Arranging Rows in Custom Order Using Dplyr

Arranging rows in custom order using dplyr

We can use factor to change the order in a custom way

df %>%
    arrange(factor(Reg, levels = LETTERS[c(3, 1, 2)]), desc(Res), desc(Pop))
#  Reg   Res    Pop
#1   C Urban 501638
#2   C Rural 499274
#3   A Urban 500414
#4   A Rural 500501
#5   B Urban 499922
#6   B Rural 500016

Or with match to get the index and arrange on it

df %>%
    arrange(match(Reg, c("C", "A", "B")), desc(Res), desc(Pop))

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If we have multiple columns to arrange in descending order

df %>%
     arrange_at(2:3, desc) %>%
     arrange(match(Reg, c("C", "A", "B")))

Arrange rows in custom order using R

You can make country an ordered factor.

library(dplyr)

country <- rep(c("AT","BE","CY","DE","EE"),10)
value <- seq(1, 50)

# chenged this to a vector rather than a data frame
target_cc <- c("DE","CY","BE","AT","EE")

df %>% 
  mutate(country = factor(country, levels = target_cc)) %>% 
  arrange(country)

Reorder rows using custom order

First, create a vector with the letters in the desired order. Then match* the vector with the variable to be sorted. match returns indices of (first) matches, which can be plugged into slice:

library(dplyr)

# create a vector with letters in the desired order
x <- c("C", "A", "B")

DT %>%
  slice(match(x, category))
#   category b
# 1        C 3
# 2        A 1
# 3        B 2

Another way would be to convert "category" to a factor, set levels to the desired order, and use arrange:

DT %>%
  mutate(category =  factor(category, levels = x)) %>%
  arrange(category)    
#   category b
# 1        C 3
# 2        A 1
# 3        B 2

*The match method is inspired by this answer.

Arranging rows in R so that year column is in a custom order, and other columns with identical entries are grouped

Is this what you want?

library(dplyr)

USA_tech %>% 
  arrange(region, supplysector, subsector, technology, year)
#>    region supplysector               subsector              technology year
#> 1      AK  agriculture      agriculture energy      agriculture energy 1975
#> 2      AK  agriculture      agriculture energy      agriculture energy 1990
#> 3      AK  agriculture      agriculture energy      agriculture energy 2005
#> 4      AK  agriculture      agriculture energy      agriculture energy 2010
#> 5      AK construction     construction energy     construction energy 1975
#> 6      AK construction     construction energy     construction energy 1990
#> 7      AK construction     construction energy     construction energy 2005
#> 8      AK construction     construction energy     construction energy 2010
#> 9      AK construction construction feedstocks construction feedstocks 1975
#> 10     AK construction construction feedstocks construction feedstocks 1990
#> 11     AK construction construction feedstocks construction feedstocks 2005
#> 12     AK construction construction feedstocks construction feedstocks 2010
#> 13     AL  agriculture      agriculture energy      agriculture energy 1975
#> 14     AL  agriculture      agriculture energy      agriculture energy 1990
#> 15     AL  agriculture      agriculture energy      agriculture energy 2005
#> 16     AL  agriculture      agriculture energy      agriculture energy 2010
#> 17     AL construction     construction energy     construction energy 1975
#> 18     AL construction     construction energy     construction energy 1990
#> 19     AL construction     construction energy     construction energy 2005
#> 20     AL construction     construction energy     construction energy 2010
#> 21     AL construction construction feedstocks construction feedstocks 1975
#> 22     AL construction construction feedstocks construction feedstocks 1990
#> 23     AL construction construction feedstocks construction feedstocks 2005
#> 24     AL construction construction feedstocks construction feedstocks 2010
#>    coefficient market.name tech_change
#> 1  0.004152838          AK          NA
#> 2  0.004285719          AK          NA
#> 3  0.005530093          AK          NA
#> 4  0.005155489          AK          NA
#> 5  0.002515274          AK          NA
#> 6  0.005207937          AK          NA
#> 7  0.007962923          AK          NA
#> 8  0.006292851          AK          NA
#> 9  0.005392370          AK          NA
#> 10 0.003550397          AK          NA
#> 11 0.002824053          AK          NA
#> 12 0.001722293          AK          NA
#> 13 0.010508707          AL          NA
#> 14 0.006022474          AL          NA
#> 15 0.007286581          AL          NA
#> 16 0.005622613          AL          NA
#> 17 0.004010048          AL          NA
#> 18 0.002468304          AL          NA
#> 19 0.002889554          AL          NA
#> 20 0.002351508          AL          NA
#> 21 0.001856025          AL          NA
#> 22 0.002650908          AL          NA
#> 23 0.004949693          AL          NA
#> 24 0.003576161          AL          NA

^{Created on 2020-09-24 by the reprex package (v0.3.0)}

When using arrange, the data gets ordered in the order you specify the columns.

Sort dataframe by custom order

If you want to keep the column as character rather than factor, you can arrange based on a match to the order vector

rating_order <- c("AAA", "AA", "A", "BBB", "BB", "B")

df <-
  data.frame(
    Rating = c("A", "AA", "AAA", "B", "BB", "BBB"),
    Value1 = c(1, 2, 3, 4, 5, 6),
    Value2 = c(2, 3, 4, 5, 3, 2)
  )

library(dplyr, warn.conflicts = FALSE)

df %>%
  arrange(match(Rating, rating_order))
#>   Rating Value1 Value2
#> 1    AAA      3      4
#> 2     AA      2      3
#> 3      A      1      2
#> 4    BBB      6      2
#> 5     BB      5      3
#> 6      B      4      5

^{Created on 2022-01-20 by the reprex package (v2.0.1)}

Arrange by custom order in dplyr using dynamic column names

You can also use as.symbol from base R

test_tbl_final <- test_tbl %>%
  dplyr::arrange(factor(!!as.symbol(group_var), levels = order_var), date)

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