update lib

module3/exo3/exercice_fr.Rmd

@@ -12,18 +12,14 @@ knitr::opts_chunk$set(echo = TRUE)
 
 
 ```{r libraries, echo=FALSE, warning=FALSE, message=FALSE}
-library(plyr) # help for data manipulation
-library(dplyr) # help splitting, applying and combining data
 library(data.table) # extension to data.frame format, optimized for huge datasets
-library(ggplot2) # The grammar of graphics, it improves the quality and aesthetic of your graphs
-library(DT)
+library(ggplot2) # for graphics
 library(plotly)
-library(RColorBrewer) # package with pre-defined color palette
 ```
 
 ## Récupération des données
 
-Le fichier est téléchargé à partir de l'url puis lu via la fonction fread pour avoir un objet data.table. Le nom des colonnes est vérifié pour remplacer les caractères spéciaux par des ".".
+Le fichier est téléchargé à partir de l'url [https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv) puis lu via la fonction fread pour avoir un objet de type data.table afin de faire facilement des graphiques avec ggplot2. Le nom des colonnes est vérifié pour remplacer les caractères spéciaux par des ".".
 
 ```{r data_dwld}
 url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv" 
@@ -43,7 +39,7 @@ On commence par réannoter les pays pour mieux extraire ceux d'intérêts :
 - la france hors DOM-TOM est renommée France Metropolitaine
 - les colonies des pays bas sont renommées Netherlands, colonies
 
-```{r}
+```{r modify_country}
 ## change China to China, Hong Kong for Hong Kong
 covid_data[covid_data$Country.Region == "China" & covid_data$Province.State == "Hong Kong", ]$Country.Region <- "China, Hong-Kong"
 
@@ -60,7 +56,7 @@ covid_data[covid_data$Country.Region == "United Kingdom" & covid_data$Province.S
 ### Creation d'un sous jeu de données pour analyse des pays d'intérêts
 
 Création d'un sous jeu de données avec les pays d'intérêts. 
-```{r}
+```{r select_country}
 ## Filter only countries of interest
 covid.subset <- filter(covid_data, Country.Region %in% c("Belgium","China","China, Hong-Kong", "France","Germany","Iran","Italy", "Japan","Korea, South","Netherlands","Portugal","Spain", "United Kingdom", "US"))
 ```
@@ -72,7 +68,7 @@ Ce sera plus facile et rapide pour l'utilisation de ggplot2 d'avoir tous les com
 
 Pour cela nous utilisons fonction melt.
 
-```{r}
+```{r table_melt}
 covid.subset <- melt(covid.subset , 
                           id.vars = c("Province.State", "Country.Region", "Lat", "Long"), 
                           variable.name = "Date", 
@@ -85,7 +81,7 @@ covid.subset$Country.Region <- as.factor(covid.subset$Country.Region)
 ### Fusion des lignes pour un même pays
 
 Afin de n'avoir qu'une valeur par pays, nous fusionnons les lignes
-```{r}
+```{r sum_cases}
 covid.byCountry <- covid.subset[, list(Case_number=sum(Case_number)),by=c("Country.Region","Date")]
 ```
 
@@ -93,8 +89,8 @@ covid.byCountry <- covid.subset[, list(Case_number=sum(Case_number)),by=c("Count
 
 Représentation du nombre de cas cumulés par jour par pays.
 
-```{r plot, echo=FALSE, fig.height=15, fig.width=12}
-ggplot(covid.byCountry, aes(x = as.Date(Date), y = Case_number, group = Country.Region)) + 
+```{r plot, echo=FALSE, warning=FALSE, fig.height=12, fig.width=10}
+p <- ggplot(covid.byCountry, aes(x = as.Date(Date), y = Case_number, group = Country.Region)) + 
   geom_line(aes(color = Country.Region), size=1) +
   scale_color_manual(values = c("#00008B","#33a02c","#e31a1c","#8470FF","#FF7F24","#7A378B","#00688B","#FFF68F","#FF82AB","#5C5C5C","#87CEEB","#D9D9D9","#8B5A00","#9ACD32","#8EE5EE","#20B2AA","#FFF0F5","#FFFF00","#FFFFFF")) +
   scale_x_date(date_breaks = 'week') +
@@ -104,12 +100,13 @@ ggplot(covid.byCountry, aes(x = as.Date(Date), y = Case_number, group = Country.
   ggtitle("Number of Covid Cases by Country") + 
   xlab("Date") +
   ylab("Number of cases") + labs(color = "Country")
-
+#p
+ggplotly(p)
 ```
 Reproduction du graphique en log10 : 
 
-```{r plot_log, echo=FALSE, fig.height=15, fig.width=12}
-ggplot(covid.byCountry, aes(x = as.Date(Date), y = Case_number, group = Country.Region)) + 
+```{r plot_log, echo=FALSE, warning=FALSE, fig.height=12, fig.width=10}
+p <- ggplot(covid.byCountry, aes(x = as.Date(Date), y = Case_number, group = Country.Region)) + 
   geom_line(aes(color = Country.Region), size = 1) +
   scale_color_manual(values = c("#00008B","#33a02c","#e31a1c","#8470FF","#FF7F24","#7A378B","#00688B","#FFF68F","#FF82AB","#5C5C5C","#87CEEB","#D9D9D9","#8B5A00","#9ACD32","#8EE5EE","#20B2AA","#FFF0F5","#FFFF00","#FFFFFF")) +
   scale_x_date(date_breaks = 'week') +
@@ -121,5 +118,6 @@ ggplot(covid.byCountry, aes(x = as.Date(Date), y = Case_number, group = Country.
   xlab("Date") +
   ylab("Number of cases (log10)") + labs(color = "Country")
 
-
+#p
+ggplotly(p)
 ```

module3/exo3/time_series_covid19_confirmed_global.csv

@@ -148,7 +148,7 @@ Martinique,France,14.6415,-61.0242,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
 ,Latvia,56.8796,24.6032,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2,6,8,10,10,17,26,30,34,49,71,86,111,124,139,180,197,221,244,280,305,347,376,398,446,458,493,509,533,542,548,577,589,612,630,651,655,657,666,675,682,712,727,739,748,761,778,784,804,812,818,836,849,858,870,871,879,896,896,900,909,928,930,939,946,950,951,962,970,997,1008,1009,1012,1016,1025,1030,1046,1047,1049,1053,1057,1061,1064,1065,1066,1066,1071,1079,1082,1085,1086,1088,1088,1089,1092,1094,1096,1097,1097,1097,1098,1104,1108,1110,1111,1111,1111,1111,1111,1111,1112,1115,1116,1117,1118,1121,1122,1122,1123,1124,1127,1134,1141,1154,1165,1173,1173,1174,1174,1178,1179,1185,1189,1192,1192,1193
 ,Lebanon,33.8547,35.8623,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,2,2,2,4,10,13,13,13,16,22,22,32,32,41,61,61,77,93,110,110,120,133,157,163,187,248,267,318,333,368,391,412,438,446,470,479,494,508,520,527,541,548,576,582,609,619,630,632,641,658,663,668,672,673,677,677,682,688,696,704,707,710,717,721,725,729,733,737,740,741,750,784,796,809,845,859,870,878,886,891,902,911,931,954,961,1024,1086,1097,1114,1119,1140,1161,1168,1172,1191,1220,1233,1242,1256,1306,1312,1320,1331,1350,1368,1388,1402,1422,1442,1446,1464,1473,1489,1495,1510,1536,1587,1603,1622,1644,1662,1697,1719,1740,1745,1778,1788,1796,1830,1855,1873,1885,1907,1946,2011,2082,2168,2334,2419,2451,2542,2599,2700,2775,2859,2905,2980
 ,Liberia,6.4280550000000005,-9.429499,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,6,6,7,10,13,14,14,31,31,37,48,50,59,59,59,59,76,76,91,99,101,101,101,117,120,124,124,141,141,141,152,154,158,166,170,178,189,199,199,199,211,211,213,215,219,223,226,229,233,238,240,249,255,265,265,266,266,269,273,280,288,296,311,316,321,334,345,359,370,383,397,410,421,446,458,498,509,516,542,581,601,626,650,652,662,681,684,729,768,770,780,804,819,833,869,874,891,917,926,957,963,998,1010,1024,1024,1056,1070,1085,1088,1091,1107,1108
-,Liechtenstein,47.14,9.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,4,4,4,7,28,28,28,37,37,51,51,51,56,56,56,56,62,68,68,75,75,77,77,77,78,78,78,79,79,79,79,79,79,79,79,79,81,81,81,81,81,81,81,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,83,86,82,82,82,82,82,82,82,82,83,83,83,83,84,84,84,84,84,84,84,84,84,84,84,85,86,86,86,86
+,Liechtenstein,47.14,9.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,4,4,4,7,28,28,28,37,37,51,51,51,56,56,56,56,62,68,68,75,76,77,77,77,78,78,78,79,79,79,79,79,79,79,79,79,81,81,81,81,81,81,81,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,83,83,83,83,84,84,84,84,84,84,84,84,84,84,84,85,86,86,86,86
 ,Lithuania,55.1694,23.8813,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,3,3,6,8,12,17,25,27,36,49,83,143,179,209,274,299,358,394,460,491,537,581,649,696,771,811,843,880,912,955,999,1026,1053,1062,1070,1091,1128,1149,1239,1298,1326,1350,1370,1398,1410,1426,1438,1449,1344,1375,1385,1399,1406,1410,1419,1423,1428,1433,1436,1444,1479,1485,1491,1505,1511,1523,1534,1541,1547,1562,1577,1593,1604,1616,1623,1635,1639,1647,1656,1662,1670,1675,1678,1682,1684,1687,1694,1705,1714,1720,1727,1733,1752,1756,1763,1768,1773,1776,1778,1784,1792,1795,1798,1801,1803,1804,1806,1808,1813,1815,1816,1817,1818,1825,1828,1831,1836,1841,1844,1854,1857,1861,1865,1869,1874,1875,1882,1902,1908,1915,1932,1947,1949
 ,Luxembourg,49.8153,6.1296,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2,2,3,3,5,7,19,34,51,59,77,140,203,335,484,670,798,875,1099,1333,1453,1605,1831,1950,1988,2178,2319,2487,2612,2729,2804,2843,2970,3034,3115,3223,3270,3281,3292,3307,3373,3444,3480,3537,3550,3558,3618,3654,3665,3695,3711,3723,3729,3741,3769,3784,3802,3812,3824,3828,3840,3851,3859,3871,3877,3886,3888,3894,3904,3915,3923,3930,3945,3947,3958,3971,3980,3981,3990,3992,3993,3995,4001,4008,4012,4016,4018,4019,4020,4024,4027,4032,4035,4039,4040,4046,4049,4052,4055,4063,4070,4072,4075,4085,4091,4099,4105,4120,4121,4133,4140,4151,4173,4217,4242,4256,4299,4345,4395,4447,4476,4522,4542,4603,4650,4719,4777,4842,4925,4956,5056,5122,5285,5409,5483,5605,5639,5725
 ,Madagascar,-18.766947,46.869107,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,3,12,17,19,23,26,26,39,43,57,57,59,70,70,72,82,88,93,93,93,102,106,106,108,110,111,117,120,121,121,121,121,121,122,123,124,128,128,128,128,132,135,149,149,151,158,193,193,193,193,186,186,212,230,238,283,304,322,326,371,405,448,488,527,542,586,612,656,698,758,771,826,845,908,957,975,1026,1052,1094,1138,1162,1203,1240,1252,1272,1290,1317,1378,1403,1443,1503,1596,1640,1724,1787,1829,1922,2005,2078,2138,2214,2303,2403,2512,2728,2941,3250,3472,3573,3782,4143,4578,4867,5080,5343,5605,6089,6467,6849,7049,7153,7548