CODEWITHSUNDEEP
rdataframesortingreshapealphabetical
Patrick
Patrick

Reputation: 23

Reshaping a dataframe in R by sorting just some fields in a row alphabetically

I have a few large dataframes in RStudio, that have this structure:

Original data structure

structure(list(CHROM = c("scaffold1000|size223437", "scaffold1000|size223437", 
"scaffold1000|size223437", "scaffold1000|size223437"), POS = c(666, 
1332, 3445, 4336), REF = c("A", "TA", "CTTGA", "GCTA"), RO = c(20, 
14, 9, 25), ALT_1 = c("GAT", "TGC", "AGC", "T"), ALT_2 = c("CAG", 
"TGA", "CGC", NA), ALT_3 = c("G", NA, "TGA", NA), ALT_4 = c("AGT", 
NA, NA, NA), AO_1 = c(13, 4, 67, 120), AO_2 = c(12, 5, 34, NA
), AO_3 = c(6, NA, 18, NA), AO_4 = c(101, NA, NA, NA), AOF_1 = c(8.55263157894737, 
17.3913043478261, 52.34375, 82.7586206896552), AOF_2 = c(7.89473684210526, 
21.7391304347826, 26.5625, NA), AOF_3 = c(3.94736842105263, NA, 
14.0625, NA), AOF_4 = c(66.4473684210526, NA, NA, NA)), class = "data.frame", row.names = c(NA, 
-4L))

But for an analysis I need it to look like this:

Desired output

 structure(list(CHROM = c("scaffold1000|size223437", "scaffold1000|size223437", 
    "scaffold1000|size223437", "scaffold1000|size223437"), POS = c(666, 
    1332, 3445, 4336), REF = c("A", "TA", "CTTGA", "GCTA"), RO = c(20, 
    14, 9, 25), ALT_1 = c("AGT", "TGA", "AGC", "T"), ALT_2 = c("CAG", 
    "TGC", "CGC", NA), ALT_3 = c("G", NA, "TGA", NA), ALT_4 = c("GAT", 
    NA, NA, NA), AO_1 = c(101, 5, 67, 120), AO_2 = c(12, 4, 34, NA
    ), AO_3 = c(6, NA, 18, NA), AO_4 = c(13, NA, NA, NA), AOF_1 = c(66.4473684210526, 
    21.7391304347826, 52.34375, 82.7586206896552), AOF_2 = c(7.89473684210526, 
    17.3913043478261, 26.5625, NA), AOF_3 = c(3.94736842105263, NA, 
    14.0625, NA), AOF_4 = c(8.55263157894737, NA, NA, NA)), class = "data.frame", row.names = c(NA, 
    -4L))

So what I would like to do is to rearrange the content of a row in a way, that the columns ALT_1, ALT_2, ALT_3, ALT_4 are alphabetically sorted, but at the same time I also need to rearrange the corresponding columns of AO and AOF, so that the values still match. (The value of AO_1 should still match with the sequence that was in ALT_1. So if ALT_1 becomes ALT_2 in the sorted dataframe, AO_1 should also become AO_2)

What I tried so far, but didn't work:

Pasting the values of ALT_1, AO_1, AOF_1 all in one field, so I have them together with

  if (is.na(X[i,6]) == FALSE) {
    X[i,6] <- paste(X[i,6],X[i,10],X[i,14],sep=" ")
  }
}

And then I wanted to extract every row as a vector to sort the values and put it back in the dataframe, but I didn't manage to do this.

So the question would be how I can order the dataframe to get the desired output? (I need to apply this to 32 dataframes with each having >100.000 values)

Upvotes: 1

Views: 73

Answers (2)

TarJae
TarJae

Reputation: 78947

Here is dplyr solution. Took me some time and I needed some help pivot_wider dissolves arrange:

library(dplyr)
library(tidyr)

df1 %>% 
  mutate(id = row_number()) %>% 
  unite("conc1", c(ALT_1, AO_1, AOF_1), sep = "_") %>% 
  unite("conc2", c(ALT_2, AO_2, AOF_2), sep = "_") %>% 
  unite("conc3", c(ALT_3, AO_3, AOF_3), sep = "_") %>% 
  unite("conc4", c(ALT_4, AO_4, AOF_4), sep = "_") %>% 
  pivot_longer(
    starts_with("conc")
  ) %>% 
  mutate(value = ifelse(value=="NA_NA_NA", NA_character_, value)) %>% 
  group_by(id) %>% 
  mutate(value = sort(value, na.last = TRUE)) %>% 
  ungroup() %>% 
  pivot_wider(
    names_from = name,
    values_from = value,
    values_fill = "0"
  ) %>% 
  separate(conc1, c("ALT_1", "AO_1", "AOF_1"), sep = "_") %>% 
  separate(conc2, c("ALT_2", "AO_2", "AOF_2"), sep = "_") %>% 
  separate(conc3, c("ALT_3", "AO_3", "AOF_3"), sep = "_") %>% 
  separate(conc4, c("ALT_4", "AO_4", "AOF_4"), sep = "_") %>% 
  select(CHROM, POS, REF, RO, starts_with("ALT"), starts_with("AO_"), starts_with("AOF_")) %>% 
  type.convert(as.is=TRUE)

  CHROM                     POS REF      RO ALT_1 ALT_2 ALT_3 ALT_4  AO_1  AO_2  AO_3  AO_4 AOF_1 AOF_2 AOF_3 AOF_4
  <chr>                   <int> <chr> <int> <chr> <chr> <chr> <chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
1 scaffold1000|size223437   666 A        20 AGT   CAG   G     GAT     101    12     6    13  66.4  7.89  3.95  8.55
2 scaffold1000|size223437  1332 TA       14 TGA   TGC   NA    NA        5     4    NA    NA  21.7 17.4  NA    NA   
3 scaffold1000|size223437  3445 CTTGA     9 AGC   CGC   TGA   NA       67    34    18    NA  52.3 26.6  14.1  NA   
4 scaffold1000|size223437  4336 GCTA     25 T     NA    NA    NA      120    NA    NA    NA  82.8 NA    NA    NA

Upvotes: 2

Wimpel
Wimpel

Reputation: 27742

here is a data.table approach

library(data.table)
# Set to data.table format
setDT(mydata)
# Melt to long format
DT.melt <- melt(mydata, measure.vars = patterns(ALT = "^ALT_", AO = "^AO_", AOF = "^AOF_"))
# order by groups, na's at the end
setorderv(DT.melt, cols = c("CHROM", "POS",  "ALT"), na.last = TRUE)
# cast to wide again, use rowid() for numbering
dcast(DT.melt, CHROM + POS + REF + RO ~ rowid(REF), value.var = list("ALT", "AO", "AOF"))
#                      CHROM  POS   REF RO ALT_1 ALT_2 ALT_3 ALT_4 AO_1 AO_2 AO_3 AO_4    AOF_1     AOF_2     AOF_3    AOF_4
# 1: scaffold1000|size223437  666     A 20   AGT   CAG     G   GAT  101   12    6   13 66.44737  7.894737  3.947368 8.552632
# 2: scaffold1000|size223437 1332    TA 14   TGA   TGC  <NA>  <NA>    5    4   NA   NA 21.73913 17.391304        NA       NA
# 3: scaffold1000|size223437 3445 CTTGA  9   AGC   CGC   TGA  <NA>   67   34   18   NA 52.34375 26.562500 14.062500       NA
# 4: scaffold1000|size223437 4336  GCTA 25     T  <NA>  <NA>  <NA>  120   NA   NA   NA 82.75862        NA        NA       NA

Upvotes: 1

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