CODEWITHSUNDEEP
rapache-sparkdplyrsparklyr
user60856839
user60856839

Reputation: 133

How to aggregate Data by 3 minutes timestamps in sparklyr?

I am using sparklyr for some quick analysis. I do have some issues in working with timestamps. I have two different dataframes: one with rows in 1-minute-interval and another with 3-minute-interval.

First dataset: (1-minute-interval)

id  timefrom    timeto  value
10  "2017-06-06 10:30:00"   "2017-06-06 10:31:00"   50
10  "2017-06-06 10:31:00"   "2017-06-06 10:32:00"   80
10  "2017-06-06 10:32:00"   "2017-06-06 10:33:00"   20
22  "2017-06-06 10:33:00"   "2017-06-06 10:34:00"   30
22  "2017-06-06 10:34:00"   "2017-06-06 10:35:00"   50
22  "2017-06-06 10:35:00"   "2017-06-06 10:36:00"   50

Second dataset: (3-minute-interval)

id  timefrom    timeto  value
10  "2017-06-06 10:30:00"   "2017-06-06 10:33:00"   30
22  "2017-06-06 10:33:00"   "2017-06-06 10:36:00"   67
32  "2017-06-06 10:36:00"   "2017-06-06 10:39:00"   28
14  "2017-06-06 10:39:00"   "2017-06-06 10:42:00"   30
27  "2017-06-06 10:42:00"   "2017-06-06 10:55:00"   90

To compare values of these 2 dataset I have to aggregate the first by 3 minutes and calculate the average of value. Furthermore I have to find the best fitting window from the second dataset.

The result should look something like this:

id  timefrom    timeto  value1  value2
10  "2017-06-06 10:30:00"   "2017-06-06 10:33:00"   30  50
22  "2017-06-06 10:33:00"   "2017-06-06 10:36:00"   67  43.3

Is it possible to achieve this only with sparklyr? I appreciate your help!

Upvotes: 2

Views: 466

Answers (1)

zero323
zero323

Reputation: 330343

Assuming your data is already parsed:

df1
# # Source:   table<df1> [?? x 4]
# # Database: spark_connection
#      id timefrom            timeto              value
#   <int> <dttm>              <dttm>              <int>
# 1    10 2017-06-06 08:30:00 2017-06-06 08:31:00    50
# 2    10 2017-06-06 08:31:00 2017-06-06 08:32:00    80
# 3    10 2017-06-06 08:32:00 2017-06-06 08:33:00    20
# 4    22 2017-06-06 08:33:00 2017-06-06 08:34:00    30
# 5    22 2017-06-06 08:34:00 2017-06-06 08:35:00    50
# 6    22 2017-06-06 08:35:00 2017-06-06 08:36:00    50

df2
# # Source:   table<df2> [?? x 4]
# # Database: spark_connection
#      id timefrom            timeto              value
#   <int> <dttm>              <dttm>              <int>
# 1    10 2017-06-06 08:30:00 2017-06-06 08:33:00    30
# 2    22 2017-06-06 08:33:00 2017-06-06 08:36:00    67
# 3    32 2017-06-06 08:36:00 2017-06-06 08:39:00    28
# 4    14 2017-06-06 08:39:00 2017-06-06 08:42:00    30
# 5    27 2017-06-06 08:42:00 2017-06-06 08:55:00    90

you can use window function:

exprs <- list(
  "id", "value as value2",
  # window generates structure struct<start: timestamp, end: timestamp>
  # we use dot syntax to access nested fields
  "window.start as timefrom", "window.end as timeto")

df1_agg <- df1 %>% 
  mutate(window = window(timefrom, "3 minutes")) %>% 
  group_by(id, window) %>% 
  summarise(value = avg(value)) %>%
  # As far as I am aware there is no sparklyr syntax 
  # for accessing struct fields, so we'll use simple SQL expression
  spark_dataframe() %>% 
  invoke("selectExpr", exprs) %>% 
  sdf_register() %>%
  print()

# Source:   table<sparklyr_tmp_472ee8ba244> [?? x 4]
# Database: spark_connection
     id value2 timefrom            timeto             
  <int>  <dbl> <dttm>              <dttm>             
1    22   43.3 2017-06-06 08:33:00 2017-06-06 08:36:00
2    10   50.0 2017-06-06 08:30:00 2017-06-06 08:33:00

Then you can just by id and timestamp columns:

df2 %>% inner_join(df1_agg, by = c("id", "timefrom", "timeto"))
# # Source:   lazy query [?? x 5]
# # Database: spark_connection
#      id timefrom            timeto              value value2
#   <int> <dttm>              <dttm>              <int>  <dbl>
# 1    10 2017-06-06 08:30:00 2017-06-06 08:33:00    30   50.0
# 2    22 2017-06-06 08:33:00 2017-06-06 08:36:00    67   43.3

Upvotes: 1

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