解决R语言排序问题的方法：
- order from base
- arrange from dplyr
- setorder and setorderv from data.table
- arrange from plyr
- sort from taRifx
- orderBy from doBy
- sortData from Deducer
- Most of the time you should use the dplyr or data.table solutions, unless having no-dependencies is important, in which case use base::order.
plyr::arrange(dd, desc(z), b) ## plyr arrange(dd, desc(z), b) ## dplyr sort(dd, f = ~ -z + b) ## taRifx dd[with(dd, order(-z, b)), ] ## base R
library(dplyr)
library(data.table)
df1 <- tbl_df(iris)
#using strings or formula
arrange_(df1, c('Petal.Length', 'Petal.Width'))
arrange_(df1, ~Petal.Length, ~Petal.Width)
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.60 3.60 1.00 0.200 setosa
2 4.30 3.00 1.10 0.100 setosa
3 5.80 4.00 1.20 0.200 setosa
4 5.00 3.20 1.20 0.200 setosa
5 4.70 3.20 1.30 0.200 setosa
6 5.50 3.50 1.30 0.200 setosa
7 4.40 3.00 1.30 0.200 setosa
8 4.40 3.20 1.30 0.200 setosa
9 5.00 3.50 1.30 0.300 setosa
10 4.50 2.30 1.30 0.300 setosa
# ... with 140 more rows
#Or using a variable
sortBy <- c('Petal.Length', 'Petal.Width')
arrange_(df1, .dots = sortBy)
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.60 3.60 1.00 0.200 setosa
2 4.30 3.00 1.10 0.100 setosa
3 5.80 4.00 1.20 0.200 setosa
4 5.00 3.20 1.20 0.200 setosa
5 4.70 3.20 1.30 0.200 setosa
6 5.50 3.50 1.30 0.200 setosa
7 4.40 3.00 1.30 0.200 setosa
8 4.40 3.20 1.30 0.200 setosa
9 5.00 3.50 1.30 0.300 setosa
10 4.50 2.30 1.30 0.300 setosa
# ... with 140 more rows
- 其次是sort（）功能
#Doing the same operation except sorting Petal.Length in descending order
sortByDesc <- c('desc(Petal.Length)', 'Petal.Width')
arrange_(df1, .dots = sortByDesc)
dt1 <- data.table(iris) #not really required, as you can work directly on your data.frame
sortBy <- c('Petal.Length', 'Petal.Width')
sortType <- c(-1, 1)
setorderv(dt1, sortBy, sortType)
dt1
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 7.7 2.6 6.9 2.3 virginica
2: 7.7 2.8 6.7 2.0 virginica
3: 7.7 3.8 6.7 2.2 virginica
4: 7.6 3.0 6.6 2.1 virginica
5: 7.9 3.8 6.4 2.0 virginica
---
146: 5.4 3.9 1.3 0.4 setosa
147: 5.8 4.0 1.2 0.2 setosa
148: 5.0 3.2 1.2 0.2 setosa
149: 4.3 3.0 1.1 0.1 setosa
150: 4.6 3.6 1.0 0.2 setosa
dd <- data.frame(b = factor(c("Hi", "Med", "Hi", "Low"),
levels = c("Low", "Med", "Hi"), ordered = TRUE),
x = c("A", "D", "A", "C"), y = c(8, 3, 9, 9),
z = c(1, 1, 1, 2))
library(taRifx)
dd1 <- sort(dd, f= ~ -z + b )
> dd
b x y z
1 Hi A 8 1
2 Med D 3 1
3 Hi A 9 1
4 Low C 9 2
> dd1
b x y z
4 Low C 9 2
2 Med D 3 1
1 Hi A 8 1
3 Hi A 9 1
doBy()
dd <- data.frame(b = factor(c("Hi", "Med", "Hi", "Low"),
levels = c("Low", "Med", "Hi"), ordered = TRUE),
x = c("A", "D", "A", "C"), y = c(8, 3, 9, 9),
z = c(1, 1, 1, 2))
library(doBy)
dd
orderBy( ~ -z + b, data = dd) ## doBy
> dd
b x y z
1 Hi A 8 1
2 Med D 3 1
3 Hi A 9 1
4 Low C 9 2
> orderBy( ~ -z + b, data = dd) ## doBy
b x y z
4 Low C 9 2
2 Med D 3 1
1 Hi A 8 1
3 Hi A 9 1
