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BigDataBERTA-NUNES
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  1. #

  2. r2 = which(r1 == TRUE)

  3. #

  4. vowels = c('a','e','i','o','u','y')

  5. alphabet = letters

  6. r1 = letters %in% vowels

  7. #Le result est un vecteur de boolean

  8. #avec true quand les deux elements sont

  9. #egaux, et false quand c l'inverse

  10. v1 = seq(6)

  11. #

  12. #r2 = which(r1 == TRUE)

  13. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  14. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  15. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  16. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  17. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  18. ?which

  19. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  20. ?letters

  21. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  22. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  23. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  24. ?which

  25. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  26. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  27. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  28. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  29. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  30. ?strsplit

  31. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  32. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  33. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  34. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  35. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  36. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  37. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  38. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  39. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  40. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  41. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  42. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  43. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  44. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  45. myname_split[0]

  46. myname_split[1]

  47. myname_split[1][0]

  48. myname_split[2

  49. myname_split[2]

  50. myname_split[2].[0]

  51. myname_split[1].[0]

  52. myname_split[[1]][0]

  53. myname_split[[1]][1]

  54. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  55. print(r6)

  56. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  57. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  58. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  59. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  60. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  61. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  62. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  63. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP1_2.R', echo=TRUE)

  64. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  65. rep(c(0,1),13)

  66. c(1,26,1)

  67. seq(1,26,1)

  68. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  69. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  70. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  71. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  72. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  73. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  74. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  75. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  76. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  77. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  78. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  79. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  80. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  81. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  82. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  83. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  84. install.packages("ggplot2")

  85. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  86. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  87. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  88. head

  89. msleep

  90. head?

  91. s

  92. ?head

  93. head(msleep)

  94. ?names

  95. ?head

  96. head(letters)

  97. letters

  98. head(letters, n = 7)

  99. head(msleep, n=2)

  100. ?str

  101. str(1:12)

  102. str(freeny)

  103. str(msleep)

  104. ?summary

  105. sumamry(letters)

  106. summary(letters)

  107. summary(msleep)

  108. msleep

  109. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  110. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  111. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  112. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  113. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  114. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  115. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  116. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  117. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  118. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  119. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  120. msleep

  121. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  122. msleep

  123. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  124. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  125. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  126. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  127. msleep

  128. msleep

  129. ?na.omit

  130. msleep

  131. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  132. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  133. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  134. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  135. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  136. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  137. summary("pauline")

  138. summary('p','a','u')

  139. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  140. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  141. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  142. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  143. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  144. summary(strsplit(Pauline, NULL))

  145. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  146. summary(strsplit("Pauline", NULL))

  147. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  148. summary(strsplit("Pauline", NULL))

  149. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  150. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  151. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  152. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  153. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  154. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  155. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  156. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  157. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  158. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  159. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  160. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  161. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  162. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  163. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  164. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  165. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  166. View(p600)

  167. p600[[1]][1]

  168. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  169. letters %in% p600

  170. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  171. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  172. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  173. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  174. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  175. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  176. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  177. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', encoding = 'ASCII', echo=TRUE)

  178. ?substr

  179. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  180. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  181. msleep

  182. which(msleep[[1]] ==Cow)

  183. which(msleep[[1]] =="Cow")

  184. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  185. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  186. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  187. msleep

  188. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  189. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  190. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  191. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  192. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  193. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  194. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  195. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  196. which( msleep[[1]] == "w")

  197. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  198. p777 <- which( msleep[[1]] == "w")

  199. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  200. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  201. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  202. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  203. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  204. install.packages("rapportools")

  205. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  206. source('C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/TP2_1.R', echo=TRUE)

  207. n

  208. n

  209. n

  210. n

  211. library(tidyverse)

  212. library(ggplot2)

  213. setwd("C:/Users/nunes/OneDrive/Bureau/WorkDay/INSA/Big_Data/Projet")

  214. ##########################

  215. #Acquisition of the data #

  216. ##########################

  217. #Variable containing all the information

  218. tab <- read.table("yob1880.txt",h=FALSE,sep=",")

  219. Year <- matrix(1880,ncol=1,nrow = nrow(tab))

  220. tab <- cbind(Year,tab)

  221. for (i in seq(1881,2019,1)){

  222. #Dynamic name for the source txt file

  223. namesource <- "yob"

  224. namesource <- paste(c(namesource,i),collapse = "")

  225. namesource <- paste (c(namesource,".txt"), collapse="")

  226. # Table used inside the for loop to extrat the informations

  227. tabinter <- read.table(namesource,h=FALSE,sep=",")

  228. #Converting the table into the format "Year,Name,Sex,Number"

  229. Year <- matrix(i,ncol=1,nrow = nrow(tabinter))

  230. tabinter <- cbind(Year,tabinter)

  231. #Adding the new informations to the final table

  232. tab <- rbind(tab,tabinter)

  233. }

  234. final_table <- tab%>%

  235. rename(

  236. Name = V1,

  237. Sex = V2,

  238. Number = V3

  239. )

  240. ####################################################################

  241. # Partie 1 - L'influence de Marylin depuis 1880                    #

  242. ####################################################################

  243. MarilynFemme <- final_table %>%

  244. filter(Name == "Marilyn")%>%

  245. group_by(Year,Number)%>%

  246. filter(Sex=='F')

  247. MarilynHomme <- final_table %>%

  248. filter(Name == "Marilyn")%>%

  249. group_by(Year,Number)%>%

  250. filter(Sex=='M')

  251. ggplot(MarilynFemme,aes(x=Year,y=Number, fill=Number))+

  252. geom_bar(stat = "identity")+

  253. ggtitle("Evolution des naissances avec le prénom Marilyn depuis 1880, femmes")

  254. ggplot(MarilynHomme,aes(x=Year,y=Number, fill=Number))+

  255. geom_bar(stat = "identity")+

  256. ggtitle("Evolution des naissances avec le prénom Marilyn depuis 1880, hommes")

  257. AdolphHomme <- final_table %>%

  258. filter(Name == "Adolph")%>%

  259. group_by(Year,Number)%>%

  260. filter(Sex=='M')

  261. ggplot(AdolphHomme,aes(x=Year,y=Number, fill=Number))+

  262. geom_bar(stat = "identity")+

  263. ggtitle("Evolution des naissances avec le prénom Adolph depuis 1880, hommes")

  264. ##################################

  265. # 1.1 - Number of births by year #

  266. ##################################

  267. NaissancesAnDepuis1880 <- final_table %>%

  268. group_by(Year)%>%

  269. summarise(

  270. NbrBirths = sum(Number)

  271. )

  272. ggplot(NaissancesAnDepuis1880,aes(x=Year,y=NbrBirths, color= NbrBirths))+

  273. geom_line(size = 3)+

  274. ggtitle("Nombre de naissances par année depuis 1880, sexes confondus")

  275. NaissancesAn6090 <- final_table %>%

  276. group_by(Year,Sex)%>%

  277. filter(Year < 1990)%>%

  278. filter(Year> 1960)%>%

  279. summarise(NbrBirths = sum(Number))

  280. ggplot(NaissancesAn6090,aes(x=Year,y=NbrBirths, color = Sex))+

  281. geom_line(size=5)+

  282. geom_point()+

  283. ggtitle("Nombre de naissances par année, entre 1960 et 1990")+

  284. NaissancesSexeSep <- final_table %>%

  285. group_by(Year,Sex)%>%

  286. summarise(NbrBirths = sum(Number))

  287. ggplot(NaissancesSexeSep,aes(x=Year,y=NbrBirths, color = Sex))+

  288. geom_line(size=5)+

  289. geom_point()+

  290. ggtitle("Nombre de naissances par année, sexe séparés")

  291. NaissancesSexeSep <- final_table %>%

  292. group_by(Year,Sex)%>%

  293. summarise(NbrBirths = sum(Number))

  294. ggplot(NaissancesSexeSep,aes(x=Year,y=NbrBirths, color = Sex))+

  295. geom_line(size=5)+

  296. geom_point()+

  297. ggtitle("Nombre de naissances par année, sexe séparés")

  298. PrenomsHommeDepuis1880 <- final_table %>%

  299. group_by(Name)%>%

  300. filter(Sex == "M")%>%

  301. summarise(

  302. NumberOfBirths = sum(Number)

  303. )%>%

  304. arrange(desc(NumberOfBirths))%>%

  305. top_n(5)

  306. ggplot(PrenomsHommeDepuis1880,aes(x=Name,y=NumberOfBirths))+

  307. geom_bar(stat = "identity")+

  308. ggtitle("Most given male names since 1880")

  309. # Create Data

  310. PrenomsHommeDepuis1880 <- data.frame(

  311. name=c('James','John','Robert','Michael','William','Others'),

  312. value= c(2.89,2.86,2.70,2.44,2.34,86.77)

  313. )

  314. # Basic piechart

  315. ggplot(data, aes(x="", y=value, fill=name)) +

  316. geom_bar(stat="identity", width=1) +

  317. coord_polar("y", start=0)+

  318. theme_void()

  319. # Create Data

  320. PrenomsHommeDepuis1880 <- data.frame(

  321. name=c('James','John','Robert','Michael','William','Others'),

  322. value= c(2.89,2.86,2.70,2.44,2.34,86.77)

  323. )

  324. # Basic piechart

  325. ggplot(data, aes(x="", y=value, fill=name)) +

  326. geom_bar(stat="identity", width=1) +

  327. coord_polar("y", start=0)+

  328. theme_void()

  329. # Create Data

  330. PrenomsHommeDepuis1880 <- data.frame(

  331. name=c('James','John','Robert','Michael','William','Others'),

  332. value= c(2.89,2.86,2.70,2.44,2.34,86.77)

  333. )

  334. # Basic piechart

  335. ggplot(PrenomsHommeDepuis1880, aes(x="", y=value, fill=name)) +

  336. geom_bar(stat="identity", width=1) +

  337. coord_polar("y", start=0)+

  338. theme_void()

  339. PrenomsFemmesDepuis1880 <- final_table %>%

  340. group_by(Name)%>%

  341. filter(Sex == "F")%>%

  342. summarise(

  343. NumberOfBirths = sum(Number)

  344. )%>%

  345. arrange(desc(NumberOfBirths))%>%

  346. top_n(5)

  347. ggplot(PrenomsFemmesDepuis1880,aes(x=Name,y=NumberOfBirths))+

  348. geom_bar(stat = "identity")+

  349. ggtitle("Most given female names since 1880")

  350. name= c('Mary','Elizabeth','Patricia','Jennifer','Linda','Others')

  351. value= c(2.35,0.94,0.89,0.84,0.83,94.15)

  352. # Create Data

  353. PrenonesFemmes1880 <- data.frame(name,value)

  354. # Basic piechart

  355. ggplot(PrenonesFemmes1880, aes(x="", y=value, fill=name)) +

  356. geom_bar(stat="identity", width=1) +

  357. coord_polar("y", start=0)+

  358. theme_void()

  359. PrenomsHommeDepuis2000 <- final_table %>%

  360. group_by(Name)%>%

  361. filter(Sex == "M")%>%

  362. filter(Year>2000)%>%

  363. summarise(

  364. NumberOfBirths = sum(Number)

  365. )%>%

  366. arrange(desc(NumberOfBirths))%>%

  367. top_n(5)

  368. ggplot(PrenomsHommeDepuis2000,aes(x=Name,y=NumberOfBirths))+

  369. geom_bar(stat = "identity")+

  370. ggtitle("Most given male names since 2000")

  371. NbrMaleBirths2000 <- final_table %>%

  372. group_by(Sex)%>%

  373. filter(Sex == "M")%>%

  374. filter(Year>2000)%>%

  375. summarise(

  376. NumberOfBirths = sum(Number)

  377. )

  378. top5MaleNames2000 <- tab233%>% summarise(

  379. N=sum(NumberOfBirths)

  380. )

  381. OtherNames2000 <- (NbrMaleBirths2000[2] - (top5MaleNames2000[1]))

  382. # Create Data

  383. PrenomsHomme2000 <- data.frame(

  384. name=c('Jacob','Michael','Ethan','William','Mathew','Others'),

  385. value= c(402290,363299,335423,333255,317778,OtherNames2000)

  386. )

  387. # Basic piechart

  388. ggplot(PrenomsHomme2000, aes(x="", y=value, fill=name)) +

  389. geom_bar(stat="identity", width=1) +

  390. coord_polar("y", start=0)+

  391. theme_void()

  392. NbrMaleBirths2000 <- final_table %>%

  393. group_by(Sex)%>%

  394. filter(Sex == "M")%>%

  395. filter(Year>2000)%>%

  396. summarise(

  397. NumberOfBirths = sum(Number)

  398. )

  399. top5MaleNames2000 <- tab233%>% summarise(

  400. N=sum(NumberOfBirths)

  401. )

  402. OtherMaleNames2000 <- (NbrMaleBirths2000[2] - (top5MaleNames2000[1]))

  403. # Create Data

  404. PrenomsHomme2000 <- data.frame(

  405. name=c('Jacob','Michael','Ethan','William','Mathew','Others'),

  406. value= c(402290,363299,335423,333255,317778,OtherMaleNames2000)

  407. )

  408. NbrMaleBirths2000 <- final_table %>%

  409. group_by(Sex)%>%

  410. filter(Sex == "M")%>%

  411. filter(Year>2000)%>%

  412. summarise(

  413. NumberOfBirths = sum(Number)

  414. )

  415. top5MaleNames2000 <- tab233%>% summarise(

  416. N=sum(NumberOfBirths)

  417. )

  418. OtherMaleNames2000 <- (NbrMaleBirths2000[2] - (top5MaleNames2000[1]))

  419. #########

  420. PrenomsHommeDepuis2000 <- final_table %>%

  421. group_by(Name)%>%

  422. filter(Sex == "M")%>%

  423. filter(Year>2000)%>%

  424. summarise(

  425. NumberOfBirths = sum(Number)

  426. )%>%

  427. arrange(desc(NumberOfBirths))%>%

  428. top_n(5)

  429. ggplot(PrenomsHommeDepuis2000,aes(x=Name,y=NumberOfBirths))+

  430. geom_bar(stat = "identity")+

  431. ggtitle("Most given male names since 2000")

  432. NbrMaleBirths2000 <- final_table %>%

  433. group_by(Sex)%>%

  434. filter(Sex == "M")%>%

  435. filter(Year>2000)%>%

  436. summarise(

  437. NumberOfBirths = sum(Number)

  438. )

  439. top5MaleNames2000 <- PrenomsHommeDepuis2000%>% summarise(

  440. N=sum(NumberOfBirths)

  441. )

  442. OtherMaleNames2000 <- (NbrMaleBirths2000[2] - (top5MaleNames2000[1]))

  443. # Create Data

  444. PrenomsHomme2000 <- data.frame(

  445. name=c('Jacob','Michael','Ethan','William','Mathew','Others'),

  446. value= c(402290,363299,335423,333255,317778,OtherMaleNames2000)

  447. )

  448. # Basic piechart

  449. ggplot(PrenomsHomme2000, aes(x="", y=value, fill=name)) +

  450. geom_bar(stat="identity", width=1) +

  451. coord_polar("y", start=0)+

  452. theme_void()

  453. #########

  454. PrenomsFemmeDepuis2000 <- final_table %>%

  455. group_by(Name)%>%

  456. filter(Sex == "F")%>%

  457. filter(Year>2000)%>%

  458. summarise(

  459. NumberOfBirths = sum(Number)

  460. )%>%

  461. arrange(desc(NumberOfBirths))%>%

  462. top_n(5)

  463. ggplot(PrenomsFemmeDepuis2000,aes(x=Name,y=NumberOfBirths))+

  464. geom_bar(stat = "identity")+

  465. ggtitle("Most given female names since 2000")

  466. NbrFemaleBirths2000 <- final_table %>%

  467. group_by(Sex)%>%

  468. filter(Sex == "F")%>%

  469. filter(Year>2000)%>%

  470. summarise(

  471. NumberOfBirths = sum(Number)

  472. )

  473. top5FemaleNames2000 <- PrenomsFemmeDepuis2000%>% summarise(

  474. N=sum(NumberOfBirths)

  475. )

  476. OtherFemaleNames2000 <- (NbrMaleBirths2000[2] - (top5FemaleNames2000[1]))

  477. name= c('Emma','Olivia','Emily','Isabella','Sophia','Others')

  478. value= c(363402,327356,315202,313471,293494,OtherFemaleNames2000)

  479. # Create Data

  480. PrenomsFemme2000 <- data.frame(name,value)

  481. # Basic piechart

  482. ggplot(PrenomsFemme2000, aes(x="", y=value, fill=name)) +

  483. geom_bar(stat="identity", width=1) +

  484. coord_polar("y", start=0)+

  485. theme_void()

  486. EvolutionJames <- final_table %>%

  487. group_by(Year)%>%

  488. filter(Name == "James")%>%

  489. summarise(

  490. NumberOfBirths = sum(Number)

  491. )

  492. ggplot(EvolutionJames,aes(x=Year,y=NumberOfBirths))+

  493. geom_line(size=5)+

  494. geom_point()+

  495. ggtitle("The evolution of the name James since 1880")

  496. # The evolution of the name Mary since 1880 #

  497. ##############################################

  498. EvolutionMary <- final_table %>%

  499. group_by(Year)%>%

  500. filter(Name == "Mary")%>%

  501. summarise(

  502. NumberOfBirths = sum(Number)

  503. )

  504. ggplot(EvolutionMary,aes(x=Year,y=NumberOfBirths))+

  505. geom_line(size=5)+

  506. geom_point()+

  507. ggtitle("The evolution of the name Mary since 1880")