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 2025-07-25 1273
#> 2 2025-07-26 1274
#> 3 2025-07-29 1275
#> 4 2025-07-30 1276
#> 5 2025-07-30 1277
#> 6 2025-07-30 1278
## 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 30 2017-12-05
#> 2 2018 20 50 2018-11-30
#> 3 2019 87 137 2019-12-18
#> 4 2020 98 235 2020-12-22
#> 5 2021 118 353 2021-12-18
#> 6 2022 158 511 2022-12-21
#> 7 2023 180 691 2023-12-27
#> 8 2024 310 1001 2024-12-28
#> 9 2025 277 1278 2025-07-30
## 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 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 2 23 2017-10-24
#> 8 2017 11 6 29 2017-11-21
#> 9 2017 12 1 30 2017-12-05
#> 10 2018 01 1 31 2018-01-16
#> 11 2018 02 4 35 2018-02-26
#> 12 2018 03 2 37 2018-03-29
#> 13 2018 05 1 38 2018-05-14
#> 14 2018 06 1 39 2018-06-12
#> 15 2018 07 1 40 2018-07-18
#> 16 2018 08 4 44 2018-08-30
#> 17 2018 09 3 47 2018-09-07
#> 18 2018 10 1 48 2018-10-12
#> 19 2018 11 2 50 2018-11-30
#> 20 2019 01 1 51 2019-01-17
#> 21 2019 02 11 62 2019-02-21
#> 22 2019 03 15 77 2019-03-22
#> 23 2019 04 9 86 2019-04-30
#> 24 2019 05 6 92 2019-05-20
#> 25 2019 06 6 98 2019-06-30
#> 26 2019 07 7 105 2019-07-29
#> 27 2019 08 4 109 2019-08-31
#> 28 2019 09 16 125 2019-09-28
#> 29 2019 10 4 129 2019-10-31
#> 30 2019 11 4 133 2019-11-26
#> 31 2019 12 4 137 2019-12-18
#> 32 2020 01 6 143 2020-01-29
#> 33 2020 02 6 149 2020-02-29
#> 34 2020 03 8 157 2020-03-25
#> 35 2020 04 6 163 2020-04-20
#> 36 2020 05 10 173 2020-05-29
#> 37 2020 06 2 175 2020-06-19
#> 38 2020 07 11 186 2020-07-28
#> 39 2020 08 15 201 2020-08-31
#> 40 2020 09 10 211 2020-09-21
#> 41 2020 10 10 221 2020-10-30
#> 42 2020 11 9 230 2020-11-19
#> 43 2020 12 5 235 2020-12-22
#> 44 2021 01 10 245 2021-01-28
#> 45 2021 02 10 255 2021-02-26
#> 46 2021 03 10 265 2021-03-25
#> 47 2021 04 6 271 2021-04-27
#> 48 2021 05 7 278 2021-05-21
#> 49 2021 06 8 286 2021-06-30
#> 50 2021 07 3 289 2021-07-27
#> 51 2021 08 12 301 2021-08-30
#> 52 2021 09 17 318 2021-09-30
#> 53 2021 10 14 332 2021-10-27
#> 54 2021 11 12 344 2021-11-25
#> 55 2021 12 9 353 2021-12-18
#> 56 2022 01 12 365 2022-01-30
#> 57 2022 02 11 376 2022-02-26
#> 58 2022 03 17 393 2022-03-31
#> 59 2022 04 9 402 2022-04-22
#> 60 2022 05 9 411 2022-05-27
#> 61 2022 06 9 420 2022-06-25
#> 62 2022 07 9 429 2022-07-27
#> 63 2022 08 16 445 2022-08-31
#> 64 2022 09 31 476 2022-09-24
#> 65 2022 10 17 493 2022-10-26
#> 66 2022 11 13 506 2022-11-29
#> 67 2022 12 5 511 2022-12-21
#> 68 2023 01 14 525 2023-01-31
#> 69 2023 02 11 536 2023-02-28
#> 70 2023 03 10 546 2023-03-24
#> 71 2023 04 12 558 2023-04-26
#> 72 2023 05 8 566 2023-05-10
#> 73 2023 06 15 581 2023-06-30
#> 74 2023 07 18 599 2023-07-29
#> 75 2023 08 19 618 2023-08-29
#> 76 2023 09 33 651 2023-09-30
#> 77 2023 10 13 664 2023-10-27
#> 78 2023 11 18 682 2023-11-29
#> 79 2023 12 9 691 2023-12-27
#> 80 2024 01 21 712 2024-01-31
#> 81 2024 02 21 733 2024-02-29
#> 82 2024 03 12 745 2024-03-27
#> 83 2024 04 21 766 2024-04-23
#> 84 2024 05 13 779 2024-05-31
#> 85 2024 06 14 793 2024-06-28
#> 86 2024 07 30 823 2024-07-31
#> 87 2024 08 32 855 2024-08-31
#> 88 2024 09 57 912 2024-09-28
#> 89 2024 10 37 949 2024-10-31
#> 90 2024 11 23 972 2024-11-28
#> 91 2024 12 29 1001 2024-12-28
#> 92 2025 01 41 1042 2025-01-31
#> 93 2025 02 41 1083 2025-02-28
#> 94 2025 03 36 1119 2025-03-25
#> 95 2025 04 48 1167 2025-04-30
#> 96 2025 05 21 1188 2025-05-28
#> 97 2025 06 30 1218 2025-06-30
#> 98 2025 07 60 1278 2025-07-30
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)
}
