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 14
#> 2 2017-04-25 15
#> 3 2017-05-23 16
#> 4 2017-07-04 17
#> 5 2017-08-10 18
#> 6 2017-08-11 19
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2025-06-13 1422
#> 2 2025-06-17 1423
#> 3 2025-06-17 1424
#> 4 2025-06-17 1425
#> 5 2025-06-18 1426
#> 6 2025-06-18 1427
## 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 22 35 2017-12-12
#> 2 2018 27 62 2018-11-30
#> 3 2019 99 161 2019-12-18
#> 4 2020 112 273 2020-12-22
#> 5 2021 131 404 2021-12-18
#> 6 2022 173 577 2022-12-21
#> 7 2023 216 793 2023-12-27
#> 8 2024 388 1181 2024-12-28
#> 9 2025 246 1427 2025-06-18
## 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: 97 × 5
#> # Groups: year, month [97]
#> year month change total per
#> <chr> <chr> <int> <int> <date>
#> 1 2017 02 1 14 2017-02-21
#> 2 2017 04 1 15 2017-04-25
#> 3 2017 05 1 16 2017-05-23
#> 4 2017 07 1 17 2017-07-04
#> 5 2017 08 2 19 2017-08-11
#> 6 2017 09 2 21 2017-09-22
#> 7 2017 10 5 26 2017-10-27
#> 8 2017 11 7 33 2017-11-21
#> 9 2017 12 2 35 2017-12-12
#> 10 2018 01 1 36 2018-01-16
#> 11 2018 02 6 42 2018-02-26
#> 12 2018 03 2 44 2018-03-29
#> 13 2018 05 2 46 2018-05-14
#> 14 2018 06 1 47 2018-06-12
#> 15 2018 07 3 50 2018-07-20
#> 16 2018 08 5 55 2018-08-30
#> 17 2018 09 3 58 2018-09-07
#> 18 2018 10 2 60 2018-10-12
#> 19 2018 11 2 62 2018-11-30
#> 20 2019 01 1 63 2019-01-17
#> 21 2019 02 11 74 2019-02-21
#> 22 2019 03 16 90 2019-03-22
#> 23 2019 04 13 103 2019-04-30
#> 24 2019 05 7 110 2019-05-20
#> 25 2019 06 6 116 2019-06-30
#> 26 2019 07 7 123 2019-07-29
#> 27 2019 08 5 128 2019-08-31
#> 28 2019 09 18 146 2019-09-28
#> 29 2019 10 6 152 2019-10-31
#> 30 2019 11 4 156 2019-11-26
#> 31 2019 12 5 161 2019-12-18
#> 32 2020 01 6 167 2020-01-29
#> 33 2020 02 7 174 2020-02-29
#> 34 2020 03 8 182 2020-03-25
#> 35 2020 04 7 189 2020-04-30
#> 36 2020 05 11 200 2020-05-29
#> 37 2020 06 3 203 2020-06-19
#> 38 2020 07 11 214 2020-07-29
#> 39 2020 08 16 230 2020-08-31
#> 40 2020 09 14 244 2020-09-22
#> 41 2020 10 12 256 2020-10-30
#> 42 2020 11 11 267 2020-11-19
#> 43 2020 12 6 273 2020-12-22
#> 44 2021 01 12 285 2021-01-28
#> 45 2021 02 11 296 2021-02-26
#> 46 2021 03 10 306 2021-03-25
#> 47 2021 04 9 315 2021-04-27
#> 48 2021 05 7 322 2021-05-21
#> 49 2021 06 8 330 2021-06-30
#> 50 2021 07 5 335 2021-07-27
#> 51 2021 08 13 348 2021-08-30
#> 52 2021 09 20 368 2021-09-30
#> 53 2021 10 16 384 2021-10-27
#> 54 2021 11 11 395 2021-11-20
#> 55 2021 12 9 404 2021-12-18
#> 56 2022 01 14 418 2022-01-30
#> 57 2022 02 11 429 2022-02-26
#> 58 2022 03 18 447 2022-03-31
#> 59 2022 04 10 457 2022-04-15
#> 60 2022 05 10 467 2022-05-27
#> 61 2022 06 11 478 2022-06-25
#> 62 2022 07 14 492 2022-07-27
#> 63 2022 08 17 509 2022-08-31
#> 64 2022 09 32 541 2022-09-24
#> 65 2022 10 17 558 2022-10-26
#> 66 2022 11 14 572 2022-11-29
#> 67 2022 12 5 577 2022-12-21
#> 68 2023 01 16 593 2023-01-31
#> 69 2023 02 10 603 2023-02-28
#> 70 2023 03 12 615 2023-03-24
#> 71 2023 04 15 630 2023-04-28
#> 72 2023 05 11 641 2023-05-10
#> 73 2023 06 18 659 2023-06-30
#> 74 2023 07 20 679 2023-07-29
#> 75 2023 08 24 703 2023-08-26
#> 76 2023 09 43 746 2023-09-30
#> 77 2023 10 17 763 2023-10-24
#> 78 2023 11 21 784 2023-11-29
#> 79 2023 12 9 793 2023-12-27
#> 80 2024 01 27 820 2024-01-31
#> 81 2024 02 24 844 2024-02-29
#> 82 2024 03 14 858 2024-03-27
#> 83 2024 04 23 881 2024-04-23
#> 84 2024 05 21 902 2024-05-31
#> 85 2024 06 27 929 2024-06-28
#> 86 2024 07 40 969 2024-07-31
#> 87 2024 08 32 1001 2024-08-31
#> 88 2024 09 68 1069 2024-09-28
#> 89 2024 10 42 1111 2024-10-31
#> 90 2024 11 31 1142 2024-11-28
#> 91 2024 12 39 1181 2024-12-28
#> 92 2025 01 48 1229 2025-01-31
#> 93 2025 02 51 1280 2025-02-28
#> 94 2025 03 43 1323 2025-03-28
#> 95 2025 04 55 1378 2025-04-30
#> 96 2025 05 29 1407 2025-05-28
#> 97 2025 06 20 1427 2025-06-18
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)
}
