Description:

These SAS code snippets showcase techniques to eliminate duplicate observations in a dataset using different methods: nodupkey and noduprec.

nodupkey:

The nodupkey option is used in the proc sort statement to remove duplicate observations based on specific variables. The dataset "ae" is sorted by the variables "subjid," "term," and "stdtc" using the by statement. Then, another proc sort statement is used with the nodupkey option to create a new dataset called "nodupkey," eliminating duplicate observations based on the "subjid" and "term" variables.

noduprec:

The noduprec option is used in the proc sort statement to remove duplicate observations by considering all variables in the dataset. The dataset "ae1" is sorted by _all_, which includes all variables in the dataset. The noduprec option is used to create a new dataset called "x," eliminating duplicate observations based on all variables.

These SAS code snippets demonstrate how to handle duplicate observations in a dataset by sorting the data and removing duplicates based on specific variables or considering all variables. The resulting datasets "nodupkey" and "x" will contain unique observations based on the defined criteria.

ae<-tribble(
~subjid,~term,~stdtc,
"1001","Headache","2010-01-05",
"1001","Headache","2010-01-08",
"1001","Nausea","2010-01-08",
"1002","Headache","2010-02-15",
)

ae1<-tribble(
~subjid,~term,~stdtc,
"1001","Headache","2010-01-05",
"1001","Headache","2010-01-08",
"1001","Nausea","2010-01-08",
"1002","Headache","2010-02-15",
"1002","Headache","2010-02-15",
)

#---------------------------------------
#duplicate based on key variables (nodupkey)
#---------------------------------------
nodupkey<-ae %>%   
  arrange(subjid,term,stdtc) %>%   
  group_by(subjid,term) %>%   
  slice(1)

#---------------------------------------
#duplicate record (noduprec/nodup)
#---------------------------------------

noduprec<-ae1 %>%   
  distinct()

Description:

These R Tidyverse code snippets demonstrate techniques to eliminate duplicate observations in a dataset using different approaches.

nodupkey:

In the first code snippet, the dataset "ae" is processed using the %>% pipe operator. The data is arranged by the variables "subjid," "term," and "stdtc" using the arrange() function. Then, the data is grouped by "subjid" and "term" using the group_by() function. Finally, the slice(1) function is applied to keep only the first observation within each group, effectively removing duplicates based on the key variables. The resulting dataset contains unique observations based on the defined key variables.

noduprec/nodup:

In the second code snippet, the dataset "ae1" is processed. The distinct() function is applied to the dataset, which removes duplicate records based on all variables. The resulting dataset contains unique observations without considering any specific key variables.

These R Tidyverse code snippets demonstrate how to handle duplicate observations in a dataset by arranging, grouping, and selecting unique observations based on specific key variables or considering all variables. The resulting datasets will contain the unique observations based on the defined criteria.