Nothing
install.packages("dmtools")
# dev-version
devtools::install_github("KonstantinRyabov/dmtools")
library(dmtools)
For checking the dataset from EDC in clinical trials. Notice, your dataset should have a postfix( _V1 ) or a prefix( V1_ ) in the names of variables. Column names should be unique.
date()
- create object date to check dates in the datasetlab()
- create object lab to check lab reference rangeshort()
- create object short to reshape the dataset in a tidy
view.check()
- check objectsget_result()
- get the final result of objectchoose_test()
- filter the final result of check()
rename_dataset()
- rename the datasetFor example, you want to check laboratory values, you need to create the excel table like in the example.
|
*column names without prefix or postfix
lab reference ranges AGELOW AGEHIGH SEX LBTEST LBORRES LBNRIND LBORNRLO LBORNRHI 18 45 f|m Glucose GLUC GLUC_IND 3.9 5.9 18 45 m Aspartate transaminase AST AST_IND 0 42 18 45 f Aspartate transaminase AST AST_IND 0 39 dataset ID AGE SEX GLUC_V1 GLUC_IND_V1 AST_V2 AST_IND_V2 01 19 f 5.5 norm 30 norm 02 20 m 4.1 NA 48 norm 03 22 m 9.7 norm 31 norm# "norm" and "no" it is an example, necessary variable for the estimate, get from the dataset
refs <- system.file("labs_refer.xlsx", package = "dmtools")
obj_lab <- lab(refs, ID, AGE, SEX, "norm", "no")
obj_lab <- obj_lab %>% check(df)
# ok - analysis, which has a correct estimate of the result
obj_lab %>% choose_test("ok")
#> ID AGE SEX LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 01 19 f Glucose GLUC _V1 3.9 5.9 5.5
#> 2 01 19 f Aspartate transaminase AST _V2 0.0 39.0 30
#> 3 03 22 m Aspartate transaminase AST _V2 0.0 42.0 31
#> LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1 norm 5.5 norm
#> 2 norm 30.0 norm
#> 3 norm 31.0 norm
# mis - analysis, which has an incorrect estimate of the result
obj_lab %>% choose_test("mis")
#> ID AGE SEX LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 02 20 m Aspartate transaminase AST _V2 0.0 42.0 48
#> 2 03 22 m Glucose GLUC _V1 3.9 5.9 9.7
#> LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1 norm 48.0 no
#> 2 norm 9.7 no
# skip - analysis, which has an empty value of the estimate
obj_lab %>% choose_test("skip")
#> ID AGE SEX LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 02 20 m Glucose GLUC _V1 3.9 5.9 4.1 <NA>
#> RES_TYPE_NUM IND_EXPECTED
#> 1 4.1 norm
strange_dataset
ID
AGE
SEX
V1_GLUC
V1_GLUC_IND
V2_AST
V2_AST_IND
01
19
f
5,5
norm
< 5
norm
02
20
m
4,1
NA
48
norm
03
22
m
9,7
norm
31
norm
# dmtools can work with the dataset as strange_df
# parameter is_post has value FALSE because a dataset has a prefix( V1_ ) in the names of variables
obj_lab <- lab(refs, ID, AGE, SEX, "norm", "no", is_post = F)
obj_lab <- obj_lab %>% check(strange_df)
# dmtools can understand the value with a comma like 6,6
obj_lab %>% choose_test("ok")
#> ID AGE SEX LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 01 19 f Glucose GLUC V1_ 3.9 5.9 5,5
#> 2 03 22 m Aspartate transaminase AST V2_ 0.0 42.0 31
#> LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1 norm 5.5 norm
#> 2 norm 31.0 norm
# Notice, if dmtools can't understand the value of lab_vals e.g. < 5, it puts Inf in the RES_TYPE_NUM
obj_lab %>% choose_test("mis")
#> ID AGE SEX LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 01 19 f Aspartate transaminase AST V2_ 0.0 39.0 < 5
#> 2 02 20 m Aspartate transaminase AST V2_ 0.0 42.0 48
#> 3 03 22 m Glucose GLUC V1_ 3.9 5.9 9,7
#> LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1 norm Inf no
#> 2 norm 48.0 no
#> 3 norm 9.7 no
obj_lab %>% choose_test("skip")
#> ID AGE SEX LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 02 20 m Glucose GLUC V1_ 3.9 5.9 4,1 <NA>
#> RES_TYPE_NUM IND_EXPECTED
#> 1 4.1 norm
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