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Asked: 2026-06-13T07:41:20+00:00

I am using reshape2 package to shape my data and use it for t-test.

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I am using reshape2 package to shape my data and use it for t-test. For me it is easier to visualize the data in separate columns. I have three treatment combinations where “wat” is nested within “spp” and “ins” is nested within water. My demo table contains 3 response variable namely “tyr”, “esc” and “esc_R”. I would be interested in seeing how ins influence response -> “tyr” in “spp” -> Bl, with treatment “wat” -> High (just an example).

Here is my data:
demo.data

## Use orderBy function to sort data
library(doBy)
demo <- orderBy(~spp+wat+ins, data = demo)
## Create an unique data frame for a specific variable
df.bl.ins.1 <- demo[demo$spp == "Bl", c(1:3, 4)]
df.bl.ins.2 <- df.bl.ins.1[df.bl.ins.1$wat == "High", ]

And then I am having trouble executing dcast function.

df.bl.ins.tmp <- dcast(df.bl.ins.2, spp + wat ~ ins, value.var = "tyr")

I have found interesting information in the following threads

  1. Dason’s suggestion – which works really well with ToothGrowth demo dataset. Unfortunately, when the table has multiple treatments (more than 2) the solution did not remain simple. I agree with Maiasaura’s suggestions that creating an unique variable is the key to this problem. However, I am having hard time understanding what function(x) does or how to use it in my table.

Any help in this regard is much appreciated.

In addition, if you have alternative suggestions to do t-test without manipulating the original data frame (demo), I will be excited to hear about it.

Thanks in advance.

Edit
Here is what I am expecting, for “tyr”. In the following format I desire to compare “No” vs. “Yes” using a t-test.

spp wat ins No  Yes
Bl  High    No  0.3036  0.1987
Bl  High    No  0.2577  0.1112
Bl  High    No  NA  0.199
Bl  High    No  0.3299  0.1886
Bl  High    No  0.3301  0.2332
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1 Answer

  1. Editorial Team

    Perhaps I don’t understand exactly what you want to do, but I think you could run linear regression directly on your data. In this way, you could do t-tests on whether the coefficients of your model were zero or not. I think this might suffice, and serve also to tease apart the effects of each of your independent variables. Here is an example:

    summary(lm(tyr~spp+wat+ins,data=read.table('http://pastebin.com/raw.php?i=sR2MvBBA')))
Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.286386   0.016500  17.356  < 2e-16 ***
sppMan      -0.159514   0.015811 -10.089  1.3e-11 ***
watLow      -0.005501   0.015858  -0.347 0.730861    
insYes      -0.066741   0.015858  -4.209 0.000185 ***

    This will get you a t test for just the groups that you showed in your example:

    t.test(tyr~ins,data=df[df$spp=='Bl' & df$wat=='High',])
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