multiple t-test with different species and treatments

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?

        [[alternative HTML version deleted]]

______________________________________________
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.

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).
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?

        [[alternative HTML version deleted]]

______________________________________________
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.

[[alternative HTML version deleted]]

______________________________________________
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.