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 8
#> 2 2017-04-25 9
#> 3 2017-05-23 10
#> 4 2017-07-04 11
#> 5 2017-08-10 12
#> 6 2017-10-23 13
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2026-09-29 928
#> 2 2026-09-30 929
#> 3 2026-10-01 930
#> 4 2026-10-02 931
#> 5 2026-10-02 932
#> 6 2026-10-03 933
## 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: 10 × 4
#> # Groups: year [10]
#> year change total per
#> <chr> <int> <int> <date>
#> 1 2017 12 19 2017-12-12
#> 2 2018 12 31 2018-11-30
#> 3 2019 57 88 2019-12-18
#> 4 2020 60 148 2020-12-16
#> 5 2021 69 217 2021-12-18
#> 6 2022 87 304 2022-12-06
#> 7 2023 94 398 2023-12-22
#> 8 2024 165 563 2024-12-28
#> 9 2025 190 753 2025-12-23
#> 10 2026 180 933 2026-10-03
## 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: 109 × 5
#> # Groups: year, month [109]
#> year month change total per
#> <chr> <chr> <int> <int> <date>
#> 1 2017 02 1 8 2017-02-21
#> 2 2017 04 1 9 2017-04-25
#> 3 2017 05 1 10 2017-05-23
#> 4 2017 07 1 11 2017-07-04
#> 5 2017 08 1 12 2017-08-10
#> 6 2017 10 1 13 2017-10-23
#> 7 2017 11 4 17 2017-11-21
#> 8 2017 12 2 19 2017-12-12
#> 9 2018 01 1 20 2018-01-26
#> 10 2018 02 2 22 2018-02-26
#> 11 2018 03 1 23 2018-03-22
#> 12 2018 05 1 24 2018-05-10
#> 13 2018 07 1 25 2018-07-18
#> 14 2018 08 2 27 2018-08-24
#> 15 2018 09 3 30 2018-09-07
#> 16 2018 11 1 31 2018-11-30
#> 17 2019 02 7 38 2019-02-21
#> 18 2019 03 15 53 2019-03-22
#> 19 2019 04 10 63 2019-04-30
#> 20 2019 05 5 68 2019-05-20
#> 21 2019 06 4 72 2019-06-30
#> 22 2019 07 1 73 2019-07-29
#> 23 2019 08 4 77 2019-08-26
#> 24 2019 09 1 78 2019-09-19
#> 25 2019 10 3 81 2019-10-31
#> 26 2019 11 5 86 2019-11-26
#> 27 2019 12 2 88 2019-12-18
#> 28 2020 01 6 94 2020-01-29
#> 29 2020 02 3 97 2020-02-18
#> 30 2020 03 5 102 2020-03-25
#> 31 2020 04 5 107 2020-04-20
#> 32 2020 05 6 113 2020-05-20
#> 33 2020 06 1 114 2020-06-02
#> 34 2020 07 9 123 2020-07-21
#> 35 2020 08 8 131 2020-08-31
#> 36 2020 09 4 135 2020-09-21
#> 37 2020 10 5 140 2020-10-30
#> 38 2020 11 6 146 2020-11-19
#> 39 2020 12 2 148 2020-12-16
#> 40 2021 01 5 153 2021-01-25
#> 41 2021 02 4 157 2021-02-26
#> 42 2021 03 4 161 2021-03-23
#> 43 2021 04 4 165 2021-04-27
#> 44 2021 05 3 168 2021-05-21
#> 45 2021 06 7 175 2021-06-30
#> 46 2021 07 2 177 2021-07-13
#> 47 2021 08 8 185 2021-08-30
#> 48 2021 09 10 195 2021-09-30
#> 49 2021 10 11 206 2021-10-27
#> 50 2021 11 4 210 2021-11-20
#> 51 2021 12 7 217 2021-12-18
#> 52 2022 01 8 225 2022-01-30
#> 53 2022 02 7 232 2022-02-26
#> 54 2022 03 8 240 2022-03-31
#> 55 2022 04 2 242 2022-04-07
#> 56 2022 05 6 248 2022-05-27
#> 57 2022 06 4 252 2022-06-29
#> 58 2022 07 4 256 2022-07-23
#> 59 2022 08 6 262 2022-08-31
#> 60 2022 09 22 284 2022-09-24
#> 61 2022 10 10 294 2022-10-25
#> 62 2022 11 9 303 2022-11-23
#> 63 2022 12 1 304 2022-12-06
#> 64 2023 01 7 311 2023-01-31
#> 65 2023 02 4 315 2023-02-24
#> 66 2023 03 6 321 2023-03-13
#> 67 2023 04 5 326 2023-04-26
#> 68 2023 05 6 332 2023-05-10
#> 69 2023 06 5 337 2023-06-30
#> 70 2023 07 7 344 2023-07-29
#> 71 2023 08 11 355 2023-08-29
#> 72 2023 09 24 379 2023-09-30
#> 73 2023 10 10 389 2023-10-24
#> 74 2023 11 7 396 2023-11-29
#> 75 2023 12 2 398 2023-12-22
#> 76 2024 01 15 413 2024-01-31
#> 77 2024 02 13 426 2024-02-29
#> 78 2024 03 6 432 2024-03-27
#> 79 2024 04 16 448 2024-04-24
#> 80 2024 05 12 460 2024-05-29
#> 81 2024 06 13 473 2024-06-28
#> 82 2024 07 8 481 2024-07-27
#> 83 2024 08 12 493 2024-08-31
#> 84 2024 09 32 525 2024-09-27
#> 85 2024 10 16 541 2024-10-31
#> 86 2024 11 5 546 2024-11-22
#> 87 2024 12 17 563 2024-12-28
#> 88 2025 01 14 577 2025-01-31
#> 89 2025 02 15 592 2025-02-28
#> 90 2025 03 5 597 2025-03-21
#> 91 2025 04 6 603 2025-04-29
#> 92 2025 05 12 615 2025-05-28
#> 93 2025 06 11 626 2025-06-21
#> 94 2025 07 9 635 2025-07-30
#> 95 2025 08 14 649 2025-08-29
#> 96 2025 09 34 683 2025-09-30
#> 97 2025 10 39 722 2025-10-30
#> 98 2025 11 20 742 2025-11-27
#> 99 2025 12 11 753 2025-12-23
#> 100 2026 01 29 782 2026-01-29
#> 101 2026 02 31 813 2026-02-24
#> 102 2026 03 18 831 2026-03-24
#> 103 2026 04 17 848 2026-04-23
#> 104 2026 05 7 855 2026-05-30
#> 105 2026 06 15 870 2026-06-26
#> 106 2026 07 21 891 2026-07-30
#> 107 2026 08 12 903 2026-08-28
#> 108 2026 09 26 929 2026-09-30
#> 109 2026 10 4 933 2026-10-03
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)
}
