Plot ANOVA error message: non-numeric argument to binary operator

chaj · June 23, 2022, 11:58am

I am trying to plot my ANOVA results on boxplot per group per time but I receive the following error message: non-numeric argument to binary operator.

I started with R not so long ago and I can't figure out what's the problem.
All the df is complete
Levene test ok
Barlett test ok

Any help would be very appreciated thanks.

Test and plot

# Test 
res.aov_totalcholesterol <- anova_test(
  data = final_clean_low_MV_totalcholesterol, dv = totalcholesterol, wid = id,
  between = group, within = time
  )
get_anova_table(res.aov_glucose)

# Post-hoc tests

# Effect of group at each time point (group has an effect pre intervention)
one.way_totalcholesterol <- final_clean_low_MV_totalcholesterol%>%
  group_by(time) %>%
  anova_test(dv = totalcholesterol, wid = id, between = group) %>%
  get_anova_table() %>%
  adjust_pvalue(method = "bonferroni")
one.way_totalcholesterol

# Pairwise comparisons between group levels and time 
pwc_totalcholesterol <- final_clean_low_MV_totalcholesterol%>%
  group_by(time) %>%
  pairwise_t_test(totalcholesterol ~ group, p.adjust.method = "bonferroni")
pwc_totalcholesterol


## Effect of time in each group
one.way2_totalcholesterol <- final_clean_low_MV_totalcholesterol %>%
  group_by(group) %>%
  anova_test(dv = totalcholesterol, wid = id, within = time) %>%
  get_anova_table() %>%
  adjust_pvalue(method = "bonferroni")
one.way2_totalcholesterol


library(ggpubr)

# Plots ERROR MESSAGE: non-numeric argument to binary operator
pwc <- pwc_totalcholesterol %>% add_xy_position(x = "time")
pwc.filtered <- pwc %>% filter(time != "t1")
bxp + 
  stat_pvalue_manual(pwc.filtered, tip.length = 0, hide.ns = TRUE) + labs(subtitle = get_test_label(res.aov_totalcholesterol, detailed = TRUE),
    caption = get_pwc_label(pwc_totalcholesterol))

Reproducible df

final_clean_low_MV_totalcholesterol<-structure(list(id = structure(c("SA01", "SA02", "SA03", "SA04", 
"SA05", "SA06", "SA07", "SA08", "SA09", "SA10", "SA11", "SA12", 
"SA13", "SA14", "SA15", "SA16", "SA17", "SA18", "SA19", "SA20", 
"SA21", "SA22", "SA23", "SA24", "SA25", "SA26", "SA27", "SA28", 
"SA29", "SA30", "SA31", "SA32", "SA33", "SA34", "SA35", "SA36", 
"SA37", "SA38", "SA39", "SA40", "SA41", "SA42", "SA43", "SA44", 
"SA45", "SA46", "SA47", "SA48", "SA49", "SA50", "SA51", "SA52", 
"SA53", "SA54", "SA56", "SA57", "SA58", "SA59", "SA60", "SA61", 
"SA62", "SA63", "SA64", "SA65", "SA66", "SA67", "SA68", "SA69", 
"SA72", "SA73", "SA74", "SA75", "SA76", "SA77", "SA78", "SA79", 
"SA80", "SA81", "SA82", "SA83", "SA84", "SA85", "SA86", "SA87", 
"SA88", "SA89", "SA90", "SA92", "SA93", "SA94", "SA95", "SA96", 
"SA97", "SA99", "SA100", "SA101", "SA102", "SA103", "SA104", 
"SA105", "SA107", "SA108", "SA109", "SA110", "SA111", "SA112", 
"SA113", "SA114", "SA115", "SA116", "SA118", "SC01", "SC02", 
"SC03", "SC04", "SC05", "SC06", "SC07", "SC08", "SC09", "SC10", 
"SC11", "SC12", "SC13", "SC14", "SC15", "SC16", "SC17", "SC18", 
"SC19", "SC20", "SC21", "SC22", "SC23", "SC24", "SC25", "SC26", 
"SC27", "SC28", "SC29", "SC30", "SC31", "SC32", "SC33", "SC34", 
"SC35", "SC36", "SC37", "SC38", "M01", "M02", "M03", "M04", "M05", 
"M06", "M07", "M08", "M09", "M10", "M11", "M12", "M13", "M14", 
"M15", "M16", "M17", "M18", "M19", "M20", "M21", "M22", "M23", 
"M24", "M25", "M26", "M27", "M28", "M29", "M30", "M31", "M32", 
"M33", "M34", "M35", "M36", "M37", "M38", "M39", "M40", "M41", 
"M42", "M43", "M44", "M45", "M46", "M47", "M48", "M49", "M50", 
"M51", "M52", "M53", "SA01", "SA02", "SA03", "SA04", "SA05", 
"SA06", "SA07", "SA08", "SA09", "SA10", "SA11", "SA12", "SA13", 
"SA14", "SA15", "SA16", "SA17", "SA18", "SA19", "SA20", "SA21", 
"SA22", "SA23", "SA24", "SA25", "SA26", "SA27", "SA28", "SA29", 
"SA30", "SA31", "SA32", "SA33", "SA34", "SA35", "SA36", "SA37", 
"SA38", "SA39", "SA40", "SA41", "SA42", "SA43", "SA44", "SA45", 
"SA46", "SA47", "SA48", "SA49", "SA50", "SA51", "SA52", "SA53", 
"SA54", "SA56", "SA57", "SA58", "SA59", "SA60", "SA61", "SA62", 
"SA63", "SA64", "SA65", "SA66", "SA67", "SA68", "SA69", "SA72", 
"SA73", "SA74", "SA75", "SA76", "SA77", "SA78", "SA79", "SA80", 
"SA81", "SA82", "SA83", "SA84", "SA85", "SA86", "SA87", "SA88", 
"SA89", "SA90", "SA92", "SA93", "SA94", "SA95", "SA96", "SA97", 
"SA99", "SA100", "SA101", "SA102", "SA103", "SA104", "SA105", 
"SA107", "SA108", "SA109", "SA110", "SA111", "SA112", "SA113", 
"SA114", "SA115", "SA116", "SA118", "SC01", "SC02", "SC03", "SC04", 
"SC05", "SC06", "SC07", "SC08", "SC09", "SC10", "SC11", "SC12", 
"SC13", "SC14", "SC15", "SC16", "SC17", "SC18", "SC19", "SC20", 
"SC21", "SC22", "SC23", "SC24", "SC25", "SC26", "SC27", "SC28", 
"SC29", "SC30", "SC31", "SC32", "SC33", "SC34", "SC35", "SC36", 
"SC37", "SC38", "M01", "M02", "M03", "M04", "M05", "M06", "M07", 
"M08", "M09", "M10", "M11", "M12", "M13", "M14", "M15", "M16", 
"M17", "M18", "M19", "M20", "M21", "M22", "M23", "M24", "M25", 
"M26", "M27", "M28", "M29", "M30", "M31", "M32", "M33", "M34", 
"M35", "M36", "M37", "M38", "M39", "M40", "M41", "M42", "M43", 
"M44", "M45", "M46", "M47", "M48", "M49", "M50", "M51", "M52", 
"M53"), label = "Code of PrevenGo", format.spss = "A5", display_width = 12L), 
    group = 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, 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, 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, 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, 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, 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, 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, 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, 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, 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), levels = c("Metab", "SA", "SC"), class = "factor"), 
    time = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 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, 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, 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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L), levels = c("1", "2"), class = "factor"), 
    totalcholesterol = structure(c(166, 172, 229, 209, 171, 211, 
    140, 161, 131, 195, 157, 149, 151, 145, 165, 163, 126, 146, 
    113, 122, 184, 202, 196, 197, 179, 199, 185, 151, 161, 162, 
    165, 164, 161, 156, 153, 199, 156, 155, 160, 163, 209, 173, 
    125, 194, 170, 226, 197, 159, 122, 112, 199, 122, 163, 154, 
    194, 146, 138, 194, 149, 174, 125, 156, 163, 200, 142, 150, 
    163, 199, 118, 147, 163, 147, 157, 173, 170, 217, 127, 249, 
    158, 201, 170, 189, 149, 172, 184, 129, 148, 123, 186, 168, 
    141, 172, 108, 155, 164, 130, 152, 150, 72, 121, 180, 155, 
    156, 191, 151, 203, 146, 152, 186, 221, 172, 210, 174, 120, 
    151, 175, 143, 180, 169, 143, 117, 142, 146, 174, 173, 158, 
    197, 120, 128, 144, 172, 168, 211, 211, 226, 192, 179, 135, 
    185, 110, 165, 228, 175, 178, 150, 173, 161, 112, 131, 181, 
    166, 134, 163, 151, 175, 195, 190, 124, 159, 161, 115, 122, 
    173, 190, 145, 168, 199, 174, 159, 167, 155, 182, 167, 168, 
    199, 126, 144, 139, 162, 208, 132, 139, 154, 165, 188, 188, 
    130, 214, 150, 146, 178, 158, 165, 148, 171, 197, 160, 145, 
    171, 188, 168, 209, 156, 156, 207, 226, 151, 212, 159, 194, 
    122, 225, 140, 141, 173, 150, 134, 133, 121, 144, 146, 125, 
    184, 145, 167, 191, 149, 178, 129, 149, 176, 162, 171, 144, 
    162, 155, 173, 166, 147, 139, 156, 173, 146, 182, 134, 190, 
    161, 168, 224, 142, 113, 115, 200, 140, 141, 156, 190, 158, 
    199, 156, 199, 171, 135, 143, 139, 175, 159, 184, 12, 162, 
    112, 144, 161, 145, 161, 177, 188, 169, 190, 197, 112, 153, 
    193, 200, 165, 176, 185, 113, 150, 12, 163, 166, 115, 158, 
    185, 143, 179, 143, 157, 176, 163, 113, 192, 130, 142, 126, 
    166, 120, 155, 155, 114, 188, 175, 235, 155, 114, 155, 186, 
    146, 144, 171, 183, 113, 128, 144, 146, 182, 168, 179, 166, 
    100, 131, 225, 158, 199, 192, 201, 164, 149, 156, 171, 113, 
    168, 166, 182, 146, 146, 175, 172, 112, 129, 171, 185, 156, 
    184, 202, 131, 176, 186, 120, 168, 178, 165, 148, 190, 228, 
    140, 168, 178, 171, 108, 163, 171, 168, 174, 172, 271, 146, 
    165, 164, 168, 195, 163, 147, 158, 130, 212, 157, 159, 212, 
    183, 179, 189, 164, 180, 137, 192, 192, 181, 105, 150, 202, 
    202, 192), format.spss = "F4.2", display_width = 11L)), class = "data.frame", row.names = c(NA, 
-404L))

system · July 14, 2022, 11:59am

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