The number of Wynton users over time
users_over_time(file = NULL, since = "2017-01-01")Arguments
Value
A tibble::tibble with columns date and total,
total the cumulative sum based on date occurances.
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
pathname <- system.file("exdata", "ldap_wynton_dates.txt", package = "wyntonquery")
signups <- users_over_time(pathname)
print(head(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2017-02-21 15
#> 2 2017-04-20 16
#> 3 2017-04-25 17
#> 4 2017-05-23 18
#> 5 2017-07-04 19
#> 6 2017-07-11 20
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2024-11-15 1389
#> 2 2024-11-15 1390
#> 3 2024-11-16 1391
#> 4 2024-11-19 1392
#> 5 2024-11-19 1393
#> 6 2024-11-19 1394
## Summarize by year and month
signups <- mutate(signups, year = format(date, "%Y"))
## Signups per calendar year
signups <- mutate(signups, month = format(date, "%m"))
signups <- group_by(signups, year)
signups_per_year <- count(signups, name = "change")
signups_end_of_year <- filter(signups, date == max(date), total == max(total))
signups_per_year <- left_join(signups_per_year, signups_end_of_year)
#> Joining with `by = join_by(year)`
signups_per_year <- select(signups_per_year, year, change, total, per = date)
print(signups_per_year, n = Inf)
#> # A tibble: 8 × 4
#> # Groups: year [8]
#> year change total per
#> <chr> <int> <int> <date>
#> 1 2017 27 41 2017-12-12
#> 2 2018 33 74 2018-11-30
#> 3 2019 112 186 2019-12-18
#> 4 2020 137 323 2020-12-22
#> 5 2021 142 465 2021-12-18
#> 6 2022 212 677 2022-12-23
#> 7 2023 256 933 2023-12-27
#> 8 2024 461 1394 2024-11-19
## Signups per calendar month
signups <- group_by(signups, year, month)
signups_per_month <- count(signups, name = "change")
signups_end_of_month <- filter(signups, date == max(date), total == max(total))
signups_per_month <- left_join(signups_per_month, signups_end_of_month)
#> Joining with `by = join_by(year, month)`
signups_per_month <- select(signups_per_month, year, month, change, total, per = date)
print(signups_per_month, n = Inf)
#> # A tibble: 90 × 5
#> # Groups: year, month [90]
#> year month change total per
#> <chr> <chr> <int> <int> <date>
#> 1 2017 02 1 15 2017-02-21
#> 2 2017 04 2 17 2017-04-25
#> 3 2017 05 1 18 2017-05-23
#> 4 2017 07 3 21 2017-07-19
#> 5 2017 08 2 23 2017-08-11
#> 6 2017 09 1 24 2017-09-20
#> 7 2017 10 6 30 2017-10-27
#> 8 2017 11 9 39 2017-11-30
#> 9 2017 12 2 41 2017-12-12
#> 10 2018 01 1 42 2018-01-16
#> 11 2018 02 6 48 2018-02-26
#> 12 2018 03 3 51 2018-03-29
#> 13 2018 05 3 54 2018-05-14
#> 14 2018 06 1 55 2018-06-12
#> 15 2018 07 3 58 2018-07-20
#> 16 2018 08 6 64 2018-08-24
#> 17 2018 09 3 67 2018-09-07
#> 18 2018 10 2 69 2018-10-12
#> 19 2018 11 5 74 2018-11-30
#> 20 2019 01 4 78 2019-01-17
#> 21 2019 02 9 87 2019-02-21
#> 22 2019 03 17 104 2019-03-22
#> 23 2019 04 13 117 2019-04-30
#> 24 2019 05 8 125 2019-05-28
#> 25 2019 06 7 132 2019-06-30
#> 26 2019 07 7 139 2019-07-29
#> 27 2019 08 7 146 2019-08-31
#> 28 2019 09 19 165 2019-09-28
#> 29 2019 10 8 173 2019-10-31
#> 30 2019 11 6 179 2019-11-26
#> 31 2019 12 7 186 2019-12-18
#> 32 2020 01 7 193 2020-01-29
#> 33 2020 02 11 204 2020-02-29
#> 34 2020 03 9 213 2020-03-25
#> 35 2020 04 10 223 2020-04-30
#> 36 2020 05 13 236 2020-05-29
#> 37 2020 06 3 239 2020-06-19
#> 38 2020 07 11 250 2020-07-29
#> 39 2020 08 21 271 2020-08-31
#> 40 2020 09 17 288 2020-09-29
#> 41 2020 10 13 301 2020-10-30
#> 42 2020 11 15 316 2020-11-19
#> 43 2020 12 7 323 2020-12-22
#> 44 2021 01 9 332 2021-01-28
#> 45 2021 02 12 344 2021-02-26
#> 46 2021 03 11 355 2021-03-25
#> 47 2021 04 8 363 2021-04-27
#> 48 2021 05 6 369 2021-05-21
#> 49 2021 06 12 381 2021-06-30
#> 50 2021 07 6 387 2021-07-27
#> 51 2021 08 16 403 2021-08-30
#> 52 2021 09 22 425 2021-09-30
#> 53 2021 10 15 440 2021-10-27
#> 54 2021 11 13 453 2021-11-25
#> 55 2021 12 12 465 2021-12-18
#> 56 2022 01 20 485 2022-01-30
#> 57 2022 02 13 498 2022-02-26
#> 58 2022 03 26 524 2022-03-31
#> 59 2022 04 11 535 2022-04-27
#> 60 2022 05 11 546 2022-05-27
#> 61 2022 06 11 557 2022-06-25
#> 62 2022 07 17 574 2022-07-27
#> 63 2022 08 21 595 2022-08-31
#> 64 2022 09 37 632 2022-09-24
#> 65 2022 10 19 651 2022-10-26
#> 66 2022 11 19 670 2022-11-29
#> 67 2022 12 7 677 2022-12-23
#> 68 2023 01 23 700 2023-01-31
#> 69 2023 02 15 715 2023-02-28
#> 70 2023 03 17 732 2023-03-24
#> 71 2023 04 17 749 2023-04-28
#> 72 2023 05 12 761 2023-05-10
#> 73 2023 06 20 781 2023-06-30
#> 74 2023 07 22 803 2023-07-29
#> 75 2023 08 21 824 2023-08-23
#> 76 2023 09 44 868 2023-09-30
#> 77 2023 10 20 888 2023-10-25
#> 78 2023 11 25 913 2023-11-29
#> 79 2023 12 20 933 2023-12-27
#> 80 2024 01 41 974 2024-01-31
#> 81 2024 02 42 1016 2024-02-29
#> 82 2024 03 35 1051 2024-03-29
#> 83 2024 04 45 1096 2024-04-27
#> 84 2024 05 28 1124 2024-05-31
#> 85 2024 06 35 1159 2024-06-28
#> 86 2024 07 42 1201 2024-07-31
#> 87 2024 08 36 1237 2024-08-31
#> 88 2024 09 77 1314 2024-09-28
#> 89 2024 10 56 1370 2024-10-31
#> 90 2024 11 24 1394 2024-11-19
if (require("ggplot2", quietly = TRUE)) {
gg <- ggplot(signups, aes(date, total)) + geom_line(linewidth = 2.0)
gg <- gg + xlab("") + ylab("Number of users")
gg <- gg + theme(text = element_text(size = 20))
print(gg)
}
