clyde | A collection of Java packages for making networked 3D games | 3D Animation library

Note that using geom_errorbar like this you can fix doing geom_errorbar((aes(ymin=Measurement_1-sd(Measurement_1), ymax=Measurement+sd(Measurement_1)))) but you do get on every group the same bar, it does not do it group wise.

I recommend using this instead, which will only show your errors for "Dante" in group "A" as your sample data has only one value for the other groups making SD=0.

I would use moment to create a date object using the information.

While reading the CSV file line by line with a fileReader, 2 days ago has to be used like this: moment().subtract(2, "days")... For example.

So the strategy is to determine which time "range" to use and then, create a moment object that will be used to sort the rows.

You need to specify which column is to be ordered/sorted even if the data frame contains only one column.

If you want to preserve the original order of names.ordered use order to create an index:

Your code is referencing an object called "df", which does not appear to exist. Also, when using kruskal.test with the arguments kruskal.test(formula, data), there is no need to reference the data frame in the formula. Providing kruskal.test a "data" argument will cause the function to search for the formula symbols first in the provided data. In other words, if data frame "x" contains columns "Height" and "Person", then the following would work:

I am the author of the example you already mentioned in a comment. One of of the commentors already pointed out correctly where the .group column came from. However, when pointing out the general difference between your code and the one in the example I find that

1. Data

   1a. Your data has 1 observation per factor level (=Person).

   1b. My data has multiple observations per factor level (=group).

2. Mean comparisons

   2a. You used FSA::Dunntest to fit a model and immediately compare means per factor level.

   2b. I used lm() to fid a model, then emmeans::emmeans() to compare means per factor level.

3. Compact Letter Display

   3a. You used rcompanion::cldList() to get the letters.

   3b. I used multcomp::cld() to get the letters.

I think point 2 and 3 are totally fine - it's simply different functions leading to the same goal. I tried your approach on my data and it worked:

You can use -

You can use bind_rows() from dplyr and separate from tidyr:

I'm not sure your purpose applying those tests. If code below is your purpose,

Remove the tilde, as in