Rearranging data frame columns in R (mutate, dplyr)

Question

I have a data frame like so

Type  Number  Species
A     1         G    
A     2         R 
A     7         Q
A     4         L
B     4         S
B     5         T
B     3         H
B     9         P
C     12        K
C     11        T
C     6         U
C     5         Q

Where I have used group_by(Type) My goal is to collapse this data by having NUMBER be the top 2 values in the number column, and then making a new column(Number_2) that is the second 2 values. Also I would want the Species values for the bottom two numbers to be deleted, so that the species corresponds to the higher number in the row I would like to use dplyr and the final would look like this

Type  Number Number_2   Species       
A     7    1               Q
A     4    2               L 
B     5    3               T
B     9    4               P
C     12   6               K
C     11   5               T

as of now the order that number_2 is in doesn't matter, as long as it is in the same type.... I don't know if this is possible but if it is does anyone know how...

thanks!

Answer 1

You can try

library(data.table)
setDT(df1)[order(-Number), list(Number1=Number[1:2], 
                                Number2=Number[3:4],
                                Species=Species[1:2]), keyby = Type]
 #   Type Number1 Number2 Species
 #1:    A       7       2       Q
 #2:    A       4       1       L
 #3:    B       9       4       P
 #4:    B       5       3       T
 #5:    C      12       6       K
 #6:    C      11       5       T

Or using dplyr with do

 library(dplyr)
 df1 %>% 
   group_by(Type) %>%
   arrange(desc(Number)) %>%
   do(data.frame(Type=.$Type[1L],
                Number1=.$Number[1:2], 
                Number2 = .$Number[3:4],
                Species=.$Species[1:2], stringsAsFactors=FALSE))
 #   Type Number1 Number2 Species
 #1    A       7       2       Q
 #2    A       4       1       L
 #3    B       9       4       P
 #4    B       5       3       T
 #5    C      12       6       K
 #6    C      11       5       T

Answer 2

Here's a different dplyr approach.

library(dplyr)

# Start creating the data set with top 2 values and store as df1:
df1 <- df %>% 
  group_by(Type) %>%
  top_n(2, Number) %>%
  ungroup() %>%
  arrange(Type, Number)

# Then, get the anti-joined data (the not top 2 values), arrange, rename and select
# the number colummn and cbind to df1:
out <- df %>%
  anti_join(df1, c("Type","Number")) %>%
  arrange(Type, Number) %>%
  select(Number2 = Number) %>%
  cbind(df1, .)

This results in:

> out
#  Type Number Species Number2
#1    A      4       L       1
#2    A      7       Q       2
#3    B      5       T       3
#4    B      9       P       4
#5    C     11       T       5
#6    C     12       K       6

Answer 3

This could be another option using ddply

library(plyr)
ddply(dat[order(Number)], .(Type), summarize, 
      Number1 = Number[4:3],  Number2 = Number[2:1], Species = Species[4:3])

#  Type Number1 Number2 Species
#1    A       7       2       Q
#2    A       4       1       L
#3    B       9       4       P
#4    B       5       3       T
#5    C      12       6       K
#6    C      11       5       T