数据框列表，尝试为每个 dataframe 创建具有归一化值的新列

Question

I'm new to r and mostly work with dataframe s. A frequent task is to normalize counts for several parameters from several data frames. I have a demo dataset: dataset

Season	Product	Quality	Sales
Winter	Apple	bad	345
Winter	Apple	good	13
Winter	Potato	bad	23
Winter	Potato	good	66
Winter	Beer	bad	345
Winter	Beer	good	34
Summer	Apple	bad	88
Summer	Apple	good	90
Summer	Potato	bad	123
Summer	Potato	good	457
Summer	Beer	bad	44
Summer	Beer	good	546

What I want to do is add a column "FC" ([tag:fold change]) for "Sales". FC must be calculated for each "Season" and "Product" according to "Quality". "Bad" is the baseline.

Desired result:

Season	Product	Quality	Sales	FC
Winter	Apple	bad	345	1.00
Winter	Apple	good	13	0.04
Winter	Potato	bad	23	1.00
Winter	Potato	good	66	2.87
Winter	Beer	bad	345	1.00
Winter	Beer	good	34	0.10
Summer	Apple	bad	88	1.00
Summer	Apple	good	90	1.02
Summer	Potato	bad	123	1.00
Summer	Potato	good	457	3.72
Summer	Beer	bad	44	1.00
Summer	Beer	good	546	12.41

One way to do it is to filter first by "Season" and then by "Product" (eg creating subset data frame subset_winter_apple ) and then calculate FC similarly to this: subset_winter_apple$FC = subset_winter_apple$Sales / subset_winter_apple$Sales[1]. Later on, I can then combine all subset dataframes again eg using rbind to reconstitute the original data frame with the FC column. However, this is highly inefficient. So I thought of split ting the data frame and creating a list : split(dataset, list(dataset$Season, dataset$Product)) .

However, now I struggle with the normalisation (FC calculation) as I do not know how to reference the specific first cell value of "Sales" in the list of data frames so that each value in that column in each listed data frame is individually normalized. I did manage to calculate an FC value for the list, however, it is an exact copy in each listed data frame from the first one using lappy :

lapply(dataset, function(DF){DF$FC = dataset[[1]]$Sales/dataset[[1]]$Sales[1]; DF})

Clearly, I do not know how to reference the first cell in a specific column to normalize the entire column for each listed data frame . Can somebody please help me?

Many thanks in advance for your suggestions.

Answer 1

dplyr solution

Using logical indexing within a grouped mutate() :

library(dplyr)

dataset %>%
  group_by(Season, Product) %>%
  mutate(FC = Sales / Sales[Quality == "bad"]) %>%
  ungroup()

# A tibble: 12 × 5
   Season Product Quality Sales      FC
   <chr>  <chr>   <chr>   <int>   <dbl>
 1 Winter Apple   bad       345  1     
 2 Winter Apple   good       13  0.0377
 3 Winter Potato  bad        23  1     
 4 Winter Potato  good       66  2.87  
 5 Winter Beer    bad       345  1     
 6 Winter Beer    good       34  0.0986
 7 Summer Apple   bad        88  1     
 8 Summer Apple   good       90  1.02  
 9 Summer Potato  bad       123  1     
10 Summer Potato  good      457  3.72  
11 Summer Beer    bad        44  1     
12 Summer Beer    good      546 12.4   

Base R solution

Using by() :

dataset <- by(
  dataset,
  list(dataset$Season, dataset$Product),
  \(x) transform(x, FC = Sales / Sales[Quality == "bad"])
)

dataset <- do.call(rbind, dataset)

dataset[order(as.numeric(rownames(dataset))), ]

   Season Product Quality Sales          FC
1  Winter   Apple     bad   345  1.00000000
2  Winter   Apple    good    13  0.03768116
3  Winter  Potato     bad    23  1.00000000
4  Winter  Potato    good    66  2.86956522
5  Winter    Beer     bad   345  1.00000000
6  Winter    Beer    good    34  0.09855072
7  Summer   Apple     bad    88  1.00000000
8  Summer   Apple    good    90  1.02272727
9  Summer  Potato     bad   123  1.00000000
10 Summer  Potato    good   457  3.71544715
11 Summer    Beer     bad    44  1.00000000
12 Summer    Beer    good   546 12.40909091