multiple t-test with different species and treatments
Dear Lingling Wen: Bert has forwarded your message to the list because one person cannot usually answer every question... so many heads are better than one. However, you seem to have neglected his other advice about providing a complete example including data. Further, you are using several specialized packages that may require more specific assistance than you may find on this list... as the Posting Guide warns you, such packages are technically out of scope here (because contributed packages may introduce hidden behavior that only people familiar with those specific packages can recognize). If someone unfamiliar with the package can run your example (not possible here because of lack of data) they might see something about the errors that can help anyway. The one R-language issue I can see in your code is the use of single-quote marks (used for entering string literals) on what should probably be a variable rather than back-tick quotes (used to denote language symbols).
On December 15, 2020 7:43:56 AM PST, Bert Gunter <bgunter.4567 at gmail.com> wrote:
Unless there is good reason not to, always cc r-help, which I have done here. Bert Gunter On Tue, Dec 15, 2020 at 1:16 AM Lingling Wen <wenlingling912 at gmail.com> wrote:
Dear Bert Gunter, Good day, Thank you for your comments about the posting policy. I am sorry for bothering you with the text against the posting policy in this list.
I will
read the policy carefully and improve it in my future posting. Regarding the question I asked, actually, it is for my experiment
data
analysis but not homework. I've tried code as followed:
library(dplyr)
library(tidyverse)
library(rstatix)
library(ggpubr)
test <- read.csv(file.choose(), header=TRUE)
print(test)
mydata <- test %>%
pivot_longer(
cols = c(3:8),
names_to = "Metabolites",
values_to = "Relative content",
values_drop_na = FALSE)
mydata
stat <- group_by(mydata, Metabolites,Treatment) %>%
t_test('Relative content' ~ Treatment) %>%
adjust_pvalue(method = "BH") %>%
add_significance()
When I run the code, it always shows error like this: Error in
terms.formula(formula) : invalid term in model formula.
Because I have a lot of metabolic data to deal with, I think R will
help
to save a lot of time so I am learning to use it. But I could not
figure
out what's the problem when it gives error feedback. It would be very appreciated if I could get help from the list. Thank you! Lingling On Mon, 14 Dec 2020 at 01:19, Bert Gunter <bgunter.4567 at gmail.com>
wrote:
1. Please read and follow the posting guide linked below. 2. No html -- this is a plain text list. 3. Use ?dput to provide us your data so that we don't have to
convert it
for you. 4. We expect you to first make an effort to do your own coding. See ?t.test, which you could also have found yourself by a web search (rseek.org is a reasonable place
to
search from for R-related stuff, though I have usually found that a plain google search does the
job).
5. Is this homework? -- this list has a no homework policy (see the posting guide). Bert Gunter "The trouble with having an open mind is that people keep coming
along
and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sun, Dec 13, 2020 at 2:33 PM Lingling Wen
<wenlingling912 at gmail.com>
wrote:
Dear R users, I would like to ask for help with the code of multiple t-test. I
have a
dataset as followed: Species Treatment var1 var2 var2 var4 var5 var6 Blue D 0.022620093 0.125079631 0.04522571 0.010105835 0.013418019 1.455646741 Blue D 0.02117295 0.073544277 0.0311234 0.008742305 0.03261776 0.982196898 Blue D 0.021896521 0.112681274 0.05664344 0.013512548 0.032380618 1.777003683 Green D 0.032749726 0.087705198 0.13699174 0.009902168 0.083534492 1.553758965 Green D 0.036468693 0.115829755 0.10941521 0.012139481 0.206929915 2.610557732 Green D 0.043594022 0.062832712 0.12232853 0.015045559 0.111687593 1.99552401 Orange D 0.022617656 0.11465489 0.02882994 0.013304181 0.018175693 1.72075866 Orange D 0.026211773 0.099294867 0.03387876 0.013408254 0.02971197 2.184955376 Orange D 0.032205662 0.057267709 0.03883165 0.007744362 0.026553323 1.27255601 White D 0.041135469 0.085531343 0.06921425 0.011496168 0.010196895 0.573205411 White D 0.045142458 0.111429194 0.03546278 0.009196729 0.009968818 0.748529991 White D 0.031471913 0.050175149 0.05233851 0.011447205 0.010424973 0.92385457 Blue W 0.022222296 0.112334911 0.04080824 0.016064488 0.031047157 0.885523847 Blue W 0.040238733 0.121941307 0.04239768 0.010310538 0.020106944 0.751643349 Blue W 0.031508947 0.131547704 0.05212774 0.015720985 0.013932284 0.881234886 Green W 0.021070032 0.121018603 0.38202466 0.022152283 0.038479532 0.662605036 Green W 0.026562365 0.108269047 0.44028708 0.019344875 0.090798566 0.746330971 Green W 0.02926478 0.084080729 0.32376224 0.012609717 0.097744041 0.969301308 Orange W 0.02456562 0.134535891 0.09135624 0.007701481 0.017310058 0.966322354 Orange W 0.032095541 0.149347595 0.06048885 0.010332579 0.017457175 0.561561725 Orange W 0.039120696 0.141941743 0.02962146 0.005889924 0.017162941 0.502529091 White W 0.033903057 0.061460583 0.0492955 0.012457767 0.029929334 0.70986421 White W 0.033630233 0.115782233 0.02675399 0.021391535 0.023774961 1.176680075 White W 0.030638581 0.065074112 0.03678494 0.014781912 0.03529703 0.805500558 I wanted to perform a t-test between the treatment "D" and "W" of
each
species for all of the variables (var1, var2,...). Could anyone
suggest
the packages or code for this analysis?
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______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Sent from my phone. Please excuse my brevity.