{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(tidyverse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deuxième partie : Manipulation des données avec dplyr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Avec la fonction `filter()`, trouvez les 8 mamifères qui dorment plus de 16 heures par jour" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 8 × 11
namegenusvoreorderconservationsleep_totalsleep_remsleep_cycleawakebrainwtbodywt
<chr><chr><chr><chr><chr><dbl><dbl><dbl><dbl><dbl><dbl>
Owl monkey Aotus omni Primates NA17.01.8 NA7.00.01550 0.480
Long-nosed armadillo Dasypus carni Cingulata lc17.43.10.38333336.60.01080 3.500
North American OpossumDidelphis omni Didelphimorphialc18.04.90.33333336.00.00630 1.700
Big brown bat Eptesicus insectiChiroptera lc19.73.90.11666674.30.00030 0.023
Thick-tailed opposum Lutreolina carni Didelphimorphialc19.46.6 NA4.6 NA 0.370
Little brown bat Myotis insectiChiroptera NA19.92.00.20000004.10.00025 0.010
Giant armadillo Priodontes insectiCingulata en18.16.1 NA5.90.0810060.000
Arctic ground squirrelSpermophilusherbi Rodentia lc16.6 NA NA7.40.00570 0.920
\n" ], "text/latex": [ "A tibble: 8 × 11\n", "\\begin{tabular}{lllllllllll}\n", " name & genus & vore & order & conservation & sleep\\_total & sleep\\_rem & sleep\\_cycle & awake & brainwt & bodywt\\\\\n", " & & & & & & & & & & \\\\\n", "\\hline\n", "\t Owl monkey & Aotus & omni & Primates & NA & 17.0 & 1.8 & NA & 7.0 & 0.01550 & 0.480\\\\\n", "\t Long-nosed armadillo & Dasypus & carni & Cingulata & lc & 17.4 & 3.1 & 0.3833333 & 6.6 & 0.01080 & 3.500\\\\\n", "\t North American Opossum & Didelphis & omni & Didelphimorphia & lc & 18.0 & 4.9 & 0.3333333 & 6.0 & 0.00630 & 1.700\\\\\n", "\t Big brown bat & Eptesicus & insecti & Chiroptera & lc & 19.7 & 3.9 & 0.1166667 & 4.3 & 0.00030 & 0.023\\\\\n", "\t Thick-tailed opposum & Lutreolina & carni & Didelphimorphia & lc & 19.4 & 6.6 & NA & 4.6 & NA & 0.370\\\\\n", "\t Little brown bat & Myotis & insecti & Chiroptera & NA & 19.9 & 2.0 & 0.2000000 & 4.1 & 0.00025 & 0.010\\\\\n", "\t Giant armadillo & Priodontes & insecti & Cingulata & en & 18.1 & 6.1 & NA & 5.9 & 0.08100 & 60.000\\\\\n", "\t Arctic ground squirrel & Spermophilus & herbi & Rodentia & lc & 16.6 & NA & NA & 7.4 & 0.00570 & 0.920\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 8 × 11\n", "\n", "| name <chr> | genus <chr> | vore <chr> | order <chr> | conservation <chr> | sleep_total <dbl> | sleep_rem <dbl> | sleep_cycle <dbl> | awake <dbl> | brainwt <dbl> | bodywt <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|\n", "| Owl monkey | Aotus | omni | Primates | NA | 17.0 | 1.8 | NA | 7.0 | 0.01550 | 0.480 |\n", "| Long-nosed armadillo | Dasypus | carni | Cingulata | lc | 17.4 | 3.1 | 0.3833333 | 6.6 | 0.01080 | 3.500 |\n", "| North American Opossum | Didelphis | omni | Didelphimorphia | lc | 18.0 | 4.9 | 0.3333333 | 6.0 | 0.00630 | 1.700 |\n", "| Big brown bat | Eptesicus | insecti | Chiroptera | lc | 19.7 | 3.9 | 0.1166667 | 4.3 | 0.00030 | 0.023 |\n", "| Thick-tailed opposum | Lutreolina | carni | Didelphimorphia | lc | 19.4 | 6.6 | NA | 4.6 | NA | 0.370 |\n", "| Little brown bat | Myotis | insecti | Chiroptera | NA | 19.9 | 2.0 | 0.2000000 | 4.1 | 0.00025 | 0.010 |\n", "| Giant armadillo | Priodontes | insecti | Cingulata | en | 18.1 | 6.1 | NA | 5.9 | 0.08100 | 60.000 |\n", "| Arctic ground squirrel | Spermophilus | herbi | Rodentia | lc | 16.6 | NA | NA | 7.4 | 0.00570 | 0.920 |\n", "\n" ], "text/plain": [ " name genus vore order conservation\n", "1 Owl monkey Aotus omni Primates NA \n", "2 Long-nosed armadillo Dasypus carni Cingulata lc \n", "3 North American Opossum Didelphis omni Didelphimorphia lc \n", "4 Big brown bat Eptesicus insecti Chiroptera lc \n", "5 Thick-tailed opposum Lutreolina carni Didelphimorphia lc \n", "6 Little brown bat Myotis insecti Chiroptera NA \n", "7 Giant armadillo Priodontes insecti Cingulata en \n", "8 Arctic ground squirrel Spermophilus herbi Rodentia lc \n", " sleep_total sleep_rem sleep_cycle awake brainwt bodywt\n", "1 17.0 1.8 NA 7.0 0.01550 0.480\n", "2 17.4 3.1 0.3833333 6.6 0.01080 3.500\n", "3 18.0 4.9 0.3333333 6.0 0.00630 1.700\n", "4 19.7 3.9 0.1166667 4.3 0.00030 0.023\n", "5 19.4 6.6 NA 4.6 NA 0.370\n", "6 19.9 2.0 0.2000000 4.1 0.00025 0.010\n", "7 18.1 6.1 NA 5.9 0.08100 60.000\n", "8 16.6 NA NA 7.4 0.00570 0.920" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filter(msleep, sleep_total > 16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Avec la fonction `arrange()`, triez les données par ordre taxonomique (`order`) puis par temps de sommeil croissant (`sleep_total`)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 11
namegenusvoreorderconservationsleep_totalsleep_remsleep_cycleawakebrainwtbodywt
<chr><chr><chr><chr><chr><dbl><dbl><dbl><dbl><dbl><dbl>
Tenrec Tenrec omni AfrosoricidaNA 15.62.3 NA 8.40.0026 0.900
Giraffe Giraffa herbiArtiodactylacd 1.90.4 NA22.1 NA899.995
Roe deerCapreolusherbiArtiodactylalc 3.0 NA NA21.00.0982 14.800
Sheep Ovis herbiArtiodactyladomesticated 3.80.6 NA20.20.1750 55.500
Cow Bos herbiArtiodactyladomesticated 4.00.70.666666720.00.4230600.000
Goat Capri herbiArtiodactylalc 5.30.6 NA18.70.1150 33.500
\n" ], "text/latex": [ "A tibble: 6 × 11\n", "\\begin{tabular}{lllllllllll}\n", " name & genus & vore & order & conservation & sleep\\_total & sleep\\_rem & sleep\\_cycle & awake & brainwt & bodywt\\\\\n", " & & & & & & & & & & \\\\\n", "\\hline\n", "\t Tenrec & Tenrec & omni & Afrosoricida & NA & 15.6 & 2.3 & NA & 8.4 & 0.0026 & 0.900\\\\\n", "\t Giraffe & Giraffa & herbi & Artiodactyla & cd & 1.9 & 0.4 & NA & 22.1 & NA & 899.995\\\\\n", "\t Roe deer & Capreolus & herbi & Artiodactyla & lc & 3.0 & NA & NA & 21.0 & 0.0982 & 14.800\\\\\n", "\t Sheep & Ovis & herbi & Artiodactyla & domesticated & 3.8 & 0.6 & NA & 20.2 & 0.1750 & 55.500\\\\\n", "\t Cow & Bos & herbi & Artiodactyla & domesticated & 4.0 & 0.7 & 0.6666667 & 20.0 & 0.4230 & 600.000\\\\\n", "\t Goat & Capri & herbi & Artiodactyla & lc & 5.3 & 0.6 & NA & 18.7 & 0.1150 & 33.500\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 11\n", "\n", "| name <chr> | genus <chr> | vore <chr> | order <chr> | conservation <chr> | sleep_total <dbl> | sleep_rem <dbl> | sleep_cycle <dbl> | awake <dbl> | brainwt <dbl> | bodywt <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|\n", "| Tenrec | Tenrec | omni | Afrosoricida | NA | 15.6 | 2.3 | NA | 8.4 | 0.0026 | 0.900 |\n", "| Giraffe | Giraffa | herbi | Artiodactyla | cd | 1.9 | 0.4 | NA | 22.1 | NA | 899.995 |\n", "| Roe deer | Capreolus | herbi | Artiodactyla | lc | 3.0 | NA | NA | 21.0 | 0.0982 | 14.800 |\n", "| Sheep | Ovis | herbi | Artiodactyla | domesticated | 3.8 | 0.6 | NA | 20.2 | 0.1750 | 55.500 |\n", "| Cow | Bos | herbi | Artiodactyla | domesticated | 4.0 | 0.7 | 0.6666667 | 20.0 | 0.4230 | 600.000 |\n", "| Goat | Capri | herbi | Artiodactyla | lc | 5.3 | 0.6 | NA | 18.7 | 0.1150 | 33.500 |\n", "\n" ], "text/plain": [ " name genus vore order conservation sleep_total sleep_rem\n", "1 Tenrec Tenrec omni Afrosoricida NA 15.6 2.3 \n", "2 Giraffe Giraffa herbi Artiodactyla cd 1.9 0.4 \n", "3 Roe deer Capreolus herbi Artiodactyla lc 3.0 NA \n", "4 Sheep Ovis herbi Artiodactyla domesticated 3.8 0.6 \n", "5 Cow Bos herbi Artiodactyla domesticated 4.0 0.7 \n", "6 Goat Capri herbi Artiodactyla lc 5.3 0.6 \n", " sleep_cycle awake brainwt bodywt \n", "1 NA 8.4 0.0026 0.900\n", "2 NA 22.1 NA 899.995\n", "3 NA 21.0 0.0982 14.800\n", "4 NA 20.2 0.1750 55.500\n", "5 0.6666667 20.0 0.4230 600.000\n", "6 NA 18.7 0.1150 33.500" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "arrange(msleep, order, sleep_total) %>%\n", " head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Avec la fonction `select()`, sélectionnez les colonnes qui commencent par \"sleep\"" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 3
sleep_totalsleep_remsleep_cycle
<dbl><dbl><dbl>
12.1 NA NA
17.01.8 NA
14.42.4 NA
14.92.30.1333333
4.00.70.6666667
14.42.20.7666667
\n" ], "text/latex": [ "A tibble: 6 × 3\n", "\\begin{tabular}{lll}\n", " sleep\\_total & sleep\\_rem & sleep\\_cycle\\\\\n", " & & \\\\\n", "\\hline\n", "\t 12.1 & NA & NA\\\\\n", "\t 17.0 & 1.8 & NA\\\\\n", "\t 14.4 & 2.4 & NA\\\\\n", "\t 14.9 & 2.3 & 0.1333333\\\\\n", "\t 4.0 & 0.7 & 0.6666667\\\\\n", "\t 14.4 & 2.2 & 0.7666667\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 3\n", "\n", "| sleep_total <dbl> | sleep_rem <dbl> | sleep_cycle <dbl> |\n", "|---|---|---|\n", "| 12.1 | NA | NA |\n", "| 17.0 | 1.8 | NA |\n", "| 14.4 | 2.4 | NA |\n", "| 14.9 | 2.3 | 0.1333333 |\n", "| 4.0 | 0.7 | 0.6666667 |\n", "| 14.4 | 2.2 | 0.7666667 |\n", "\n" ], "text/plain": [ " sleep_total sleep_rem sleep_cycle\n", "1 12.1 NA NA \n", "2 17.0 1.8 NA \n", "3 14.4 2.4 NA \n", "4 14.9 2.3 0.1333333 \n", "5 4.0 0.7 0.6666667 \n", "6 14.4 2.2 0.7666667 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "select(msleep, starts_with(\"sleep\")) %>%\n", " head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Avec l'opérateur `%>%`, enchainez les opérations suivantes :\n", "Sélectionnez les colonnes `name`, `order` et `sleep_total`, triez en fonction de `order`puis de `sleep_total` et enfin filtrer pour les mamifères dormant 16h ou plus." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 8 × 3
nameordersleep_total
<chr><chr><dbl>
Big brown bat Chiroptera 19.7
Little brown bat Chiroptera 19.9
Long-nosed armadillo Cingulata 17.4
Giant armadillo Cingulata 18.1
North American OpossumDidelphimorphia18.0
Thick-tailed opposum Didelphimorphia19.4
Owl monkey Primates 17.0
Arctic ground squirrelRodentia 16.6
\n" ], "text/latex": [ "A tibble: 8 × 3\n", "\\begin{tabular}{lll}\n", " name & order & sleep\\_total\\\\\n", " & & \\\\\n", "\\hline\n", "\t Big brown bat & Chiroptera & 19.7\\\\\n", "\t Little brown bat & Chiroptera & 19.9\\\\\n", "\t Long-nosed armadillo & Cingulata & 17.4\\\\\n", "\t Giant armadillo & Cingulata & 18.1\\\\\n", "\t North American Opossum & Didelphimorphia & 18.0\\\\\n", "\t Thick-tailed opposum & Didelphimorphia & 19.4\\\\\n", "\t Owl monkey & Primates & 17.0\\\\\n", "\t Arctic ground squirrel & Rodentia & 16.6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 8 × 3\n", "\n", "| name <chr> | order <chr> | sleep_total <dbl> |\n", "|---|---|---|\n", "| Big brown bat | Chiroptera | 19.7 |\n", "| Little brown bat | Chiroptera | 19.9 |\n", "| Long-nosed armadillo | Cingulata | 17.4 |\n", "| Giant armadillo | Cingulata | 18.1 |\n", "| North American Opossum | Didelphimorphia | 18.0 |\n", "| Thick-tailed opposum | Didelphimorphia | 19.4 |\n", "| Owl monkey | Primates | 17.0 |\n", "| Arctic ground squirrel | Rodentia | 16.6 |\n", "\n" ], "text/plain": [ " name order sleep_total\n", "1 Big brown bat Chiroptera 19.7 \n", "2 Little brown bat Chiroptera 19.9 \n", "3 Long-nosed armadillo Cingulata 17.4 \n", "4 Giant armadillo Cingulata 18.1 \n", "5 North American Opossum Didelphimorphia 18.0 \n", "6 Thick-tailed opposum Didelphimorphia 19.4 \n", "7 Owl monkey Primates 17.0 \n", "8 Arctic ground squirrel Rodentia 16.6 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msleep %>%\n", " select(name, order, sleep_total) %>%\n", " arrange(order, sleep_total) %>%\n", " filter(sleep_total > 16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5. Avec la fonction `mutate()`, créez une nouvelle variable `rem_proportion` qui calcule la proportion de sommeil paradoxal (`sleep_rem`) par rapport au sommeil total (`sleep_total`)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 4
nameordersleep_totalrem_proportion
<chr><chr><dbl><dbl>
Cheetah Carnivora 12.1 NA
Owl monkey Primates 17.00.1058824
Mountain beaver Rodentia 14.40.1666667
Greater short-tailed shrewSoricomorpha14.90.1543624
Cow Artiodactyla 4.00.1750000
Three-toed sloth Pilosa 14.40.1527778
\n" ], "text/latex": [ "A tibble: 6 × 4\n", "\\begin{tabular}{llll}\n", " name & order & sleep\\_total & rem\\_proportion\\\\\n", " & & & \\\\\n", "\\hline\n", "\t Cheetah & Carnivora & 12.1 & NA\\\\\n", "\t Owl monkey & Primates & 17.0 & 0.1058824\\\\\n", "\t Mountain beaver & Rodentia & 14.4 & 0.1666667\\\\\n", "\t Greater short-tailed shrew & Soricomorpha & 14.9 & 0.1543624\\\\\n", "\t Cow & Artiodactyla & 4.0 & 0.1750000\\\\\n", "\t Three-toed sloth & Pilosa & 14.4 & 0.1527778\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 4\n", "\n", "| name <chr> | order <chr> | sleep_total <dbl> | rem_proportion <dbl> |\n", "|---|---|---|---|\n", "| Cheetah | Carnivora | 12.1 | NA |\n", "| Owl monkey | Primates | 17.0 | 0.1058824 |\n", "| Mountain beaver | Rodentia | 14.4 | 0.1666667 |\n", "| Greater short-tailed shrew | Soricomorpha | 14.9 | 0.1543624 |\n", "| Cow | Artiodactyla | 4.0 | 0.1750000 |\n", "| Three-toed sloth | Pilosa | 14.4 | 0.1527778 |\n", "\n" ], "text/plain": [ " name order sleep_total rem_proportion\n", "1 Cheetah Carnivora 12.1 NA \n", "2 Owl monkey Primates 17.0 0.1058824 \n", "3 Mountain beaver Rodentia 14.4 0.1666667 \n", "4 Greater short-tailed shrew Soricomorpha 14.9 0.1543624 \n", "5 Cow Artiodactyla 4.0 0.1750000 \n", "6 Three-toed sloth Pilosa 14.4 0.1527778 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msleep %>%\n", " mutate(rem_proportion = sleep_rem/sleep_total) %>%\n", " select(name, order, sleep_total, rem_proportion) %>%\n", " head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Produite des résumés informatifs avec `summarise`" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 4
avg_sleepmin_sleepmax_sleeptotal
<dbl><dbl><dbl><int>
10.433731.919.983
\n" ], "text/latex": [ "A tibble: 1 × 4\n", "\\begin{tabular}{llll}\n", " avg\\_sleep & min\\_sleep & max\\_sleep & total\\\\\n", " & & & \\\\\n", "\\hline\n", "\t 10.43373 & 1.9 & 19.9 & 83\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 4\n", "\n", "| avg_sleep <dbl> | min_sleep <dbl> | max_sleep <dbl> | total <int> |\n", "|---|---|---|---|\n", "| 10.43373 | 1.9 | 19.9 | 83 |\n", "\n" ], "text/plain": [ " avg_sleep min_sleep max_sleep total\n", "1 10.43373 1.9 19.9 83 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msleep %>% \n", " summarise(avg_sleep = mean(sleep_total), \n", " min_sleep = min(sleep_total),\n", " max_sleep = max(sleep_total),\n", " total = n())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6. Avec les fonctions `group_by()` et `summarise()` calculez, pour chaque ordre taxonomique : le nombre d'animaux, le temps de sommeil moyen, le temps de sommeil minimal et le temps de sommeil maximal." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 19 × 5
orderavg_sleepmin_sleepmax_sleeptotal
<chr><dbl><dbl><dbl><int>
Afrosoricida 15.60000015.615.6 1
Artiodactyla 4.516667 1.9 9.1 6
Carnivora 10.116667 3.515.812
Cetacea 4.500000 2.7 5.6 3
Chiroptera 19.80000019.719.9 2
Cingulata 17.75000017.418.1 2
Didelphimorphia18.70000018.019.4 2
Diprotodontia 12.40000011.113.7 2
Erinaceomorpha 10.20000010.110.3 2
Hyracoidea 5.666667 5.3 6.3 3
Lagomorpha 8.400000 8.4 8.4 1
Monotremata 8.600000 8.6 8.6 1
Perissodactyla 3.466667 2.9 4.4 3
Pilosa 14.40000014.414.4 1
Primates 10.500000 8.017.012
Proboscidea 3.600000 3.3 3.9 2
Rodentia 12.468182 7.016.622
Scandentia 8.900000 8.9 8.9 1
Soricomorpha 11.100000 8.414.9 5
\n" ], "text/latex": [ "A tibble: 19 × 5\n", "\\begin{tabular}{lllll}\n", " order & avg\\_sleep & min\\_sleep & max\\_sleep & total\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t Afrosoricida & 15.600000 & 15.6 & 15.6 & 1\\\\\n", "\t Artiodactyla & 4.516667 & 1.9 & 9.1 & 6\\\\\n", "\t Carnivora & 10.116667 & 3.5 & 15.8 & 12\\\\\n", "\t Cetacea & 4.500000 & 2.7 & 5.6 & 3\\\\\n", "\t Chiroptera & 19.800000 & 19.7 & 19.9 & 2\\\\\n", "\t Cingulata & 17.750000 & 17.4 & 18.1 & 2\\\\\n", "\t Didelphimorphia & 18.700000 & 18.0 & 19.4 & 2\\\\\n", "\t Diprotodontia & 12.400000 & 11.1 & 13.7 & 2\\\\\n", "\t Erinaceomorpha & 10.200000 & 10.1 & 10.3 & 2\\\\\n", "\t Hyracoidea & 5.666667 & 5.3 & 6.3 & 3\\\\\n", "\t Lagomorpha & 8.400000 & 8.4 & 8.4 & 1\\\\\n", "\t Monotremata & 8.600000 & 8.6 & 8.6 & 1\\\\\n", "\t Perissodactyla & 3.466667 & 2.9 & 4.4 & 3\\\\\n", "\t Pilosa & 14.400000 & 14.4 & 14.4 & 1\\\\\n", "\t Primates & 10.500000 & 8.0 & 17.0 & 12\\\\\n", "\t Proboscidea & 3.600000 & 3.3 & 3.9 & 2\\\\\n", "\t Rodentia & 12.468182 & 7.0 & 16.6 & 22\\\\\n", "\t Scandentia & 8.900000 & 8.9 & 8.9 & 1\\\\\n", "\t Soricomorpha & 11.100000 & 8.4 & 14.9 & 5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 19 × 5\n", "\n", "| order <chr> | avg_sleep <dbl> | min_sleep <dbl> | max_sleep <dbl> | total <int> |\n", "|---|---|---|---|---|\n", "| Afrosoricida | 15.600000 | 15.6 | 15.6 | 1 |\n", "| Artiodactyla | 4.516667 | 1.9 | 9.1 | 6 |\n", "| Carnivora | 10.116667 | 3.5 | 15.8 | 12 |\n", "| Cetacea | 4.500000 | 2.7 | 5.6 | 3 |\n", "| Chiroptera | 19.800000 | 19.7 | 19.9 | 2 |\n", "| Cingulata | 17.750000 | 17.4 | 18.1 | 2 |\n", "| Didelphimorphia | 18.700000 | 18.0 | 19.4 | 2 |\n", "| Diprotodontia | 12.400000 | 11.1 | 13.7 | 2 |\n", "| Erinaceomorpha | 10.200000 | 10.1 | 10.3 | 2 |\n", "| Hyracoidea | 5.666667 | 5.3 | 6.3 | 3 |\n", "| Lagomorpha | 8.400000 | 8.4 | 8.4 | 1 |\n", "| Monotremata | 8.600000 | 8.6 | 8.6 | 1 |\n", "| Perissodactyla | 3.466667 | 2.9 | 4.4 | 3 |\n", "| Pilosa | 14.400000 | 14.4 | 14.4 | 1 |\n", "| Primates | 10.500000 | 8.0 | 17.0 | 12 |\n", "| Proboscidea | 3.600000 | 3.3 | 3.9 | 2 |\n", "| Rodentia | 12.468182 | 7.0 | 16.6 | 22 |\n", "| Scandentia | 8.900000 | 8.9 | 8.9 | 1 |\n", "| Soricomorpha | 11.100000 | 8.4 | 14.9 | 5 |\n", "\n" ], "text/plain": [ " order avg_sleep min_sleep max_sleep total\n", "1 Afrosoricida 15.600000 15.6 15.6 1 \n", "2 Artiodactyla 4.516667 1.9 9.1 6 \n", "3 Carnivora 10.116667 3.5 15.8 12 \n", "4 Cetacea 4.500000 2.7 5.6 3 \n", "5 Chiroptera 19.800000 19.7 19.9 2 \n", "6 Cingulata 17.750000 17.4 18.1 2 \n", "7 Didelphimorphia 18.700000 18.0 19.4 2 \n", "8 Diprotodontia 12.400000 11.1 13.7 2 \n", "9 Erinaceomorpha 10.200000 10.1 10.3 2 \n", "10 Hyracoidea 5.666667 5.3 6.3 3 \n", "11 Lagomorpha 8.400000 8.4 8.4 1 \n", "12 Monotremata 8.600000 8.6 8.6 1 \n", "13 Perissodactyla 3.466667 2.9 4.4 3 \n", "14 Pilosa 14.400000 14.4 14.4 1 \n", "15 Primates 10.500000 8.0 17.0 12 \n", "16 Proboscidea 3.600000 3.3 3.9 2 \n", "17 Rodentia 12.468182 7.0 16.6 22 \n", "18 Scandentia 8.900000 8.9 8.9 1 \n", "19 Soricomorpha 11.100000 8.4 14.9 5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msleep %>% \n", " group_by(order) %>%\n", " summarise(avg_sleep = mean(sleep_total), \n", " min_sleep = min(sleep_total),\n", " max_sleep = max(sleep_total),\n", " total = n())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Troisième partie : Ranger des données avec tidyr" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 5
Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies
<dbl><dbl><dbl><dbl><fct>
15.13.51.40.2setosa
24.93.01.40.2setosa
34.73.21.30.2setosa
44.63.11.50.2setosa
55.03.61.40.2setosa
65.43.91.70.4setosa
\n" ], "text/latex": [ "A data.frame: 6 × 5\n", "\\begin{tabular}{r|lllll}\n", " & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n", "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n", "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n", "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n", "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n", "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 5\n", "\n", "| | Sepal.Length <dbl> | Sepal.Width <dbl> | Petal.Length <dbl> | Petal.Width <dbl> | Species <fct> |\n", "|---|---|---|---|---|---|\n", "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n", "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n", "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n", "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n", "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n", "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n", "\n" ], "text/plain": [ " Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n", "1 5.1 3.5 1.4 0.2 setosa \n", "2 4.9 3.0 1.4 0.2 setosa \n", "3 4.7 3.2 1.3 0.2 setosa \n", "4 4.6 3.1 1.5 0.2 setosa \n", "5 5.0 3.6 1.4 0.2 setosa \n", "6 5.4 3.9 1.7 0.4 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(iris)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. En utilisant la fonction `pivot_longer()`, créez un jeu de données où les étiquettes `Sepal.Length` `Sepal.Width` `Petal.Length` `Petal.Width` sont bien rangées dans une colonne appelée `Variables`. Les mesures correspondantes sont rangées dans une colonne appelée `Mesures`." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 3
Speciesnamevalue
<fct><chr><dbl>
setosaSepal.Length5.1
setosaSepal.Width 3.5
setosaPetal.Length1.4
setosaPetal.Width 0.2
setosaSepal.Length4.9
setosaSepal.Width 3.0
\n" ], "text/latex": [ "A tibble: 6 × 3\n", "\\begin{tabular}{lll}\n", " Species & name & value\\\\\n", " & & \\\\\n", "\\hline\n", "\t setosa & Sepal.Length & 5.1\\\\\n", "\t setosa & Sepal.Width & 3.5\\\\\n", "\t setosa & Petal.Length & 1.4\\\\\n", "\t setosa & Petal.Width & 0.2\\\\\n", "\t setosa & Sepal.Length & 4.9\\\\\n", "\t setosa & Sepal.Width & 3.0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 3\n", "\n", "| Species <fct> | name <chr> | value <dbl> |\n", "|---|---|---|\n", "| setosa | Sepal.Length | 5.1 |\n", "| setosa | Sepal.Width | 3.5 |\n", "| setosa | Petal.Length | 1.4 |\n", "| setosa | Petal.Width | 0.2 |\n", "| setosa | Sepal.Length | 4.9 |\n", "| setosa | Sepal.Width | 3.0 |\n", "\n" ], "text/plain": [ " Species name value\n", "1 setosa Sepal.Length 5.1 \n", "2 setosa Sepal.Width 3.5 \n", "3 setosa Petal.Length 1.4 \n", "4 setosa Petal.Width 0.2 \n", "5 setosa Sepal.Length 4.9 \n", "6 setosa Sepal.Width 3.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pivot_longer(iris, cols = -Species) %>%\n", " head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. À partir du jeu de données précédent, créez le boxplot attendu. " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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i1D0z59pu/Xjv2/jV7WlWcwAwBQvBkMBhG5lpq6/eoD/bVjbafEifzsP2tKvzCo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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "pivot_longer(iris, cols = -Species) %>%\n", " ggplot(mapping = aes(x = name, y = value)) + geom_boxplot(mapping = aes(fill = name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Avec la fonction `separate()` séparez le data frame suivant en trois colonnes : Year, Month et Day " ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 13 × 1
Date
<date>
2017-01-01
2017-01-02
2017-01-03
2017-01-04
2017-01-05
2017-01-06
2017-01-07
2017-01-08
2017-01-09
2017-01-10
2017-01-11
2017-01-12
2017-01-13
\n" ], "text/latex": [ "A data.frame: 13 × 1\n", "\\begin{tabular}{l}\n", " Date\\\\\n", " \\\\\n", "\\hline\n", "\t 2017-01-01\\\\\n", "\t 2017-01-02\\\\\n", "\t 2017-01-03\\\\\n", "\t 2017-01-04\\\\\n", "\t 2017-01-05\\\\\n", "\t 2017-01-06\\\\\n", "\t 2017-01-07\\\\\n", "\t 2017-01-08\\\\\n", "\t 2017-01-09\\\\\n", "\t 2017-01-10\\\\\n", "\t 2017-01-11\\\\\n", "\t 2017-01-12\\\\\n", "\t 2017-01-13\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 13 × 1\n", "\n", "| Date <date> |\n", "|---|\n", "| 2017-01-01 |\n", "| 2017-01-02 |\n", "| 2017-01-03 |\n", "| 2017-01-04 |\n", "| 2017-01-05 |\n", "| 2017-01-06 |\n", "| 2017-01-07 |\n", "| 2017-01-08 |\n", "| 2017-01-09 |\n", "| 2017-01-10 |\n", "| 2017-01-11 |\n", "| 2017-01-12 |\n", "| 2017-01-13 |\n", "\n" ], "text/plain": [ " Date \n", "1 2017-01-01\n", "2 2017-01-02\n", "3 2017-01-03\n", "4 2017-01-04\n", "5 2017-01-05\n", "6 2017-01-06\n", "7 2017-01-07\n", "8 2017-01-08\n", "9 2017-01-09\n", "10 2017-01-10\n", "11 2017-01-11\n", "12 2017-01-12\n", "13 2017-01-13" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.frame(Date = as.Date('2017-01-01') + 0:12)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 13 × 3
YearMonthDay
<chr><chr><chr>
20170101
20170102
20170103
20170104
20170105
20170106
20170107
20170108
20170109
20170110
20170111
20170112
20170113
\n" ], "text/latex": [ "A data.frame: 13 × 3\n", "\\begin{tabular}{lll}\n", " Year & Month & Day\\\\\n", " & & \\\\\n", "\\hline\n", "\t 2017 & 01 & 01\\\\\n", "\t 2017 & 01 & 02\\\\\n", "\t 2017 & 01 & 03\\\\\n", "\t 2017 & 01 & 04\\\\\n", "\t 2017 & 01 & 05\\\\\n", "\t 2017 & 01 & 06\\\\\n", "\t 2017 & 01 & 07\\\\\n", "\t 2017 & 01 & 08\\\\\n", "\t 2017 & 01 & 09\\\\\n", "\t 2017 & 01 & 10\\\\\n", "\t 2017 & 01 & 11\\\\\n", "\t 2017 & 01 & 12\\\\\n", "\t 2017 & 01 & 13\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 13 × 3\n", "\n", "| Year <chr> | Month <chr> | Day <chr> |\n", "|---|---|---|\n", "| 2017 | 01 | 01 |\n", "| 2017 | 01 | 02 |\n", "| 2017 | 01 | 03 |\n", "| 2017 | 01 | 04 |\n", "| 2017 | 01 | 05 |\n", "| 2017 | 01 | 06 |\n", "| 2017 | 01 | 07 |\n", "| 2017 | 01 | 08 |\n", "| 2017 | 01 | 09 |\n", "| 2017 | 01 | 10 |\n", "| 2017 | 01 | 11 |\n", "| 2017 | 01 | 12 |\n", "| 2017 | 01 | 13 |\n", "\n" ], "text/plain": [ " Year Month Day\n", "1 2017 01 01 \n", "2 2017 01 02 \n", "3 2017 01 03 \n", "4 2017 01 04 \n", "5 2017 01 05 \n", "6 2017 01 06 \n", "7 2017 01 07 \n", "8 2017 01 08 \n", "9 2017 01 09 \n", "10 2017 01 10 \n", "11 2017 01 11 \n", "12 2017 01 12 \n", "13 2017 01 13 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.frame(Date = as.Date('2017-01-01') + 0:12) %>%\n", "separate(col = Date, into = c(\"Year\", \"Month\", \"Day\"), sep = \"-\")" ] } ], "metadata": { "kernelspec": { "display_name": "R 4.0.2", "language": "R", "name": "r-4.0.2" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.0.2" } }, "nbformat": 4, "nbformat_minor": 4 }