Arrange_() Multiple Columns with Descending Order

arrange_() multiple columns with descending order

We can paste 'desc' as a string to evaluate it.

myCol1 <- paste0("desc(", "x)")
df1 %>% 
     arrange_(.dots = c("grp", myCol1))
#  grp    x
#1   1 6.16
#2   1 3.39
#3   1 2.82
#4   2 9.17
#5   2 4.35

Or with 'myCol'

df1 %>% 
      arrange_(.dots = c("grp", paste0("desc(", myCol, ")")))

Or use lazyeval

library(lazyeval)
df1 %>%
     arrange_(.dots = c("grp", interp(~ desc(n1), n1 = as.name(myCol))))
#  grp    x
#1   1 6.16
#2   1 3.39
#3   1 2.82
#4   2 9.17
#5   2 4.35

---

By using "desc(myCol)", it is a single string and the value of the 'myCol' is not evaluated.

Update

Or another option is parse_expr (from rlang) and evaluate with !!

df1 %>%
    arrange(grp, !! rlang::parse_expr(myCol1))
#grp    x
#1   1 6.16
#2   1 3.39
#3   1 2.82
#4   2 9.17
#5   2 4.35

---

Or using the original string in the OP's post. Convert the string to symbol (sym), evaluate (!!) and arrange it in descending (desc) order

myCol <- "x"
df1 %>% 
    arrange(grp, desc(!! rlang::sym(myCol)))
# grp    x
#1   1 6.16
#2   1 3.39
#3   1 2.82
#4   2 9.17
#5   2 4.35

 

How to sort a Pandas DataFrame with multiple columns, some in ascending order and others descending order?

The DataFrame.sort_values method can handle this very easily. Just use the ascending argument and provide a list of boolean values.

import pandas as pd

my_df = pd.DataFrame({'col1':['a','a','a','a','b','b','b','b','c','c','c','c'],
                      'col2':[1,1,2,2,1,1,2,2,1,1,2,2],
                      'col3':[1,2,1,2,1,2,1,2,1,2,1,2]})

my_df = my_df.sort_values(by=['col1','col2','col3'], 
                          ascending=[False, False, True])

Note that the list provided in the ascending argument must have the same length as the one provided in the by argument.

Descending sorting in numpy by several columns

Use numpy.lexsort to sort on multiple columns at the same time.

arr = np.array([
    [150, 8],
    [105, 20],
    [90, 100],
    [101, 12],
    [110, 80],
    [105, 100],
])

order = np.lexsort([arr[:, 1], arr[:, 0]])[::-1]
arr[order]

yields:

array([[150,   8],
       [110,  80],
       [105, 100],
       [105,  20],
       [101,  12],
       [ 90, 100]])

Sort (order) data frame rows by multiple columns

You can use the order() function directly without resorting to add-on tools -- see this simpler answer which uses a trick right from the top of the example(order) code:

R> dd[with(dd, order(-z, b)), ]
    b x y z
4 Low C 9 2
2 Med D 3 1
1  Hi A 8 1
3  Hi A 9 1

Edit some 2+ years later: It was just asked how to do this by column index. The answer is to simply pass the desired sorting column(s) to the order() function:

R> dd[order(-dd[,4], dd[,1]), ]
    b x y z
4 Low C 9 2
2 Med D 3 1
1  Hi A 8 1
3  Hi A 9 1
R> 

rather than using the name of the column (and with() for easier/more direct access).

Set Ascending Descending for each column when sorting by multiple columns

From the docs:

reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed.

So what you want instead is something like:

d.sort(key=lambda x: (x[0], -x[1]))

If x[1] is not a number, try:

d.sort(key=lambda x: x[1], reverse=True)
d.sort(key=lambda x: x[0])

R: sort by multiple columns with a specific order in each column (not ascending)

We can create factor columns and specify the level to arrange the column. The last mutate_if is optional, which is just converting the factor back to character.

library(dplyr)

dat2 <- dat %>%
  mutate(A = factor(A, level = c("yellow", "green", "red")),
         B = factor(B, c("X", "Z", "Y"))) %>%
  arrange(A, B, desc(C)) %>%
  mutate_if(is.factor, as.character)
dat2
#        A B   C    D   E
# 1 yellow X 0.8 bbbb 126
# 2 yellow X 0.5 cccc 324
# 3 yellow X 0.4 zzzz 222
# 4 yellow Z 0.6 dddd 333
# 5 yellow Y 0.2 dddd 222
# 6  green X 0.3 cccc 111
# 7  green Y 0.1 aaaa 123
# 8    red X 0.8 aaaa 111

DATA

dat <- read.table(text = "     A          B         C          D          E        
1  red         X        0.8        aaaa        111
2  yellow      Y        0.2        dddd        222
3  green       X        0.3        cccc        111
4  yellow      Z        0.6        dddd        333
5  green       Y        0.1        aaaa        123
6  yellow      X        0.5        cccc        324
7  yellow      X        0.4        zzzz        222
8  yellow      X        0.8        bbbb        126",
                  header = TRUE, stringsAsFactors = FALSE)

SQL multiple column ordering

ORDER BY column1 DESC, column2

This sorts everything by column1 (descending) first, and then by column2 (ascending, which is the default) whenever the column1 fields for two or more rows are equal.

Arranging summarized NAs in descending order after calculating them in each column in tidyverse

I think the issue is caused by the summarise function returning a single column ("col_name : value") instead of two columns ("col_name" and "variable"). One potential solution is to use the pivot_longer() tidyverse function to split the output into two columns, e.g.

library(tidyverse)

data(airquality)
dat1 <- airquality
dat1 %>%
  summarise(across(everything(), ~ sum(is.na(.)))) %>% 
  pivot_longer(cols = everything(), names_to = "names", values_to = "values") %>% 
  arrange(desc(values))
#> # A tibble: 6 x 2
#>   names   values
#>   <chr>    <int>
#> 1 Ozone       37
#> 2 Solar.R      7
#> 3 Wind         0
#> 4 Temp         0
#> 5 Month        0
#> 6 Day          0

^{Created on 2021-07-21 by the reprex package (v2.0.0)}

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