1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 | getwd() setwd("C:/Users/aseandi/My Library/COURSERA/Passion-Driven Statistics/datasets") getwd() # fileurlcsv <- "http://spark-public.s3.amazonaws.com/pdstatistics/data_sets/nesarc_pds.csv" # download.file(fileurlcsv, destfile="./nesarc_pds.csv") # list.files(".") # dateDownloaded <-date() # dateDownloaded nesarc <- read.csv("./nesarc_pds.csv") str(nesarc) # summary(nesarc) # sapply(nesarc[1, ], class) # sum(is.na(nesarc)) # table(is.na(nesarc)) library(Hmisc) library(RColorBrewer) library(reshape2) library(stringr) library(plyr) # # -------------------------------------------------------------------------------------------------------- # # # DEFINING THE VARIABLES UNDER STUDY # # # # study variables: Sex, average daily quantity of alcohol consumed and cigarettes smoked in past 12 months, and use of sedatives, tranquilizers, cannabis, opioids, amphetamines, cocaine, heroine, hallucinogens, and inhalants # codes: Sex, s2aq8b, s3aq3c1, S3bq1a1, S3bq1a2, S3bq1a3, S3bq1a4, S3bq1a5, S3bq1a6, S3bq1a7, S3bq1a8, S3bq1a9a #number of alcohol consumed in past 12 months table(is.na(nesarc$S2AQ8B)) table(nesarc$S2AQ8B) sum(table(nesarc$S2AQ8B)) #usual quantity of cigarettes smoked table(is.na(nesarc$S3AQ3C1)) table(nesarc$S3AQ3C1) sum(table(nesarc$S3AQ3C1)) # # -------------------------------------------------------------------------------------------------------- # # # CREATE NEW DATAFRAME WITH FEWER VARIABLES # # # mddold <- data.frame(nesarc$MAJORDEPLIFE, nesarc$SEX, nesarc$S3AQ3B1, nesarc$S3AQ3C1, nesarc$S2AQ8A, nesarc$S2AQ8B, nesarc$S3BQ1A1, nesarc$S3BQ1A2, nesarc$S3BQ1A3, nesarc$S3BQ1A4, nesarc$S3BQ1A5, nesarc$S3BQ1A6, nesarc$S3BQ1A7, nesarc$S3BQ1A8, nesarc$S3BQ1A9A) str(mddold) names(mddold) <- c("mdd", "sex", "smokefreq", "smoke", "drinkfreq", "alcohol", "sedatives", "tranquilizers", "cannabis", "opioids", "amphetamines", "cocaine", "heroine", "hallucinogens", "inhalants") str(mddold) # # -------------------------------------------------------------------------------------------------------- # # # DATA MUNGING # # # # for the following variables: "sex", "sedatives", "tranquilizers", "cannabis", "opioids", "amphetamines", "cocaine", "heroine", "hallucinogens", "inhalants" # change the binomial setting [1,2] --> [1,0] # change [9] to [NA] # # # DATA MUNGING # # # # for the following variables: "alcohol", "smoke", "smokefreq", "drinkfreq" # change [99] for smoke quantity, drinks consumed to [NA] # change [99] for drinkfreq to [NA] # change [9] for smokefreq to [NA] mddold$sex[mddold$sex==2]=0 # sex=0 female; sex=1 male mddold$sedatives[mddold$sedatives==2]=0 mddold$tranquilizers[mddold$tranquilizers==2]=0 mddold$cannabis[mddold$cannabis==2]=0 mddold$opioids[mddold$opioids==2]=0 mddold$amphetamines[mddold$amphetamines==2]=0 mddold$cocaine[mddold$cocaine==2]=0 mddold$heroine[mddold$heroine==2]=0 mddold$hallucinogens[mddold$hallucinogens==2]=0 mddold$inhalants[mddold$inhalants==2]=0 mddold$sedatives[mddold$sedatives==9]=NA mddold$tranquilizers[mddold$tranquilizers==9]=NA mddold$cannabis[mddold$cannabis==9]=NA mddold$opioids[mddold$opioids==9]=NA mddold$amphetamines[mddold$amphetamines==9]=NA mddold$cocaine[mddold$cocaine==9]=NA mddold$heroine[mddold$heroine==9]=NA mddold$hallucinogens[mddold$hallucinogens==9]=NA mddold$inhalants[mddold$inhalants==9]=NA mddold$alcohol[mddold$alcohol==99]=NA mddold$smoke[mddold$smoke==99]=NA mddold$smokefreq[mddold$smokefreq==9]=NA mddold$drinkfreq[mddold$drinkfreq==99]=NA # # # ADD NEW VARIABLES: PACKYEARS and DRINKYEARS # # # mddold$smokefreqyr[mddold$smokefreq==1]= 364 mddold$smokefreqyr[mddold$smokefreq==2]= 286 mddold$smokefreqyr[mddold$smokefreq==3]= 182 mddold$smokefreqyr[mddold$smokefreq==4]= 78 mddold$smokefreqyr[mddold$smokefreq==5]= 30 mddold$smokefreqyr[mddold$smokefreq==6]= 1 mddold$smokefreqyr[mddold$smokefreq==NA]= NA mddold$alcoholfreqyr[mddold$drinkfreq==1]=364 mddold$alcoholfreqyr[mddold$drinkfreq==2]=286 mddold$alcoholfreqyr[mddold$drinkfreq==3]=182 mddold$alcoholfreqyr[mddold$drinkfreq==4]=104 mddold$alcoholfreqyr[mddold$drinkfreq==5]=52 mddold$alcoholfreqyr[mddold$drinkfreq==6]=30 mddold$alcoholfreqyr[mddold$drinkfreq==7]=12 mddold$alcoholfreqyr[mddold$drinkfreq==8]=9 mddold$alcoholfreqyr[mddold$drinkfreq==9]=4.5 mddold$alcoholfreqyr[mddold$drinkfreq==10]=1.5 mddold$alcoholfreqyr[mddold$drinkfreq==NA]=NA mddold$cigsperyear <- (mddold$smokefreqyr * mddold$smoke) mddold$swigsperyear <- (mddold$alcoholfreqyr * mddold$alcohol) summary(mddold$smokefreqyr) summary(mddold$alcoholfreqyr) summary(mddold$cigsperyear) summary(mddold$swigsperyear) table(mddold$smokefreqyr) table(mddold$alcoholfreqyr) table(mddold$cigsperyear) table(mddold$swigsperyear) mddnew <- mddold str(mddnew) # # -------------------------------------------------------------------------------------------------------- # FREQUENCY TABLES # ("mdd", "sex", "drinkyears", "packyears", "sedatives", "tranquilizers", "cannabis", "opioids", "amphetamines", "cocaine", "heroine", "hallucinogens", "inhalants") library(gmodels) CrossTable(mddnew$mdd, mddnew$sex) CrossTable(mddnew$mdd, mddnew$sedatives) CrossTable(mddnew$mdd, mddnew$tranquilizers) CrossTable(mddnew$mdd, mddnew$cannabis) CrossTable(mddnew$mdd, mddnew$opioids) CrossTable(mddnew$mdd, mddnew$amphetamines) CrossTable(mddnew$mdd, mddnew$cocaine) CrossTable(mddnew$mdd, mddnew$heroine) CrossTable(mddnew$mdd, mddnew$hallucinogens) CrossTable(mddnew$mdd, mddnew$inhalants) library(Hmisc) mddnew$g4cigs <- cut2(mddnew$cigsperyear, g = 4) CrossTable(mddnew$mdd, mddnew$g4cigs) # intervals # [ 1, 2002) | [2002, 4550) | [4550, 7644) | [7644,35672] mddnew$g4swigs <- cut2(mddnew$swigsperyear, g = 4) CrossTable(mddnew$mdd, mddnew$g4swigs) # intervals # [ 1.5, 10.5) | [ 10.5, 63.0) | [ 63.0, 360.0) | [360.0,35672.0] # # -------------------------------------------------------------------------------------------------------- # # # MULTIVARIATE GRAPHS FOR EXPLORATORY ANALYSIS # # # library(RColorBrewer) mypar <- function(a = 1, b = 1, brewer.n = 4, brewer.name = "RdYlGn", ...) { par(mar = c(2.5, 2.5, 1.6, 1.1), mgp = c(1.5, 0.5, 0)) par(mfrow = c(a, b), ...) palette(brewer.pal(brewer.n, brewer.name)) } # create table for mdd vs cigs category mddvg4cigs = table(mddnew$mdd,mddnew$g4cigs) # To get the graph we want, we need to exchange the rows in this table mddvg4cigs = rbind(mddvg4cigs[2,],mddvg4cigs[1,]) # and turn them into percents (dividing by the num. of observations # in each cigs category) mddvg4cigs[1,]=mddvg4cigs[1,]/table(mddnew$g4cigs) mddvg4cigs[2,]=mddvg4cigs[2,]/table(mddnew$g4cigs) str(mddvg4cigs) # create table for mdd vs alcohol category mddvg4swigs = table(mddnew$mdd,mddnew$g4swigs) # To get the graph we want, we need to exchange the rows in this table mddvg4swigs = rbind(mddvg4swigs[2,],mddvg4swigs[1,]) # and turn them into percents (dividing by the num. of observations # in each swigs category) mddvg4swigs[1,]=mddvg4swigs[1,]/table(mddnew$g4swigs) mddvg4swigs[2,]=mddvg4swigs[2,]/table(mddnew$g4swigs) str(mddvg4swigs) # create table for mdd vs sex mddvsex = table(mddnew$mdd,mddnew$sex) # To get the graph we want, we need to exchange the rows in this table mddvsex = rbind(mddvsex[2,],mddvsex[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans sex) mddvsex[1,]=mddvsex[1,]/table(mddnew$sex) mddvsex[2,]=mddvsex[2,]/table(mddnew$sex) str(mddvsex) # create table for mdd vs sedatives mddvsedatives = table(mddnew$mdd,mddnew$sedatives) # To get the graph we want, we need to exchange the rows in this table mddvsedatives = rbind(mddvsedatives[2,],mddvsedatives[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans sedatives) mddvsedatives[1,]=mddvsedatives[1,]/table(mddnew$sedatives) mddvsedatives[2,]=mddvsedatives[2,]/table(mddnew$sedatives) str(mddvsedatives) # create table for mdd vs cannabis mddvcannabis = table(mddnew$mdd,mddnew$cannabis) # To get the graph we want, we need to exchange the rows in this table mddvcannabis = rbind(mddvcannabis[2,],mddvcannabis[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans cannabis) mddvcannabis[1,]=mddvcannabis[1,]/table(mddnew$cannabis) mddvcannabis[2,]=mddvcannabis[2,]/table(mddnew$cannabis) str(mddvcannabis) # create table for mdd vs opioids mddvopioids = table(mddnew$mdd,mddnew$opioids) # To get the graph we want, we need to exchange the rows in this table mddvopioids = rbind(mddvopioids[2,],mddvopioids[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans opioids) mddvopioids[1,]=mddvopioids[1,]/table(mddnew$opioids) mddvopioids[2,]=mddvopioids[2,]/table(mddnew$opioids) str(mddvopioids) # create table for mdd vs tranquilizers mddvtranquilizers = table(mddnew$mdd,mddnew$tranquilizers) # To get the graph we want, we need to exchange the rows in this table mddvtranquilizers = rbind(mddvtranquilizers[2,],mddvtranquilizers[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans tranquilizers) mddvtranquilizers[1,]=mddvtranquilizers[1,]/table(mddnew$tranquilizers) mddvtranquilizers[2,]=mddvtranquilizers[2,]/table(mddnew$tranquilizers) str(mddvtranquilizers) # create table for mdd vs amphetamines mddvamphetamines = table(mddnew$mdd,mddnew$amphetamines) # To get the graph we want, we need to exchange the rows in this table mddvamphetamines = rbind(mddvamphetamines[2,],mddvamphetamines[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans amphetamines) mddvamphetamines[1,]=mddvamphetamines[1,]/table(mddnew$amphetamines) mddvamphetamines[2,]=mddvamphetamines[2,]/table(mddnew$amphetamines) str(mddvamphetamines) # create table for mdd vs cocaine mddvcocaine = table(mddnew$mdd,mddnew$cocaine) # To get the graph we want, we need to exchange the rows in this table mddvcocaine = rbind(mddvcocaine[2,],mddvcocaine[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans cocaine) mddvcocaine[1,]=mddvcocaine[1,]/table(mddnew$cocaine) mddvcocaine[2,]=mddvcocaine[2,]/table(mddnew$cocaine) str(mddvcocaine) # create table for mdd vs heroine mddvheroine = table(mddnew$mdd,mddnew$heroine) # To get the graph we want, we need to exchange the rows in this table mddvheroine = rbind(mddvheroine[2,],mddvheroine[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans heroine) mddvheroine[1,]=mddvheroine[1,]/table(mddnew$heroine) mddvheroine[2,]=mddvheroine[2,]/table(mddnew$heroine) str(mddvheroine) # create table for mdd vs hallucinogens mddvhallucinogens = table(mddnew$mdd,mddnew$hallucinogens) # To get the graph we want, we need to exchange the rows in this table mddvhallucinogens = rbind(mddvhallucinogens[2,],mddvhallucinogens[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans hallucinogens) mddvhallucinogens[1,]=mddvhallucinogens[1,]/table(mddnew$hallucinogens) mddvhallucinogens[2,]=mddvhallucinogens[2,]/table(mddnew$hallucinogens) str(mddvhallucinogens) # create table for mdd vs inhalants mddvinhalants = table(mddnew$mdd,mddnew$inhalants) # To get the graph we want, we need to exchange the rows in this table mddvinhalants = rbind(mddvinhalants[2,],mddvinhalants[1,]) # and turn them into percents (dividing by the num. of observations # in either con or sans inhalants) mddvinhalants[1,]=mddvinhalants[1,]/table(mddnew$inhalants) mddvinhalants[2,]=mddvinhalants[2,]/table(mddnew$inhalants) str(mddvinhalants) mypar(mfrow = c(2,2)) # MDD diagnosis {RESPONSE} by Estimated Cigarette Use per Year {EXPLANATORY} among all Adults in the NESARC Study bp_mddvg4cigs <- barplot(mddvg4cigs[1,], col=unique(mddnew$g4cigs), xlab="cigarettes per year", ylab="diagnosed depression", cex.axis=0.8) # MDD diagnosis {RESPONSE} by Estimated Cigarette Use per Year {EXPLANATORY} among all Adults in the NESARC Study bp_mddvg4cigs <- barplot(mddvg4cigs, col=unique(mddnew$g4cigs), xlab="cigarettes per year", ylab="diagnosed depression", cex.axis=0.8) # MDD diagnosis {RESPONSE} by Estimated Alcohol Consumed per Year {EXPLANATORY} among all Adults in the NESARC Study bp_mddvg4swigs <- barplot(mddvg4swigs[1,], col=unique(mddnew$g4swigs), xlab="alcohol per year", ylab="diagnosed depression", cex.axis=0.8) # MDD diagnosis {RESPONSE} by Estimated Alcohol Consumed per Year {EXPLANATORY} among all Adults in the NESARC Study bp_mddvg4swigs <- barplot(mddvg4swigs, col=unique(mddnew$g4swigs), xlab="alcohol per year", ylab="diagnosed depression", cex.axis=0.8) # dev.copy2pdf(file="mdd_cigarettes_alcohol.pdf", height =8, width = 11) mypar(mfrow = c(2, 5)) # MDD diagnosis {RESPONSE} by Biological Sex {EXPLANATORY} among all Adults in the NESARC Study bp_mddvsex <- barplot(mddvsex[1,], col=unique(mddnew$sex), xlab="biological sex, 0 - female, 1 - male", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by Sedatives Use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvsedatives <- barplot(mddvsedatives[1,], col=unique(mddnew$sedatives), xlab="sedative use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by Cannabis Use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvcannabis <- barplot(mddvcannabis[1,], col=unique(mddnew$cannabis), xlab="cannabis use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by tranquilizers use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvtranquilizers <- barplot(mddvtranquilizers[1,], col=unique(mddnew$tranquilizers), xlab="tranquilizers use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by opioids use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvopioids <- barplot(mddvopioids[1,], col=unique(mddnew$opioids), xlab="opioids use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by cocaine use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvcocaine <- barplot(mddvcocaine[1,], col=unique(mddnew$cocaine), xlab="cocaine use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by amphetamines use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvamphetamines <- barplot(mddvamphetamines[1,], col=unique(mddnew$amphetamines), xlab="amphetamines use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by heroine use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvheroine <- barplot(mddvheroine[1,], col=unique(mddnew$heroine), xlab="heroine use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by hallucinogens use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvhallucinogens <- barplot(mddvhallucinogens[1,], col=unique(mddnew$hallucinogens), xlab="hallucinogens use", ylab="diagnosed depression") # MDD diagnosis {RESPONSE} by inhalants use {EXPLANATORY} among all Adults in the NESARC Study bp_mddvinhalants <- barplot(mddvinhalants[1,], col=unique(mddnew$inhalants), xlab="inhalants use", ylab="diagnosed depression") # dev.copy2pdf(file="mdd_sex_substances.pdf", height =8, width = 11) |
in any case, i think Google will eventually be able to crawl this R post, and hopefully other beginner programmers like me will find the code useful for their basic graphing.
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