R - How to rearrange rows in a data frame while maintaining their grouping?

Question

I have the following data frame. The rows are currently ordered in groups of species, i.e. the sorgho group followed by the poacee group.

# create a dataset
specie <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) )
condition <- rep(c("normal" , "stress" , "Nitrogen") , 4)
value <- abs(rnorm(12 , 0 , 15))
data <- data.frame(specie,condition,value)
print(data)

     specie condition     value
1    sorgho    normal 12.623696
2    sorgho    stress 11.394047
3    sorgho  Nitrogen  0.498003
4    poacee    normal 14.589322
5    poacee    stress 10.744153
6    poacee  Nitrogen  7.299742
7    banana    normal  9.845850
8    banana    stress  9.416088
9    banana  Nitrogen  4.178521
10 triticum    normal 13.230663
11 triticum    stress 30.658355
12 triticum  Nitrogen  9.402721

How can I rearrange these groupings so that they are in order of decreasing nitrogen value? I want the data frame to be reorganized to resemble this:

     specie condition     value
10 triticum    normal 13.230663
11 triticum    stress 30.658355
12 triticum  Nitrogen  9.402721
4    poacee    normal 14.589322
5    poacee    stress 10.744153
6    poacee  Nitrogen  7.299742
7    banana    normal  9.845850
8    banana    stress  9.416088
9    banana  Nitrogen  4.178521
1    sorgho    normal 12.623696
2    sorgho    stress 11.394047
3    sorgho  Nitrogen  0.498003

Answer 1

We can filter the 'Nitrogen' rows, arrange 'value' in the descending order, extract the 'specie' and use that as levels to `arrange the 'specie' column

library(dplyr)
lvls <- data %>% 
      filter(condition == 'Nitrogen') %>% 
      arrange(desc(value)) %>% 
      pull(specie) 
data %>% 
        arrange(factor(specie, levels = lvls))%>%
        as_tibble

-output

# A tibble: 12 x 3
#   specie   condition  value
#   <chr>    <chr>      <dbl>
# 1 triticum normal    13.2  
# 2 triticum stress    30.7  
# 3 triticum Nitrogen   9.40 
# 4 poacee   normal    14.6  
# 5 poacee   stress    10.7  
# 6 poacee   Nitrogen   7.30 
# 7 banana   normal     9.85 
# 8 banana   stress     9.42 
# 9 banana   Nitrogen   4.18 
#10 sorgho   normal    12.6  
#11 sorgho   stress    11.4  
#12 sorgho   Nitrogen   0.498

---

Or doing this in a single pipe

data %>%
    arrange(factor(specie, levels = 
          unique(specie)[order(-value[condition == 'Nitrogen'])]))

---

Or using base R

data[order(with(data, factor(specie, levels = 
     unique(specie)[order(-value[condition == "Nitrogen"])]))),]

data

data <- structure(list(specie = c("sorgho", "sorgho", "sorgho", "poacee", 
"poacee", "poacee", "banana", "banana", "banana", "triticum", 
"triticum", "triticum"), condition = c("normal", "stress", "Nitrogen", 
"normal", "stress", "Nitrogen", "normal", "stress", "Nitrogen", 
"normal", "stress", "Nitrogen"), value = c(12.623696, 11.394047, 
0.498003, 14.589322, 10.744153, 7.299742, 9.84585, 9.416088, 
4.178521, 13.230663, 30.658355, 9.402721)), class = "data.frame",
row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))

Answer 2

Another base R option (less elegant than @akrun's base R solution)

with(
  data,
  do.call(
    rbind,
    split(data, factor(specie, levels = unique(specie)))[order(-value[condition == "Nitrogen"])]
  )
)

which gives

              specie condition     value
triticum.10 triticum    normal 13.230663
triticum.11 triticum    stress 30.658355
triticum.12 triticum  Nitrogen  9.402721
poacee.4      poacee    normal 14.589322
poacee.5      poacee    stress 10.744153
poacee.6      poacee  Nitrogen  7.299742
banana.7      banana    normal  9.845850
banana.8      banana    stress  9.416088
banana.9      banana  Nitrogen  4.178521
sorgho.1      sorgho    normal 12.623696
sorgho.2      sorgho    stress 11.394047
sorgho.3      sorgho  Nitrogen  0.498003