#> # ℹ 313 more rows # Total each year (.by is set to "year" now) m4_daily %>% group_by ( id ) %>% summarise_by_time (. type = "ceiling" ) %>% # Shift to the last day of the month mutate (date = date %-time% "1 day" ) #>. #> # ℹ 313 more rows # Last value in each month (day is first day of next month with ceiling option) m4_daily %>% group_by ( id ) %>% summarise_by_time (. by = "month", # Setup for monthly aggregation # Summarization value = first ( value ) ) #> # A tibble: 323 × 3 #> # Groups: id #> id date value #> #> -07-01 2076. #> `summarise()` regrouping output by 'cut' (override with `.groups` argument)Ĭreated on by the reprex package (v0.3.# Libraries library ( timetk ) library ( dplyr ) # First value in each month m4_daily %>% group_by ( id ) %>% summarise_by_time (. set.seed(1234)ĭsmall I1 SI2 VVS2 VS1 VVS1 VS2 SI1 IF In those cases, summarize() generates one new row per value generated. Second, in some cases a function simply returns a vector as output. #> # … with 2 more variables: kurtosis, se #> cut vars n mean sd median trimmed mad min max range skew #> `summarise()` ungrouping output (override with `.groups` argument) #> vars n mean sd median trimmed mad min max range skew If we use such a function in summarize(), it generates a data-frame column, which we can turn into separate columns via unpack(). In recent versions of the tidyverse, this is possible.įirst, in the example you provided, the function returns a one-row data frame. We can write own summary function which returns a list: fun cut n min median mean sd max So we are handling it by using na.rmTRUE functon as shown in the code. #> cut vars n mean sd median trimmed mad min max range skew kurtosis se Solution based on the purrr ( purrrlyr since 2017) package: library(ggplot2)īy_slice(~ describe(.x$price). #> cut n mean sd median trimmed mad min max range skew kurtosis se With dplyr >= 0.2 we can use do function for this: library(ggplot2)
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