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 13
#> 2 2017-04-25 14
#> 3 2017-05-23 15
#> 4 2017-07-04 16
#> 5 2017-08-10 17
#> 6 2017-08-11 18
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2025-08-26 1292
#> 2 2025-08-26 1293
#> 3 2025-08-26 1294
#> 4 2025-08-26 1295
#> 5 2025-08-27 1296
#> 6 2025-08-27 1297
## 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: 9 × 4
#> # Groups: year [9]
#> year change total per
#> <chr> <int> <int> <date>
#> 1 2017 17 29 2017-12-05
#> 2 2018 20 49 2018-11-30
#> 3 2019 85 134 2019-12-18
#> 4 2020 100 234 2020-12-22
#> 5 2021 116 350 2021-12-18
#> 6 2022 153 503 2022-12-21
#> 7 2023 180 683 2023-12-27
#> 8 2024 306 989 2024-12-28
#> 9 2025 308 1297 2025-08-27
## 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: 98 × 5
#> # Groups: year, month [98]
#> year month change total per
#> <chr> <chr> <int> <int> <date>
#> 1 2017 02 1 13 2017-02-21
#> 2 2017 04 1 14 2017-04-25
#> 3 2017 05 1 15 2017-05-23
#> 4 2017 07 1 16 2017-07-04
#> 5 2017 08 2 18 2017-08-11
#> 6 2017 09 2 20 2017-09-22
#> 7 2017 10 2 22 2017-10-24
#> 8 2017 11 6 28 2017-11-21
#> 9 2017 12 1 29 2017-12-05
#> 10 2018 01 1 30 2018-01-16
#> 11 2018 02 4 34 2018-02-26
#> 12 2018 03 2 36 2018-03-29
#> 13 2018 05 2 38 2018-05-14
#> 14 2018 07 1 39 2018-07-18
#> 15 2018 08 4 43 2018-08-30
#> 16 2018 09 3 46 2018-09-07
#> 17 2018 10 1 47 2018-10-12
#> 18 2018 11 2 49 2018-11-30
#> 19 2019 01 1 50 2019-01-17
#> 20 2019 02 11 61 2019-02-21
#> 21 2019 03 14 75 2019-03-22
#> 22 2019 04 9 84 2019-04-30
#> 23 2019 05 6 90 2019-05-20
#> 24 2019 06 6 96 2019-06-30
#> 25 2019 07 7 103 2019-07-29
#> 26 2019 08 4 107 2019-08-31
#> 27 2019 09 14 121 2019-09-28
#> 28 2019 10 5 126 2019-10-31
#> 29 2019 11 4 130 2019-11-26
#> 30 2019 12 4 134 2019-12-18
#> 31 2020 01 6 140 2020-01-29
#> 32 2020 02 6 146 2020-02-29
#> 33 2020 03 8 154 2020-03-25
#> 34 2020 04 6 160 2020-04-20
#> 35 2020 05 11 171 2020-05-29
#> 36 2020 06 2 173 2020-06-19
#> 37 2020 07 12 185 2020-07-29
#> 38 2020 08 15 200 2020-08-31
#> 39 2020 09 11 211 2020-09-21
#> 40 2020 10 10 221 2020-10-30
#> 41 2020 11 9 230 2020-11-19
#> 42 2020 12 4 234 2020-12-22
#> 43 2021 01 10 244 2021-01-28
#> 44 2021 02 10 254 2021-02-26
#> 45 2021 03 10 264 2021-03-25
#> 46 2021 04 6 270 2021-04-27
#> 47 2021 05 7 277 2021-05-21
#> 48 2021 06 8 285 2021-06-30
#> 49 2021 07 5 290 2021-07-27
#> 50 2021 08 11 301 2021-08-30
#> 51 2021 09 16 317 2021-09-30
#> 52 2021 10 13 330 2021-10-27
#> 53 2021 11 11 341 2021-11-25
#> 54 2021 12 9 350 2021-12-18
#> 55 2022 01 11 361 2022-01-30
#> 56 2022 02 10 371 2022-02-26
#> 57 2022 03 18 389 2022-03-31
#> 58 2022 04 9 398 2022-04-27
#> 59 2022 05 10 408 2022-05-27
#> 60 2022 06 8 416 2022-06-25
#> 61 2022 07 9 425 2022-07-27
#> 62 2022 08 14 439 2022-08-31
#> 63 2022 09 29 468 2022-09-24
#> 64 2022 10 17 485 2022-10-26
#> 65 2022 11 14 499 2022-11-29
#> 66 2022 12 4 503 2022-12-21
#> 67 2023 01 14 517 2023-01-31
#> 68 2023 02 11 528 2023-02-28
#> 69 2023 03 10 538 2023-03-24
#> 70 2023 04 13 551 2023-04-26
#> 71 2023 05 9 560 2023-05-10
#> 72 2023 06 13 573 2023-06-30
#> 73 2023 07 18 591 2023-07-29
#> 74 2023 08 19 610 2023-08-29
#> 75 2023 09 34 644 2023-09-30
#> 76 2023 10 13 657 2023-10-24
#> 77 2023 11 17 674 2023-11-29
#> 78 2023 12 9 683 2023-12-27
#> 79 2024 01 23 706 2024-01-31
#> 80 2024 02 22 728 2024-02-29
#> 81 2024 03 13 741 2024-03-27
#> 82 2024 04 21 762 2024-04-23
#> 83 2024 05 13 775 2024-05-31
#> 84 2024 06 15 790 2024-06-28
#> 85 2024 07 26 816 2024-07-31
#> 86 2024 08 30 846 2024-08-31
#> 87 2024 09 56 902 2024-09-28
#> 88 2024 10 36 938 2024-10-31
#> 89 2024 11 21 959 2024-11-28
#> 90 2024 12 30 989 2024-12-28
#> 91 2025 01 42 1031 2025-01-31
#> 92 2025 02 42 1073 2025-02-28
#> 93 2025 03 36 1109 2025-03-25
#> 94 2025 04 48 1157 2025-04-30
#> 95 2025 05 19 1176 2025-05-28
#> 96 2025 06 27 1203 2025-06-30
#> 97 2025 07 56 1259 2025-07-30
#> 98 2025 08 38 1297 2025-08-27
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)
}
