How can I show mean value, maximum value, minimum value, variance and z-values using tidyverse/dplyr?

[Laurent227]

Fellow R-Users,

I'm trying to convert these Base-R Codes into tidyverse-Codes but can't manage to get them to work. I tried using summarise() but I couldn't manage any of these tasks.

Would be thankful for any help!

[nirgrahamuk]

What have you tried so far? what is your specific problem?, we are more inclined towards helping you with specific coding problems rather than doing your work for you.

Could you please turn this into a self-contained REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[Matthias]

1. you need to have your data in "long"-format, see the ToothGrowth dataset:

> head(ToothGrowth, 20)
    len supp dose
1   4.2   VC  0.5
2  11.5   VC  0.5
3   7.3   VC  0.5
4   5.8   VC  0.5
5   6.4   VC  0.5
6  10.0   VC  0.5
7  11.2   VC  0.5
8  11.2   VC  0.5
9   5.2   VC  0.5
10  7.0   VC  0.5
11 16.5   VC  1.0
12 16.5   VC  1.0
13 15.2   VC  1.0
14 17.3   VC  1.0
15 22.5   VC  1.0
16 17.3   VC  1.0
17 13.6   VC  1.0
18 14.5   VC  1.0
19 18.8   VC  1.0
20 15.5   VC  1.0

2. You may need to group your data as you want to calculate these values on multiple conditions at the same time, here by dose and supp.
3. with summarise() the results are summarised in a single line per condition.
4. adding na.rm = TRUE helps when there migth be missing values (not needed for the example data but is better to add it - just in case)
5. you just define the column containing the data you want to calculate, without the $ notation.
6. you may want to read this book:
   5 Data transformation | R for Data Science

summarised = ToothGrowth %>% 
  group_by(supp, dose) %>% 
  summarise(mean = mean(len, na.rm = TRUE),
            sd = sd(len, na.rm = TRUE))

summarised
# A tibble: 6 x 4
# Groups:   supp [2]
  supp   dose  mean    sd
  <fct> <dbl> <dbl> <dbl>
1 OJ      0.5 13.2   4.46
2 OJ      1   22.7   3.91
3 OJ      2   26.1   2.66
4 VC      0.5  7.98  2.75
5 VC      1   16.8   2.52
6 VC      2   26.1   4.80