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  1. ```{r}
  2. library(aws.s3)
  3. library('lehmansociology')
  4. library(ggplot2)
  5. library(dplyr)
  6. library(lsr)
  7. s3load('gss100.Rdata', bucket = 'lehmansociologydata')
  8. gss100<-droplevels(gss100)
  9. ```
  10.  
  11. #GRASS
  12. #create a dichotomized variable where TRUE or 1 = supports legalization of grass and
  13. #FALSE or 0 = does not support legalization of grass
  14. ```{r}
  15. frequency(gss100$grass)
  16. frequency(as.numeric(gss100$grass))
  17. gss100$favorgrass<-as.numeric(gss100$grass)==1
  18. frequency(gss100$favorgrass)
  19. ```
  20. #HEALTH
  21. #create a dichotomized variable where TRUE or 1=excellent or good
  22. #health and FALSE or 0=fair or poor health
  23. ```{r}
  24. frequency(gss100$health)
  25. frequency(as.numeric(gss100$health))
  26. gss100$goodhealth<-as.numeric(gss100$health)<3
  27. frequency(gss100$goodhealth)
  28. ```
  29. ```{r}
  30. mean(gss100$goodhealth, na.rm = TRUE)
  31. ciMean(as.numeric(gss100$goodhealth), na.rm=TRUE, conf =0.95)
  32. ciMean(as.numeric(gss100$goodhealth), na.rm=TRUE, conf =0.99)
  33. ```
  34. ```{r}
  35. mean(gss100$favorgrass, na.rm = TRUE)
  36. ciMean(as.numeric(gss100$favorgrass), na.rm=TRUE, conf =0.95)
  37. ciMean(as.numeric(gss100$favorgrass), na.rm=TRUE, conf =0.99)
  38. ```
  39. #HEALTH INDEPENDENT, GRASS DEPENDENT
  40. #REMEMBER TO USE YOUR DICHOTOMIZED VARIABLES
  41. #SO THIS WILL BE GOODHEALTH INDEPENDENT, FAVORGRASS DEPENDENT
  42. ```{r}
  43. goodhealth<-dplyr::filter(gss100, goodhealth=="TRUE")
  44. ciMean(as.numeric(goodhealth$favorgrass), na.rm=TRUE, conf =0.95)
  45. ciMean(as.numeric(goodhealth$favorgrass), na.rm=TRUE, conf =0.99)
  46. badhealth<-dplyr::filter(gss100, goodhealth=="FALSE")
  47. ciMean(as.numeric(badhealth$favorgrass), na.rm=TRUE, conf =0.95)
  48. ciMean(as.numeric(badhealth$favorgrass), na.rm=TRUE, conf =0.99)
  49. ```
  50.  
  51. #GRASS INDEPENDENT, HEALTH DEPENDENT
  52. #REMEMBER TO USE YOUR DICHOTOMIZED VARIABLES
  53. #SO THIS WILL BE FAVORGRASS INDEPENDENT, GOODHEALTH DEPENDENT
  54. ```{r}
  55. yesgrass<-dplyr::filter(gss100, favorgrass=="TRUE")
  56. ciMean(as.numeric(yesgrass$goodhealth), na.rm=TRUE, conf =0.95)
  57. ciMean(as.numeric(yesgrass$goodhealth), na.rm=TRUE, conf =0.99)
  58. nograss<-dplyr::filter(gss100, favorgrass=="FALSE")
  59. ciMean(as.numeric(nograss$goodhealth), na.rm=TRUE, conf =0.95)
  60. ciMean(as.numeric(nograss$goodhealth), na.rm=TRUE, conf =0.99)
  61. ```
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