I am in the process of learning the tidyverse and am loving the flow the pipe operator offers. I was wondering, is it possible to split a pipe at all so that the output from one part of the pipe can go to two separate commands? I have done a little research on this and have seen nothing about this being possible. So that instead of doing something like this where you would have to save the first step.
iris_filter <- iris %>%
filter(Sepal.Length <= 5.8)
iris_filter %>%
summarise(n= n())
iris_filter %>%
arrange(Sepal.Length)
Could you instead have filter passed to two separate commands and continue down two distinct pipe paths? A little image to clarify what I am curious is possible.
The %T>% operator from the magrittr-package seems to be what you are looking for.
However for that specific problem I would write a custom function which outputs the original data:
library(tidyverse)
custom.function <- function(x) {
summarise(x, n = n()) %>%
print()
return(x)
}
iris %>%
filter(Sepal.Length <= 5.8) %>%
custom.function() %>%
arrange(Sepal.Length)
#> n
#> 1 80
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 4.3 3.0 1.1 0.1 setosa
#> 2 4.4 2.9 1.4 0.2 setosa
#> 3 4.4 3.0 1.3 0.2 setosa
#> 4 4.4 3.2 1.3 0.2 setosa
#> 5 4.5 2.3 1.3 0.3 setosa
#> 6 4.6 3.1 1.5 0.2 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 4.6 3.6 1.0 0.2 setosa
#> 9 4.6 3.2 1.4 0.2 setosa
#> 10 4.7 3.2 1.3 0.2 setosa
#> 11 4.7 3.2 1.6 0.2 setosa
#> 12 4.8 3.4 1.6 0.2 setosa
#> 13 4.8 3.0 1.4 0.1 setosa
#> 14 4.8 3.4 1.9 0.2 setosa
#> 15 4.8 3.1 1.6 0.2 setosa
#> 16 4.8 3.0 1.4 0.3 setosa
#> 17 4.9 3.0 1.4 0.2 setosa
#> 18 4.9 3.1 1.5 0.1 setosa
#> 19 4.9 3.1 1.5 0.2 setosa
#> 20 4.9 3.6 1.4 0.1 setosa
#> 21 4.9 2.4 3.3 1.0 versicolor
#> 22 4.9 2.5 4.5 1.7 virginica
#> 23 5.0 3.6 1.4 0.2 setosa
#> 24 5.0 3.4 1.5 0.2 setosa
#> 25 5.0 3.0 1.6 0.2 setosa
#> 26 5.0 3.4 1.6 0.4 setosa
#> 27 5.0 3.2 1.2 0.2 setosa
#> 28 5.0 3.5 1.3 0.3 setosa
#> 29 5.0 3.5 1.6 0.6 setosa
#> 30 5.0 3.3 1.4 0.2 setosa
#> 31 5.0 2.0 3.5 1.0 versicolor
#> 32 5.0 2.3 3.3 1.0 versicolor
#> 33 5.1 3.5 1.4 0.2 setosa
#> 34 5.1 3.5 1.4 0.3 setosa
#> 35 5.1 3.8 1.5 0.3 setosa
#> 36 5.1 3.7 1.5 0.4 setosa
#> 37 5.1 3.3 1.7 0.5 setosa
#> 38 5.1 3.4 1.5 0.2 setosa
#> 39 5.1 3.8 1.9 0.4 setosa
#> 40 5.1 3.8 1.6 0.2 setosa
#> 41 5.1 2.5 3.0 1.1 versicolor
#> 42 5.2 3.5 1.5 0.2 setosa
#> 43 5.2 3.4 1.4 0.2 setosa
#> 44 5.2 4.1 1.5 0.1 setosa
#> 45 5.2 2.7 3.9 1.4 versicolor
#> 46 5.3 3.7 1.5 0.2 setosa
#> 47 5.4 3.9 1.7 0.4 setosa
#> 48 5.4 3.7 1.5 0.2 setosa
#> 49 5.4 3.9 1.3 0.4 setosa
#> 50 5.4 3.4 1.7 0.2 setosa
#> 51 5.4 3.4 1.5 0.4 setosa
#> 52 5.4 3.0 4.5 1.5 versicolor
#> 53 5.5 4.2 1.4 0.2 setosa
#> 54 5.5 3.5 1.3 0.2 setosa
#> 55 5.5 2.3 4.0 1.3 versicolor
#> 56 5.5 2.4 3.8 1.1 versicolor
#> 57 5.5 2.4 3.7 1.0 versicolor
#> 58 5.5 2.5 4.0 1.3 versicolor
#> 59 5.5 2.6 4.4 1.2 versicolor
#> 60 5.6 2.9 3.6 1.3 versicolor
#> 61 5.6 3.0 4.5 1.5 versicolor
#> 62 5.6 2.5 3.9 1.1 versicolor
#> 63 5.6 3.0 4.1 1.3 versicolor
#> 64 5.6 2.7 4.2 1.3 versicolor
#> 65 5.6 2.8 4.9 2.0 virginica
#> 66 5.7 4.4 1.5 0.4 setosa
#> 67 5.7 3.8 1.7 0.3 setosa
#> 68 5.7 2.8 4.5 1.3 versicolor
#> 69 5.7 2.6 3.5 1.0 versicolor
#> 70 5.7 3.0 4.2 1.2 versicolor
#> 71 5.7 2.9 4.2 1.3 versicolor
#> 72 5.7 2.8 4.1 1.3 versicolor
#> 73 5.7 2.5 5.0 2.0 virginica
#> 74 5.8 4.0 1.2 0.2 setosa
#> 75 5.8 2.7 4.1 1.0 versicolor
#> 76 5.8 2.7 3.9 1.2 versicolor
#> 77 5.8 2.6 4.0 1.2 versicolor
#> 78 5.8 2.7 5.1 1.9 virginica
#> 79 5.8 2.8 5.1 2.4 virginica
#> 80 5.8 2.7 5.1 1.9 virginica
Created on 2018-11-04 by the reprex package (v0.2.1)
I don't think this is possible. One workaround is to save the intermediate values in the full dataframe, for example:
iris %>%
add_tally() %>%
filter(Sepal.Length <= 5.8) %>%
arrange(Sepal.Length)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species n
<dbl> <dbl> <dbl> <dbl> <fct> <int>
1 4.3 3 1.1 0.1 setosa 150
2 4.4 2.9 1.4 0.2 setosa 150
3 4.4 3 1.3 0.2 setosa 150
4 4.4 3.2 1.3 0.2 setosa 150
5 4.5 2.3 1.3 0.3 setosa 150
Here you can use functions such as add_tally() or add_count(group1, group2, ...), which are basically equivalents of more verbose mutate(n = n()), and group_by(group1, group2, ..) %>% mutate(n = n()).
You can always use the values stored for further calculations / charts then.
Related
This question already has answers here:
Arranging rows in custom order using dplyr
(3 answers)
Reorder rows using custom order
(2 answers)
Arrange rows in custom order using R [duplicate]
(4 answers)
Order data frame rows according to vector with specific order
(6 answers)
Closed last month.
For example, I have the typical dataframe:
library(tidyverse)
my_data <- as_tibble(iris)
my_data
# A tibble: 150 × 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
I just want to reorder the dataset by the column "Species" (which has 3 values: setosa, virginica and versicolor), specifying an exact order of rows. For example: virginica, then setosa, then versicolor.
You can use arrange and match:
library(dplyr)
iris %>%
arrange(match(Species, c("virginica", "setosa", "versicolor")))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 6.3 3.3 6.0 2.5 virginica
#> 2 5.8 2.7 5.1 1.9 virginica
#> 3 7.1 3.0 5.9 2.1 virginica
#> 4 6.3 2.9 5.6 1.8 virginica
#> 5 6.5 3.0 5.8 2.2 virginica
#> 6 7.6 3.0 6.6 2.1 virginica
#> 7 4.9 2.5 4.5 1.7 virginica
#> 8 7.3 2.9 6.3 1.8 virginica
#> 9 6.7 2.5 5.8 1.8 virginica
#> 10 7.2 3.6 6.1 2.5 virginica
#> 11 6.5 3.2 5.1 2.0 virginica
#> 12 6.4 2.7 5.3 1.9 virginica
#> 13 6.8 3.0 5.5 2.1 virginica
#> 14 5.7 2.5 5.0 2.0 virginica
#> 15 5.8 2.8 5.1 2.4 virginica
#> 16 6.4 3.2 5.3 2.3 virginica
#> 17 6.5 3.0 5.5 1.8 virginica
#> 18 7.7 3.8 6.7 2.2 virginica
#> 19 7.7 2.6 6.9 2.3 virginica
#> 20 6.0 2.2 5.0 1.5 virginica
#> 21 6.9 3.2 5.7 2.3 virginica
#> 22 5.6 2.8 4.9 2.0 virginica
#> 23 7.7 2.8 6.7 2.0 virginica
#> 24 6.3 2.7 4.9 1.8 virginica
#> 25 6.7 3.3 5.7 2.1 virginica
#> 26 7.2 3.2 6.0 1.8 virginica
#> 27 6.2 2.8 4.8 1.8 virginica
#> 28 6.1 3.0 4.9 1.8 virginica
#> 29 6.4 2.8 5.6 2.1 virginica
#> 30 7.2 3.0 5.8 1.6 virginica
#> 31 7.4 2.8 6.1 1.9 virginica
#> 32 7.9 3.8 6.4 2.0 virginica
#> 33 6.4 2.8 5.6 2.2 virginica
#> 34 6.3 2.8 5.1 1.5 virginica
#> 35 6.1 2.6 5.6 1.4 virginica
#> 36 7.7 3.0 6.1 2.3 virginica
#> 37 6.3 3.4 5.6 2.4 virginica
#> 38 6.4 3.1 5.5 1.8 virginica
#> 39 6.0 3.0 4.8 1.8 virginica
#> 40 6.9 3.1 5.4 2.1 virginica
#> 41 6.7 3.1 5.6 2.4 virginica
#> 42 6.9 3.1 5.1 2.3 virginica
#> 43 5.8 2.7 5.1 1.9 virginica
#> 44 6.8 3.2 5.9 2.3 virginica
#> 45 6.7 3.3 5.7 2.5 virginica
#> 46 6.7 3.0 5.2 2.3 virginica
#> 47 6.3 2.5 5.0 1.9 virginica
#> 48 6.5 3.0 5.2 2.0 virginica
#> 49 6.2 3.4 5.4 2.3 virginica
#> 50 5.9 3.0 5.1 1.8 virginica
#> 51 5.1 3.5 1.4 0.2 setosa
#> 52 4.9 3.0 1.4 0.2 setosa
#> 53 4.7 3.2 1.3 0.2 setosa
#> 54 4.6 3.1 1.5 0.2 setosa
#> 55 5.0 3.6 1.4 0.2 setosa
#> 56 5.4 3.9 1.7 0.4 setosa
#> 57 4.6 3.4 1.4 0.3 setosa
#> 58 5.0 3.4 1.5 0.2 setosa
#> 59 4.4 2.9 1.4 0.2 setosa
#> 60 4.9 3.1 1.5 0.1 setosa
#> 61 5.4 3.7 1.5 0.2 setosa
#> 62 4.8 3.4 1.6 0.2 setosa
#> 63 4.8 3.0 1.4 0.1 setosa
#> 64 4.3 3.0 1.1 0.1 setosa
#> 65 5.8 4.0 1.2 0.2 setosa
#> 66 5.7 4.4 1.5 0.4 setosa
#> 67 5.4 3.9 1.3 0.4 setosa
#> 68 5.1 3.5 1.4 0.3 setosa
#> 69 5.7 3.8 1.7 0.3 setosa
#> 70 5.1 3.8 1.5 0.3 setosa
#> 71 5.4 3.4 1.7 0.2 setosa
#> 72 5.1 3.7 1.5 0.4 setosa
#> 73 4.6 3.6 1.0 0.2 setosa
#> 74 5.1 3.3 1.7 0.5 setosa
#> 75 4.8 3.4 1.9 0.2 setosa
#> 76 5.0 3.0 1.6 0.2 setosa
#> 77 5.0 3.4 1.6 0.4 setosa
#> 78 5.2 3.5 1.5 0.2 setosa
#> 79 5.2 3.4 1.4 0.2 setosa
#> 80 4.7 3.2 1.6 0.2 setosa
#> 81 4.8 3.1 1.6 0.2 setosa
#> 82 5.4 3.4 1.5 0.4 setosa
#> 83 5.2 4.1 1.5 0.1 setosa
#> 84 5.5 4.2 1.4 0.2 setosa
#> 85 4.9 3.1 1.5 0.2 setosa
#> 86 5.0 3.2 1.2 0.2 setosa
#> 87 5.5 3.5 1.3 0.2 setosa
#> 88 4.9 3.6 1.4 0.1 setosa
#> 89 4.4 3.0 1.3 0.2 setosa
#> 90 5.1 3.4 1.5 0.2 setosa
#> 91 5.0 3.5 1.3 0.3 setosa
#> 92 4.5 2.3 1.3 0.3 setosa
#> 93 4.4 3.2 1.3 0.2 setosa
#> 94 5.0 3.5 1.6 0.6 setosa
#> 95 5.1 3.8 1.9 0.4 setosa
#> 96 4.8 3.0 1.4 0.3 setosa
#> 97 5.1 3.8 1.6 0.2 setosa
#> 98 4.6 3.2 1.4 0.2 setosa
#> 99 5.3 3.7 1.5 0.2 setosa
#> 100 5.0 3.3 1.4 0.2 setosa
#> 101 7.0 3.2 4.7 1.4 versicolor
#> 102 6.4 3.2 4.5 1.5 versicolor
#> 103 6.9 3.1 4.9 1.5 versicolor
#> 104 5.5 2.3 4.0 1.3 versicolor
#> 105 6.5 2.8 4.6 1.5 versicolor
#> 106 5.7 2.8 4.5 1.3 versicolor
#> 107 6.3 3.3 4.7 1.6 versicolor
#> 108 4.9 2.4 3.3 1.0 versicolor
#> 109 6.6 2.9 4.6 1.3 versicolor
#> 110 5.2 2.7 3.9 1.4 versicolor
#> 111 5.0 2.0 3.5 1.0 versicolor
#> 112 5.9 3.0 4.2 1.5 versicolor
#> 113 6.0 2.2 4.0 1.0 versicolor
#> 114 6.1 2.9 4.7 1.4 versicolor
#> 115 5.6 2.9 3.6 1.3 versicolor
#> 116 6.7 3.1 4.4 1.4 versicolor
#> 117 5.6 3.0 4.5 1.5 versicolor
#> 118 5.8 2.7 4.1 1.0 versicolor
#> 119 6.2 2.2 4.5 1.5 versicolor
#> 120 5.6 2.5 3.9 1.1 versicolor
#> 121 5.9 3.2 4.8 1.8 versicolor
#> 122 6.1 2.8 4.0 1.3 versicolor
#> 123 6.3 2.5 4.9 1.5 versicolor
#> 124 6.1 2.8 4.7 1.2 versicolor
#> 125 6.4 2.9 4.3 1.3 versicolor
#> 126 6.6 3.0 4.4 1.4 versicolor
#> 127 6.8 2.8 4.8 1.4 versicolor
#> 128 6.7 3.0 5.0 1.7 versicolor
#> 129 6.0 2.9 4.5 1.5 versicolor
#> 130 5.7 2.6 3.5 1.0 versicolor
#> 131 5.5 2.4 3.8 1.1 versicolor
#> 132 5.5 2.4 3.7 1.0 versicolor
#> 133 5.8 2.7 3.9 1.2 versicolor
#> 134 6.0 2.7 5.1 1.6 versicolor
#> 135 5.4 3.0 4.5 1.5 versicolor
#> 136 6.0 3.4 4.5 1.6 versicolor
#> 137 6.7 3.1 4.7 1.5 versicolor
#> 138 6.3 2.3 4.4 1.3 versicolor
#> 139 5.6 3.0 4.1 1.3 versicolor
#> 140 5.5 2.5 4.0 1.3 versicolor
#> 141 5.5 2.6 4.4 1.2 versicolor
#> 142 6.1 3.0 4.6 1.4 versicolor
#> 143 5.8 2.6 4.0 1.2 versicolor
#> 144 5.0 2.3 3.3 1.0 versicolor
#> 145 5.6 2.7 4.2 1.3 versicolor
#> 146 5.7 3.0 4.2 1.2 versicolor
#> 147 5.7 2.9 4.2 1.3 versicolor
#> 148 6.2 2.9 4.3 1.3 versicolor
#> 149 5.1 2.5 3.0 1.1 versicolor
#> 150 5.7 2.8 4.1 1.3 versicolor
I would like to shuffle all the rows in a column unless a row contains one particular value, in which case that row should not be included in the shuffling process (i.e. all rows containing the specified value stay where they are).
Using the iris example below, let's say we want to shuffle all rows of the column Species except those rows of Species which contain the value setosa. How do I do that?
Thanks!
library(dplyr)
set.seed(123)
my_df <- iris %>%
mutate(species2 = sample(Species), # shuffles whole column
# attempted conditional shuffle of non-setosa values (does not work!)
species3 = sample(nrow(Species != "setosa")))
We can try making an external function that does that. conditional_shuffle selects cases that don't fit a condition and just uses sample on them:
library(tidyverse)
conditional_shuffle <- function(vec, condition, seed = 123) {
set.seed(seed)
cases2shuffle <- vec != condition
vec[cases2shuffle] <- sample(vec[cases2shuffle])
return(vec)
}
iris %>%
mutate(species2 = conditional_shuffle(Species, "setosa"))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species species2
#> 1 5.1 3.5 1.4 0.2 setosa setosa
#> 2 4.9 3.0 1.4 0.2 setosa setosa
#> 3 4.7 3.2 1.3 0.2 setosa setosa
#> 4 4.6 3.1 1.5 0.2 setosa setosa
#> 5 5.0 3.6 1.4 0.2 setosa setosa
#> 6 5.4 3.9 1.7 0.4 setosa setosa
#> 7 4.6 3.4 1.4 0.3 setosa setosa
#> 8 5.0 3.4 1.5 0.2 setosa setosa
#> 9 4.4 2.9 1.4 0.2 setosa setosa
#> 10 4.9 3.1 1.5 0.1 setosa setosa
#> 11 5.4 3.7 1.5 0.2 setosa setosa
#> 12 4.8 3.4 1.6 0.2 setosa setosa
#> 13 4.8 3.0 1.4 0.1 setosa setosa
#> 14 4.3 3.0 1.1 0.1 setosa setosa
#> 15 5.8 4.0 1.2 0.2 setosa setosa
#> 16 5.7 4.4 1.5 0.4 setosa setosa
#> 17 5.4 3.9 1.3 0.4 setosa setosa
#> 18 5.1 3.5 1.4 0.3 setosa setosa
#> 19 5.7 3.8 1.7 0.3 setosa setosa
#> 20 5.1 3.8 1.5 0.3 setosa setosa
#> 21 5.4 3.4 1.7 0.2 setosa setosa
#> 22 5.1 3.7 1.5 0.4 setosa setosa
#> 23 4.6 3.6 1.0 0.2 setosa setosa
#> 24 5.1 3.3 1.7 0.5 setosa setosa
#> 25 4.8 3.4 1.9 0.2 setosa setosa
#> 26 5.0 3.0 1.6 0.2 setosa setosa
#> 27 5.0 3.4 1.6 0.4 setosa setosa
#> 28 5.2 3.5 1.5 0.2 setosa setosa
#> 29 5.2 3.4 1.4 0.2 setosa setosa
#> 30 4.7 3.2 1.6 0.2 setosa setosa
#> 31 4.8 3.1 1.6 0.2 setosa setosa
#> 32 5.4 3.4 1.5 0.4 setosa setosa
#> 33 5.2 4.1 1.5 0.1 setosa setosa
#> 34 5.5 4.2 1.4 0.2 setosa setosa
#> 35 4.9 3.1 1.5 0.2 setosa setosa
#> 36 5.0 3.2 1.2 0.2 setosa setosa
#> 37 5.5 3.5 1.3 0.2 setosa setosa
#> 38 4.9 3.6 1.4 0.1 setosa setosa
#> 39 4.4 3.0 1.3 0.2 setosa setosa
#> 40 5.1 3.4 1.5 0.2 setosa setosa
#> 41 5.0 3.5 1.3 0.3 setosa setosa
#> 42 4.5 2.3 1.3 0.3 setosa setosa
#> 43 4.4 3.2 1.3 0.2 setosa setosa
#> 44 5.0 3.5 1.6 0.6 setosa setosa
#> 45 5.1 3.8 1.9 0.4 setosa setosa
#> 46 4.8 3.0 1.4 0.3 setosa setosa
#> 47 5.1 3.8 1.6 0.2 setosa setosa
#> 48 4.6 3.2 1.4 0.2 setosa setosa
#> 49 5.3 3.7 1.5 0.2 setosa setosa
#> 50 5.0 3.3 1.4 0.2 setosa setosa
#> 51 7.0 3.2 4.7 1.4 versicolor versicolor
#> 52 6.4 3.2 4.5 1.5 versicolor virginica
#> 53 6.9 3.1 4.9 1.5 versicolor virginica
#> 54 5.5 2.3 4.0 1.3 versicolor versicolor
#> 55 6.5 2.8 4.6 1.5 versicolor virginica
#> 56 5.7 2.8 4.5 1.3 versicolor versicolor
#> 57 6.3 3.3 4.7 1.6 versicolor versicolor
#> 58 4.9 2.4 3.3 1.0 versicolor versicolor
#> 59 6.6 2.9 4.6 1.3 versicolor virginica
#> 60 5.2 2.7 3.9 1.4 versicolor versicolor
#> 61 5.0 2.0 3.5 1.0 versicolor virginica
#> 62 5.9 3.0 4.2 1.5 versicolor virginica
#> 63 6.0 2.2 4.0 1.0 versicolor virginica
#> 64 6.1 2.9 4.7 1.4 versicolor versicolor
#> 65 5.6 2.9 3.6 1.3 versicolor virginica
#> 66 6.7 3.1 4.4 1.4 versicolor versicolor
#> 67 5.6 3.0 4.5 1.5 versicolor versicolor
#> 68 5.8 2.7 4.1 1.0 versicolor virginica
#> 69 6.2 2.2 4.5 1.5 versicolor virginica
#> 70 5.6 2.5 3.9 1.1 versicolor versicolor
#> 71 5.9 3.2 4.8 1.8 versicolor virginica
#> 72 6.1 2.8 4.0 1.3 versicolor virginica
#> 73 6.3 2.5 4.9 1.5 versicolor virginica
#> 74 6.1 2.8 4.7 1.2 versicolor versicolor
#> 75 6.4 2.9 4.3 1.3 versicolor versicolor
#> 76 6.6 3.0 4.4 1.4 versicolor virginica
#> 77 6.8 2.8 4.8 1.4 versicolor virginica
#> 78 6.7 3.0 5.0 1.7 versicolor versicolor
#> 79 6.0 2.9 4.5 1.5 versicolor versicolor
#> 80 5.7 2.6 3.5 1.0 versicolor versicolor
#> 81 5.5 2.4 3.8 1.1 versicolor virginica
#> 82 5.5 2.4 3.7 1.0 versicolor virginica
#> 83 5.8 2.7 3.9 1.2 versicolor virginica
#> 84 6.0 2.7 5.1 1.6 versicolor virginica
#> 85 5.4 3.0 4.5 1.5 versicolor virginica
#> 86 6.0 3.4 4.5 1.6 versicolor versicolor
#> 87 6.7 3.1 4.7 1.5 versicolor virginica
#> 88 6.3 2.3 4.4 1.3 versicolor versicolor
#> 89 5.6 3.0 4.1 1.3 versicolor versicolor
#> 90 5.5 2.5 4.0 1.3 versicolor versicolor
#> 91 5.5 2.6 4.4 1.2 versicolor versicolor
#> 92 6.1 3.0 4.6 1.4 versicolor versicolor
#> 93 5.8 2.6 4.0 1.2 versicolor versicolor
#> 94 5.0 2.3 3.3 1.0 versicolor versicolor
#> 95 5.6 2.7 4.2 1.3 versicolor versicolor
#> 96 5.7 3.0 4.2 1.2 versicolor virginica
#> 97 5.7 2.9 4.2 1.3 versicolor virginica
#> 98 6.2 2.9 4.3 1.3 versicolor virginica
#> 99 5.1 2.5 3.0 1.1 versicolor versicolor
#> 100 5.7 2.8 4.1 1.3 versicolor virginica
#> 101 6.3 3.3 6.0 2.5 virginica virginica
#> 102 5.8 2.7 5.1 1.9 virginica versicolor
#> 103 7.1 3.0 5.9 2.1 virginica virginica
#> 104 6.3 2.9 5.6 1.8 virginica virginica
#> 105 6.5 3.0 5.8 2.2 virginica versicolor
#> 106 7.6 3.0 6.6 2.1 virginica versicolor
#> 107 4.9 2.5 4.5 1.7 virginica versicolor
#> 108 7.3 2.9 6.3 1.8 virginica virginica
#> 109 6.7 2.5 5.8 1.8 virginica virginica
#> 110 7.2 3.6 6.1 2.5 virginica versicolor
#> 111 6.5 3.2 5.1 2.0 virginica virginica
#> 112 6.4 2.7 5.3 1.9 virginica versicolor
#> 113 6.8 3.0 5.5 2.1 virginica versicolor
#> 114 5.7 2.5 5.0 2.0 virginica versicolor
#> 115 5.8 2.8 5.1 2.4 virginica virginica
#> 116 6.4 3.2 5.3 2.3 virginica versicolor
#> 117 6.5 3.0 5.5 1.8 virginica virginica
#> 118 7.7 3.8 6.7 2.2 virginica virginica
#> 119 7.7 2.6 6.9 2.3 virginica versicolor
#> 120 6.0 2.2 5.0 1.5 virginica virginica
#> 121 6.9 3.2 5.7 2.3 virginica versicolor
#> 122 5.6 2.8 4.9 2.0 virginica virginica
#> 123 7.7 2.8 6.7 2.0 virginica versicolor
#> 124 6.3 2.7 4.9 1.8 virginica virginica
#> 125 6.7 3.3 5.7 2.1 virginica virginica
#> 126 7.2 3.2 6.0 1.8 virginica virginica
#> 127 6.2 2.8 4.8 1.8 virginica virginica
#> 128 6.1 3.0 4.9 1.8 virginica versicolor
#> 129 6.4 2.8 5.6 2.1 virginica virginica
#> 130 7.2 3.0 5.8 1.6 virginica versicolor
#> 131 7.4 2.8 6.1 1.9 virginica virginica
#> 132 7.9 3.8 6.4 2.0 virginica versicolor
#> 133 6.4 2.8 5.6 2.2 virginica versicolor
#> 134 6.3 2.8 5.1 1.5 virginica versicolor
#> 135 6.1 2.6 5.6 1.4 virginica virginica
#> 136 7.7 3.0 6.1 2.3 virginica virginica
#> 137 6.3 3.4 5.6 2.4 virginica virginica
#> 138 6.4 3.1 5.5 1.8 virginica virginica
#> 139 6.0 3.0 4.8 1.8 virginica versicolor
#> 140 6.9 3.1 5.4 2.1 virginica versicolor
#> 141 6.7 3.1 5.6 2.4 virginica virginica
#> 142 6.9 3.1 5.1 2.3 virginica versicolor
#> 143 5.8 2.7 5.1 1.9 virginica virginica
#> 144 6.8 3.2 5.9 2.3 virginica versicolor
#> 145 6.7 3.3 5.7 2.5 virginica versicolor
#> 146 6.7 3.0 5.2 2.3 virginica versicolor
#> 147 6.3 2.5 5.0 1.9 virginica virginica
#> 148 6.5 3.0 5.2 2.0 virginica versicolor
#> 149 6.2 3.4 5.4 2.3 virginica virginica
#> 150 5.9 3.0 5.1 1.8 virginica versicolor
Calling print(tibblevariable) by default only prints a few of its rows. This can be changed by setting tibble.print_min to a higher value.
Even when adjusting the minimum number of rows to print, I can't get more rows than how many can fit in my terminal window. Scrolling up after trying to do this just shows my earlier terminal input and output.
The situation I'm asking about may or may not be controlled in the same way. if I run print(tibblevariable) four times, and each tibblevariable is a tibble with 10 rows. and my terminal window is 24 characters tall, I will only see the last 24 rows of the output I asked for.
My question is how I can get R to just print everything I ask it to without cutting it off when it extends beyond the window.
I'm using the vim plugin Nvim-R, if that's relevant for the answer.
Just use the print function with n as an argument
print(tibblevariable,n = Inf)
Pray this doesn't break anything on memory
Added reprex as proof
library(tidyverse)
iris %>%
tibble() %>%
print(n = Inf)
#> # A tibble: 150 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> 11 5.4 3.7 1.5 0.2 setosa
#> 12 4.8 3.4 1.6 0.2 setosa
#> 13 4.8 3 1.4 0.1 setosa
#> 14 4.3 3 1.1 0.1 setosa
#> 15 5.8 4 1.2 0.2 setosa
#> 16 5.7 4.4 1.5 0.4 setosa
#> 17 5.4 3.9 1.3 0.4 setosa
#> 18 5.1 3.5 1.4 0.3 setosa
#> 19 5.7 3.8 1.7 0.3 setosa
#> 20 5.1 3.8 1.5 0.3 setosa
#> 21 5.4 3.4 1.7 0.2 setosa
#> 22 5.1 3.7 1.5 0.4 setosa
#> 23 4.6 3.6 1 0.2 setosa
#> 24 5.1 3.3 1.7 0.5 setosa
#> 25 4.8 3.4 1.9 0.2 setosa
#> 26 5 3 1.6 0.2 setosa
#> 27 5 3.4 1.6 0.4 setosa
#> 28 5.2 3.5 1.5 0.2 setosa
#> 29 5.2 3.4 1.4 0.2 setosa
#> 30 4.7 3.2 1.6 0.2 setosa
#> 31 4.8 3.1 1.6 0.2 setosa
#> 32 5.4 3.4 1.5 0.4 setosa
#> 33 5.2 4.1 1.5 0.1 setosa
#> 34 5.5 4.2 1.4 0.2 setosa
#> 35 4.9 3.1 1.5 0.2 setosa
#> 36 5 3.2 1.2 0.2 setosa
#> 37 5.5 3.5 1.3 0.2 setosa
#> 38 4.9 3.6 1.4 0.1 setosa
#> 39 4.4 3 1.3 0.2 setosa
#> 40 5.1 3.4 1.5 0.2 setosa
#> 41 5 3.5 1.3 0.3 setosa
#> 42 4.5 2.3 1.3 0.3 setosa
#> 43 4.4 3.2 1.3 0.2 setosa
#> 44 5 3.5 1.6 0.6 setosa
#> 45 5.1 3.8 1.9 0.4 setosa
#> 46 4.8 3 1.4 0.3 setosa
#> 47 5.1 3.8 1.6 0.2 setosa
#> 48 4.6 3.2 1.4 0.2 setosa
#> 49 5.3 3.7 1.5 0.2 setosa
#> 50 5 3.3 1.4 0.2 setosa
#> 51 7 3.2 4.7 1.4 versicolor
#> 52 6.4 3.2 4.5 1.5 versicolor
#> 53 6.9 3.1 4.9 1.5 versicolor
#> 54 5.5 2.3 4 1.3 versicolor
#> 55 6.5 2.8 4.6 1.5 versicolor
#> 56 5.7 2.8 4.5 1.3 versicolor
#> 57 6.3 3.3 4.7 1.6 versicolor
#> 58 4.9 2.4 3.3 1 versicolor
#> 59 6.6 2.9 4.6 1.3 versicolor
#> 60 5.2 2.7 3.9 1.4 versicolor
#> 61 5 2 3.5 1 versicolor
#> 62 5.9 3 4.2 1.5 versicolor
#> 63 6 2.2 4 1 versicolor
#> 64 6.1 2.9 4.7 1.4 versicolor
#> 65 5.6 2.9 3.6 1.3 versicolor
#> 66 6.7 3.1 4.4 1.4 versicolor
#> 67 5.6 3 4.5 1.5 versicolor
#> 68 5.8 2.7 4.1 1 versicolor
#> 69 6.2 2.2 4.5 1.5 versicolor
#> 70 5.6 2.5 3.9 1.1 versicolor
#> 71 5.9 3.2 4.8 1.8 versicolor
#> 72 6.1 2.8 4 1.3 versicolor
#> 73 6.3 2.5 4.9 1.5 versicolor
#> 74 6.1 2.8 4.7 1.2 versicolor
#> 75 6.4 2.9 4.3 1.3 versicolor
#> 76 6.6 3 4.4 1.4 versicolor
#> 77 6.8 2.8 4.8 1.4 versicolor
#> 78 6.7 3 5 1.7 versicolor
#> 79 6 2.9 4.5 1.5 versicolor
#> 80 5.7 2.6 3.5 1 versicolor
#> 81 5.5 2.4 3.8 1.1 versicolor
#> 82 5.5 2.4 3.7 1 versicolor
#> 83 5.8 2.7 3.9 1.2 versicolor
#> 84 6 2.7 5.1 1.6 versicolor
#> 85 5.4 3 4.5 1.5 versicolor
#> 86 6 3.4 4.5 1.6 versicolor
#> 87 6.7 3.1 4.7 1.5 versicolor
#> 88 6.3 2.3 4.4 1.3 versicolor
#> 89 5.6 3 4.1 1.3 versicolor
#> 90 5.5 2.5 4 1.3 versicolor
#> 91 5.5 2.6 4.4 1.2 versicolor
#> 92 6.1 3 4.6 1.4 versicolor
#> 93 5.8 2.6 4 1.2 versicolor
#> 94 5 2.3 3.3 1 versicolor
#> 95 5.6 2.7 4.2 1.3 versicolor
#> 96 5.7 3 4.2 1.2 versicolor
#> 97 5.7 2.9 4.2 1.3 versicolor
#> 98 6.2 2.9 4.3 1.3 versicolor
#> 99 5.1 2.5 3 1.1 versicolor
#> 100 5.7 2.8 4.1 1.3 versicolor
#> 101 6.3 3.3 6 2.5 virginica
#> 102 5.8 2.7 5.1 1.9 virginica
#> 103 7.1 3 5.9 2.1 virginica
#> 104 6.3 2.9 5.6 1.8 virginica
#> 105 6.5 3 5.8 2.2 virginica
#> 106 7.6 3 6.6 2.1 virginica
#> 107 4.9 2.5 4.5 1.7 virginica
#> 108 7.3 2.9 6.3 1.8 virginica
#> 109 6.7 2.5 5.8 1.8 virginica
#> 110 7.2 3.6 6.1 2.5 virginica
#> 111 6.5 3.2 5.1 2 virginica
#> 112 6.4 2.7 5.3 1.9 virginica
#> 113 6.8 3 5.5 2.1 virginica
#> 114 5.7 2.5 5 2 virginica
#> 115 5.8 2.8 5.1 2.4 virginica
#> 116 6.4 3.2 5.3 2.3 virginica
#> 117 6.5 3 5.5 1.8 virginica
#> 118 7.7 3.8 6.7 2.2 virginica
#> 119 7.7 2.6 6.9 2.3 virginica
#> 120 6 2.2 5 1.5 virginica
#> 121 6.9 3.2 5.7 2.3 virginica
#> 122 5.6 2.8 4.9 2 virginica
#> 123 7.7 2.8 6.7 2 virginica
#> 124 6.3 2.7 4.9 1.8 virginica
#> 125 6.7 3.3 5.7 2.1 virginica
#> 126 7.2 3.2 6 1.8 virginica
#> 127 6.2 2.8 4.8 1.8 virginica
#> 128 6.1 3 4.9 1.8 virginica
#> 129 6.4 2.8 5.6 2.1 virginica
#> 130 7.2 3 5.8 1.6 virginica
#> 131 7.4 2.8 6.1 1.9 virginica
#> 132 7.9 3.8 6.4 2 virginica
#> 133 6.4 2.8 5.6 2.2 virginica
#> 134 6.3 2.8 5.1 1.5 virginica
#> 135 6.1 2.6 5.6 1.4 virginica
#> 136 7.7 3 6.1 2.3 virginica
#> 137 6.3 3.4 5.6 2.4 virginica
#> 138 6.4 3.1 5.5 1.8 virginica
#> 139 6 3 4.8 1.8 virginica
#> 140 6.9 3.1 5.4 2.1 virginica
#> 141 6.7 3.1 5.6 2.4 virginica
#> 142 6.9 3.1 5.1 2.3 virginica
#> 143 5.8 2.7 5.1 1.9 virginica
#> 144 6.8 3.2 5.9 2.3 virginica
#> 145 6.7 3.3 5.7 2.5 virginica
#> 146 6.7 3 5.2 2.3 virginica
#> 147 6.3 2.5 5 1.9 virginica
#> 148 6.5 3 5.2 2 virginica
#> 149 6.2 3.4 5.4 2.3 virginica
#> 150 5.9 3 5.1 1.8 virginica
Created on 2021-02-01 by the reprex package (v1.0.0)
This question already has answers here:
Having trouble viewing more than 10 rows in a tibble [duplicate]
(3 answers)
Closed 2 years ago.
df <- data.frame(x=1:10000, y=1:10000, z=1:10000)
print(df)
...
330 330 330 330
331 331 331 331
332 332 332 332
333 333 333 333
[ reached 'max' / getOption("max.print") -- omitted 667 rows ]
How can i set the number of rows (50 for example) of a data frame i want to be printed into the console?
Regards
Using the n argument in print() in tibbles.
library(tibble)
iris_tbl <- as_tibble(iris)
print(iris_tbl)
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# ... with 140 more rows
print(iris_tbl, n = 30)
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3 1.4 0.1 setosa
14 4.3 3 1.1 0.1 setosa
15 5.8 4 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5 3 1.6 0.2 setosa
27 5 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
# ... with 120 more rows
A real question. Whenever I need to write dplyr functions, I play by the ear.
I am aware of the curly-curly operator which simplifies a lot the task.
https://www.tidyverse.org/blog/2019/06/rlang-0-4-0/
and
https://www.tidyverse.org/blog/2020/02/glue-strings-and-tidy-eval/
What is unclear to me is when to use the simple "=" and the Walrus operator ":=".
For instance consider the snippet at the end of the post.
The functions mean_by and mean_by2 differ only because the former relies on "=" and the latter on ":=", but the result is the same.
However, if I try writing a function which relies on mutate to add a new column, I get an error message if I use "=" instead of ":=" when I create the new column.
Can someone clarify to me why the difference? Does it mean it is safer to use the Walrus operator instead of "="?
Thanks!
library(tidyverse)
mean_by <- function(data, by, var) {
data %>%
group_by({{ by }}) %>%
summarise(avg = mean({{ var }}, na.rm = TRUE))
}
mean_by2 <- function(data, by, var) {
data %>%
group_by({{ by }}) %>%
summarise(avg := mean({{ var }}, na.rm = TRUE))
}
add_new_col <- function(data, old_col, new_col){
data %>%
mutate({{new_col}}:={{old_col}})
}
iris %>% mean_by(Species, Sepal.Width)
#> # A tibble: 3 x 2
#> Species avg
#> <fct> <dbl>
#> 1 setosa 3.43
#> 2 versicolor 2.77
#> 3 virginica 2.97
iris %>% mean_by2(Species, Sepal.Width)
#> # A tibble: 3 x 2
#> Species avg
#> <fct> <dbl>
#> 1 setosa 3.43
#> 2 versicolor 2.77
#> 3 virginica 2.97
iris %>% add_new_col(Species, New_species)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species New_species
#> 1 5.1 3.5 1.4 0.2 setosa setosa
#> 2 4.9 3.0 1.4 0.2 setosa setosa
#> 3 4.7 3.2 1.3 0.2 setosa setosa
#> 4 4.6 3.1 1.5 0.2 setosa setosa
#> 5 5.0 3.6 1.4 0.2 setosa setosa
#> 6 5.4 3.9 1.7 0.4 setosa setosa
#> 7 4.6 3.4 1.4 0.3 setosa setosa
#> 8 5.0 3.4 1.5 0.2 setosa setosa
#> 9 4.4 2.9 1.4 0.2 setosa setosa
#> 10 4.9 3.1 1.5 0.1 setosa setosa
#> 11 5.4 3.7 1.5 0.2 setosa setosa
#> 12 4.8 3.4 1.6 0.2 setosa setosa
#> 13 4.8 3.0 1.4 0.1 setosa setosa
#> 14 4.3 3.0 1.1 0.1 setosa setosa
#> 15 5.8 4.0 1.2 0.2 setosa setosa
#> 16 5.7 4.4 1.5 0.4 setosa setosa
#> 17 5.4 3.9 1.3 0.4 setosa setosa
#> 18 5.1 3.5 1.4 0.3 setosa setosa
#> 19 5.7 3.8 1.7 0.3 setosa setosa
#> 20 5.1 3.8 1.5 0.3 setosa setosa
#> 21 5.4 3.4 1.7 0.2 setosa setosa
#> 22 5.1 3.7 1.5 0.4 setosa setosa
#> 23 4.6 3.6 1.0 0.2 setosa setosa
#> 24 5.1 3.3 1.7 0.5 setosa setosa
#> 25 4.8 3.4 1.9 0.2 setosa setosa
#> 26 5.0 3.0 1.6 0.2 setosa setosa
#> 27 5.0 3.4 1.6 0.4 setosa setosa
#> 28 5.2 3.5 1.5 0.2 setosa setosa
#> 29 5.2 3.4 1.4 0.2 setosa setosa
#> 30 4.7 3.2 1.6 0.2 setosa setosa
#> 31 4.8 3.1 1.6 0.2 setosa setosa
#> 32 5.4 3.4 1.5 0.4 setosa setosa
#> 33 5.2 4.1 1.5 0.1 setosa setosa
#> 34 5.5 4.2 1.4 0.2 setosa setosa
#> 35 4.9 3.1 1.5 0.2 setosa setosa
#> 36 5.0 3.2 1.2 0.2 setosa setosa
#> 37 5.5 3.5 1.3 0.2 setosa setosa
#> 38 4.9 3.6 1.4 0.1 setosa setosa
#> 39 4.4 3.0 1.3 0.2 setosa setosa
#> 40 5.1 3.4 1.5 0.2 setosa setosa
#> 41 5.0 3.5 1.3 0.3 setosa setosa
#> 42 4.5 2.3 1.3 0.3 setosa setosa
#> 43 4.4 3.2 1.3 0.2 setosa setosa
#> 44 5.0 3.5 1.6 0.6 setosa setosa
#> 45 5.1 3.8 1.9 0.4 setosa setosa
#> 46 4.8 3.0 1.4 0.3 setosa setosa
#> 47 5.1 3.8 1.6 0.2 setosa setosa
#> 48 4.6 3.2 1.4 0.2 setosa setosa
#> 49 5.3 3.7 1.5 0.2 setosa setosa
#> 50 5.0 3.3 1.4 0.2 setosa setosa
#> 51 7.0 3.2 4.7 1.4 versicolor versicolor
#> 52 6.4 3.2 4.5 1.5 versicolor versicolor
#> 53 6.9 3.1 4.9 1.5 versicolor versicolor
#> 54 5.5 2.3 4.0 1.3 versicolor versicolor
#> 55 6.5 2.8 4.6 1.5 versicolor versicolor
#> 56 5.7 2.8 4.5 1.3 versicolor versicolor
#> 57 6.3 3.3 4.7 1.6 versicolor versicolor
#> 58 4.9 2.4 3.3 1.0 versicolor versicolor
#> 59 6.6 2.9 4.6 1.3 versicolor versicolor
#> 60 5.2 2.7 3.9 1.4 versicolor versicolor
#> 61 5.0 2.0 3.5 1.0 versicolor versicolor
#> 62 5.9 3.0 4.2 1.5 versicolor versicolor
#> 63 6.0 2.2 4.0 1.0 versicolor versicolor
#> 64 6.1 2.9 4.7 1.4 versicolor versicolor
#> 65 5.6 2.9 3.6 1.3 versicolor versicolor
#> 66 6.7 3.1 4.4 1.4 versicolor versicolor
#> 67 5.6 3.0 4.5 1.5 versicolor versicolor
#> 68 5.8 2.7 4.1 1.0 versicolor versicolor
#> 69 6.2 2.2 4.5 1.5 versicolor versicolor
#> 70 5.6 2.5 3.9 1.1 versicolor versicolor
#> 71 5.9 3.2 4.8 1.8 versicolor versicolor
#> 72 6.1 2.8 4.0 1.3 versicolor versicolor
#> 73 6.3 2.5 4.9 1.5 versicolor versicolor
#> 74 6.1 2.8 4.7 1.2 versicolor versicolor
#> 75 6.4 2.9 4.3 1.3 versicolor versicolor
#> 76 6.6 3.0 4.4 1.4 versicolor versicolor
#> 77 6.8 2.8 4.8 1.4 versicolor versicolor
#> 78 6.7 3.0 5.0 1.7 versicolor versicolor
#> 79 6.0 2.9 4.5 1.5 versicolor versicolor
#> 80 5.7 2.6 3.5 1.0 versicolor versicolor
#> 81 5.5 2.4 3.8 1.1 versicolor versicolor
#> 82 5.5 2.4 3.7 1.0 versicolor versicolor
#> 83 5.8 2.7 3.9 1.2 versicolor versicolor
#> 84 6.0 2.7 5.1 1.6 versicolor versicolor
#> 85 5.4 3.0 4.5 1.5 versicolor versicolor
#> 86 6.0 3.4 4.5 1.6 versicolor versicolor
#> 87 6.7 3.1 4.7 1.5 versicolor versicolor
#> 88 6.3 2.3 4.4 1.3 versicolor versicolor
#> 89 5.6 3.0 4.1 1.3 versicolor versicolor
#> 90 5.5 2.5 4.0 1.3 versicolor versicolor
#> 91 5.5 2.6 4.4 1.2 versicolor versicolor
#> 92 6.1 3.0 4.6 1.4 versicolor versicolor
#> 93 5.8 2.6 4.0 1.2 versicolor versicolor
#> 94 5.0 2.3 3.3 1.0 versicolor versicolor
#> 95 5.6 2.7 4.2 1.3 versicolor versicolor
#> 96 5.7 3.0 4.2 1.2 versicolor versicolor
#> 97 5.7 2.9 4.2 1.3 versicolor versicolor
#> 98 6.2 2.9 4.3 1.3 versicolor versicolor
#> 99 5.1 2.5 3.0 1.1 versicolor versicolor
#> 100 5.7 2.8 4.1 1.3 versicolor versicolor
#> 101 6.3 3.3 6.0 2.5 virginica virginica
#> 102 5.8 2.7 5.1 1.9 virginica virginica
#> 103 7.1 3.0 5.9 2.1 virginica virginica
#> 104 6.3 2.9 5.6 1.8 virginica virginica
#> 105 6.5 3.0 5.8 2.2 virginica virginica
#> 106 7.6 3.0 6.6 2.1 virginica virginica
#> 107 4.9 2.5 4.5 1.7 virginica virginica
#> 108 7.3 2.9 6.3 1.8 virginica virginica
#> 109 6.7 2.5 5.8 1.8 virginica virginica
#> 110 7.2 3.6 6.1 2.5 virginica virginica
#> 111 6.5 3.2 5.1 2.0 virginica virginica
#> 112 6.4 2.7 5.3 1.9 virginica virginica
#> 113 6.8 3.0 5.5 2.1 virginica virginica
#> 114 5.7 2.5 5.0 2.0 virginica virginica
#> 115 5.8 2.8 5.1 2.4 virginica virginica
#> 116 6.4 3.2 5.3 2.3 virginica virginica
#> 117 6.5 3.0 5.5 1.8 virginica virginica
#> 118 7.7 3.8 6.7 2.2 virginica virginica
#> 119 7.7 2.6 6.9 2.3 virginica virginica
#> 120 6.0 2.2 5.0 1.5 virginica virginica
#> 121 6.9 3.2 5.7 2.3 virginica virginica
#> 122 5.6 2.8 4.9 2.0 virginica virginica
#> 123 7.7 2.8 6.7 2.0 virginica virginica
#> 124 6.3 2.7 4.9 1.8 virginica virginica
#> 125 6.7 3.3 5.7 2.1 virginica virginica
#> 126 7.2 3.2 6.0 1.8 virginica virginica
#> 127 6.2 2.8 4.8 1.8 virginica virginica
#> 128 6.1 3.0 4.9 1.8 virginica virginica
#> 129 6.4 2.8 5.6 2.1 virginica virginica
#> 130 7.2 3.0 5.8 1.6 virginica virginica
#> 131 7.4 2.8 6.1 1.9 virginica virginica
#> 132 7.9 3.8 6.4 2.0 virginica virginica
#> 133 6.4 2.8 5.6 2.2 virginica virginica
#> 134 6.3 2.8 5.1 1.5 virginica virginica
#> 135 6.1 2.6 5.6 1.4 virginica virginica
#> 136 7.7 3.0 6.1 2.3 virginica virginica
#> 137 6.3 3.4 5.6 2.4 virginica virginica
#> 138 6.4 3.1 5.5 1.8 virginica virginica
#> 139 6.0 3.0 4.8 1.8 virginica virginica
#> 140 6.9 3.1 5.4 2.1 virginica virginica
#> 141 6.7 3.1 5.6 2.4 virginica virginica
#> 142 6.9 3.1 5.1 2.3 virginica virginica
#> 143 5.8 2.7 5.1 1.9 virginica virginica
#> 144 6.8 3.2 5.9 2.3 virginica virginica
#> 145 6.7 3.3 5.7 2.5 virginica virginica
#> 146 6.7 3.0 5.2 2.3 virginica virginica
#> 147 6.3 2.5 5.0 1.9 virginica virginica
#> 148 6.5 3.0 5.2 2.0 virginica virginica
#> 149 6.2 3.4 5.4 2.3 virginica virginica
#> 150 5.9 3.0 5.1 1.8 virginica virginica
Created on 2020-04-18 by the reprex package (v0.3.0)
The rule of thumb is simple: if you are using any form of quasiquation (i.e., !! and {{ operators) on the left-hand side of the assignment, you need the walrus operator.
s = sym("abc")
## Quasi-quotation on the left-hand of the assignment
iris %>% mutate( !!s = Petal.Length * Petal.Width ) # Error: unexpected '='
## Using walrus fixes the issue
iris %>% mutate( !!s := Petal.Length * Petal.Width ) # Works, creates column abc
## No quasi-quotation on the left-hand side, so = is enough
iris %>% mutate( s = Petal.Length * Petal.Width ) # Also works, creates column s
Note that the walrus operator only works in functions that support quasiquotation. It is not supported in the general case:
list( a := 5 ) ## Error
rlang::list2( a := 5 ) ## Works
In the second case, we are creating a column named 'avg' and it is not the one provided by the user as unquoted argument. So, we can use = while in the last case, it is an argument that is provided in the function and here we use {{}} to do the evaluation