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  1. #Alcohol Syntax
  2. setwd("C:\\Users\\thepo\\OneDrive\\Desktop\\R stuff")
  3. raw_mlm <- read.csv("fixed_mixed.csv")
  4.  
  5. names(raw_mlm)[names(raw_mlm)=='R0000100']<- 'id'
  6. names(raw_mlm)[names(raw_mlm)=='R0033000']<- 'grade_currently_attend'
  7. names(raw_mlm)[names(raw_mlm)=='R0358900']<- 'ever_use_MJ'
  8. names(raw_mlm)[names(raw_mlm)=='R0536300']<- 'SEX'
  9. names(raw_mlm)[names(raw_mlm)=='R0536401']<- 'month'
  10. names(raw_mlm)[names(raw_mlm)=='R0536402']<- 'year'
  11. names(raw_mlm)[names(raw_mlm)=='R1235800']<- 'cross_secc'
  12. names(raw_mlm)[names(raw_mlm)=='R1482600']<- 'ethnicity'
  13.  
  14. names(raw_mlm)[names(raw_mlm)=='R1204500']<- 'HH_income97'
  15. names(raw_mlm)[names(raw_mlm)=='R2563300']<- 'HH_income98'
  16. names(raw_mlm)[names(raw_mlm)=='R3884900']<- 'HH_income99'
  17. names(raw_mlm)[names(raw_mlm)=='R5464100']<- 'HH_income00'
  18. names(raw_mlm)[names(raw_mlm)=='R7227800']<- 'HH_income01'
  19. names(raw_mlm)[names(raw_mlm)=='S1541700']<- 'HH_income02'
  20. names(raw_mlm)[names(raw_mlm)=='S2011500']<- 'HH_income03'
  21.  
  22. names(raw_mlm)[names(raw_mlm)=='R9789700']<- 'number_absences97'
  23. names(raw_mlm)[names(raw_mlm)=='R9789800']<- 'number_absences98'
  24. names(raw_mlm)[names(raw_mlm)=='R9789900']<- 'number_absences99'
  25. names(raw_mlm)[names(raw_mlm)=='R9829700']<- 'number_absences00'
  26. names(raw_mlm)[names(raw_mlm)=='R9829800']<- 'number_absences01'
  27. names(raw_mlm)[names(raw_mlm)=='R9829900']<- 'number_absences02'
  28. names(raw_mlm)[names(raw_mlm)=='R9830000']<- 'number_absences03'
  29.  
  30.  
  31.  
  32.  
  33. names(raw_mlm)[names(raw_mlm)=='R0358300']<- 'ever_drink_97'
  34. names(raw_mlm)[names(raw_mlm)=='R0358600']<- '#_of_drinks97'
  35. names(raw_mlm)[names(raw_mlm)=='R0358500']<- '#_of_days_drank_97'
  36.  
  37. names(raw_mlm)[names(raw_mlm)=='R2190200']<- 'drank_DLI_98'
  38. names(raw_mlm)[names(raw_mlm)=='R2190300']<- '#_of_days_drank_98'
  39. names(raw_mlm)[names(raw_mlm)=='R2190400']<- '#_of_drinks98'
  40. names(raw_mlm)[names(raw_mlm)=='R2191200']<- 'used_mj_DLI_98'
  41.  
  42. names(raw_mlm)[names(raw_mlm)=='R3509300']<- 'drank_DLI_99'
  43. names(raw_mlm)[names(raw_mlm)=='R3509500']<- '#_of_drinks99'
  44. names(raw_mlm)[names(raw_mlm)=='R3510300']<- 'used_MJ_DLI_99'
  45. names(raw_mlm)[names(raw_mlm)=='R3509400']<- '#_of_days_drank_99'
  46.  
  47. names(raw_mlm)[names(raw_mlm)=='R4907400']<- 'drank_DLI_00'
  48. names(raw_mlm)[names(raw_mlm)=='R4907500']<- '#_of_days_drank_00'
  49. names(raw_mlm)[names(raw_mlm)=='R4907600']<- '#_of_drinks00'
  50. names(raw_mlm)[names(raw_mlm)=='R4908400']<- 'used_MJ_DLI_00'
  51.  
  52. names(raw_mlm)[names(raw_mlm)=='R6534700']<- 'drank_DLI_01'
  53. names(raw_mlm)[names(raw_mlm)=='R6534800']<- '#_of_days_drank_01'
  54. names(raw_mlm)[names(raw_mlm)=='R6534900']<- '#_of_drinks01'
  55. names(raw_mlm)[names(raw_mlm)=='R6535600']<- 'used_MJ_DLI_01'
  56.  
  57. names(raw_mlm)[names(raw_mlm)=='S0922200']<- 'drank_DLI_02'
  58. names(raw_mlm)[names(raw_mlm)=='S0922300']<- '#_of_days_drank_02'
  59. names(raw_mlm)[names(raw_mlm)=='S0922400']<- '#_of_drinks02'
  60. names(raw_mlm)[names(raw_mlm)=='S0923200']<- 'used_MJ_DLI_02'
  61.  
  62. names(raw_mlm)[names(raw_mlm)=='S2988900']<- 'drank_DLI_03'
  63. names(raw_mlm)[names(raw_mlm)=='S2989000']<- '#_of_days_drank_03'
  64. names(raw_mlm)[names(raw_mlm)=='S2989100']<- '#_of_drinks03'
  65. names(raw_mlm)[names(raw_mlm)=='S2989700']<- 'used_MJ_DLI_03'
  66.  
  67. names(raw_mlm)[names(raw_mlm)=='S4683700']<- 'drank_DLI_04'
  68. names(raw_mlm)[names(raw_mlm)=='S4683800']<- '#_of_days_drank_04'
  69. names(raw_mlm)[names(raw_mlm)=='S4683900']<- '#_of_drinks04'
  70. names(raw_mlm)[names(raw_mlm)=='S4684700']<- 'used_MJ_DLI_04'
  71.  
  72. names(raw_mlm)[names(raw_mlm)=='S6319300']<- 'drank_DLI_05'
  73. names(raw_mlm)[names(raw_mlm)=='S6319400']<- '#_of_days_drank_05'
  74. names(raw_mlm)[names(raw_mlm)=='S6319500']<- '#_of_drinks05'
  75. names(raw_mlm)[names(raw_mlm)=='S6320300']<- 'used_MJ_DLI_05'
  76.  
  77. names(raw_mlm)[names(raw_mlm)=='S8333800']<- 'drank_DLI_06'
  78. names(raw_mlm)[names(raw_mlm)=='S8333900']<- '#_of_days_drank_06'
  79. names(raw_mlm)[names(raw_mlm)=='S8334000']<- '#_of_drinks06'
  80. names(raw_mlm)[names(raw_mlm)=='S8334300']<- 'used_MJ_DLI_06'
  81.  
  82. names(raw_mlm)[names(raw_mlm)=='T0740600']<- 'drank_DLI_07'
  83. names(raw_mlm)[names(raw_mlm)=='T0740700']<- '#_of_days_drank_07'
  84. names(raw_mlm)[names(raw_mlm)=='T0740800']<- '#_of_drinks07'
  85. names(raw_mlm)[names(raw_mlm)=='T0741100']<- 'used_MJ_DLI_07'
  86.  
  87. names(raw_mlm)[names(raw_mlm)=='T2784100']<- 'drank_DLI_08'
  88. names(raw_mlm)[names(raw_mlm)=='T2784200']<- '#_of_days_drank_08'
  89. names(raw_mlm)[names(raw_mlm)=='T2784300']<- '#_of_drinks08'
  90. names(raw_mlm)[names(raw_mlm)=='T2784600']<- 'used_MJ_DLI_08'
  91.  
  92. names(raw_mlm)[names(raw_mlm)=='T4495800']<-'drank_DLI_09'
  93. names(raw_mlm)[names(raw_mlm)=='T4495900']<-'#_of_days_drank_09'
  94. names(raw_mlm)[names(raw_mlm)=='T4496000']<-'#_of_drinks09'
  95. names(raw_mlm)[names(raw_mlm)=='T4496300']<-'used_MJ_DLI_09'
  96.  
  97. names(raw_mlm)[names(raw_mlm)=='T6144700']<-'drank_DLI_10'
  98. names(raw_mlm)[names(raw_mlm)=='T6144800']<-'#_of_days_drank_10'
  99. names(raw_mlm)[names(raw_mlm)=='T6144900']<-'#_of_drinks10'
  100. names(raw_mlm)[names(raw_mlm)=='T6145200']<-'used_MJ_DLI_10'
  101.  
  102. names(raw_mlm)[names(raw_mlm)=='T7639200']<-'drank_DLI_11'
  103. names(raw_mlm)[names(raw_mlm)=='T7639300']<-'#_of_days_drank_11'
  104. names(raw_mlm)[names(raw_mlm)=='T7639400']<-'#_of_drinks11'
  105. names(raw_mlm)[names(raw_mlm)=='T7639700']<-'used_MJ_DLI_11'
  106.  
  107. names(raw_mlm)[names(raw_mlm)=='T9041200']<-'drank_DLI_13'
  108. names(raw_mlm)[names(raw_mlm)=='T9041300']<-'#_of_days_drank_13'
  109. names(raw_mlm)[names(raw_mlm)=='T9041400']<-'#_of_drinks13'
  110.  
  111. names(raw_mlm)[names(raw_mlm)=='U1031700']<-'drank_DLI_15'
  112. names(raw_mlm)[names(raw_mlm)=='U1031800']<-'#_of_days_drank_15'
  113. names(raw_mlm)[names(raw_mlm)=='U1031900']<-'#_of_drinks15'
  114. names(raw_mlm)[names(raw_mlm)=='U1032200']<-'used_MJ_DLI_15'
  115.  
  116. names(raw_mlm)[names(raw_mlm)=='R0359100']<- '#_of_days_used_MJ_97'
  117. names(raw_mlm)[names(raw_mlm)=='R2191300']<- '#_of_days_used_MJ_98'
  118. names(raw_mlm)[names(raw_mlm)=='R3510400']<- '#_of_days_used_MJ_99'
  119. names(raw_mlm)[names(raw_mlm)=='R4908500']<- '#_of_days_used_MJ_00'
  120. names(raw_mlm)[names(raw_mlm)=='R6535700']<- '#_of_days_used_MJ_01'
  121. names(raw_mlm)[names(raw_mlm)=='S0923300']<- '#_of_days_used_MJ_02'
  122. names(raw_mlm)[names(raw_mlm)=='S2989800']<- '#_of_days_used_MJ_03'
  123. names(raw_mlm)[names(raw_mlm)=='S4684800']<- '#_of_days_used_MJ_04'
  124. names(raw_mlm)[names(raw_mlm)=='S6320400']<- '#_of_days_used_MJ_05'
  125. names(raw_mlm)[names(raw_mlm)=='S8334400']<- '#_of_days_used_MJ_06'
  126. names(raw_mlm)[names(raw_mlm)=='T0741200']<- '#_of_days_used_MJ_07'
  127. names(raw_mlm)[names(raw_mlm)=='T2784700']<- '#_of_days_used_MJ_08'
  128. names(raw_mlm)[names(raw_mlm)=='T4496400']<- '#_of_days_used_MJ_09'
  129. names(raw_mlm)[names(raw_mlm)=='T6145300']<- '#_of_days_used_MJ_10'
  130. names(raw_mlm)[names(raw_mlm)=='T7639800']<- '#_of_days_used_MJ_11'
  131. names(raw_mlm)[names(raw_mlm)=='U1032300']<- '#_of_days_used_MJ_15'
  132.  
  133. sixthgrade97 <- subset(raw_mlm,grade_currently_attend==6)
  134.  
  135. #library("dplyr")
  136. #only needed first run of code : install.packages("tidyr")
  137. library("tidyr")
  138.  
  139. ever_drank<-gather(sixthgrade97,grade,ever_drank, c("ever_drink_97","drank_DLI_98","drank_DLI_99","drank_DLI_00","drank_DLI_01","drank_DLI_02","drank_DLI_03"))
  140.  
  141. detach("package:tidyr", unload=TRUE)
  142.  
  143. #only needed to run the first time install.packages("car")
  144. library("car")
  145.  
  146. ever_drank$grade<-recode(ever_drank$grade,"'ever_drink_97'= 6")
  147. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_98'= 7")
  148. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_99'= 8")
  149. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_00'= 9")
  150. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_01'= 10")
  151. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_02'= 11")
  152. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_03'= 12")
  153. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_04'= 13")
  154. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_05'= 14")
  155. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_06'= 15")
  156. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_07'= 16")
  157. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_08'= 17")
  158. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_09'= 18")
  159. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_10'= 19")
  160. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_11'= 20")
  161. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_13'= 22")
  162. ever_drank$grade<-recode(ever_drank$grade,"'drank_DLI_15'= 24")
  163.  
  164. library("tidyr")
  165.  
  166. income<-gather(sixthgrade97,grade,income, c("HH_income97","HH_income98","HH_income99","HH_income00","HH_income01","HH_income02","HH_income03"))
  167.  
  168. detach("package:tidyr", unload=TRUE)
  169.  
  170. library("car")
  171.  
  172. income$grade<-recode(income$grade,"'HH_income97'= 6")
  173. income$grade<-recode(income$grade,"'HH_income98'= 7")
  174. income$grade<-recode(income$grade,"'HH_income99'= 8")
  175. income$grade<-recode(income$grade,"'HH_income00'= 9")
  176. income$grade<-recode(income$grade,"'HH_income01'= 10")
  177. income$grade<-recode(income$grade,"'HH_income02'= 11")
  178. income$grade<-recode(income$grade,"'HH_income03'= 12")
  179.  
  180. income[income==-4]<-NA
  181.  
  182.  
  183.  
  184. library("tidyr")
  185.  
  186. attendance<-gather(sixthgrade97,grade,attendance, c("number_absences97","number_absences98","number_absences99","number_absences00","number_absences01","number_absences02","number_absences03"))
  187.  
  188. detach("package:tidyr", unload=TRUE)
  189.  
  190. library("car")
  191.  
  192. attendance$grade<-recode(attendance$grade,"'number_absences97'= 6")
  193. attendance$grade<-recode(attendance$grade,"'number_absences98'= 7")
  194. attendance$grade<-recode(attendance$grade,"'number_absences99'= 8")
  195. attendance$grade<-recode(attendance$grade,"'number_absences00'= 9")
  196. attendance$grade<-recode(attendance$grade,"'number_absences01'= 10")
  197. attendance$grade<-recode(attendance$grade,"'number_absences02'= 11")
  198. attendance$grade<-recode(attendance$grade,"'number_absences03'= 12")
  199.  
  200.  
  201. library("tidyr")
  202.  
  203. days_drank<-gather(sixthgrade97,grade,days_drank, c("#_of_days_drank_97", "#_of_days_drank_98","#_of_days_drank_99","#_of_days_drank_00","#_of_days_drank_01","#_of_days_drank_02","#_of_days_drank_03"))
  204.  
  205. detach("package:tidyr", unload=TRUE)
  206.  
  207. library("car")
  208.  
  209. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_97'= 6")
  210. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_98'= 7")
  211. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_99'= 8")
  212. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_00'= 9")
  213. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_01'= 10")
  214. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_02'= 11")
  215. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_03'= 12")
  216. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_04'= 13")
  217. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_05'= 14")
  218. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_06'= 15")
  219. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_07'= 16")
  220. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_08'= 17")
  221. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_09'= 18")
  222. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_10'= 19")
  223. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_11'= 20")
  224. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_13'= 22")
  225. days_drank$grade<-recode(days_drank$grade,"'#_of_days_drank_15'= 24")
  226.  
  227.  
  228. library("tidyr")
  229.  
  230. drinksdrank<-gather(sixthgrade97,grade,amount_drank, c("#_of_drinks97", "#_of_drinks98","#_of_drinks99","#_of_drinks00","#_of_drinks01","#_of_drinks02","#_of_drinks03"))
  231.  
  232. detach("package:tidyr", unload=TRUE)
  233.  
  234. library("car")
  235.  
  236. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks97'= 6")
  237. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks98'= 7")
  238. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks99'= 8")
  239. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks00'= 9")
  240. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks01'= 10")
  241. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks02'= 11")
  242. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks03'= 12")
  243. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks04'= 13")
  244. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks05'= 14")
  245. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks06'= 15")
  246. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks07'= 16")
  247. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks08'= 17")
  248. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks09'= 18")
  249. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks10'= 19")
  250. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks11'= 20")
  251. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks13'= 22")
  252. drinksdrank$grade<-recode(drinksdrank$grade,"'#_of_drinks15'= 24")
  253.  
  254.  
  255. library("tidyr")
  256.  
  257. mj_ever_used <-gather(sixthgrade97,grade,mj_ever_used, c("ever_use_MJ","used_mj_DLI_98","used_MJ_DLI_99","used_MJ_DLI_01","used_MJ_DLI_01","used_MJ_DLI_02","used_MJ_DLI_03"))
  258.  
  259. detach("package:tidyr", unload=TRUE)
  260.  
  261. library("car")
  262.  
  263. mj_ever_used$grade<-recode(mj_ever_used$grade, "'ever_use_MJ'=6")
  264. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_mj_DLI_98'=7")
  265. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_99'=8")
  266. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_01'=9")
  267. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_02'=10")
  268. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_03'=11")
  269. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_04'=12")
  270. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_05'=13")
  271. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_06'=14")
  272. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_07'=15")
  273. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_08'=16")
  274. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_09'=17")
  275. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_10'=18")
  276. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_11'=19")
  277. mj_ever_used$grade<-recode(mj_ever_used$grade, "'used_MJ_DLI_15'=24")
  278.  
  279.  
  280. library("tidyr")
  281.  
  282. mj_days <-gather(sixthgrade97, grade, mj_days, c("#_of_days_used_MJ_97","#_of_days_used_MJ_98","#_of_days_used_MJ_99","#_of_days_used_MJ_01","#_of_days_used_MJ_02","#_of_days_used_MJ_03"))
  283.  
  284. detach("package:tidyr", unload=TRUE)
  285.  
  286.  
  287. library("car")
  288.  
  289.  
  290. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_97'= 6")
  291. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_98'= 7")
  292. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_99'= 8")
  293. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_01'= 9")
  294. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_02'= 10")
  295. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_03'= 11")
  296. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_04'= 12")
  297. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_05'= 13")
  298. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_06'= 14")
  299. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_07'= 15")
  300. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_08'= 16")
  301. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_09'= 17")
  302. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_10'= 18")
  303. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_11'= 19")
  304. mj_days$grade<-recode(mj_days$grade,"'#_of_days_used_MJ_15'= 24")
  305.  
  306.  
  307.  
  308. drinkdat<-data.frame(ever_drank[,c(1,7,10,11,107,108)],drinksdrank[,"amount_drank"],days_drank[,"days_drank"],income[,"income"],attendance["attendance"])
  309.  
  310.  
  311. names(drinkdat)[names(drinkdat)=='drinksdrank....amount_drank..']<- 'amount_drank'
  312. names(drinkdat)[names(drinkdat)=='days_drank....days_drank..']<- 'days_drank'
  313. names(drinkdat)[names(drinkdat)=='income....income..']<- 'income'
  314.  
  315.  
  316. drinkdat[drinkdat==-4]<-0
  317. drinkdat[drinkdat==-3]<-NA
  318. drinkdat[drinkdat==-2]<-NA
  319. drinkdat[drinkdat==-1]<-NA
  320. drinkdat[drinkdat==-5]<-NA
  321.  
  322. drinkdat$grade_c <- drinkdat$grade -6
  323.  
  324. drinkdat$amount_drank_trim<-drinkdat$amount_drank
  325. is.na(drinkdat$amount_drank_trim)<-drinkdat$amount_drank_trim >=15
  326.  
  327. onlyday12th<- subset(drinkdat,drinkdat$days_drank>0)
  328. onlydrink12th<- subset(drinkdat,drinkdat$amount_drank_trim>0)
  329.  
  330. a<-drinkdat$days_drank
  331. b<-drinkdat$grade_c
  332. c<-drinkdat$amount_drank_trim
  333. d<-drinkdat$ethnicity
  334. e<-drinkdat$attendance
  335. f<-drinkdat$income
  336. id<-drinkdat$id
  337.  
  338. d<-as.factor(d)
  339. id<-as.factor(id)
  340.  
  341. #install.packages("ggplot2")
  342. library("ggplot2")
  343.  
  344. #install.packages("lme4")
  345. library("lme4")
  346.  
  347. table(drinkdat$amount_drank_trim,drinkdat$grade)
  348. table(drinkdat$days_drank,drinkdat$grade)
  349.  
  350.  
  351.  
  352. mixslope_day<-lmer(a~b+e+f+d+(0+b|id),data = drinkdat)
  353.  
  354. mixslope_drink<-lmer(c~b+e+f+d+(0+b|id),data = drinkdat)
  355.  
  356. summary(mixslope_day)
  357.  
  358. #install.packages("strengejacke")
  359.  
  360. # load required packages
  361. library(sjPlot) # table functions
  362. library(sjmisc) # sample data
  363. library(lme4) # fitting models
  364.  
  365.  
  366.  
  367. # prepare grouping variables
  368.  
  369. d <- as.factor(rec(d, "1=1;2=2;3:4=4"))
  370. levels(x = d) <- c("Black", "Hispanic", "Mixed Race","Non-black/Hispanic")
  371.  
  372. # data frame for fitted model
  373. mydf <- data.frame(neg_c_7 = as.numeric(efc$neg_c_7),
  374. sex = as.factor(efc$c161sex),
  375. c12hour = as.numeric(efc$c12hour),
  376. barthel = as.numeric(efc$barthtot),
  377. education = as.factor(efc$c172code),
  378. grp = efc$grp,
  379. carelevel = efc$care.level)
  380.  
  381. # fit sample models
  382. fit1 <- lmer(neg_c_7 ~ sex + c12hour + barthel + (1|grp), data = mydf)
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