CODEWITHSUNDEEP
rmatrixdplyr
Raphaël Bilgraer
Raphaël Bilgraer

Reputation: 31

Apply dplyr functions to several columns of a matrix

I have a 38467*59 data frame. Here is a subset of the whole dataset

mydata.2
   Unique.Groups Protein.group.IDs Intensity.R1 Intensity.R2
1            yes                 0      9701600     17971000
2            yes                 0            0     29308000
3            yes                 1     49083000     75300000
4            yes                 2     53926000     13926000
5            yes                 3     35255000     35750000
6            yes                 3    302600000    295410000
7            yes                 4     72766000     51914000
8            yes                 5     33657000     31027000
9            yes                 5     16504000     20689000
10           yes                 5    191170000    209230000
11           yes                 5    155710000    150280000
12           yes                 5    402890000    529300000
13           yes                 5    633480000    522180000
14           yes                 5     92339000            0
15           yes                 6            0     30646000
16           yes                 6     67494000     92326000
17           yes                 6     20205000            0
18           yes                 7     94183000     76848000
19           yes                 7     15584000            0
20           yes                 7     27247000     38961000
21           yes                 7     98433000    390870000
22           yes                 8            0     36542000
23           yes                 8     83978000            0
24           yes                 8     53744000     95173000
25           yes                 8            0     53494000

I applied the following piece of code to Intensity.R1 column and I'd like to apply it automatically to all the Intensities column of the data frame. At each round I need to run the same code on Intensity.R1, Intensity.R2, and so on.

     data.normalized <- filter(mydata.2, Unique.Groups == "yes") %>% 
  group_by(Protein.group.IDs) %>%
  arrange(desc(Intensity.R1)) %>% 
  top_n(3, Intensity.R1) %>%
  mutate(sum.top.3.R1 = sum(Intensity.R1)) %>%
  filter(!duplicated(Protein.group.IDs)) %>%
  drop.levels() %>%
  arrange(Protein.group.IDs)

Each round should generate a new column (i.e sum.top.3) that I want to bind together in a final table. Here is an example just for the Intensity.R1 column.

data.normalized    
Source: local data frame [9 x 5]
    Groups: Protein.group.IDs

      Unique.Groups Protein.group.IDs Intensity.R1 Intensity.R2 sum.top.3.R1
    1           yes                 0      9701600     17971000      9701600
    2           yes                 1     49083000     75300000     49083000
    3           yes                 2     53926000     13926000     53926000
    4           yes                 3    302600000    295410000    337855000
    5           yes                 4     72766000     51914000     72766000
    6           yes                 5    633480000    522180000   1227540000
    7           yes                 6     67494000     92326000     87699000
    8           yes                 7     98433000    390870000    219863000
    9           yes                 8     83978000            0    137722000

Upvotes: 0

Views: 2813

Answers (1)

AntoniosK
AntoniosK

Reputation: 16121

I've created a simple example with same column names for you to modify.

library(dplyr)
library(gdata)

mydata.2 = data.frame(Unique.Groups = rep("yes",8),
                      Protein.group.IDs = c(1,1,1,1,2,2,2,2),
                      Intensity.R1 = c(10,40,20,30,40,50,70,60),
                      Intensity.R2 = c(70,40,50,60,40,10,20,30))

# function that orders a vector and keeps top 3 values and calculates the sum
ff = function(x) {x[order(-x)][1:3] %>% sum()}


filter(mydata.2, Unique.Groups == "yes") %>% 
  group_by(Protein.group.IDs) %>%
  select(-Unique.Groups) %>% # keep only grouping variable and the ones you need to calculate
  summarise_each(funs(ff))

Even in your code if you have used "summarise" instead of "mutate" you wouldn't have to to filter out duplicated ids. I don't know if I'm missing something with the drop.levels here. Also, i don't really think you needed the first "arrange" as top_n will keep the top 3, but not in the right order (which is not a problem here).

Upvotes: 1

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