Ошибка: столбец `` имеет неподдерживаемый тип NULL

Question

structure(list(Date = c("2019.01.26", "2019.01.26", "2019.01.26", 
"2019.01.26", "2019.01.26", "2019.01.26", "2019.01.26", "2019.01.26", 
"2019.01.26", "2019.01.26", "2019.01.26", "2019.01.26", "2019.01.26", 
"2019.01.26", "2019.01.26", "2019.01.26", "2019.01.26", "2019.01.26", 
"2019.01.26", "2019.01.26"), Participant = c("CV", "CV", "CV", 
"CV", "CV", "CV", "CV", "CV", "CV", "CV", "CV", "CV", "CV", "CV", 
"CV", "CV", "CV", "CV", "CV", "CV"), Machine_ASVZ = c("A1", "A1", 
"A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", 
"A1", "A1", "A1", "A1", "A1", "A1", "A1"), Machine = c("LEG PRESS", 
"LEG PRESS", "LEG PRESS", "LEG PRESS", "LEG PRESS", "LEG PRESS", 
"LEG PRESS", "LEG PRESS", "LEG PRESS", "LEG PRESS", "LEG PRESS", 
"LEG PRESS", "LEG PRESS", "LEG PRESS", "LEG PRESS", "LEG PRESS", 
"LEG PRESS", "LEG PRESS", "LEG PRESS", "LEG PRESS"), Set = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), Contraction_Mode = c("Con01", 
"Con02", "Con03", "Con04", "Con05", "Con06", "Con07", "Con08", 
"Con09", "Con10", "Con01", "Con02", "Con03", "Con04", "Con05", 
"Con06", "Con07", "Con08", "Con09", "Con10"), Time_Video_CV = c(1340, 
1160, 1220, 1260, 1560, 1020, 1060, 1100, 1060, 1040, 1080, 980, 
1020, 1000, 940, 1000, 960, 1000, 900, 980), Time_Video_GRFD = c(1360, 
1180, 1240, 1280, 1180, 1060, 1080, 1100, 1060, 1060, 1100, 980, 
1020, 1020, 980, 1020, 960, 980, 920, 1040), Time_Smartphone_1 = c(1650, 
1350, 1400, 1400, 1350, 1250, 1550, 1500, 1600, 1650, 2500, 1100, 
1100, 1150, 1100, 1200, 1350, 1450, 1200, 1600), Time_Smartphone_3 = c(1700, 
1350, 1350, 1350, 1300, 1250, 1600, 1500, 1650, 1650, 1300, 1100, 
1150, 1150, 1100, 1150, 1200, 1400, 1400, 1700), Rater_Mean = c(1350, 
1170, 1230, 1270, 1370, 1040, 1070, 1100, 1060, 1050, 1090, 980, 
1020, 1010, 960, 1010, 960, 990, 910, 1010), Smartphone_Mean = c(1675, 
1350, 1375, 1375, 1325, 1250, 1575, 1500, 1625, 1650, 1900, 1100, 
1125, 1150, 1100, 1175, 1275, 1425, 1300, 1650), Relative_Diff = c(0.241, 
0.154, 0.118, 0.083, 0.033, 0.202, 0.472, 0.364, 0.533, 0.571, 
0.743, 0.122, 0.103, 0.139, 0.146, 0.163, 0.328, 0.439, 0.429, 
0.634), RaterSmartphone_Diff = c(-325, -180, -145, -105, 45, 
-210, -505, -400, -565, -600, -810, -120, -105, -140, -140, -165, 
-315, -435, -390, -640), RaterSmartphone_Mean = c(1512.5, 1260, 
1302.5, 1322.5, 1347.5, 1145, 1322.5, 1300, 1342.5, 1350, 1495, 
1040, 1072.5, 1080, 1030, 1092.5, 1117.5, 1207.5, 1105, 1330), 
    Contraction_Mode_Levels = c("Con", "Con", "Con", "Con", "Con", 
    "Con", "Con", "Con", "Con", "Con", "Con", "Con", "Con", "Con", 
    "Con", "Con", "Con", "Con", "Con", "Con"), Rater_Diff = c(-20, 
    -20, -20, -20, 380, -40, -20, 0, 0, -20, -20, 0, 0, -20, 
    -40, -20, 0, 20, -20, -60), Smartphone_Diff = c(-50, 0, 50, 
    50, 50, 0, -50, 0, -50, 0, 1200, 0, -50, 0, 0, 50, 150, 50, 
    -200, -100), RaterSmartphone_Diff_Potential_Outlier = c(FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, 
    TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, 
    TRUE), Rater_Diff_Potential_Outlier = c(FALSE, FALSE, FALSE, 
    FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), 
    Smartphone_Diff_Potential_Outlier = c(FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE), Normalized_Error_Smartphone = c(19.4, 
    13.33, 10.55, 7.64, 3.4, 16.8, 32.06, 26.67, 34.77, 36.36, 
    42.63, 10.91, 9.33, 12.17, 12.73, 14.04, 24.71, 30.53, 30, 
    38.79), Participant_Age_Levels = structure(c(1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L), .Label = c("old", "young"), class = "factor"), Participant_Age = c(42, 
    42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 
    42, 42, 42, 42)), row.names = c(NA, 20L), class = "data.frame")

Вы можете найти пример моего data.frame. Я пытаюсь сгруппировать данные по Machine и Contraction_Mode_Levels, а затем суммировать тест Манна-Уинни-U для обоих двух факторов: «молодой» и «старый».

Я пытаюсь запустить Манна-Уни -U проверить в качестве следующего:

wilcox<-all_data_wide_outlier_levels %>% 
  group_by(Machine,Contraction_Mode_Levels) %>% 
  summarise_each(funs(wilcox.test(.[Participant_Age_Levels == "young"], 
                                  .[Participant_Age_Levels == "old"],
                                  paired=FALSE, alternative = c("two.sided"))$parameter,
                      wilcox.test(.[Participant_Age_Levels == "young"], 
                                  .[Participant_Age_Levels == "old"],
                                  paired=FALSE, alternative = c("two.sided"))$statistic,
                      wilcox.test(.[Participant_Age_Levels == "young"], 
                                  .[Participant_Age_Levels == "old"],
                                  paired=FALSE, alternative = c("two.sided"))$p.value),
                 vars = Rater_Mean)

, который возвращает ошибку:

Ошибка: столбец vars_$..1 имеет неподдерживаемый тип NULL

Answer 1

Поскольку на одном уровне были только ваши данные, я использую приведенный ниже пример, чтобы показать, как использовать метлу для получения статистики c, которая вам нужна:

library(broom)
library(dplyr)

df = data.frame(
Machine = sample(c("LEG PRESS","Y PRESS"),100,replace=TRUE),
Contraction_Mode_Levels = sample(c("Con01","Con02","Con03"),100,replace=TRUE),
Rater_Mean = runif(100),
Participant_Age_Levels = sample(c("young","old"),100,replace=TRUE))

df %>% group_by(Machine,Contraction_Mode_Levels) %>% do(tidy(wilcox.test(Rater_Mean ~  Participant_Age_Levels,data=. )))

# A tibble: 6 x 6
# Groups:   Machine, Contraction_Mode_Levels [6]
  Machine   Contraction_Mode_Lev… statistic p.value method           alternative
  <fct>     <fct>                     <dbl>   <dbl> <chr>            <chr>      
1 LEG PRESS Con01                        22   0.607 Wilcoxon rank s… two.sided  
2 LEG PRESS Con02                        45   0.730 Wilcoxon rank s… two.sided  
3 LEG PRESS Con03                        22   0.607 Wilcoxon rank s… two.sided  
4 Y PRESS   Con01                        38   0.604 Wilcoxon rank s… two.sided  
5 Y PRESS   Con02                        33   0.613 Wilcoxon rank s… two.sided  
6 Y PRESS   Con03                        45   0.696 Wilcoxon rank s… two.sided  

Из вашего кода, я полагаю, вы интересует только разница в Rater_Mean между «молодыми» и «старыми» группами в Participant_Age_Levels, поэтому вы можете написать wilcox.test(Rater_Mean ~ Participant_Age_Levels).