binning runtimes

Hello,

Suppose I have the dataset shown below. The amount of observations is too massive to get a nice geom_point and smoother on top. What I would like to do is to bin the data first. The data is indexed by Time (minutes from 1 to 120 i.e. two hours of System benchmarking).

Option 1) group the data by Time i.e. minute 1, minute 2, etc and within each group create bins of N consecutive observations and average them into one observation, the bins become the new data points to use for the geom_point plot. How can I do this? Shingle? how to do that?

Option 2) ?Another option is to again group by Time i.e. minute 1, minute 2, etc and within each group draw a random observation to be the representative for the corresponding bin. I could not clearly see how to use Random.

dfs <- subset(df, Partitioning == "Sharding")
head(dfs)

?Time Partitioning Workload Runtime
1 ? ?1 ? ? Sharding ? ?Query ? ?3301
2 ? ?1 ? ? Sharding ? ?Query ? ?3268
3 ? ?1 ? ? Sharding ? ?Query ? ?2878
4 ? ?1 ? ? Sharding ? ?Query ? ?2819
5 ? ?1 ? ? Sharding ? ?Query ? ?3310
6 ? ?1 ? ? Sharding ? ?Query ? ?3428

str(dfs)

'data.frame': ? 102384 obs. of ?4 variables:
?$ Time ? ? ? ?: int ?1 1 1 1 1 1 1 1 1 1 ...
?$ Partitioning: Factor w/ 2 levels "Replication",..: 2 2 2 2 2 2 2 2 2 2 ...
?$ Workload ? ?: Factor w/ 2 levels "Query","Refresh": 1 1 1 1 1 1 1 1 1 1 ...
?$ Runtime ? ? : int ?3301 3268 2878 2819 3310 3428 2837 2954 2902 2936 ...

Many thanks in advance,
Best regards,
Giovanni