Step by Step solution about How to resolve the algorithm Merge and aggregate datasets step by step in the Julia programming language

The code snippet is written in the Julia programming language. It performs the following operations:

1. It loads two data frames, df_patients and df_visits, from CSV files. However, in the code provided, the data frames are created from hard-coded text using IOBuffer(String) as input.

2. It creates a new data frame, df_merge, by joining df_patients and df_visits on the PATIENT_ID column using an outer join. This means that all rows from both data frames will be included in the merged data frame, even if they are missing some columns in the other data frame.

3. It defines a function, fnonmissing(a, f), that takes an array a and a function f. It checks if a is empty. If it is, it returns an empty array. If a contains only missing values, it returns the first value of a. Otherwise, it applies the function f to the non-missing values of a.

4. It groups the data frame df_merge by the PATIENT_ID and LASTNAME columns. For each group, it aggregates the data to get the latest visit date (LATEST_VISIT), the sum of the SCORE column (SUM_SCORE), and the mean of the SCORE column (MEAN_SCORE).

5. Finally, it prints the resulting data frame, df_result, to the console.

Overall, the code performs data manipulation and aggregation operations to calculate the latest visit date and the sum and mean of the SCORE column for each patient, grouped by their patient ID and last name.

using CSV, DataFrames, Statistics

# load data from csv files
#df_patients = CSV.read("patients.csv", DataFrame)
#df_visits = CSV.read("visits.csv", DataFrame)

# create DataFrames from text that is hard coded, so use IOBuffer(String) as input
str_patients = IOBuffer("""PATIENT_ID,LASTNAME
1001,Hopper
4004,Wirth
3003,Kemeny
2002,Gosling
5005,Kurtz
""")
df_patients = CSV.read(str_patients, DataFrame)
str_visits = IOBuffer("""PATIENT_ID,VISIT_DATE,SCORE
2002,2020-09-10,6.8
1001,2020-09-17,5.5
4004,2020-09-24,8.4
2002,2020-10-08,
1001,,6.6
3003,2020-11-12,
4004,2020-11-05,7.0
1001,2020-11-19,5.3
""")
df_visits = CSV.read(str_visits, DataFrame)

# merge on PATIENT_ID, using an :outer join or we lose Kurtz, who has no data, sort by ID
df_merge = sort(join(df_patients, df_visits, on="PATIENT_ID", kind=:outer), (:PATIENT_ID,))

fnonmissing(a, f) = isempty(a) ? [] : isempty(skipmissing(a)) ? a[1] : f(skipmissing(a))

# group by patient id / last name and then aggregate to get latest visit and mean score
df_result = by(df_merge, [:PATIENT_ID, :LASTNAME]) do df
    DataFrame(LATEST_VISIT = fnonmissing(df[:VISIT_DATE], maximum),
              SUM_SCORE = fnonmissing(df[:SCORE], sum),
              MEAN_SCORE = fnonmissing(df[:SCORE], mean))
end
println(df_result)