Forexkurs_Kapitel 1 — Forex Freiheit

Adobe XD Portrait UI UX Design list

Adobe XD Portrait UI UX Design list:
  1. 61 screen lisk furniture ecommerce sketch template
  2. aahar food recipe ui kit
  3. admenes dashboard admin template
  4. admin dashboard design
  5. admjetbuzz admin page
  6. adventure newsletter template
  7. agencies web template
  8. aimboted dashboard admin template
  9. alecya psd web template
  10. antlatian web template
  11. apet food order delivery ui kit
  12. apparel app design
  13. app finance
  14. app login signup T262M5L
  15. app phonebook A4H5R85
  16. app resto longue
  17. arc creative website ui kit
  18. architects landing page illustration
  19. asionate resto hero header
  20. asseto psd web template
  21. auerelie admin page
  22. automotive newsletter template
  23. aviation apps onboarding screens
  24. balak kindergarten pre school ui kit
  25. barelle dashboard admin template
  26. bestblu newsletter
  27. biking newsletter
  28. bizconf login signup app template
  29. biz dashboard admin template
  30. bizzie newsletter template
  31. black business newsletter template
  32. blue admin page
  33. blue finance app
  34. blue finance app design template
  35. bluerain newsletter template
  36. blue user profile app
  37. bluezking admin page template
  38. blusoft newsletter template
  39. boardmin dashboard admin template
  40. booking app design template
  41. book store app
  42. boosiness newsletter template
  43. boxcraft newsletter template
  44. brand agency newsletter template
  45. brand co newsletter template
  46. brand new newsletter template
  47. brand story newsletter
  48. breiden dashboard admin template
  49. brewer newsletter
  50. bridgo dashboard admin template
  51. business corp psd web template
  52. business newsletter
  53. business newsletter template
  54. business trust newsletter template
  55. calendar app
  56. calender remin app template
  57. cannow newsletter
  58. carrable dashboard admin template
  59. catalina admin page
  60. celebgram newsletter
  61. cerise dashboard admin template
  62. chat inbox app design template
  63. chatinbox app template
  64. chating app template
  65. chatty chatting sharing app
  66. chrisov dashboard admin template
  67. clean admin page
  68. clean finance app design template
  69. clean sign in login
  70. clever dashboard admin template
  71. clinic newsletter
  72. closun login app design template
  73. clover dashboard admin template
  74. codename admin page
  75. coffee taste newsletter
  76. coffe newsletter
  77. company admin dashboard
  78. conservation newsletter template
  79. conserv newsletter template
  80. construction web template
  81. construct newsletter
  82. consultant newsletter template
  83. consultant newsletter
  84. countnews newsletter template
  85. creacky web template
  86. creative brand newsletter
  87. creative finance app template
  88. creative login sign up
  89. criner dashboard admin template
  90. crush green admin page
  91. cubex admin page template
  92. cutslash admin page
  93. d admin dashboard admin template
  94. dark file manager app design template
  95. deepdark newsletter template
  96. desk company newsletter template
  97. diamond dashboard admin template
  98. directory search listing yellow pages mobile app
  99. discupto newsletter template
  100. diving newsletter template
  101. dosti social media mobile app ui kit
  102. drone flight apps onboarding screens
  103. dukaan ecommerce shopping
  104. duri travel flight booking ui kit
  105. dventurez login sign up app design template
  106. easy booking travelling onboarding screens
  107. educa newsletter template
  108. education podcast apps onboarding screen
  109. efficient dashboard admin template
  110. e learning education study oboarding screens
  111. electriccode newsletter 37WN7P6 2020 02 04.zip"
  112. electricity dashboard admin template
  113. elfastre admin page
  114. employee dashboard admin template
  115. enterpre newsletter
  116. evolut dashboard admin template
  117. exam centre app
  118. extra dashboard admin template
  119. eyewear newsletter template
  120. fashiobrand newsletter
  121. fashion market template
  122. fashion newsletter
  123. fashion newsletter template
  124. fashion newsletter
  125. fashion style instagram story templates
  126. fida dating meeting networking app
  127. file manaer template
  128. file manager app
  129. financeapp app design
  130. finance app design template
  131. finance app
  132. finance app template
  133. financial app
  134. financial podcast apps onboarding screens
  135. fitness gym workout ui kit mobile app
  136. fitness newsletter template
  137. flawadmin admin page
  138. flight book app design template
  139. foodie food delivery mobile app
  140. foodie newsletter
  141. foods newsletter
  142. forcen newsletter template
  143. forest newsletter template
  144. forex psd web template
  145. franse dashboard admin template
  146. franse dashboard admin template
  147. freemium psd web template
  148. fresh health newsletter
  149. furniture newsletter template
  150. furniture store app
  151. furniture web template
  152. futuristic admin page
  153. gaadi taxi cab booking ui kit
  154. gerdone hero header
  155. get travel newsletter
  156. global login app design template
  157. glorient dashboard admin template
  158. gold and precious metal apps onboarding screens
  159. gold finance app
  160. gold login sign up app design template
  161. golf newsletter template
  162. gotzen dashboard admin template
  163. grady finance app design template
  164. greatest food newsletter template
  165. greenshop newsletter template
  166. greeny admin page
  167. grein dashboard admin template
  168. grocery online shopping store ui kit
  169. hat store app template
  170. holiday newsletter template
  171. home activity instagram story templates
  172. hospital health medical mobile app
  173. hospitalize web template
  174. i future newsletter
  175. incorp psd web template
  176. infinite dashboard admin template
  177. inspire log in app template
  178. instagram story template v10 fashion black and whi
  179. instagram story template v 21 man fashio
  180. instagram story template vol 23 elegant fashion
  181. jacket o fashion newsletter template
  182. kaaj buy sell trade ui kit mobile app
  183. kaaj daily task management ui kit
  184. kaar car rental ui kit
  185. khushi gifts flowers shop ui kit
  186. kindergarten newsletter template
  187. kiray car rental ui kit mobile app
  188. landing page v05 online course
  189. lets donut newsletter
  190. letur mobile wireframe kit
  191. levians dashboard admin template
  192. lichi social media ui kit
  193. likh blogging platform ui kit
  194. link login signup app template
  195. live chat
  196. loginpag login app design template
  197. machine t newsletter template
  198. mail admin page
  199. mantis dashboard admin template
  200. master admin page
  201. masterclass web template
  202. masterlog app design template
  203. mediabrand newsletter
  204. mediatech psd web template
  205. medicalize newsletter
  206. medkit newsletter
  207. medusainc newsletter
  208. mehat newsletter template
  209. messages app template
  210. modella newsletter template
  211. model newsletter template
  212. moderz login sign up app design template
  213. mogyo newsletter template
  214. music course newsletter template
  215. music player app design
  216. music player app
  217. netflag admin page
  218. newadmin dashboard admin template
  219. new techno newsletter template
  220. northem dashboard admin template
  221. onboarding app
  222. onboarding app design template
  223. onboarding app design
  224. onboarding login app design template
  225. onboarding screen delivery service
  226. onboarding screen for marketing app
  227. online study education onboarding screen
  228. order food app template
  229. ownwallet app template
  230. page x dashboard admin template
  231. panel dashboard admin template
  232. panel inc psd web template
  233. paneling x dashboard admin template
  234. personal psd web template
  235. persona web template
  236. pet house newsletter template
  237. phonebook app template
  238. phone contact app design template
  239. photo social media onboarding screens
  240. pile dashboard admin template
  241. pinkuard admin page
  242. pizzalicious newsletter
  243. portalmusic psd web template
  244. pro admin page
  245. project admin page
  246. property newsletter
  247. prowitch dashboard admin template
  248. purpos web template
  249. qualtagh businees psd web template
  250. raag music player ui kit
  251. race psd web template
  252. rachna blogging platform mobile app ui kit
  253. range dashboard admin template
  254. raxor sign in sign up app design
  255. real estate newsletter
  256. receipes app design template
  257. recepies newsletter
  258. rectangle dashboard admin template
  259. render newsletter template
  260. reports admin page
  261. restonews newsletter template
  262. rish dating meeting networking ui kit
  263. roma id admin template
  264. romaid dashboard admin template
  265. sair tour travel mobile app ui kit
  266. sajavat furniture ecommerce store
  267. sajja furniture store ecommerce ui kit
  268. salefor newsletter template
  269. samaaj social media community ui kit
  270. sama news events ui kit
  271. sampark directory search listing app
  272. sandesh news events ui kit
  273. search flight app design template
  274. seekh online learning ui kit
  275. sharevid video sharing platform app
  276. shipment transport logistic freight app
  277. shishu kindergarten play school mobile app
  278. shopperz newsletter
  279. shopping newsletter template
  280. shoppy ecommerce mobile app ui kit
  281. simply finance app
  282. smothey dashboard admin template
  283. social media newsletter
  284. socio social media mobile app ui kit
  285. softte admin page
  286. spectre dashboard admin template
  287. sport newsletter
  288. sport series newsletter template
  289. sportzone newsletter
  290. startup newsletter template
  291. statistica admin page
  292. stock market onboarding screen
  293. store dashboard admin template
  294. svaas hospital medical ui kit
  295. tax planning solution email enewsletter template
  296. tconnection newsletter template
  297. tech biz psd web template
  298. technology newsletter template
  299. techprise newsletter template
  300. techpro psd web template
  301. tech store app template
  302. terrance dashboard admin template
  303. the babys newsletter template
  304. the conferences psd web template
  305. the new arrival newsletter template
  306. ticket concert newsletter template
  307. todo daily task management mobile app
  308. tracked dashboard admin template
  309. tracker login app
  310. travel and vacation apps onboarding screens
  311. travelapp app template
  312. travelio web template
  313. travella psd web template
  314. travelling and holiday apps onboarding screens
  315. travelogy newsletter
  316. udaan travel flight booking mobile app
  317. university education learning ui kit
  318. unprenten newsletter
  319. uphaar gifts flowers shop ui kit
  320. user profile app
  321. user profile app template
  322. v admin admin page
  323. vanguard dashboard admin template
  324. vibenes psd web template
  325. virtualie newsletter template
  326. visionary newsletter template
  327. walletapps app design
  328. watch newsletter template
  329. weather app template
  330. wellfood psd web template
  331. west login app template
  332. yaad chatting sharing ui kit
  333. yaatra travel tour ui kit
  334. yellow newsletter
  335. yoga psd web template
  336. z admindo dashboard admin template
  337. zodiac app design
  338. zoo newsletter
submitted by AdProfessional9918 to u/AdProfessional9918 [link] [comments]

loading from Tiingo euro adjusted etfs prices

I'm trying to modify an R script that calculates portfolio statistics.
library(tidyquant) library(timetk) library(dplyr) # Create the tickers and weights vector tickers = c('edv', 'spy') forex = c('EURUSD') tiingo_api_key("XXXXX") #wts = c(0.1,0.2,0.25,0.25,0.2) price_data <- tq_get(tickers, from = '2020-06-01', to = '2020-07-31', get = "tiingo") forex_data <- tq_get(forex, from = '2020-06-01', to = '2020-07-31', get = "tiingo") forex_data <- forex_data[ ,c(2, 8)] 
I get a price_data obj like this:
structure(list(symbol = c("EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "EDV", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY"), date = structure(c(1590969600, 1591056000, 1591142400, 1591228800, 1591315200, 1591574400, 1591660800, 1591747200, 1591833600, 1591920000, 1592179200, 1592265600, 1592352000, 1592438400, 1592524800, 1592784000, 1592870400, 1592956800, 1593043200, 1593129600, 1593388800, 1593475200, 1593561600, 1593648000, 1593993600, 1594080000, 1594166400, 1594252800, 1594339200, 1594598400, 1594684800, 1594771200, 1594857600, 1594944000, 1595203200, 1595289600, 1595376000, 1595462400, 1595548800, 1595808000, 1595894400, 1595980800, 1596067200, 1596153600, 1590969600, 1591056000, 1591142400, 1591228800, 1591315200, 1591574400, 1591660800, 1591747200, 1591833600, 1591920000, 1592179200, 1592265600, 1592352000, 1592438400, 1592524800, 1592784000, 1592870400, 1592956800, 1593043200, 1593129600, 1593388800, 1593475200, 1593561600, 1593648000, 1593993600, 1594080000, 1594166400, 1594252800, 1594339200, 1594598400, 1594684800, 1594771200, 1594857600, 1594944000, 1595203200, 1595289600, 1595376000, 1595462400, 1595548800, 1595808000, 1595894400, 1595980800, 1596067200, 1596153600), tzone = "UTC", class = c("POSIXct", "POSIXt")), open = c(163.69, 163.08, 160.94, 159.33, 153.62, 154.14, 159.81, 159.94, 165.2, 164.34, 166.39, 160.17, 161.05, 163.36, 162.13, 165.07, 162.87, 162.95, 166.62, 166.53, 167.58, 167.99, 163.95, 164.18, 163.64, 165.12, 166.39, 166.89, 171.43, 168.22, 171.48, 168.8, 170.79, 170.72, 170.93, 170.84, 171.81, 172.98, 173.14, 174.9, 173.88, 174.66, 175.88, 174.67, 303.62, 306.55, 310.24, 311.11, 317.23, 320.22, 320.3, 321.42, 311.46, 308.24, 298.02, 315.48, 314.07, 310.005, 314.17, 307.99, 313.4801, 309.84, 303.47, 306.16, 301.41, 303.99, 309.57, 314.24, 316.37, 315.38, 314.61, 316.84, 314.31, 320.13, 313.3, 322.41, 319.79, 321.88, 321.43, 326.45, 324.62, 326.47, 320.95, 321.63, 322.43, 322.12, 321.9, 325.9), high = c(163.94, 163.63, 161.25, 159.4, 155.75, 156.56, 160.41, 161.99, 166.48, 165.9, 166.68, 162.34, 161.83, 164.02, 164.04, 165.3, 163.4, 165.14, 166.73, 168.2, 168.07, 167.99, 165.16, 165.59, 164.43, 167.52, 167.1, 170.42, 171.77, 170.39, 171.71, 170.1, 171.09, 170.76, 170.95, 171.19, 172.11, 174.105, 174.33, 174.9, 174.96, 174.93, 176.15, 176.31, 306.205, 308.13, 313.22, 313, 321.275, 323.41, 323.2849, 322.39, 312.15, 309.08, 308.28, 315.64, 314.39, 312.3, 314.38, 311.05, 314.5, 310.51, 307.64, 306.39, 304.61, 310.2, 311.89, 315.7, 317.68, 317.52, 316.3, 317.1, 317.88, 322.71, 319.76, 323.04, 321.28, 322.57, 325.13, 326.93, 327.2, 327.23, 321.99, 323.41, 323.64, 325.73, 324.41, 326.63), low = c(163.07, 162.35, 159.42, 157.01, 151.58, 154.09, 158.63, 159.65, 164.09, 163.6, 163.95, 159.2, 159.96, 162.98, 162.09, 163.69, 162.35, 162.84, 165.4, 166.53, 166.87, 165.6, 163.28, 163.58, 162.96, 164.87, 165.75, 166.87, 169.1, 167.78, 170.28, 168.6, 170.18, 169.59, 170, 170.41, 171.16, 172.32, 172.9, 173.1271, 173.835, 173.25, 175.4778, 174.266, 303.06, 305.1, 309.94, 309.08, 317.16, 319.63, 319.36, 318.2209, 300.01, 298.6, 296.74, 307.67, 310.86, 309.51, 306.53, 306.75, 311.6101, 302.1, 301.28, 299.42, 298.93, 303.82, 309.07, 311.51, 315.56, 313.37, 312.7, 310.68, 312.76, 314.13, 312, 319.27, 319.09, 319.74, 320.62, 323.94, 324.5, 321.48, 319.246, 320.775, 320.85, 322.075, 319.64, 321.33), close = c(163.87, 162.92, 160.29, 157.05, 155.69, 156.39, 158.84, 161.99, 165.96, 164, 164.45, 160.88, 161.55, 163.82, 163.76, 164.11, 162.7, 164.87, 165.6, 168.07, 167.3, 166.03, 165.12, 165.15, 164.33, 167.29, 166.5, 170.13, 169.34, 170.39, 170.44, 169.37, 170.43, 169.88, 170.34, 170.63, 171.29, 173.82, 174.2, 173.2, 174.87, 174.51, 175.85, 175.62, 305.55, 308.08, 312.18, 311.36, 319.34, 323.2, 320.79, 319, 300.61, 304.21, 307.05, 312.96, 311.66, 311.78, 308.64, 310.62, 312.05, 304.09, 307.35, 300.05, 304.46, 308.36, 310.52, 312.23, 317.05, 313.78, 316.18, 314.38, 317.59, 314.84, 318.92, 321.85, 320.79, 321.72, 324.32, 325.01, 326.86, 322.96, 320.88, 323.22, 321.17, 325.12, 323.96, 326.52), volume = c(293800L, 153600L, 405800L, 231900L, 666300L, 428300L, 871200L, 194300L, 392300L, 316300L, 252400L, 431500L, 257200L, 160600L, 133900L, 142600L, 93200L, 208500L, 165000L, 299500L, 168000L, 240500L, 171400L, 138500L, 143200L, 246700L, 107100L, 301000L, 195900L, 815300L, 116000L, 175700L, 122000L, 384200L, 81364L, 91683L, 129757L, 164894L, 201237L, 97041L, 161419L, 250529L, 79400L, 95940L, 55696013L, 73635043L, 91750087L, 75471787L, 150302461L, 73310274L, 77174695L, 93944722L, 208285190L, 194450402L, 134968796L, 137048347L, 82814592L, 80355844L, 135211345L, 74007212L, 68066900L, 132067392L, 88966079L, 127811745L, 79773260L, 112828251L, 71910372L, 69214995L, 61149258L, 82583406L, 54202602L, 83079092L, 57454436L, 102549097L, 92791839L, 86921534L, 54433414L, 64421802L, 56150230L, 57245315L, 57792915L, 75737989L, 73766597L, 48292970L, 57494979L, 48454159L, 61861714L, 85210755L ), adjusted = c(163.0818500705, 162.136419195, 159.5190684555, 156.2946515749, 154.9411926373, 155.637825914, 158.0760423824, 161.2108921274, 165.1617979966, 163.2112248219, 163.6590604998, 160.1062307887, 160.7730083535, 163.0320905508, 162.972379127, 163.3206957654, 161.9174773081, 164.0770404658, 164.8035294543, 167.2616497306, 166.4953531262, 165.2314613243, 165.12, 165.15, 164.33, 167.29, 166.5, 170.13, 169.34, 170.39, 170.44, 169.37, 170.43, 169.88, 170.34, 170.63, 171.29, 173.82, 174.2, 173.2, 174.87, 174.51, 175.85, 175.62, 304.2034385119, 306.72228878, 310.8042200446, 309.9878337917, 317.9326658628, 321.7756548095, 319.3762757003, 317.5941642457, 299.2852091345, 302.8693439034, 305.6968279989, 311.5807825779, 310.2865116891, 310.4059828481, 308.64, 310.62, 312.05, 304.09, 307.35, 300.05, 304.46, 308.36, 310.52, 312.23, 317.05, 313.78, 316.18, 314.38, 317.59, 314.84, 318.92, 321.85, 320.79, 321.72, 324.32, 325.01, 326.86, 322.96, 320.88, 323.22, 321.17, 325.12, 323.96, 326.52), adjHigh = c(163.1515133982, 162.8430043757, 160.4744512349, 158.6333490037, 155.000904061, 155.8070082812, 159.6384913029, 161.2108921274, 165.6792970021, 165.1020865729, 165.8783350812, 161.5592087658, 161.0516616642, 163.2311286298, 163.2510324377, 164.5049723357, 162.6141105847, 164.3457418725, 165.9280946009, 167.391024482, 167.2616497306, 167.182034499, 165.16, 165.59, 164.43, 167.52, 167.1, 170.42, 171.77, 170.39, 171.71, 170.1, 171.09, 170.76, 170.95, 171.19, 172.11, 174.105, 174.33, 174.9, 174.96, 174.93, 176.15, 176.31, 304.8555519212, 306.7720684296, 311.8396367557, 311.6206062976, 319.8591383011, 321.9847293377, 321.8601806545, 320.9692244865, 310.7743522549, 307.7178817714, 306.9214073783, 314.2489717948, 313.0044805556, 310.9236912036, 314.38, 311.05, 314.5, 310.51, 307.64, 306.39, 304.61, 310.2, 311.89, 315.7, 317.68, 317.52, 316.3, 317.1, 317.88, 322.71, 319.76, 323.04, 321.28, 322.57, 325.13, 326.93, 327.2, 327.23, 321.99, 323.41, 323.64, 325.73, 324.41, 326.63), adjLow = c(162.2856977543, 161.5691606697, 158.6532528116, 156.2548439591, 150.8509601128, 153.3488880049, 157.8670523994, 158.8821466025, 163.3007919575, 162.8131486638, 163.1614653021, 158.4343109247, 159.1906556251, 162.1961306187, 161.310411167, 162.9027157994, 161.5691606697, 162.0568039634, 164.6044913753, 165.7290565219, 166.0674212563, 164.8035294543, 163.28, 163.58, 162.96, 164.87, 165.75, 166.87, 169.1, 167.78, 170.28, 168.6, 170.18, 169.59, 170, 170.41, 171.16, 172.32, 172.9, 173.1271, 173.835, 173.25, 175.4778, 174.266, 301.7244119634, 303.7554216658, 308.574091744, 307.7178817714, 315.7622731416, 318.2213878303, 317.9525777226, 316.8184977462, 298.6878533397, 297.2840672219, 295.4322642579, 306.3140956536, 309.490037296, 308.1459867577, 306.53, 306.75, 311.6101, 302.1, 301.28, 299.42, 298.93, 303.82, 309.07, 311.51, 315.56, 313.37, 312.7, 310.68, 312.76, 314.13, 312, 319.27, 319.09, 319.74, 320.62, 323.94, 324.5, 321.48, 319.246, 320.775, 320.85, 322.075, 319.64, 321.33), adjOpen = c(162.9027157994, 162.2956496583, 160.1659422124, 158.5636856761, 152.8811485191, 153.3986475247, 159.0413770658, 159.1707518172, 164.4054532962, 163.5495895563, 165.5897298666, 159.3996456081, 160.2754131559, 162.5743029689, 161.3502187828, 164.2760785448, 162.0866596753, 162.1662749069, 165.8186236575, 165.7290565219, 166.7740064369, 167.182034499, 163.95, 164.18, 163.64, 165.12, 166.39, 166.89, 171.43, 168.22, 171.48, 168.8, 170.79, 170.72, 170.93, 170.84, 171.81, 172.98, 173.14, 174.9, 173.88, 174.66, 175.88, 174.67, 302.2819440385, 305.1990315032, 308.8727696414, 309.7389355439, 315.831964651, 318.8087876952, 318.8884351345, 320.0034992849, 310.0873930908, 306.8815836586, 296.7066232869, 314.0896769161, 312.6858907983, 308.6388052884, 314.17, 307.99, 313.4801, 309.84, 303.47, 306.16, 301.41, 303.99, 309.57, 314.24, 316.37, 315.38, 314.61, 316.84, 314.31, 320.13, 313.3, 322.41, 319.79, 321.88, 321.43, 326.45, 324.62, 326.47, 320.95, 321.63, 322.43, 322.12, 321.9, 325.9), adjVolume = c(293800L, 153600L, 405800L, 231900L, 666300L, 428300L, 871200L, 194300L, 392300L, 316300L, 252400L, 431500L, 257200L, 160600L, 133900L, 142600L, 93200L, 208500L, 165000L, 299500L, 168000L, 240500L, 171400L, 138500L, 143200L, 246700L, 107100L, 301000L, 195900L, 815300L, 116000L, 175700L, 122000L, 384200L, 81364L, 91683L, 129757L, 164894L, 201237L, 97041L, 161419L, 250529L, 79400L, 95940L, 55696013L, 73635043L, 91750087L, 75471787L, 150302461L, 73310274L, 77174695L, 93944722L, 208285190L, 194450402L, 134968796L, 137048347L, 82814592L, 80355844L, 135211345L, 74007212L, 68066900L, 132067392L, 88966079L, 127811745L, 79773260L, 112828251L, 71910372L, 69214995L, 61149258L, 82583406L, 54202602L, 83079092L, 57454436L, 102549097L, 92791839L, 86921534L, 54433414L, 64421802L, 56150230L, 57245315L, 57792915L, 75737989L, 73766597L, 48292970L, 57494979L, 48454159L, 61861714L, 85210755L), divCash = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.798, 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, 1.3662, 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), splitFactor = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), row.names = c(NA, -88L), class = c("tbl_df", "tbl", "data.frame"))
and a forex_data obj like this:
structure(list(date = structure(c(1590969600, 1591056000, 1591142400, 1591228800, 1591315200, 1591574400, 1591660800, 1591747200, 1591833600, 1591920000, 1592179200, 1592265600, 1592352000, 1592438400, 1592524800, 1592784000, 1592870400, 1592956800, 1593043200, 1593129600, 1593388800, 1593475200, 1593561600, 1593648000, 1593734400, 1593993600, 1594080000, 1594166400, 1594252800, 1594339200, 1594598400, 1594684800, 1594771200, 1594857600, 1594944000, 1595203200, 1595289600, 1595376000, 1595462400, 1595548800, 1595808000, 1595894400, 1595980800, 1596067200, 1596153600 ), tzone = "UTC", class = c("POSIXct", "POSIXt")), adjusted = c(1.1137097672, 1.1183180497, 1.1218308279, 1.1345586567, 1.1313497002, 1.1299435028, 1.1354604292, 1.1350737798, 1.1381743683, 1.1246063878, 1.1268875366, 1.1257458066, 1.1223344557, 1.1220825853, 1.1179429849, 1.1252391133, 1.1330160888, 1.1275228323, 1.1214534036, 1.1203226529, 1.1247328759, 1.123343069, 1.1261261261, 1.1228385358, 1.1241007194, 1.1318619128, 1.1285407967, 1.1328877308, 1.1294330246, 1.1318619128, 1.1367511652, 1.139860937, 1.1418131994, 1.1431184271, 1.1423349326, 1.1440338634, 1.1488970588, 1.1592858799, 1.159554731, 1.1626555052, 1.1774402449, 1.1728829463, 1.1774402449, 1.179245283, 1.1828720132)), row.names = c(NA, -45L), class = c("tbl_df", "tbl", "data.frame"))
I want to obtain a price_data obj in which the values in 'adjusted' column are given by the old values divided by the values in forex_data adjusted column.
First I need to know what operator I have to use in order to divide the adjclose columns by the adjclose value of eurusd.
Of course is granted that I'm going to operate with two objects that have different number of rows. How do I deal with NAs and missing values? What if in forex_data 2020/06/03 row has a 0 or NA value? What if in forex_data 2020/06/03 row is missing ?
Thanx for every suggestion
submitted by eternal_studen to Rlanguage [link] [comments]

What factors predict the success of a Steam game? (An analysis)

What factors predict the success of a Steam game?

I've seen quite a few discussions, comments and questions on /gamedev about what determines a game's success. How much does quality matter? Is establishing market awareness before launch the only thing that matters? Does a demo help or hurt? If your game has a poor launch, how likely is it to recover? Is it possible to roughly predict the sales of a game before launch?
In preparation for my game's launch, I spent a lot of time monitoring upcoming releases trying to find the answer to these questions. I compiled a spreadsheet, noted followers, whether it was Early Access or not, and saw how many reviews it received in the first week, month and quarter.
I'm sharing this data now in the hopes that it helps other developers understand and predict their games' sales.
First some notes on the data:
Game Price Launch Discount Week Guess Week actual 3 Month 3 Month/week Followers Early Access Demo Review Score
Pit of Doom 9.99 0 7 27 43 1.592592593 295 Y N 0.8
Citrouille 9.99 0.2 16 8 12 1.5 226 N N
Corspe Party: Book 14.99 0.1 32 40 79 1.975 1015 N N 0.95
Call of Cthulhu 44.99 0 800 875 1595 1.822857143 26600 N N 0.74
On Space 0.99 0.4 0 0 0 4 N N
Orphan 14.99 0 50 0 8 732 N N
Black Bird 19.99 0 20 13 34 2.615384615 227 N N
Gloom 6.99 0 20 8 17 2.125 159 N N
Gilded Rails 5.99 0.35 2 3 7 2.333333333 11 N Y
The Quiet Man 14.99 0.1 120 207 296 1.429951691 5596 N N 0.31
KartKraft 19.99 0.1 150 90 223 2.477777778 7691 Y N 0.84
The Other Half 7.99 0 2 3 27 9 91 N Y 0.86
Parabolus 14.99 0.15 0 0 0 16 N Y
Yet Another Tower Defense 1.99 0.4 20 22 38 1.727272727 396 N N 0.65
Galaxy Squad 9.99 0.25 8 42 5.25 3741 Y N 0.87
Swords and Soldiers 2 14.99 0.1 65 36 63 1.75 1742 N N 0.84
SpitKiss 2.99 0 3 1 2 2 63 N N
Holy Potatoes 14.99 0 24 11 22 2 617 N N 0.7
Kursk 29.99 0.15 90 62 98 1.580645161 2394 N N 0.57
SimpleRockets 2 14.99 0.15 90 142 272 1.915492958 3441 Y N 0.85
Egress 14.99 0.15 160 44 75 1.704545455 7304 Y N 0.67
Kynseed 9.99 0 600 128 237 1.8515625 12984 Y N 0.86
11-11 Memories 29.99 0 30 10 69 6.9 767 N N 0.96
Rage in Peace 12.99 0.1 15 10 42 4.2 377 N N 0.85
One Hour One Life 19.99 0 12 153 708 4.62745098 573 N N 0.81
Optica 9.99 0 0 2 3 1.5 18 N N
Cybarian 5.99 0.15 8 4 18 4.5 225 N N
Zeon 25 3.99 0.3 3 11 12 1.090909091 82 Y N
Of Gods and Men 7.99 0.4 3 10 18 1.8 111 N Y
Welcome to Princeland 4.99 0.1 1 15 55 3.666666667 30 N N 0.85
Zero Caliber VR 24.99 0.1 100 169 420 2.485207101 5569 Y N 0.73
HellSign 14.99 0 100 131 334 2.549618321 3360 Y N 0.85
Thief Simulator 19.99 0.15 400 622 1867 3.001607717 10670 N N 0.81
Last Stanza 7.99 0.1 8 2 4 2 228 N Y
Evil Bank Manager 11.99 0.1 106 460 4.339622642 8147 Y N 0.78
Oppai Puzzle 0.99 0.3 36 93 2.583333333 54 N N 0.92
Hexen Hegemony 9.99 0.15 3 1 5 5 55 Y N
Blokin 2.99 0 0 0 0 0 10 N N
Light Fairytale Ep 1 9.99 0.1 80 23 54 2.347826087 4694 Y N 0.89
The Last Sphinx 2.99 0.1 0 0 1 0 17 N N
Glassteroids 9.99 0.2 0 0 0 0 5 Y N
Hitman 2 59.99 0 2000 2653 3677 1.385978138 52226 N N 0.88
Golf Peaks 4.99 0.1 1 8 25 3.125 46 N N 1
Sipho 13.99 0 24 5 14 2.8 665 Y N
Distraint 2 8.99 0.1 40 104 321 3.086538462 1799 N N 0.97
Healing Harem 12.99 0.1 24 10 15 1.5 605 N N
Spark Five 2.99 0.3 0 0 0 0 7 N N
Bad Dream: Fever 9.99 0.2 30 78 134 1.717948718 907 N N 0.72
Underworld Ascendant 29.99 0.15 200 216 288 1.333333333 8870 N N 0.34
Reentry 19.99 0.15 8 24 78 3.25 202 Y N 0.95
Zvezda 5.99 0 2 0 0 0 25 Y Y
Space Gladiator 2.99 0 0 1 2 2 5 N N
Bad North 14.99 0.1 500 360 739 2.052777778 15908 N N 0.8
Sanctus Mortem 9.99 0.15 3 3 3 1 84 N Y
The Occluder 1.99 0.2 1 1 1 1 13 N N
Dark Fantasy: Jigsaw 2.99 0.2 1 9 36 4 32 N N 0.91
Farming Simulator 19 34.99 0 1500 3895 5759 1.478562259 37478 N N 0.76
Don't Forget Our Esports Dream 14.99 0.13 3 16 22 1.375 150 N N 1
Space Toads Mayhem 3.99 0.15 1 2 3 1.5 18 N N
Cattle Call 11.99 0.1 10 19 53 2.789473684 250 Y N 0.71
Ralf 9.99 0.2 0 0 2 0 6 N N
Elite Archery 0.99 0.4 0 2 3 1.5 5 Y N
Evidence of Life 4.99 0 0 2 4 2 10 N N
Trinity VR 4.99 0 2 8 15 1.875 61 N N
Quiet as a Stone 9.99 0.1 1 1 4 4 42 N N
Overdungeon 14.99 0 3 86 572 6.651162791 77 Y N 0.91
Protocol 24.99 0.15 60 41 117 2.853658537 1764 N N 0.68
Scraper: First Strike 29.99 0 3 3 15 5 69 N N
Experiment Gone Rogue 16.99 0 1 1 5 5 27 Y N
Emerald Shores 9.99 0.2 0 1 2 2 12 N N
Age of Civilizations II 4.99 0 600 1109 2733 2.464382326 18568 N N 0.82
Dereliction 4.99 0 0 0 0 #DIV/0! 18 N N
Poopy Philosophy 0.99 0 0 6 10 1.666666667 6 N N
NOCE 17.99 0.1 1 3 4 1.333333333 35 N N
Qu-tros 2.99 0.4 0 3 7 2.333333333 4 N N
Mosaics Galore. Challenging Journey 4.99 0.2 1 1 8 8 14 N N
Zquirrels Jump 2.99 0.4 0 1 4 4 9 N N
Dark Siders III 59.99 0 2400 1721 2708 1.573503777 85498 N N 0.67
R-Type Dimensions Ex 14.99 0.2 10 48 64 1.333333333 278 N N 0.92
Artifact 19.99 0 7000 9700 16584 1.709690722 140000 N N 0.53
Crimson Keep 14.99 0.15 20 5 6 1.2 367 N N
Rival Megagun 14.99 0 35 26 31 1.192307692 818 N N
Santa's Workshop 1.99 0.1 3 1 1 1 8 N N
Hentai Shadow 1.99 0.3 2 12 6 14 N N
Ricky Runner 12.99 0.3 3 6 13 2.166666667 66 Y N 0.87
Pro Fishing Simulator 39.99 0.15 24 20 19 0.95 609 N N 0.22
Broken Reality 14.99 0.1 60 58 138 2.379310345 1313 N Y 0.98
Rapture Rejects 19.99 0 200 82 151 1.841463415 9250 Y N 0.64
Lost Cave 19.99 0 3 8 11 1.375 43 Y N
Epic Battle Fantasy 5 14.99 0 300 395 896 2.26835443 4236 N N 0.97
Ride 3 49.99 0 75 161 371 2.304347826 1951 N N 0.74
Escape Doodland 9.99 0.2 25 16 19 1.1875 1542 N N
Hillbilly Apocalypse 5.99 0.1 0 1 2 2 8 N N
X4 49.99 0 1500 2638 4303 1.63115997 38152 N N 0.7
Splotches 9.99 0.15 0 2 1 0.5 10 N N
Above the Fold 13.99 0.15 5 2 6 3 65 Y N
The Seven Chambers 12.99 0.3 3 0 0 #DIV/0! 55 N N
Terminal Conflict 29.99 0 5 4 11 2.75 125 Y N
Just Cause 4 59.99 0 2400 2083 3500 1.680268843 50000 N N 0.34
Grapple Force Rena 14.99 0 11 12 29 2.416666667 321 N Y
Beholder 2 14.99 0.1 479 950 1.983298539 16000 N N 0.84
Blueprint Word 1.99 0 12 15 1.25 244 N Y
Aeon of Sands 19.99 0.1 20 12 25 2.083333333 320 N N
Oakwood 4.99 0.1 32 68 2.125 70 N N 0.82
Endhall 4.99 0 4 22 42 1.909090909 79 N N 0.84
Dr. Cares - Family Practice 12.99 0.25 6 3 8 2.666666667 39 N N
Treasure Hunter 16.99 0.15 200 196 252 1.285714286 4835 N N 0.6
Forex Trading 1.99 0.4 7 10 14 1.4 209 N N
Ancient Frontier 14.99 0 24 5 16 3.2 389 N N
Fear the Night 14.99 0.25 25 201 440 2.189054726 835 Y N 0.65
Subterraneus 12.99 0.1 4 0 3 #DIV/0! 82 N N
Starcom: Nexus 14.99 0.15 53 119 2.245283019 1140 Y N 0.93
Subject 264 14.99 0.2 25 2 3 1.5 800 N N
Gris 16.9 0 100 1484 4650 3.133423181 5779 N N 0.96
Exiled to the Void 7.99 0.3 9 4 11 2.75 84 Y N
Column Explanations
For the columns that are not self-explanatory:

Question 1: Does Quality Predict Success?

There was a recent blog post stating that the #1 metric for indie games' success is how good it is.
Quality is obviously a subjective metric. The most obvious objective measure of quality for Steam games is their % Favorable Review score. This is the percentage of reviews by purchasers of the game that gave the game a positive rating. I excluded any game that did not have at least 20 user reviews in the first month, which limited the sample size to 56.
The (Pearson) correlation of a game's review score to its number of reviews three months after its release was -0.2. But 0.2 (plus or minus) isn't a very strong correlation at all. More importantly, Pearson correlation can be swayed if the data contains some big outliers. Looking at the actual games, we can see that the difference is an artifact of an outlier. Literally. Valve's Artifact by far had the most reviews after three months and had one of the lowest review scores (53% at the time). Removing this game from the data changed the correlation to essentially zero.
Spearman's Rho, an alternative correlation model that correlates rank position and minimizes the effect of huge outliers produced a similar result.
Conclusion: If there is correlation between a game's quality (as measured by Steam review score) and first quarter sales (as measured by total review count), it is too subtle to be detected in this data.

Question 2: Do Demos, Early Access or Launch Discounts Affect Success/Failure?

Unfortunately, there were so few games that had demos prior to release (10) that only a very strong correlation would really tell us anything. As it happens, there was no meaningful correlation one way or another.
There were more Early Access titles (28), but again the correlation was too small to be meaningful.
More than half the titles had a launch week discount and there was actually a moderate negative correlation of -0.3 between having a launch discount and first week review count. However it appears that this is primarily the result of the tendency of AAA titles (which sell the most copies) to not do launch discounts. Removing the titles that likely grossed over a $1 million in the first week reduced the correlation to basically zero.
Conclusion: Insufficient data. No clear correlation between demos, Early Access or launch discount and review counts: if they help or hurt the effect is not consistent enough to be seen here.

Question 3: Does pre-launch awareness (i.e., Steam followers) predict success?

You can see the number of "followers" for any game on Steam by searching for its automatically-created Community Group. Prior to launch, this is a good rough indicator of market awareness.
The correlation between group followers shortly before launch and review count at 3 months was 0.89. That's a very strong positive correlation. The rank correlation was also high (0.85) suggesting that this wasn't the result of a few highly anticipated games.
Save for a single outlier (discussed later), the ratio of 3 month review counts to pre-launch followers ranged from 0 (for the handful of games that never received any reviews) to 1.8, with a median value of 0.1. If you have 1000 followers just prior to launch, then at the end of the first quarter you should expect "about" 100 reviews.
One thing I noticed was that there were a few games that had follower counts that seemed too high compared to secondary indicators of market awareness, such as discussion forum threads and Twitter engagement. After some investigation I came to the conclusion that pre-launch key activations are treated as followers by Steam. If a game gave away a lot of Steam keys before launch (say as Kickstarter rewards or part of beta testing) this would cause the game to appear to have more followers than it had gained "organically."
Conclusion: Organic followers prior to launch are a strong predictor of a game's eventual success.

Question 4: What about price?

The correlation between price and review count at 3 month is 0.36, which is moderate correlation. I'm not sure how useful that data point is: it is somewhat obvious that higher budget games have larger marketing budgets.
There is a correlation between price and review score of -0.41. It seems likely that players do factor price into their reviews and a game priced at $60 has a higher bar to clear to earn a thumbs up review than a game priced at $10.

Question 5: Do first week sales predict first quarter results?

The correlation between number of reviews after 1 week and number of reviews after 3 months was 0.99. The Spearman correlation was 0.97. This is the highest correlation I found in the data.
Excluding games that sold very few copies (fewer than 5 reviews after the first week), most games had around twice as many reviews after 3 months as they did after 1 week. This suggests that games sell about as many copies in their first week as they do in the next 12 weeks combined. The vast majority of games had a tail ratio (ratio of reviews at 3 months to 1 week) of between 1.3 to 3.2.
I have seen a number of questions from developers whose game had a poor launch on Steam and wanted to know what they can do to improve sales. While I'm certain post-launch marketing can have an effect on continuing sales, your first week does seem to set hard bounds on your results.
Conclusion: ALL SIGNS POINT TO YES

Question 6: Does Quality Help with a Game's "Tail"?

As discussed in the last question while first week sales are very strongly correlated with first quarter, there's still quite a wide range of ratios. Defining a game's Tail Ratio as the ratio of reviews after 3 months to after 1 week, the lowest value was 0.95 for "Pro Fishing Simulator" which actually managed to lose 1 review. The highest ratio was 6.9, an extreme outlier that I'll talk about later. It is perhaps not a coincidence that the worst tail had a Steam score of 22% and the best tail had a Steam score of 96%.
The overall correlation between the Tail Ratio and Steam score was 0.42.
Conclusion: Even though there is no clear correlation between quality and overall review count/sales, there is a moderate correlation between a game's review score and its tail. This suggests that "good games" do better in the long run than "bad games," but the effect is small compared to the more important factor of pre-launch awareness.

Question 7: Is it possible to predict a game's success before launch without knowing its wishlists?

While I was compiling the data for each game, sometime prior to its scheduled launch date, I would make a prediction of how many reviews I thought it would receive in its first week and add that prediction to the spreadsheet.
The #1 factor I used in making my prediction was group follower count. In some cases I would adjust my prediction if I thought that value was off, using secondary sources such as Steam forum activity and Twitter engagement.
The correlation between my guess and the actual value was 0.96, which is a very strong correlation. As you can see in the data, the predictions are, for the most part, in the right ballpack with a few cases where I was way off.
Based on my experience, multiplying the group follower count by 0.1 will, in most cases, give you a ballpark sense of the first week quarter review count. If a game doesn't have at least one question in the discussion forum for every 100 followers, that may indicate that there are large number of "inorganic" followers and you may need to adjust your estimate.
Conclusion: Yes, with a few exceptions, using follower data and other indicators you can predict first week results approximately. Given the strong correlation between first week and quarter sales, it should also be possible to have a ballpark idea of first quarter results before launch.

Final Question: What about the outliers you mentioned?

There were a few games in the data that stood out significantly in one way or another.
Outlier #1: Overdungeon. This game had 77 group followers shortly before launch, a fairly small number and based solely on that number I would have expected fewer than a dozen reviews in the first week. It ended up with 86. Not only that, it had a strong tail and finished its first quarter with 572 reviews. This was by a wide margin the highest review count to follower ratio in the sample.
Based on the reviews, it appears to basically be Slay the Spire, but huge in Asia. 90% of the reviews seem to be in Japanese or Chinese. If anyone has some insight to this game's unusual apparent success, I'm very curious.
This seems to be the only clear example in the data of a game with minimal following prior to launch going on to having a solid first quarter.
Outlier #2: 11-11 Memories Retold. This game had 767 group followers shortly before launch, ten times as many as Overdungeon. That's still not a large number for even a small indie title. It had a fair amount going for it, though: it was directed by Yoan Fanise, who co-directed the critally acclaimed Valiant Hearts, a game with a similar theme. It was animated by Aardman Studios of "Wallace and Gromit" fame. Its publisher was Bandai Namco Europe, a not inexperienced publisher. The voice acting was by Sebastian Koch and Elijah Wood. It has dozens of good reviews in both gaming and traditional press. It currently has a 95% positive review rating on Steam.
Despite all that, nobody bought it. 24 hours after it came out it had literally zero reviews on Steam. One week after it came out it had just 10. Three months later it had demonstrated the largest tail in the data, but even then it had only climbed to 69 reviews. Now it's at about 100, an incredible tail ratio, but almost certainly a commercial failure.
This is a solid example that good game + good production values does necessarily equal good sales.

Final notes:
The big take-aways from this analysis are:
Thanks for reading!
submitted by justkevin to gamedev [link] [comments]

Adult Content Websites Record Increase In Crypto Transactions Due To COVID-19

Adult Content Websites Record Increase In Crypto Transactions Due To COVID-19

The Porn Industry Reported Increased Interest From Crypto Users Worldwide
As the world struggles with the ongoing COVID-19 virus outbreak, people are trying to get used to living indoors. The world of online entertainment experienced a massive boost in terms of a raised demand, as streaming and gaming services increased in popularity. Content providers like Netflix recorded a surge in China and Italy, just days after the countries entered a never-witnessed before lockdown.
As the world of online entertainment flourishes amid the global crisis, so does the adult entertainment industry. Business Insider recently reported one of the leading adult content providers, PornHub, to be going up with 11,6% in traffic globally. Also, countries with a positive stance on the sex industry – like Germany, Switzerland, and the Netherlands, showed a boost in traffic amid the lockdown.
However, the correlation between the porn industry and the crypto sector became more evident, as cryptos saw a by-proxy benefit from the aggregated surge in demand for online adult content.
Crypto payments processing company CoinGate reported an increase in crypto transactions in March, related to adult content websites. Some of the leaders in live webcam shows, LiveJasmin and ManyVids, managed to attract 8% and 17% increases in purchases, respectively. Other websites reported up to 35% increase in demand.
The crypto payment processor also noted that the Forex markets correspond to the COVID-19 outbreak as well. CoinGate reported a 13% increase in Bitcoin (BTC) transactions, Ethereum (ETH) transactions jumped with 14%, while Bitcoin Cash (BCH) recorded a 12% increase in the total number of transactions.
“People may start relying on the usage of blockchain technology and cryptocurrencies more often. The traditional financial market showed it’s not immune to higher volatility than Bitcoin, for example.” CoinGate reported.
Despite the positive outcomes in terms of using cryptocurrencies as payment methods and the increase in transaction amounts, the ground beneath the crypto sector is still shaky. Bitcoin, as well as the entire crypto sector, fell into the whirlpool of financial breakdowns, mostly due to the unstable markets from the COVID-19 virus.
https://preview.redd.it/b5o8ntall7r41.png?width=897&format=png&auto=webp&s=01223f318a14bd2a8cf98edf5c42ba62715277e3
Data from Blockchain.com shows a rough March for the crypto sector. The slump this time was in the number of transactions, as well as the amount of the transactions. In early February, Bitcoin recorded a 7-day average of 321,880 transactions per day. Bitcoin hit a two-month bottom of 251,630, before rebounding to 273,000 as of press time.
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Raspberry Pi Home Dashboard

I had a few requests of my home dashboard, and wanted to share with everyone how I put it together.
What you'll need:
Step 1: Instructions to Set Up the Raspberry Pi Itself:
Step 2: Set up the HTML File to Display
/home/pi/html
  1. Background.png
  2. Dashboard.html
  3. News.html
  4. Map.html
  5. Stocks.html
  6. ToDo.html
  7. Weather.html
  8. Calendar.html
FINAL Comments. This project probably took me 1h to set up the pi. And 4ish hours stumbling around to get the dashboard set up. My only real outlay was a monitor mount and a new monitor. Best of luck!
EDIT Here is the link for the current version of the dashboard. I removed the traffic for the weekend, but this is the dashboard. I have some formatting I really want to do (headings et al), but this should be a decent start. I have also included the color scheme I used.
submitted by fuzzyaces to raspberry_pi [link] [comments]

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