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# If they haven't been loaded yet, load all the DataSHIELD libraries.
library(opal)
library(dsBaseClient)
library(dsStatsClient)
library(dsGraphicsClient)
library(dsModellingClient)
# build a new dataframe by login to the table "DASIM" which is included in three cloud based Opals
server <- c("study1", "study2", "study3")
url <- c("http://XXXXXX:8080")
table <- c("DASIM.DASIM1", "DASIM.DASIM2", "DASIM.DASIM3")
logindata <- data.frame(server,url,user="administrator",password="datashield_test&",table)
# login and assign the whole dataset
opals <- datashield.login(logins=logindata,assign=TRUE) |
Questions
Use functions provided in DataSHIELD packages to solve the following problems:
Subsets and Statistics
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Calculate the mean and the variance of the continuous variable BMI of obese males.
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Check the levels for the variables PM_BMI_CATEGORICAL and GENDER using ds.levels(). |
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BMI is categorised in three levels: 1=normal, 2=overweight, 3=obese and gender is categorised in two levels: 0=male, 1=female. |
Assign and Plots
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- Find the quantile mean and plot a histogram of pooled data for the exponent and for the logarithm of LAB_HDL measurement.
2-dimensional contingency tables
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- What percentage of females (pooled data) are diabetics?
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- What percentage of males in each study separately have stroke (DIS_CVA)?
Generalized Linear Models
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- Apply a generalised linear model that predicts the level of glucose between males and females. What is the predicted average level of glucose for males? What is this value for females?
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- Apply a GLM to predict the level of glucose using gender and continuous
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- BMI. How much the level of glucose is increasing with the increase of bmi by one unit? What is the predicted glucose level of a female with bmi=22?
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| When you complete the questions, check your answers here. |
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