From 3d142954143a52fa9a460631ed38653a27aa61e8 Mon Sep 17 00:00:00 2001
From: Carine Rey <carine.rey@ens-lyon.fr>
Date: Mon, 27 Jun 2022 20:44:36 +0200
Subject: [PATCH] Typos
---
README.Rmd | 23 ++-
README.html | 407 ++++++++++------------------------------------------
2 files changed, 94 insertions(+), 336 deletions(-)
diff --git a/README.Rmd b/README.Rmd
index ebbbe03..4e4473c 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -522,13 +522,32 @@ library(tidyverse)
> Pour autant, les tibbles restent compatibles avec les data frames. On peut ainsi facilement convertir un data frame en tibble avec as_tibble :
+<details>
+
+<summary>
+
+build a little dataset
+
+</summary>
+
+<p>
+
```{r}
+#build a little dataset
set.seed(123)
mini_iris = iris %>% select(Species, Petal.Length, Petal.Width) %>%
sample_n(10)
mini_iris
```
+</p>
+
+</details>
+
+```{r}
+mini_iris
+```
+
```{r}
as_tibble(mini_iris)
```
@@ -538,7 +557,7 @@ as_tibble(mini_iris)
```{r}
-d <- as_tibble(rownames_to_column(iris))
+d <- as_tibble(rownames_to_column(mini_iris))
d
```
@@ -976,7 +995,7 @@ library(esquisse)
# Bilan
::: cadre_rouge
-Vous ne pouvez pas tout connaître, il faut savoir ce qui existe (mais rester curieux (!) ) et savoir retrouver comment utiliser une fonctionnalité : ce qui est essentiel pour vous se mémorisera tout seul.
+Vous ne pouvez pas tout connaître, il faut savoir ce qui existe (mais rester curieux (!) ) et savoir retrouver comment utiliser une fonctionnalité : ce qui est essentiel pour vous se mémorisera par lui même.
:::
diff --git a/README.html b/README.html
index bafe275..033c863 100644
--- a/README.html
+++ b/README.html
@@ -1891,19 +1891,19 @@ for(arg in arguments_list) {
res_list</code></pre>
<pre><code>## [[1]]
-## [1] 2.286517
+## [1] 20.23327
##
## [[2]]
-## [1] 0.1241522
+## [1] 1.204921
##
## [[3]]
-## [1] -4.807494
+## [1] -4.486249e-06
##
## [[4]]
-## [1] -0.07111893
+## [1] 11.49865
##
## [[5]]
-## [1] 6.609874</code></pre>
+## [1] 9.525572</code></pre>
</div>
<div id="les-boucles-lapply" class="section level2" number="5.2">
<h2><span class="header-section-number">5.2</span> Les boucles
@@ -1912,19 +1912,19 @@ lapply</h2>
res_list2 = lapply(arguments_list, function(arg) {arg ^ 3})
res_list2</code></pre>
<pre><code>## [[1]]
-## [1] 2.286517
+## [1] 20.23327
##
## [[2]]
-## [1] 0.1241522
+## [1] 1.204921
##
## [[3]]
-## [1] -4.807494
+## [1] -4.486249e-06
##
## [[4]]
-## [1] -0.07111893
+## [1] 11.49865
##
## [[5]]
-## [1] 6.609874</code></pre>
+## [1] 9.525572</code></pre>
</div>
<div id="les-boucles-sapplylapply" class="section level2" number="5.3">
<h2><span class="header-section-number">5.3</span> Les boucles
@@ -1932,7 +1932,7 @@ sapply/lapply</h2>
<pre class="r"><code>#l'output est un vecteur
res_list3 = sapply(arguments_list, function(arg) {arg ^ 3})
res_list3</code></pre>
-<pre><code>## [1] 2.28651679 0.12415223 -4.80749381 -0.07111893 6.60987359</code></pre>
+<pre><code>## [1] 2.023327e+01 1.204921e+00 -4.486249e-06 1.149865e+01 9.525572e+00</code></pre>
</div>
</div>
<div id="la-parralélisation-simple-avec-r" class="section level1" number="6">
@@ -1962,7 +1962,7 @@ stopCluster(clus)
end_time <- Sys.time()
duration = (end_time - start_time)
print(duration)</code></pre>
-<pre><code>## Time difference of 2.600084 secs</code></pre>
+<pre><code>## Time difference of 2.516307 secs</code></pre>
<details>
<summary>
<p>Comparaison avec lapply et for</p>
@@ -1974,7 +1974,7 @@ results_list = lapply(arguments_list, function_to_run_in_parallel)
end_time <- Sys.time()
duration = (end_time - start_time)
print(duration)</code></pre>
-<pre><code>## Time difference of 4.058037 secs</code></pre>
+<pre><code>## Time difference of 3.956438 secs</code></pre>
<pre class="r"><code>start_time <- Sys.time()
res_list = list()
@@ -1988,7 +1988,7 @@ for(arg in arguments_list) {
end_time <- Sys.time()
duration = (end_time - start_time)
print(duration)</code></pre>
-<pre><code>## Time difference of 6.015718 secs</code></pre>
+<pre><code>## Time difference of 5.871506 secs</code></pre>
</details>
<div id="attention-aux-erreurs" class="section level2" number="6.1">
<h2><span class="header-section-number">6.1</span> Attention aux
@@ -2166,7 +2166,13 @@ n’existe pas</li>
peut ainsi facilement convertir un data frame en tibble avec as_tibble
:</p>
</blockquote>
-<pre class="r"><code>set.seed(123)
+<details>
+<summary>
+<p>build a little dataset</p>
+</summary>
+<p>
+<pre class="r"><code>#build a little dataset
+set.seed(123)
mini_iris = iris %>% select(Species, Petal.Length, Petal.Width) %>%
sample_n(10)
mini_iris</code></pre>
@@ -2181,6 +2187,20 @@ mini_iris</code></pre>
## 8 versicolor 4.4 1.2
## 9 virginica 5.1 1.9
## 10 versicolor 4.6 1.4</code></pre>
+</p>
+</details>
+<pre class="r"><code>mini_iris</code></pre>
+<pre><code>## Species Petal.Length Petal.Width
+## 1 setosa 1.1 0.1
+## 2 setosa 1.4 0.2
+## 3 virginica 6.7 2.2
+## 4 setosa 1.3 0.2
+## 5 virginica 5.1 1.8
+## 6 virginica 5.2 2.0
+## 7 versicolor 4.0 1.3
+## 8 versicolor 4.4 1.2
+## 9 virginica 5.1 1.9
+## 10 versicolor 4.6 1.4</code></pre>
<pre class="r"><code>as_tibble(mini_iris)</code></pre>
<pre><code>## # A tibble: 10 × 3
## Species Petal.Length Petal.Width
@@ -2199,334 +2219,53 @@ mini_iris</code></pre>
<p>Si le data frame d’origine a des rownames, on peut d’abord les
convertir en colonnes avec rownames_to_columns :</p>
</blockquote>
-<pre class="r"><code>d <- as_tibble(rownames_to_column(iris))
+<pre class="r"><code>d <- as_tibble(rownames_to_column(mini_iris))
d</code></pre>
-<pre><code>## # A tibble: 150 × 6
-## rowname Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-## <chr> <dbl> <dbl> <dbl> <dbl> <fct>
-## 1 1 5.1 3.5 1.4 0.2 setosa
-## 2 2 4.9 3 1.4 0.2 setosa
-## 3 3 4.7 3.2 1.3 0.2 setosa
-## 4 4 4.6 3.1 1.5 0.2 setosa
-## 5 5 5 3.6 1.4 0.2 setosa
-## 6 6 5.4 3.9 1.7 0.4 setosa
-## 7 7 4.6 3.4 1.4 0.3 setosa
-## 8 8 5 3.4 1.5 0.2 setosa
-## 9 9 4.4 2.9 1.4 0.2 setosa
-## 10 10 4.9 3.1 1.5 0.1 setosa
-## # … with 140 more rows</code></pre>
+<pre><code>## # A tibble: 10 × 4
+## rowname Species Petal.Length Petal.Width
+## <chr> <fct> <dbl> <dbl>
+## 1 1 setosa 1.1 0.1
+## 2 2 setosa 1.4 0.2
+## 3 3 virginica 6.7 2.2
+## 4 4 setosa 1.3 0.2
+## 5 5 virginica 5.1 1.8
+## 6 6 virginica 5.2 2
+## 7 7 versicolor 4 1.3
+## 8 8 versicolor 4.4 1.2
+## 9 9 virginica 5.1 1.9
+## 10 10 versicolor 4.6 1.4</code></pre>
<blockquote>
<p>À l’inverse, on peut à tout moment convertir un tibble en data frame
avec as.data.frame :</p>
</blockquote>
<pre class="r"><code>as.data.frame(d)</code></pre>
-<pre><code>## rowname Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-## 1 1 5.1 3.5 1.4 0.2 setosa
-## 2 2 4.9 3.0 1.4 0.2 setosa
-## 3 3 4.7 3.2 1.3 0.2 setosa
-## 4 4 4.6 3.1 1.5 0.2 setosa
-## 5 5 5.0 3.6 1.4 0.2 setosa
-## 6 6 5.4 3.9 1.7 0.4 setosa
-## 7 7 4.6 3.4 1.4 0.3 setosa
-## 8 8 5.0 3.4 1.5 0.2 setosa
-## 9 9 4.4 2.9 1.4 0.2 setosa
-## 10 10 4.9 3.1 1.5 0.1 setosa
-## 11 11 5.4 3.7 1.5 0.2 setosa
-## 12 12 4.8 3.4 1.6 0.2 setosa
-## 13 13 4.8 3.0 1.4 0.1 setosa
-## 14 14 4.3 3.0 1.1 0.1 setosa
-## 15 15 5.8 4.0 1.2 0.2 setosa
-## 16 16 5.7 4.4 1.5 0.4 setosa
-## 17 17 5.4 3.9 1.3 0.4 setosa
-## 18 18 5.1 3.5 1.4 0.3 setosa
-## 19 19 5.7 3.8 1.7 0.3 setosa
-## 20 20 5.1 3.8 1.5 0.3 setosa
-## 21 21 5.4 3.4 1.7 0.2 setosa
-## 22 22 5.1 3.7 1.5 0.4 setosa
-## 23 23 4.6 3.6 1.0 0.2 setosa
-## 24 24 5.1 3.3 1.7 0.5 setosa
-## 25 25 4.8 3.4 1.9 0.2 setosa
-## 26 26 5.0 3.0 1.6 0.2 setosa
-## 27 27 5.0 3.4 1.6 0.4 setosa
-## 28 28 5.2 3.5 1.5 0.2 setosa
-## 29 29 5.2 3.4 1.4 0.2 setosa
-## 30 30 4.7 3.2 1.6 0.2 setosa
-## 31 31 4.8 3.1 1.6 0.2 setosa
-## 32 32 5.4 3.4 1.5 0.4 setosa
-## 33 33 5.2 4.1 1.5 0.1 setosa
-## 34 34 5.5 4.2 1.4 0.2 setosa
-## 35 35 4.9 3.1 1.5 0.2 setosa
-## 36 36 5.0 3.2 1.2 0.2 setosa
-## 37 37 5.5 3.5 1.3 0.2 setosa
-## 38 38 4.9 3.6 1.4 0.1 setosa
-## 39 39 4.4 3.0 1.3 0.2 setosa
-## 40 40 5.1 3.4 1.5 0.2 setosa
-## 41 41 5.0 3.5 1.3 0.3 setosa
-## 42 42 4.5 2.3 1.3 0.3 setosa
-## 43 43 4.4 3.2 1.3 0.2 setosa
-## 44 44 5.0 3.5 1.6 0.6 setosa
-## 45 45 5.1 3.8 1.9 0.4 setosa
-## 46 46 4.8 3.0 1.4 0.3 setosa
-## 47 47 5.1 3.8 1.6 0.2 setosa
-## 48 48 4.6 3.2 1.4 0.2 setosa
-## 49 49 5.3 3.7 1.5 0.2 setosa
-## 50 50 5.0 3.3 1.4 0.2 setosa
-## 51 51 7.0 3.2 4.7 1.4 versicolor
-## 52 52 6.4 3.2 4.5 1.5 versicolor
-## 53 53 6.9 3.1 4.9 1.5 versicolor
-## 54 54 5.5 2.3 4.0 1.3 versicolor
-## 55 55 6.5 2.8 4.6 1.5 versicolor
-## 56 56 5.7 2.8 4.5 1.3 versicolor
-## 57 57 6.3 3.3 4.7 1.6 versicolor
-## 58 58 4.9 2.4 3.3 1.0 versicolor
-## 59 59 6.6 2.9 4.6 1.3 versicolor
-## 60 60 5.2 2.7 3.9 1.4 versicolor
-## 61 61 5.0 2.0 3.5 1.0 versicolor
-## 62 62 5.9 3.0 4.2 1.5 versicolor
-## 63 63 6.0 2.2 4.0 1.0 versicolor
-## 64 64 6.1 2.9 4.7 1.4 versicolor
-## 65 65 5.6 2.9 3.6 1.3 versicolor
-## 66 66 6.7 3.1 4.4 1.4 versicolor
-## 67 67 5.6 3.0 4.5 1.5 versicolor
-## 68 68 5.8 2.7 4.1 1.0 versicolor
-## 69 69 6.2 2.2 4.5 1.5 versicolor
-## 70 70 5.6 2.5 3.9 1.1 versicolor
-## 71 71 5.9 3.2 4.8 1.8 versicolor
-## 72 72 6.1 2.8 4.0 1.3 versicolor
-## 73 73 6.3 2.5 4.9 1.5 versicolor
-## 74 74 6.1 2.8 4.7 1.2 versicolor
-## 75 75 6.4 2.9 4.3 1.3 versicolor
-## 76 76 6.6 3.0 4.4 1.4 versicolor
-## 77 77 6.8 2.8 4.8 1.4 versicolor
-## 78 78 6.7 3.0 5.0 1.7 versicolor
-## 79 79 6.0 2.9 4.5 1.5 versicolor
-## 80 80 5.7 2.6 3.5 1.0 versicolor
-## 81 81 5.5 2.4 3.8 1.1 versicolor
-## 82 82 5.5 2.4 3.7 1.0 versicolor
-## 83 83 5.8 2.7 3.9 1.2 versicolor
-## 84 84 6.0 2.7 5.1 1.6 versicolor
-## 85 85 5.4 3.0 4.5 1.5 versicolor
-## 86 86 6.0 3.4 4.5 1.6 versicolor
-## 87 87 6.7 3.1 4.7 1.5 versicolor
-## 88 88 6.3 2.3 4.4 1.3 versicolor
-## 89 89 5.6 3.0 4.1 1.3 versicolor
-## 90 90 5.5 2.5 4.0 1.3 versicolor
-## 91 91 5.5 2.6 4.4 1.2 versicolor
-## 92 92 6.1 3.0 4.6 1.4 versicolor
-## 93 93 5.8 2.6 4.0 1.2 versicolor
-## 94 94 5.0 2.3 3.3 1.0 versicolor
-## 95 95 5.6 2.7 4.2 1.3 versicolor
-## 96 96 5.7 3.0 4.2 1.2 versicolor
-## 97 97 5.7 2.9 4.2 1.3 versicolor
-## 98 98 6.2 2.9 4.3 1.3 versicolor
-## 99 99 5.1 2.5 3.0 1.1 versicolor
-## 100 100 5.7 2.8 4.1 1.3 versicolor
-## 101 101 6.3 3.3 6.0 2.5 virginica
-## 102 102 5.8 2.7 5.1 1.9 virginica
-## 103 103 7.1 3.0 5.9 2.1 virginica
-## 104 104 6.3 2.9 5.6 1.8 virginica
-## 105 105 6.5 3.0 5.8 2.2 virginica
-## 106 106 7.6 3.0 6.6 2.1 virginica
-## 107 107 4.9 2.5 4.5 1.7 virginica
-## 108 108 7.3 2.9 6.3 1.8 virginica
-## 109 109 6.7 2.5 5.8 1.8 virginica
-## 110 110 7.2 3.6 6.1 2.5 virginica
-## 111 111 6.5 3.2 5.1 2.0 virginica
-## 112 112 6.4 2.7 5.3 1.9 virginica
-## 113 113 6.8 3.0 5.5 2.1 virginica
-## 114 114 5.7 2.5 5.0 2.0 virginica
-## 115 115 5.8 2.8 5.1 2.4 virginica
-## 116 116 6.4 3.2 5.3 2.3 virginica
-## 117 117 6.5 3.0 5.5 1.8 virginica
-## 118 118 7.7 3.8 6.7 2.2 virginica
-## 119 119 7.7 2.6 6.9 2.3 virginica
-## 120 120 6.0 2.2 5.0 1.5 virginica
-## 121 121 6.9 3.2 5.7 2.3 virginica
-## 122 122 5.6 2.8 4.9 2.0 virginica
-## 123 123 7.7 2.8 6.7 2.0 virginica
-## 124 124 6.3 2.7 4.9 1.8 virginica
-## 125 125 6.7 3.3 5.7 2.1 virginica
-## 126 126 7.2 3.2 6.0 1.8 virginica
-## 127 127 6.2 2.8 4.8 1.8 virginica
-## 128 128 6.1 3.0 4.9 1.8 virginica
-## 129 129 6.4 2.8 5.6 2.1 virginica
-## 130 130 7.2 3.0 5.8 1.6 virginica
-## 131 131 7.4 2.8 6.1 1.9 virginica
-## 132 132 7.9 3.8 6.4 2.0 virginica
-## 133 133 6.4 2.8 5.6 2.2 virginica
-## 134 134 6.3 2.8 5.1 1.5 virginica
-## 135 135 6.1 2.6 5.6 1.4 virginica
-## 136 136 7.7 3.0 6.1 2.3 virginica
-## 137 137 6.3 3.4 5.6 2.4 virginica
-## 138 138 6.4 3.1 5.5 1.8 virginica
-## 139 139 6.0 3.0 4.8 1.8 virginica
-## 140 140 6.9 3.1 5.4 2.1 virginica
-## 141 141 6.7 3.1 5.6 2.4 virginica
-## 142 142 6.9 3.1 5.1 2.3 virginica
-## 143 143 5.8 2.7 5.1 1.9 virginica
-## 144 144 6.8 3.2 5.9 2.3 virginica
-## 145 145 6.7 3.3 5.7 2.5 virginica
-## 146 146 6.7 3.0 5.2 2.3 virginica
-## 147 147 6.3 2.5 5.0 1.9 virginica
-## 148 148 6.5 3.0 5.2 2.0 virginica
-## 149 149 6.2 3.4 5.4 2.3 virginica
-## 150 150 5.9 3.0 5.1 1.8 virginica</code></pre>
+<pre><code>## rowname Species Petal.Length Petal.Width
+## 1 1 setosa 1.1 0.1
+## 2 2 setosa 1.4 0.2
+## 3 3 virginica 6.7 2.2
+## 4 4 setosa 1.3 0.2
+## 5 5 virginica 5.1 1.8
+## 6 6 virginica 5.2 2.0
+## 7 7 versicolor 4.0 1.3
+## 8 8 versicolor 4.4 1.2
+## 9 9 virginica 5.1 1.9
+## 10 10 versicolor 4.6 1.4</code></pre>
<blockquote>
<p>Là encore, on peut convertir la colonne rowname en “vrais” rownames
avec column_to_rownames :</p>
</blockquote>
<pre class="r"><code>column_to_rownames(as.data.frame(d))</code></pre>
-<pre><code>## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-## 1 5.1 3.5 1.4 0.2 setosa
-## 2 4.9 3.0 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 5.0 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 5.0 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 3.0 1.4 0.1 setosa
-## 14 4.3 3.0 1.1 0.1 setosa
-## 15 5.8 4.0 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 0.2 setosa
-## 24 5.1 3.3 1.7 0.5 setosa
-## 25 4.8 3.4 1.9 0.2 setosa
-## 26 5.0 3.0 1.6 0.2 setosa
-## 27 5.0 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 5.0 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 3.0 1.3 0.2 setosa
-## 40 5.1 3.4 1.5 0.2 setosa
-## 41 5.0 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 5.0 3.5 1.6 0.6 setosa
-## 45 5.1 3.8 1.9 0.4 setosa
-## 46 4.8 3.0 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 5.0 3.3 1.4 0.2 setosa
-## 51 7.0 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 4.0 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 1.0 versicolor
-## 59 6.6 2.9 4.6 1.3 versicolor
-## 60 5.2 2.7 3.9 1.4 versicolor
-## 61 5.0 2.0 3.5 1.0 versicolor
-## 62 5.9 3.0 4.2 1.5 versicolor
-## 63 6.0 2.2 4.0 1.0 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 3.0 4.5 1.5 versicolor
-## 68 5.8 2.7 4.1 1.0 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 4.0 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 3.0 4.4 1.4 versicolor
-## 77 6.8 2.8 4.8 1.4 versicolor
-## 78 6.7 3.0 5.0 1.7 versicolor
-## 79 6.0 2.9 4.5 1.5 versicolor
-## 80 5.7 2.6 3.5 1.0 versicolor
-## 81 5.5 2.4 3.8 1.1 versicolor
-## 82 5.5 2.4 3.7 1.0 versicolor
-## 83 5.8 2.7 3.9 1.2 versicolor
-## 84 6.0 2.7 5.1 1.6 versicolor
-## 85 5.4 3.0 4.5 1.5 versicolor
-## 86 6.0 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 3.0 4.1 1.3 versicolor
-## 90 5.5 2.5 4.0 1.3 versicolor
-## 91 5.5 2.6 4.4 1.2 versicolor
-## 92 6.1 3.0 4.6 1.4 versicolor
-## 93 5.8 2.6 4.0 1.2 versicolor
-## 94 5.0 2.3 3.3 1.0 versicolor
-## 95 5.6 2.7 4.2 1.3 versicolor
-## 96 5.7 3.0 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 3.0 1.1 versicolor
-## 100 5.7 2.8 4.1 1.3 versicolor
-## 101 6.3 3.3 6.0 2.5 virginica
-## 102 5.8 2.7 5.1 1.9 virginica
-## 103 7.1 3.0 5.9 2.1 virginica
-## 104 6.3 2.9 5.6 1.8 virginica
-## 105 6.5 3.0 5.8 2.2 virginica
-## 106 7.6 3.0 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 2.0 virginica
-## 112 6.4 2.7 5.3 1.9 virginica
-## 113 6.8 3.0 5.5 2.1 virginica
-## 114 5.7 2.5 5.0 2.0 virginica
-## 115 5.8 2.8 5.1 2.4 virginica
-## 116 6.4 3.2 5.3 2.3 virginica
-## 117 6.5 3.0 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 6.0 2.2 5.0 1.5 virginica
-## 121 6.9 3.2 5.7 2.3 virginica
-## 122 5.6 2.8 4.9 2.0 virginica
-## 123 7.7 2.8 6.7 2.0 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 6.0 1.8 virginica
-## 127 6.2 2.8 4.8 1.8 virginica
-## 128 6.1 3.0 4.9 1.8 virginica
-## 129 6.4 2.8 5.6 2.1 virginica
-## 130 7.2 3.0 5.8 1.6 virginica
-## 131 7.4 2.8 6.1 1.9 virginica
-## 132 7.9 3.8 6.4 2.0 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 3.0 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 6.0 3.0 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 3.0 5.2 2.3 virginica
-## 147 6.3 2.5 5.0 1.9 virginica
-## 148 6.5 3.0 5.2 2.0 virginica
-## 149 6.2 3.4 5.4 2.3 virginica
-## 150 5.9 3.0 5.1 1.8 virginica</code></pre>
+<pre><code>## Species Petal.Length Petal.Width
+## 1 setosa 1.1 0.1
+## 2 setosa 1.4 0.2
+## 3 virginica 6.7 2.2
+## 4 setosa 1.3 0.2
+## 5 virginica 5.1 1.8
+## 6 virginica 5.2 2.0
+## 7 versicolor 4.0 1.3
+## 8 versicolor 4.4 1.2
+## 9 virginica 5.1 1.9
+## 10 versicolor 4.6 1.4</code></pre>
</div>
<div id="les-fonctions-indispensables-pour-manipuler-vos-données" class="section level2" number="7.2">
<h2><span class="header-section-number">7.2</span> les fonctions
@@ -3113,8 +2852,8 @@ library(esquisse)</code></pre>
<div class="cadre_rouge">
<p>Vous ne pouvez pas tout connaître, il faut savoir ce qui existe (mais
rester curieux (!) ) et savoir retrouver comment utiliser une
-fonctionnalité : ce qui est essentiel pour vous se mémorisera tout
-seul.</p>
+fonctionnalité : ce qui est essentiel pour vous se mémorisera par lui
+même.</p>
</div>
<p><img 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" /></p>
</div>
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GitLab