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 2017-09-20 19
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2025-05-02 1445
#> 2 2025-05-03 1446
#> 3 2025-05-03 1447
#> 4 2025-05-03 1448
#> 5 2025-05-06 1449
#> 6 2025-05-06 1450
## 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 28 63 2018-11-30
#> 3 2019 101 164 2019-12-18
#> 4 2020 116 280 2020-12-22
#> 5 2021 133 413 2021-12-18
#> 6 2022 178 591 2022-12-14
#> 7 2023 225 816 2023-12-27
#> 8 2024 419 1235 2024-12-28
#> 9 2025 215 1450 2025-05-06
## 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: 96 × 5
#> # Groups: year, month [96]
#> 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 1 18 2017-08-10
#> 6 2017 09 2 20 2017-09-22
#> 7 2017 10 6 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 5 41 2018-02-26
#> 12 2018 03 2 43 2018-03-29
#> 13 2018 05 2 45 2018-05-14
#> 14 2018 06 1 46 2018-06-12
#> 15 2018 07 3 49 2018-07-20
#> 16 2018 08 5 54 2018-08-30
#> 17 2018 09 3 57 2018-09-07
#> 18 2018 10 2 59 2018-10-12
#> 19 2018 11 4 63 2018-11-30
#> 20 2019 01 2 65 2019-01-17
#> 21 2019 02 11 76 2019-02-21
#> 22 2019 03 16 92 2019-03-22
#> 23 2019 04 13 105 2019-04-30
#> 24 2019 05 7 112 2019-05-20
#> 25 2019 06 6 118 2019-06-30
#> 26 2019 07 7 125 2019-07-29
#> 27 2019 08 5 130 2019-08-31
#> 28 2019 09 18 148 2019-09-28
#> 29 2019 10 7 155 2019-10-31
#> 30 2019 11 4 159 2019-11-26
#> 31 2019 12 5 164 2019-12-18
#> 32 2020 01 6 170 2020-01-29
#> 33 2020 02 7 177 2020-02-29
#> 34 2020 03 8 185 2020-03-25
#> 35 2020 04 8 193 2020-04-30
#> 36 2020 05 11 204 2020-05-29
#> 37 2020 06 3 207 2020-06-19
#> 38 2020 07 11 218 2020-07-29
#> 39 2020 08 18 236 2020-08-31
#> 40 2020 09 14 250 2020-09-22
#> 41 2020 10 12 262 2020-10-30
#> 42 2020 11 12 274 2020-11-19
#> 43 2020 12 6 280 2020-12-22
#> 44 2021 01 12 292 2021-01-28
#> 45 2021 02 11 303 2021-02-26
#> 46 2021 03 10 313 2021-03-25
#> 47 2021 04 10 323 2021-04-27
#> 48 2021 05 7 330 2021-05-21
#> 49 2021 06 6 336 2021-06-30
#> 50 2021 07 5 341 2021-07-27
#> 51 2021 08 13 354 2021-08-30
#> 52 2021 09 19 373 2021-09-30
#> 53 2021 10 17 390 2021-10-27
#> 54 2021 11 12 402 2021-11-25
#> 55 2021 12 11 413 2021-12-18
#> 56 2022 01 14 427 2022-01-30
#> 57 2022 02 11 438 2022-02-26
#> 58 2022 03 19 457 2022-03-31
#> 59 2022 04 11 468 2022-04-15
#> 60 2022 05 10 478 2022-05-27
#> 61 2022 06 11 489 2022-06-25
#> 62 2022 07 14 503 2022-07-27
#> 63 2022 08 17 520 2022-08-31
#> 64 2022 09 32 552 2022-09-24
#> 65 2022 10 19 571 2022-10-26
#> 66 2022 11 16 587 2022-11-29
#> 67 2022 12 4 591 2022-12-14
#> 68 2023 01 16 607 2023-01-31
#> 69 2023 02 11 618 2023-02-28
#> 70 2023 03 13 631 2023-03-24
#> 71 2023 04 15 646 2023-04-28
#> 72 2023 05 13 659 2023-05-10
#> 73 2023 06 20 679 2023-06-30
#> 74 2023 07 21 700 2023-07-29
#> 75 2023 08 22 722 2023-08-23
#> 76 2023 09 45 767 2023-09-30
#> 77 2023 10 17 784 2023-10-24
#> 78 2023 11 21 805 2023-11-29
#> 79 2023 12 11 816 2023-12-27
#> 80 2024 01 27 843 2024-01-31
#> 81 2024 02 25 868 2024-02-29
#> 82 2024 03 14 882 2024-03-27
#> 83 2024 04 39 921 2024-04-27
#> 84 2024 05 25 946 2024-05-31
#> 85 2024 06 30 976 2024-06-28
#> 86 2024 07 41 1017 2024-07-31
#> 87 2024 08 33 1050 2024-08-31
#> 88 2024 09 70 1120 2024-09-28
#> 89 2024 10 44 1164 2024-10-31
#> 90 2024 11 31 1195 2024-11-28
#> 91 2024 12 40 1235 2024-12-28
#> 92 2025 01 50 1285 2025-01-31
#> 93 2025 02 51 1336 2025-02-28
#> 94 2025 03 43 1379 2025-03-28
#> 95 2025 04 58 1437 2025-04-30
#> 96 2025 05 13 1450 2025-05-06
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)
}
