SMHS DataSimulation

Scientific Methods for Health Sciences - Data Simulation

Importing observed data for exploratory analytics

Using the SOCR Health Evaluation and Linkage to Primary (HELP) Care Dataset we can extract some sample data (00_Tiny_SOCR_HELP_Data_Simmulation.csv).

# data_1 <- read.csv('C:\\Users\\Dinov\\Desktop\\00_Tiny_SOCR_HELP_Data_Simmulation.csv',as.is=T, header=T)
# data_1 = read.csv(file.choose( ))
# data_1 <- read.table('C:\\Users\\Dinov\\Desktop\\00_Tiny_SOCR_HELP_Data_Simmulation.csv', header=TRUE,   sep=",", row.names="ID")

attach(data_1)  
# to ensure all variables are accessible within R, e.g., using “age” instead of data_1$age

#  i2 maximum number of drinks (standard units) consumed per day (in the past 30 days range 0–184) see also i1
# treat randomization group (0=usual care, 1=HELP clinic)
# pcs SF-36 Physical Component Score (range 14-75)
# mcs SF-36 Mental Component Score(range 7-62)
# cesd Center for Epidemiologic Studies Depression scale (range 0–60)
#  indtot Inventory of Drug Use Con-sequences (InDUC) total score (range 4–45)
# pss_fr perceived social supports (friends, range 0–14) see also dayslink
# drugrisk Risk-Assessment Battery(RAB) drug risk score (range0–21)
# satreat any BSAS substance abuse treatment at baseline (0=no,1=yes)

ID	i2	age	treat	homeless	pcs	mcs	cesd	indtot	pss_fr	drugrisk	sexrisk	satreat	female	substance	racegrp
1	0	25	0	0	49	7	46	37	0	1	6	0	0	cocaine	black
2	18	31	0	0	48	34	17	48	0	0	11	0	0	alcohol	white
3	39	36	0	0	76	9	33	41	12	19	4	0	0	heroin	black
…
100	81	22	0	0	37	17	19	30	3	0	10	0	0	alcohol	other

Testing section

summary(data_1)

x.norm <- rnorm(n=200, m=10, sd=20)
hist(x.norm, main="N(10,20) Histogram")
hist(x.norm, main="N(10,20) Histogram")
mean(data_1$age)
sd(data_1$age)

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• SOCR Home page: http://www.socr.umich.edu

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