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 10
#> 2 2017-04-25 11
#> 3 2017-05-23 12
#> 4 2017-07-04 13
#> 5 2017-08-10 14
#> 6 2017-08-11 15
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2026-03-20 1027
#> 2 2026-03-24 1028
#> 3 2026-04-01 1029
#> 4 2026-04-01 1030
#> 5 2026-04-02 1031
#> 6 2026-04-04 1032
## 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 14 23 2017-12-12
#> 2 2018 14 37 2018-11-30
#> 3 2019 60 97 2019-12-11
#> 4 2020 67 164 2020-12-22
#> 5 2021 84 248 2021-12-18
#> 6 2022 107 355 2022-12-21
#> 7 2023 112 467 2023-12-22
#> 8 2024 193 660 2024-12-28
#> 9 2025 280 940 2025-12-23
#> 10 2026 92 1032 2026-04-04
## 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: 103 × 5
#> # Groups: year, month [103]
#> year month change total per
#> <chr> <chr> <int> <int> <date>
#> 1 2017 02 1 10 2017-02-21
#> 2 2017 04 1 11 2017-04-25
#> 3 2017 05 1 12 2017-05-23
#> 4 2017 07 1 13 2017-07-04
#> 5 2017 08 2 15 2017-08-11
#> 6 2017 10 1 16 2017-10-23
#> 7 2017 11 5 21 2017-11-21
#> 8 2017 12 2 23 2017-12-12
#> 9 2018 01 1 24 2018-01-26
#> 10 2018 02 2 26 2018-02-26
#> 11 2018 03 2 28 2018-03-29
#> 12 2018 05 2 30 2018-05-14
#> 13 2018 07 1 31 2018-07-18
#> 14 2018 08 2 33 2018-08-24
#> 15 2018 09 3 36 2018-09-07
#> 16 2018 11 1 37 2018-11-30
#> 17 2019 02 10 47 2019-02-21
#> 18 2019 03 15 62 2019-03-22
#> 19 2019 04 8 70 2019-04-30
#> 20 2019 05 6 76 2019-05-28
#> 21 2019 06 4 80 2019-06-30
#> 22 2019 07 1 81 2019-07-29
#> 23 2019 08 3 84 2019-08-26
#> 24 2019 09 2 86 2019-09-26
#> 25 2019 10 4 90 2019-10-31
#> 26 2019 11 5 95 2019-11-26
#> 27 2019 12 2 97 2019-12-11
#> 28 2020 01 6 103 2020-01-29
#> 29 2020 02 4 107 2020-02-29
#> 30 2020 03 5 112 2020-03-25
#> 31 2020 04 3 115 2020-04-20
#> 32 2020 05 6 121 2020-05-20
#> 33 2020 06 1 122 2020-06-02
#> 34 2020 07 9 131 2020-07-28
#> 35 2020 08 10 141 2020-08-31
#> 36 2020 09 7 148 2020-09-21
#> 37 2020 10 6 154 2020-10-30
#> 38 2020 11 7 161 2020-11-19
#> 39 2020 12 3 164 2020-12-22
#> 40 2021 01 6 170 2021-01-25
#> 41 2021 02 6 176 2021-02-26
#> 42 2021 03 5 181 2021-03-25
#> 43 2021 04 4 185 2021-04-27
#> 44 2021 05 6 191 2021-05-21
#> 45 2021 06 6 197 2021-06-30
#> 46 2021 07 4 201 2021-07-27
#> 47 2021 08 9 210 2021-08-30
#> 48 2021 09 12 222 2021-09-30
#> 49 2021 10 12 234 2021-10-27
#> 50 2021 11 6 240 2021-11-25
#> 51 2021 12 8 248 2021-12-18
#> 52 2022 01 9 257 2022-01-30
#> 53 2022 02 8 265 2022-02-26
#> 54 2022 03 11 276 2022-03-31
#> 55 2022 04 4 280 2022-04-27
#> 56 2022 05 9 289 2022-05-27
#> 57 2022 06 5 294 2022-06-25
#> 58 2022 07 7 301 2022-07-27
#> 59 2022 08 7 308 2022-08-31
#> 60 2022 09 23 331 2022-09-24
#> 61 2022 10 14 345 2022-10-26
#> 62 2022 11 8 353 2022-11-23
#> 63 2022 12 2 355 2022-12-21
#> 64 2023 01 8 363 2023-01-31
#> 65 2023 02 4 367 2023-02-24
#> 66 2023 03 7 374 2023-03-21
#> 67 2023 04 10 384 2023-04-26
#> 68 2023 05 5 389 2023-05-10
#> 69 2023 06 10 399 2023-06-30
#> 70 2023 07 6 405 2023-07-29
#> 71 2023 08 13 418 2023-08-29
#> 72 2023 09 27 445 2023-09-30
#> 73 2023 10 11 456 2023-10-24
#> 74 2023 11 9 465 2023-11-29
#> 75 2023 12 2 467 2023-12-22
#> 76 2024 01 11 478 2024-01-31
#> 77 2024 02 14 492 2024-02-29
#> 78 2024 03 8 500 2024-03-27
#> 79 2024 04 21 521 2024-04-24
#> 80 2024 05 12 533 2024-05-31
#> 81 2024 06 15 548 2024-06-28
#> 82 2024 07 19 567 2024-07-27
#> 83 2024 08 14 581 2024-08-31
#> 84 2024 09 39 620 2024-09-28
#> 85 2024 10 15 635 2024-10-31
#> 86 2024 11 6 641 2024-11-22
#> 87 2024 12 19 660 2024-12-28
#> 88 2025 01 16 676 2025-01-31
#> 89 2025 02 16 692 2025-02-28
#> 90 2025 03 12 704 2025-03-25
#> 91 2025 04 17 721 2025-04-30
#> 92 2025 05 20 741 2025-05-28
#> 93 2025 06 22 763 2025-06-30
#> 94 2025 07 30 793 2025-07-30
#> 95 2025 08 29 822 2025-08-29
#> 96 2025 09 39 861 2025-09-30
#> 97 2025 10 41 902 2025-10-30
#> 98 2025 11 24 926 2025-11-27
#> 99 2025 12 14 940 2025-12-23
#> 100 2026 01 32 972 2026-01-30
#> 101 2026 02 33 1005 2026-02-24
#> 102 2026 03 23 1028 2026-03-24
#> 103 2026 04 4 1032 2026-04-04
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)
}
