVA_audpc) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = ANTRACNOSE.Sev ~ TRATAMENTO, data = df1_audpc)
$TRATAMENTO
diff lwr upr p adj
T10-T1 -1.875000e+00 -13.131512 9.381512 0.9999806
T11-T1 -3.625000e+00 -14.881512 7.631512 0.9913842
T12-T1 -3.000000e+00 -14.256512 8.256512 0.9982624
T2-T1 -2.125000e+00 -13.381512 9.131512 0.9999322
T3-T1 -2.125000e+00 -13.381512 9.131512 0.9999322
T4-T1 -4.500000e+00 -15.756512 6.756512 0.9570224
T5-T1 -4.000000e+00 -15.256512 7.256512 0.9814877
T6-T1 -4.375000e+00 -15.631512 6.881512 0.9645895
T7-T1 -2.000000e+00 -13.256512 9.256512 0.9999628
T8-T1 -4.875000e+00 -16.131512 6.381512 0.9275904
T9-T1 -2.750000e+00 -14.006512 8.506512 0.9992070
T11-T10 -1.750000e+00 -13.006512 9.506512 0.9999904
T12-T10 -1.125000e+00 -12.381512 10.131512 0.9999999
T2-T10 -2.500000e-01 -11.506512 11.006512 1.0000000
T3-T10 -2.500000e-01 -11.506512 11.006512 1.0000000
T4-T10 -2.625000e+00 -13.881512 8.631512 0.9994841
T5-T10 -2.125000e+00 -13.381512 9.131512 0.9999322
T6-T10 -2.500000e+00 -13.756512 8.756512 0.9996736
T7-T10 -1.250000e-01 -11.381512 11.131512 1.0000000
T8-T10 -3.000000e+00 -14.256512 8.256512 0.9982624
T9-T10 -8.750000e-01 -12.131512 10.381512 1.0000000
T12-T11 6.250000e-01 -10.631512 11.881512 1.0000000
T2-T11 1.500000e+00 -9.756512 12.756512 0.9999980
T3-T11 1.500000e+00 -9.756512 12.756512 0.9999980
T4-T11 -8.750000e-01 -12.131512 10.381512 1.0000000
T5-T11 -3.750000e-01 -11.631512 10.881512 1.0000000
T6-T11 -7.500000e-01 -12.006512 10.506512 1.0000000
T7-T11 1.625000e+00 -9.631512 12.881512 0.9999955
T8-T11 -1.250000e+00 -12.506512 10.006512 0.9999997
T9-T11 8.750000e-01 -10.381512 12.131512 1.0000000
T2-T12 8.750000e-01 -10.381512 12.131512 1.0000000
T3-T12 8.750000e-01 -10.381512 12.131512 1.0000000
T4-T12 -1.500000e+00 -12.756512 9.756512 0.9999980
T5-T12 -1.000000e+00 -12.256512 10.256512 1.0000000
T6-T12 -1.375000e+00 -12.631512 9.881512 0.9999992
T7-T12 1.000000e+00 -10.256512 12.256512 1.0000000
T8-T12 -1.875000e+00 -13.131512 9.381512 0.9999806
T9-T12 2.500000e-01 -11.006512 11.506512 1.0000000
T3-T2 -1.776357e-15 -11.256512 11.256512 1.0000000
T4-T2 -2.375000e+00 -13.631512 8.881512 0.9997998
T5-T2 -1.875000e+00 -13.131512 9.381512 0.9999806
T6-T2 -2.250000e+00 -13.506512 9.006512 0.9998813
T7-T2 1.250000e-01 -11.131512 11.381512 1.0000000
T8-T2 -2.750000e+00 -14.006512 8.506512 0.9992070
T9-T2 -6.250000e-01 -11.881512 10.631512 1.0000000
T4-T3 -2.375000e+00 -13.631512 8.881512 0.9997998
T5-T3 -1.875000e+00 -13.131512 9.381512 0.9999806
T6-T3 -2.250000e+00 -13.506512 9.006512 0.9998813
T7-T3 1.250000e-01 -11.131512 11.381512 1.0000000
T8-T3 -2.750000e+00 -14.006512 8.506512 0.9992070
T9-T3 -6.250000e-01 -11.881512 10.631512 1.0000000
T5-T4 5.000000e-01 -10.756512 11.756512 1.0000000
T6-T4 1.250000e-01 -11.131512 11.381512 1.0000000
T7-T4 2.500000e+00 -8.756512 13.756512 0.9996736
T8-T4 -3.750000e-01 -11.631512 10.881512 1.0000000
T9-T4 1.750000e+00 -9.506512 13.006512 0.9999904
T6-T5 -3.750000e-01 -11.631512 10.881512 1.0000000
T7-T5 2.000000e+00 -9.256512 13.256512 0.9999628
T8-T5 -8.750000e-01 -12.131512 10.381512 1.0000000
T9-T5 1.250000e+00 -10.006512 12.506512 0.9999997
T7-T6 2.375000e+00 -8.881512 13.631512 0.9997998
T8-T6 -5.000000e-01 -11.756512 10.756512 1.0000000
T9-T6 1.625000e+00 -9.631512 12.881512 0.9999955
T8-T7 -2.875000e+00 -14.131512 8.381512 0.9988121
T9-T7 -7.500000e-01 -12.006512 10.506512 1.0000000
T9-T8 2.125000e+00 -9.131512 13.381512 0.9999322##################################################---
title: "Análise AUDPC - ANOVA e TukeyHSD"
output: html_notebook
---
```{r}
library(tidyverse)
library(dplyr)
library(agricolae)
```

```{r}
df = read.csv("Dataset.csv", stringsAsFactors = FALSE)
#df = read.csv("Database-Labim.csv")

############# SIMPLE CODE
## Separa em 12 dataframes por tipo de tratamento
T1 <- filter(df, str_detect(TRATAMENTO, "T1R"))
T2 <- filter(df, str_detect(TRATAMENTO, "T2R"))
T3 <- filter(df, str_detect(TRATAMENTO, "T3R"))
T4 <- filter(df, str_detect(TRATAMENTO, "T4R"))
T5 <- filter(df, str_detect(TRATAMENTO, "T5R"))
T6 <- filter(df, str_detect(TRATAMENTO, "T6R"))
T7 <- filter(df, str_detect(TRATAMENTO, "T7R"))
T8 <- filter(df, str_detect(TRATAMENTO, "T8R"))
T9 <- filter(df, str_detect(TRATAMENTO, "T9R"))
T10 <- filter(df, str_detect(TRATAMENTO, "T10R"))
T11 <- filter(df, str_detect(TRATAMENTO, "T11R"))
T12 <- filter(df, str_detect(TRATAMENTO, "T12R"))
```

```{r}
## Separa em outros 12 dataframes com o cálculo da AUDPC considerando
## as 4 repetições para cada doença
T1_AUDPC <- T1 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA)) 

T2_AUDPC <- T2 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))

T3_AUDPC <- T3 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))

T4_AUDPC <- T4 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))

T5_AUDPC <- T5 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))


T6_AUDPC <- T6 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))

T7_AUDPC <- T7 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))


T8_AUDPC <- T8 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))

T9_AUDPC <- T9 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))


T10_AUDPC <- T10 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))

T11_AUDPC <- T11 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))

T12_AUDPC <- T12 %>% 
  group_by(TRATAMENTO) %>% 
  summarise(TRATAMENTO = unique(TRATAMENTO),
            DIPLOCARPON.In=audpc(DIPLOCARPON.In,SEMANA),
            DIPLOCARPON.Sev=audpc(DIPLOCARPON.Sev,SEMANA),
            MICOSFAERELA.In=audpc(MICOSFAERELA.In,SEMANA),
            MICOSFAERELA.Sev=audpc(MICOSFAERELA.Sev,SEMANA),
            ANTRACNOSE.In=audpc(ANTRACNOSE.In,SEMANA),
            ANTRACNOSE.Sev=audpc(ANTRACNOSE.Sev,SEMANA),
            BOTRYTIS.Inf=audpc(BOTRYTIS.Inf,SEMANA),
            BOTRYTIS.Total=audpc(BOTRYTIS.Total,SEMANA))
###################################################################################
```

```{r}
#############RENAME TREATMENT #######################
#Diseases
T1$TRATAMENTO = 'T1'
T2$TRATAMENTO = 'T2'
T3$TRATAMENTO = 'T3'
T4$TRATAMENTO = 'T4'
T5$TRATAMENTO = 'T5'
T6$TRATAMENTO = 'T6'
T7$TRATAMENTO = 'T7'
T8$TRATAMENTO = 'T8'
T9$TRATAMENTO = 'T9'
T10$TRATAMENTO = 'T10'
T11$TRATAMENTO = 'T11'
T12$TRATAMENTO = 'T12'

#AUDPC
T1_AUDPC$TRATAMENTO = 'T1'
T2_AUDPC$TRATAMENTO = 'T2'
T3_AUDPC$TRATAMENTO = 'T3'
T4_AUDPC$TRATAMENTO = 'T4'
T5_AUDPC$TRATAMENTO = 'T5'
T6_AUDPC$TRATAMENTO = 'T6'
T7_AUDPC$TRATAMENTO = 'T7'
T8_AUDPC$TRATAMENTO = 'T8'
T9_AUDPC$TRATAMENTO = 'T9'
T10_AUDPC$TRATAMENTO = 'T10'
T11_AUDPC$TRATAMENTO = 'T11'
T12_AUDPC$TRATAMENTO = 'T12'
####################################################

```

```{r}
########## Create new dataframes in easy style for statistical analysis
df1 <- bind_rows(T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12)
df1_audpc <- bind_rows(T1_AUDPC,T2_AUDPC,T3_AUDPC,T4_AUDPC,T5_AUDPC,T6_AUDPC,T7_AUDPC,T8_AUDPC,T9_AUDPC,T10_AUDPC,T11_AUDPC,T12_AUDPC)
##########################################################################################################################

```

```{r}
#### AUDPC for each disease and all 12 treatments
#### Note: Each treatment has 4 replicates 
print(df1_audpc)
```

```{r}
### DIPLOCARPON - incidência	
df1_audpc %>% 
  ggplot(aes(TRATAMENTO, DIPLOCARPON.In)) +
  geom_jitter(size=3, width = .1)+
  geom_boxplot(size=1)

#################### ANOVA AUDPC #######################
 ANOVA_audpc<-aov(DIPLOCARPON.In~TRATAMENTO,df1_audpc)
 res <- ANOVA_audpc$residuals
 shapiro.test(res)
 summary(ANOVA_audpc)
 TukeyHSD(ANOVA_audpc)
##################################################

```
```{r}
### DIPLOCARPON - severidade	
df1_audpc %>% 
  ggplot(aes(TRATAMENTO, DIPLOCARPON.Sev)) +
  geom_jitter(size=3, width = .1)+
  geom_boxplot(size=1)

#################### ANOVA AUDPC #######################
 ANOVA_audpc<-aov(DIPLOCARPON.Sev~TRATAMENTO,df1_audpc)
 res <- ANOVA_audpc$residuals
 shapiro.test(res)
 summary(ANOVA_audpc)
 TukeyHSD(ANOVA_audpc)
##################################################

```
```{r}
### MICOSFAERELA - incidência	
df1_audpc %>% 
  ggplot(aes(TRATAMENTO, MICOSFAERELA.In)) +
  geom_jitter(size=3, width = .1)+
  geom_boxplot(size=1)

#################### ANOVA AUDPC #######################
 ANOVA_audpc<-aov(MICOSFAERELA.In~TRATAMENTO,df1_audpc)
 res <- ANOVA_audpc$residuals
 shapiro.test(res)
 summary(ANOVA_audpc)
 TukeyHSD(ANOVA_audpc)
##################################################
```
```{r}
### MICOSFAERELA - severidade	
df1_audpc %>% 
  ggplot(aes(TRATAMENTO, MICOSFAERELA.Sev)) +
  geom_jitter(size=3, width = .1)+
  geom_boxplot(size=1)

#################### ANOVA AUDPC #######################
 ANOVA_audpc<-aov(MICOSFAERELA.Sev~TRATAMENTO,df1_audpc)
 res <- ANOVA_audpc$residuals
 shapiro.test(res)
 summary(ANOVA_audpc)
 TukeyHSD(ANOVA_audpc)
##################################################
```
```{r}
### ANTRACNOSE - incidência	
df1_audpc %>% 
  ggplot(aes(TRATAMENTO, ANTRACNOSE.In)) +
  geom_jitter(size=3, width = .1)+
  geom_boxplot(size=1)

#################### ANOVA AUDPC #######################
 ANOVA_audpc<-aov(ANTRACNOSE.In~TRATAMENTO,df1_audpc)
 res <- ANOVA_audpc$residuals
 shapiro.test(res)
 summary(ANOVA_audpc)
 TukeyHSD(ANOVA_audpc)
##################################################
```
```{r}
### ANTRACNOSE - severidade	
df1_audpc %>% 
  ggplot(aes(TRATAMENTO, ANTRACNOSE.Sev)) +
  geom_jitter(size=3, width = .1)+
  geom_boxplot(size=1)

#################### ANOVA AUDPC #######################
 ANOVA_audpc<-aov(ANTRACNOSE.Sev~TRATAMENTO,df1_audpc)
 res <- ANOVA_audpc$residuals
 shapiro.test(res)
 summary(ANOVA_audpc)
 TukeyHSD(ANOVA_audpc)
##################################################
```

