Hello,
I'm trying to do a One-Way Anova on multiple components and then a TuckeyHSD and store the pvalue of the all permutation
So far this is my code but when I did reprex it work and then it stopped working ... Now I have the error message :
Error: Must group by variables found in `.data`.
* Column `Compound.Name` is not found.
Run `rlang::last_error()` to see where the error occurred.
So I did the following :
> rlang::last_trace()
<error/rlang_error>
Must group by variables found in `.data`.
* Column `Compound.Name` is not found.
Backtrace:
x
1. +-`%>%`(...)
2. +-dplyr::ungroup(.)
3. +-dplyr::do(., broom::tidy(TukeyHSD(aov(Measured.Area ~ size, data = .))))
4. +-dplyr::group_by(., Compound.Name)
5. \-dplyr:::group_by.data.frame(., Compound.Name)
6. \-dplyr::group_by_prepare(.data, ..., .add = .add)
any idea as to why it suddently stopped working ?
Cheers,
Arzock
read.csv(file = 'https://filebin.net/9s9pbeg0n5of10jm/test-file-anova.csv?t=8vzw4ugs', header = T) %>% group_by(Compound.Name) %>% do(broom::tidy(TukeyHSD(aov(Measured.Area ~ size, data =.)))) %>% ungroup
#> Warning in qtukey(conf.level, length(means), x$df.residual): NaNs produced
#> Warning in qtukey(conf.level, length(means), x$df.residual): NaNs produced
#> Warning in ptukey(abs(est), length(means), x$df.residual, lower.tail = FALSE):
#> NaNs produced
#> # A tibble: 37 x 8
#> Compound.Name term contrast null.value estimate conf.low conf.high
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1-Methyladenosine,tri~ size M-L 0 -4.22e7 -2.45e8 1.60e8
#> 2 1-Methyladenosine,tri~ size p-L 0 1.15e6 -2.85e8 2.88e8
#> 3 1-Methyladenosine,tri~ size S-L 0 -4.05e7 -2.43e8 1.62e8
#> 4 1-Methyladenosine,tri~ size p-M 0 4.34e7 -2.43e8 3.30e8
#> 5 1-Methyladenosine,tri~ size S-M 0 1.68e6 -2.01e8 2.04e8
#> 6 1-Methyladenosine,tri~ size S-p 0 -4.17e7 -3.28e8 2.45e8
#> 7 1-Monopalmitin, 2TMS ~ size M-L 0 3.27e6 -4.47e6 1.10e7
#> 8 1-Monopalmitin, 2TMS ~ size p-L 0 1.21e7 1.15e6 2.30e7
#> 9 1-Monopalmitin, 2TMS ~ size S-L 0 7.28e6 -4.55e5 1.50e7
#> 10 1-Monopalmitin, 2TMS ~ size p-M 0 8.82e6 -2.12e6 1.98e7
#> # ... with 27 more rows, and 1 more variable: adj.p.value <dbl>
#> Warning in qtukey(conf.level, length(means), x$df.residual): NaNs produced
#> Warning in qtukey(conf.level, length(means), x$df.residual): NaNs produced
#> Warning in ptukey(abs(est), length(means), x$df.residual, lower.tail = FALSE):
#> NaNs produced
#> # A tibble: 37 x 8
#> Compound.Name term contrast null.value estimate conf.low conf.high
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1-Methyladenosine,tri~ size M-L 0 -4.22e7 -2.45e8 1.60e8
#> 2 1-Methyladenosine,tri~ size p-L 0 1.15e6 -2.85e8 2.88e8
#> 3 1-Methyladenosine,tri~ size S-L 0 -4.05e7 -2.43e8 1.62e8
#> 4 1-Methyladenosine,tri~ size p-M 0 4.34e7 -2.43e8 3.30e8
#> 5 1-Methyladenosine,tri~ size S-M 0 1.68e6 -2.01e8 2.04e8
#> 6 1-Methyladenosine,tri~ size S-p 0 -4.17e7 -3.28e8 2.45e8
#> 7 1-Monopalmitin, 2TMS ~ size M-L 0 3.27e6 -4.47e6 1.10e7
#> 8 1-Monopalmitin, 2TMS ~ size p-L 0 1.21e7 1.15e6 2.30e7
#> 9 1-Monopalmitin, 2TMS ~ size S-L 0 7.28e6 -4.55e5 1.50e7
#> 10 1-Monopalmitin, 2TMS ~ size p-M 0 8.82e6 -2.12e6 1.98e7
#> # ... with 27 more rows, and 1 more variable: adj.p.value <dbl>
Created on 2021-04-16 by the reprex package (v2.0.0)