Как создать граненый коробочный график с существенными отличиями и 2 измеряемыми переменными? - PullRequest
0 голосов
/ 09 июля 2019

Мне удалось создать граненый коробочный график с моими 2 количественными переменными;Я знаю, как запустить kruskal-wallis с последующим тестом Уилкоксона и показать существенные различия с буквами на блокпосту, но только в простом блокпосте, с одной переменной и без фасета.Как я могу сделать ?(Если возможно, я хотел бы поставить существенные различия с буквами, я хотел бы, чтобы я мог опубликовать фотографии того, что я уже сделал, но, видимо, мне не разрешено) Кроме того, у меня есть еще один вопрос;Какой тест выполняет функция stat_function_mean?Я пытался использовать эту функцию, но я не знаю, как ее использовать ... Вот мой код без теста, только граненый блокпост с моими двумя переменными:

Код для моего фасетного блокпоста с 2измеряемые переменные (FF и FM)

dat.m2 <- melt(pheno,id.vars=c("fusion","Genotype","Hormone"),
               measure.vars=c('FF','MF'))

dat.m2$fusion<-factor(dat.m2$fusion, levels=c("Control", "CK 20 mg/L", "CK 100 mg/L", "CK 500 mg/L", "GA 20 mg/L", "GA 100 mg/L", "GA 500 mg/L"))
levels(dat.m2$fusion)

ggplot(dat.m2) +
  geom_boxplot(aes(x=fusion, y=value, colour=variable))+
  facet_wrap(~Genotype)+
  xlab(" ")+
  ylab("Days after sowing")

Код для добавления существенных различий на графике, с буквами, но только с 1 измеряемой переменной (FF), без фасета

mymat <-tri.to.squ(pp$p.value)
mymat

myletters <- multcompLetters(mymat,compare="<=",threshold=0.05,Letters=letters)
myletters

myletters_df <- data.frame(fusion=names(myletters$Letters),letter = myletters$Letters )
myletters_df


ggplot(pheno, aes(x=fusion, y=FF, colour=fusion))+
  geom_boxplot()+
  geom_text(data = myletters_df, aes(label = letter, y = 30 ), colour="black", size=5)+
  ylab("Days after sowing")+
  xlab("")+
  labs(title="Days to female flower production")+
  theme(plot.title = element_text(hjust = 0.5))+


    > dput(pheno)
structure(list(Genotype = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("F1045", 
"FF", "M1585", "M1610"), class = "factor"), X = structure(c(1L, 
105L, 116L, 127L, 138L, 149L, 160L, 171L, 182L, 2L, 13L, 24L, 
35L, 46L, 57L, 68L, 79L, 90L, 101L, 106L, 107L, 108L, 109L, 110L, 
111L, 112L, 113L, 114L, 115L, 117L, 118L, 119L, 120L, 121L, 122L, 
123L, 124L, 125L, 126L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 
135L, 136L, 137L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 
147L, 148L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 
159L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 
172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 183L, 
184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 22L, 23L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 
34L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 47L, 48L, 
49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 58L, 59L, 60L, 61L, 62L, 
63L, 64L, 65L, 66L, 67L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 
77L, 78L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 91L, 
92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 102L, 103L, 104L
), .Label = c("H1", "H10", "H100", "H101", "H102", "H103", "H104", 
"H105", "H106", "H107", "H108", "H109", "H11", "H110", "H111", 
"H112", "H113", "H114", "H115", "H116", "H117", "H118", "H119", 
"H12", "H120", "H121", "H122", "H123", "H124", "H125", "H126", 
"H127", "H128", "H129", "H13", "H130", "H131", "H132", "H133", 
"H134", "H135", "H136", "H137", "H138", "H139", "H14", "H140", 
"H141", "H142", "H143", "H144", "H145", "H146", "H147", "H148", 
"H149", "H15", "H150", "H151", "H152", "H153", "H154", "H155", 
"H156", "H157", "H158", "H159", "H16", "H160", "H161", "H162", 
"H163", "H164", "H165", "H166", "H167", "H168", "H169", "H17", 
"H170", "H171", "H172", "H173", "H174", "H175", "H176", "H177", 
"H178", "H179", "H18", "H180", "H181", "H182", "H183", "H184", 
"H185", "H186", "H187", "H188", "H189", "H19", "H190", "H191", 
"H192", "H2", "H20", "H21", "H22", "H23", "H24", "H25", "H26", 
"H27", "H28", "H29", "H3", "H30", "H31", "H32", "H33", "H34", 
"H35", "H36", "H37", "H38", "H39", "H4", "H40", "H41", "H42", 
"H43", "H44", "H45", "H46", "H47", "H48", "H49", "H5", "H50", 
"H51", "H52", "H53", "H54", "H55", "H56", "H57", "H58", "H59", 
"H6", "H60", "H61", "H62", "H63", "H64", "H65", "H66", "H67", 
"H68", "H69", "H7", "H70", "H71", "H72", "H73", "H74", "H75", 
"H76", "H77", "H78", "H79", "H8", "H80", "H81", "H82", "H83", 
"H84", "H85", "H86", "H87", "H88", "H89", "H9", "H90", "H91", 
"H92", "H93", "H94", "H95", "H96", "H97", "H98", "H99"), class = "factor"), 
    Hormone = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L), .Label = c("CK", "Control", "GA"), class = "factor"), 
    Hormone.quantity = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L), .Label = c("100", "20", "500", "Control"
    ), class = "factor"), fusion = structure(c(4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("CK 100 mg/L", 
    "CK 20 mg/L", "CK 500 mg/L", "Control", "GA 100 mg/L", "GA 20 mg/L", 
    "GA 500 mg/L"), class = "factor"), Sowing.date = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "25-mrt", class = "factor"), 
    BT = structure(c(6L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 6L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 2L, 
    2L, 2L, 2L, 2L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 8L, 4L, 6L, 6L, 6L, 4L, 3L, 4L, 4L, 3L, 
    4L, 3L, 3L, 3L, 3L, 6L, 6L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 4L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 6L, 6L, 8L, 6L, 4L, 4L, 
    4L, 8L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 6L, 3L, 5L, 4L, 5L, 5L, 
    4L, 3L), .Label = c("16-apr", "17-apr", "18-apr", "19-apr", 
    "21-mei", "23-apr", "26-apr", "30-apr"), class = "factor"), 
    ff = structure(c(14L, 20L, 4L, 10L, 20L, 3L, 1L, 14L, 9L, 
    11L, 20L, 11L, 9L, 9L, 9L, 11L, 12L, 12L, 6L, 12L, 12L, 16L, 
    12L, 12L, 17L, 17L, 12L, 16L, 17L, 18L, 12L, 6L, 20L, 20L, 
    15L, 15L, 15L, 20L, 20L, 11L, 11L, 11L, 9L, 9L, 9L, 9L, 20L, 
    20L, 20L, 4L, 1L, 4L, 4L, 4L, 8L, 20L, 4L, 20L, 12L, 4L, 
    14L, 14L, 11L, 11L, 15L, 15L, 11L, 11L, 9L, 15L, 9L, 9L, 
    11L, 11L, 14L, 1L, 5L, 4L, 4L, 20L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 15L, 15L, 14L, 13L, 15L, 15L, 11L, 9L, 9L, 
    11L, 9L, 11L, 1L, 20L, 1L, 20L, 20L, 20L, 20L, 1L, 1L, 4L, 
    20L, 20L, 20L, 15L, 15L, 14L, 15L, 1L, 15L, 15L, 20L, 11L, 
    11L, 11L, 11L, 15L, 10L, 10L, 16L, 10L, 12L, 10L, 17L, 8L, 
    16L, 12L, 8L, 4L, 4L, 8L, 20L, 10L, 1L, 20L, NA, 12L, 10L, 
    20L, 20L, 20L, 1L, 20L, 1L, 20L, 12L, 16L, 12L, 2L, 8L, 4L, 
    10L, 4L, 4L, 4L, 10L, 8L, 4L, 8L, 20L, 20L, 20L, NA, 20L, 
    1L, 20L, 1L, 8L, 20L, 1L, 1L, 7L, 17L, 19L, 19L, 12L, 10L, 
    12L, 19L, 10L, 10L, 10L, 17L), .Label = c("10-mei", "13-jun", 
    "14-apr", "14-mei", "17-mei", "18-jun", "21-jun", "21-mei", 
    "23-apr", "24-mei", "26-apr", "28-mei", "3-apr", "3-mei", 
    "30-apr", "31-mei", "4-jun", "5-jul", "7-jun", "7-mei"), class = "factor"), 
    FH = c(3.5, 6, 9, 16, 5.5, 12, 11.5, 4, 4.5, 6, 8, 5, 4.5, 
    3.5, 4, 5, 20, 42, 14, 40, 27, 42, 27, 26, 16, 18, 35, 17, 
    20, 28, 15, 20, 33, 32, 14.5, 14.5, 14.5, 35, 32, 12.5, 13.5, 
    12, 14.5, 12, 15, 14.5, 18, 18, 18.5, 35, 23, 25, 30, 37, 
    53, 27.5, 37, 25.5, 35, 47, 8.5, 20.5, 13, 14.5, 13.5, 18.5, 
    10.5, 10, 14.3, 18.5, 15.3, 11.7, 16, 15, 13.5, 26, 36, 30, 
    43, 23.5, 23.5, 31.5, 29, 30.5, 30, 29, 30, 24.5, 19, 23, 
    21.5, 26.5, 18.5, 20, 15, 12.3, 17, 12, 15, 13, 43614, 25, 
    27, 22.5, 35, 23.5, 30, 42, 42, 55, 32.5, 26, 26, 9.5, 4.5, 
    5.5, 5, 15.5, 10, 4.5, 8.5, 6, 5, 5.5, 5, 4.5, 30, 20, 16, 
    16, 20, 22, 30, 22, 25, 11, 13.5, 11, 11, 14, 6, NA, 5.5, 
    7, NA, 12, 14, 7, 9.5, 6.5, 9, 8.5, 12.5, 8, 27, 33, 35, 
    32, 17, 14, 22, 11, 17, 12, 25, 22, 15, 10, 5, 3, 4, NA, 
    5, 8, 4.5, 6, 7, 5, 5.5, 7, 42, 23, 23, 21, 14, 21, 17, 22, 
    19, 18, 17, 17), SRDT = structure(c(2L, 7L, 14L, NA, 7L, 
    8L, 7L, NA, NA, NA, 3L, NA, 18L, 15L, 17L, 17L, 18L, 18L, 
    NA, 18L, 15L, 17L, 15L, 20L, 2L, NA, 11L, 17L, 18L, 2L, 2L, 
    2L, 14L, 12L, 17L, 15L, 12L, 9L, 9L, 6L, 6L, 15L, 15L, 15L, 
    15L, NA, 17L, 15L, 10L, 11L, 11L, 10L, 11L, 17L, 5L, 21L, 
    6L, NA, 20L, 5L, 12L, 7L, NA, 17L, 17L, 15L, 15L, 10L, 10L, 
    6L, 10L, 10L, 21L, NA, 15L, 15L, 5L, 15L, 15L, 11L, 10L, 
    21L, 1L, 21L, 21L, 21L, 1L, 5L, 18L, 2L, 9L, 9L, NA, 12L, 
    10L, NA, 16L, 6L, 6L, 15L, 6L, 10L, 10L, 10L, 1L, 10L, 1L, 
    21L, 21L, 1L, 21L, 5L, 18L, 2L, 17L, 20L, 9L, 14L, 5L, 9L, 
    9L, 11L, NA, 18L, 10L, 18L, 20L, 4L, 9L, 7L, 2L, 2L, 7L, 
    5L, 17L, 17L, 11L, 10L, 12L, 2L, 14L, 19L, 19L, 19L, NA, 
    NA, 2L, 11L, 17L, 14L, 17L, 9L, 10L, 10L, 2L, 7L, 17L, 14L, 
    2L, 11L, 20L, 2L, 15L, 15L, 11L, 5L, NA, 10L, NA, 2L, 8L, 
    NA, NA, 14L, 5L, 15L, 15L, NA, 22L, NA, 9L, 9L, 19L, 9L, 
    9L, 22L, 20L, 13L, 7L, 20L, 15L, 20L), .Label = c("10-mei", 
    "11-jun", "13-jun", "13-mei", "14-mei", "17-mei", "18-jun", 
    "2-jul", "21-jun", "21-mei", "24-mei", "25-jun", "26-jun", 
    "28-jun", "28-mei", "3-mei", "31-mei", "4-jun", "5-jul", 
    "7-jun", "7-mei", "9-jul"), class = "factor"), MH = c(26, 
    50, 58, NA, 46, 58, 61, NA, NA, NA, 40, NA, 68, 48, 47, 42, 
    26, 50, NA, 48, 27, 42, 27, 48, 25, NA, 25, 17, 20, 18, 32, 
    19, 75, 75, 65, 70, 73, 73, 71, 65, 70, 60, 80, 70, 70, NA, 
    54, 45, 45, 45, 45, 40, 49, 53, 45, 27.5, 44, NA, NA, 47, 
    47, 62, NA, 75, 60, 75, 70, 65, 80, 67, 80, 75, 52, NA, 67, 
    68, 26, 55, 60, 60, 60, 31.5, 39, 30.5, 30, 29, 39, 39, 86, 
    74, 80, 76, NA, 69, 80, NA, 44, 70, 70, 65, 43, 60, 57, 57, 
    45, 60, 39, 35, 32.5, 27, 32.5, 43, 70, 75, 60, 66, 58, 48, 
    41, NA, 44, 42, NA, 44, 39, 40, 48, 53, 50, 50, 45, 45, 50, 
    13, 25, 11, 21, 20.5, 46, 44, 54, 25, 20, 25, NA, NA, 28, 
    33, 36, 40, 21, 36, 23.5, 21, 44, 60, 37, 37, 55, 24, 45, 
    45, 35, 30, 25, 12, 27, 10, NA, 53, 35, NA, NA, 43, 11, 13, 
    7, NA, 22, NA, 42, 46, NA, 41, 43, 40, 26, 45, 35, 29, 17, 
    22), SEEDT = structure(c(2L, 4L, 9L, NA, 4L, 5L, 4L, NA, 
    NA, NA, 4L, NA, 12L, 11L, 11L, 11L, 4L, 3L, NA, 4L, 15L, 
    4L, 8L, 5L, 7L, NA, 2L, 2L, 8L, 13L, 8L, NA, 13L, 8L, 15L, 
    15L, 8L, 7L, 7L, 10L, 10L, 11L, 6L, 10L, 10L, NA, 3L, 11L, 
    12L, 12L, 12L, 12L, 4L, 4L, 12L, 12L, 12L, NA, 9L, 12L, NA, 
    4L, NA, 2L, 15L, 2L, 15L, 14L, 10L, 12L, 12L, 11L, 11L, NA, 
    2L, 12L, 8L, 3L, 15L, 11L, 11L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 2L, 2L, 7L, 7L, NA, 8L, 10L, NA, 10L, 10L, 10L, 
    15L, 10L, 12L, 12L, 10L, 11L, 11L, 10L, 10L, 10L, 11L, 10L, 
    11L, 12L, 2L, 12L, 4L, 7L, 9L, 10L, 7L, 7L, 10L, NA, 12L, 
    10L, 15L, 2L, 4L, 8L, 8L, 4L, 4L, 13L, 12L, NA, NA, 4L, 7L, 
    NA, 7L, 13L, 13L, 13L, NA, NA, NA, 2L, 2L, NA, NA, NA, 8L, 
    NA, NA, 4L, 4L, 2L, NA, 4L, 2L, 7L, 7L, 7L, 2L, 2L, 15L, 
    1L, 15L, NA, 2L, 5L, NA, NA, 5L, 13L, NA, NA, NA, NA, NA, 
    16L, 16L, 13L, 16L, 7L, 1L, 7L, 16L, 7L, 7L, 7L, NA), .Label = c("11-jul", 
    "11-jun", "13-jun", "18-jun", "2-jul", "20-mei", "21-jun", 
    "25-jun", "28-jun", "28-mei", "31-mei", "4-jun", "5-jul", 
    "6-apr", "7-jun", "9-jul"), class = "factor"), FERMK = c(7L, 
    8L, 8L, 7L, 8L, 8L, 8L, 4L, NA, NA, 5L, 7L, 7L, 6L, 7L, 6L, 
    4L, 6L, NA, 4L, 3L, 4L, 4L, 4L, 2L, NA, 2L, 2L, 2L, 1L, 2L, 
    2L, 8L, 6L, 6L, 6L, 7L, 7L, 7L, 6L, 6L, 7L, 7L, 6L, 4L, 6L, 
    6L, 5L, 6L, 5L, 5L, 6L, 5L, 4L, 2L, 5L, NA, NA, 4L, 2L, 5L, 
    5L, NA, 7L, 7L, 8L, 6L, 6L, 7L, NA, 7L, 7L, 6L, 5L, 5L, 5L, 
    4L, 4L, 6L, 7L, 6L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 8L, 7L, 7L, 
    7L, 7L, 7L, 7L, NA, 7L, 7L, 7L, 7L, 5L, 5L, 4L, 5L, 6L, 4L, 
    6L, 2L, 2L, 2L, 5L, 4L, 7L, 6L, 8L, 7L, 6L, 6L, 8L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L, 4L, 4L, 4L, 4L, 2L, 2L, NA, 
    3L, 2L, NA, 3L, 6L, 5L, 5L, 6L, NA, 6L, 4L, 6L, 5L, 5L, 5L, 
    5L, 4L, 5L, 4L, 4L, 6L, 5L, 6L, 5L, 7L, 7L, 7L, 3L, 2L, 3L, 
    3L, 4L, NA, 5L, 5L, NA, 5L, 5L, 3L, 2L, 3L, NA, 4L, NA, 5L, 
    4L, 5L, 5L, 6L, 4L, 4L, 3L, 3L, 4L, 5L, NA), PLRMK = c(1L, 
    2L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L, 2L, 0L, 0L, 0L, 0L, 
    1L, 1L, NA, 1L, 1L, 2L, 1L, 1L, 4L, NA, 5L, 5L, 4L, 5L, 3L, 
    4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, NA, 
    2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 4L, 5L, NA, NA, 5L, 6L, 1L, 
    1L, NA, 1L, 1L, 0L, 1L, 1L, 1L, NA, 2L, 1L, 2L, NA, 2L, NA, 
    4L, 3L, 2L, 2L, 1L, 4L, 5L, 5L, 4L, 5L, 7L, 6L, 1L, 1L, 1L, 
    1L, NA, 1L, 2L, NA, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 4L, 5L, 2L, 
    4L, 7L, 5L, 8L, 5L, 2L, 0L, 1L, 1L, 1L, 7L, 1L, 0L, 1L, 1L, 
    0L, 0L, 0L, 0L, NA, 2L, 3L, 1L, 1L, 2L, 1L, 2L, 6L, 6L, NA, 
    4L, 4L, NA, 2L, 2L, 1L, 1L, 1L, NA, 1L, 1L, 3L, 1L, 1L, 1L, 
    1L, NA, NA, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 5L, 5L, 4L, 
    1L, 4L, NA, 2L, 1L, NA, NA, 2L, 2L, 0L, 0L, NA, 1L, NA, 4L, 
    2L, 1L, 2L, 1L, 2L, 4L, 1L, 2L, 4L, 3L, NA), FF = c(39L, 
    43L, 50L, 60L, 43L, 20L, 46L, 39L, 29L, 32L, 43L, 32L, 29L, 
    29L, 29L, 32L, 64L, 64L, 85L, 64L, 64L, 67L, 64L, 64L, 71L, 
    71L, 64L, 67L, 71L, 102L, 64L, 85L, 43L, 43L, 36L, 36L, 36L, 
    43L, 43L, 32L, 32L, 32L, 29L, 29L, 29L, 29L, 43L, 43L, 43L, 
    50L, 46L, 50L, 50L, 50L, 57L, 43L, 50L, 43L, 64L, 50L, 39L, 
    39L, 32L, 32L, 36L, 36L, 32L, 32L, 29L, 36L, 29L, 29L, 32L, 
    32L, 39L, 46L, 53L, 50L, 50L, 43L, 43L, 43L, 43L, 43L, 43L, 
    43L, 43L, 43L, 36L, 36L, 39L, 9L, 36L, 36L, 32L, 29L, 29L, 
    32L, 29L, 32L, 46L, 43L, 46L, 43L, 43L, 43L, 43L, 46L, 46L, 
    50L, 43L, 43L, 43L, 36L, 36L, 39L, 36L, 46L, 36L, 36L, 43L, 
    32L, 32L, 32L, 32L, 36L, 60L, 60L, 67L, 60L, 64L, 60L, 71L, 
    57L, 67L, 64L, 57L, 50L, 50L, 57L, 43L, 60L, 46L, 43L, NA, 
    64L, 60L, 43L, 43L, 43L, 46L, 43L, 46L, 43L, 64L, 67L, 64L, 
    80L, 57L, 50L, 60L, 50L, 50L, 50L, 60L, 57L, 50L, 57L, 43L, 
    43L, 43L, NA, 43L, 46L, 43L, 46L, 57L, 43L, 46L, 46L, 88L, 
    71L, 74L, 74L, 64L, 60L, 64L, 74L, 60L, 60L, 60L, 71L), MF = c(78L, 
    85L, 95L, NA, 85L, 99L, 85L, NA, NA, NA, 80L, NA, 71L, 64L, 
    67L, 67L, 71L, 71L, NA, 71L, 64L, 67L, 64L, 74L, 78L, NA, 
    60L, 67L, 71L, 78L, 78L, 78L, 95L, 92L, 67L, 64L, 92L, 88L, 
    88L, 53L, 53L, 64L, 64L, 64L, 64L, NA, 67L, 64L, 57L, 60L, 
    60L, 57L, 60L, 67L, 50L, 43L, 53L, NA, 74L, 50L, 92L, 85L, 
    NA, 67L, 67L, 64L, 64L, 57L, 57L, 53L, 57L, 57L, 43L, NA, 
    64L, 64L, 50L, 64L, 64L, 60L, 57L, 43L, 46L, 43L, 43L, 43L, 
    46L, 50L, 71L, 78L, 88L, 88L, NA, 92L, 57L, NA, 39L, 53L, 
    53L, 64L, 53L, 57L, 57L, 57L, 46L, 57L, 46L, 43L, 43L, 46L, 
    43L, 50L, 71L, 78L, 67L, 74L, 88L, 95L, 50L, 88L, 88L, 60L, 
    NA, 71L, 57L, 71L, 74L, 49L, 88L, 85L, 78L, 78L, 85L, 50L, 
    67L, 67L, 60L, 57L, 92L, 78L, 95L, 102L, 102L, 102L, NA, 
    NA, 78L, 60L, 67L, 95L, 67L, 88L, 57L, 57L, 78L, 85L, 67L, 
    95L, 78L, 60L, 74L, 78L, 64L, 64L, 60L, 50L, NA, 57L, NA, 
    78L, 99L, NA, NA, 95L, 50L, 64L, 64L, NA, 106L, NA, 88L, 
    88L, 102L, 88L, 88L, 106L, 74L, 93L, 85L, 74L, 64L, 74L), 
    speed = c(0.08974359, 0.139534884, 0.18, 0.266666667, 0.127906977, 
    0.6, 0.25, 0.102564103, 0.155172414, 0.1875, 0.186046512, 
    0.15625, 0.155172414, 0.120689655, 0.137931034, 0.15625, 
    0.3125, 0.65625, 0.164705882, 0.625, 0.421875, 0.626865672, 
    0.421875, 0.40625, 0.225352113, 0.253521127, 0.546875, 0.253731343, 
    0.281690141, 0.274509804, 0.234375, 0.235294118, 0.76744186, 
    0.744186047, 0.402777778, 0.402777778, 0.402777778, 0.813953488, 
    0.744186047, 0.390625, 0.421875, 0.375, 0.5, 0.413793103, 
    0.517241379, 0.5, 0.418604651, 0.418604651, 0.430232558, 
    0.7, 0.5, 0.5, 0.6, 0.74, 0.929824561, 0.639534884, 0.74, 
    0.593023256, 0.546875, 0.94, 0.217948718, 0.525641026, 0.40625, 
    0.453125, 0.375, 0.513888889, 0.328125, 0.3125, 0.493103448, 
    0.513888889, 0.527586207, 0.403448276, 0.5, 0.46875, 0.346153846, 
    0.565217391, 0.679245283, 0.6, 0.86, 0.546511628, 0.546511628, 
    0.73255814, 0.674418605, 0.709302326, 0.697674419, 0.674418605, 
    0.697674419, 0.569767442, 0.527777778, 0.638888889, 0.551282051, 
    2.944444444, 0.513888889, 0.555555556, 0.46875, 0.424137931, 
    0.586206897, 0.375, 0.517241379, 0.40625, 948.1304348, 0.581395349, 
    0.586956522, 0.523255814, 0.813953488, 0.546511628, 0.697674419, 
    0.913043478, 0.913043478, 1.1, 0.755813953, 0.604651163, 
    0.604651163, 0.263888889, 0.125, 0.141025641, 0.138888889, 
    0.336956522, 0.277777778, 0.125, 0.197674419, 0.1875, 0.15625, 
    0.171875, 0.15625, 0.125, 0.5, 0.333333333, 0.23880597, 0.266666667, 
    0.3125, 0.366666667, 0.422535211, 0.385964912, 0.373134328, 
    0.171875, 0.236842105, 0.22, 0.22, 0.245614035, 0.139534884, 
    NA, 0.119565217, 0.162790698, NA, 0.1875, 0.233333333, 0.162790698, 
    0.220930233, 0.151162791, 0.195652174, 0.197674419, 0.27173913, 
    0.186046512, 0.421875, 0.492537313, 0.546875, 0.4, 0.298245614, 
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    0.3, 0.175438596, 0.11627907, 0.069767442, 0.093023256, NA, 
    0.11627907, 0.173913043, 0.104651163, 0.130434783, 0.122807018, 
    0.11627907, 0.119565217, 0.152173913, 0.477272727, 0.323943662, 
    0.310810811, 0.283783784, 0.21875, 0.35, 0.265625, 0.297297297, 
    0.316666667, 0.3, 0.283333333, 0.23943662), ratiofm = c(7, 
    4, 8, 7, 8, 8, 8, NA, NA, NA, 5, 3.5, NA, NA, NA, NA, 4, 
    6, NA, 4, 3, 2, 4, 4, 0.5, NA, 0.4, 0.4, 0.5, 0.2, 0.666666667, 
    0.5, 8, 6, 6, 6, 7, 7, 7, 3, 3, 3.5, 3.5, 6, 4, NA, 3, 2.5, 
    3, 5, 2.5, 3, 5, 4, 0.5, 1, NA, NA, 0.8, 0.333333333, 5, 
    5, NA, 7, 7, NA, 6, 6, 7, NA, 3.5, 7, 3, NA, 2.5, NA, 1, 
    1.333333333, 3, 3.5, 6, 1, 0.4, 0.4, 0.5, 0.4, 0.285714286, 
    0.333333333, 8, 7, 7, 7, NA, 7, 3.5, NA, 7, 7, 7, 7, 1.666666667, 
    1.666666667, 4, 1.25, 1.2, 2, 1.5, 0.285714286, 0.4, 0.25, 
    1, 2, NA, 6, 8, 7, 0.857142857, 6, NA, 7, 7, NA, NA, NA, 
    NA, NA, 3.5, 1.666666667, 5, 4, 2, 4, 2, 0.333333333, 0.333333333, 
    NA, 0.75, 0.5, NA, 1.5, 3, 5, 5, 6, NA, 6, 4, 2, 5, 5, 5, 
    5, NA, NA, 4, 4, 3, 2.5, 3, 2.5, 2.333333333, 3.5, 3.5, 0.6, 
    0.4, 0.75, 3, 1, NA, 2.5, 5, NA, NA, 2.5, 1.5, NA, NA, NA, 
    4, NA, 1.25, 2, 5, 2.5, 6, 2, 1, 3, 1.5, 1, 1.666666667, 
    NA)), class = "data.frame", row.names = c(NA, -192L))

Itбыло бы более понятно с изображениями моих графиков, но, видимо, мне пока не разрешено включать изображения в мои сообщения, извините

Заранее спасибо за вашу помощь

1 Ответ

0 голосов
/ 18 июля 2019

вы можете попробовать

library(tidyverse)
df %>% 
  as_tibble() %>%  
 ggplot(aes(x=fusion, y=FF)) +
  geom_boxplot(aes(colour=fusion))+
  ggsignif::geom_signif(comparisons = combn(levels(df$fusion), 2, simplify = F), step_increase = 0.3) +
  ggpubr::stat_compare_means() +
  facet_wrap(~Genotype)+
  xlab(" ")+
  ylab("Days after sowing")

enter image description here

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