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 2017-04-20 15
#> 3 2017-04-25 16
#> 4 2017-05-23 17
#> 5 2017-07-04 18
#> 6 2017-08-10 19
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2025-01-17 1390
#> 2 2025-01-17 1391
#> 3 2025-01-17 1392
#> 4 2025-01-17 1393
#> 5 2025-01-18 1394
#> 6 2025-01-18 1395
## 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 26 39 2017-12-12
#> 2 2018 32 71 2018-11-30
#> 3 2019 104 175 2019-12-18
#> 4 2020 124 299 2020-12-22
#> 5 2021 140 439 2021-12-18
#> 6 2022 201 640 2022-12-21
#> 7 2023 231 871 2023-12-27
#> 8 2024 492 1363 2024-12-28
#> 9 2025 32 1395 2025-01-18
## 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: 93 × 5
#> # Groups: year, month [93]
#> year month change total per
#> <chr> <chr> <int> <int> <date>
#> 1 2017 02 1 14 2017-02-21
#> 2 2017 04 2 16 2017-04-25
#> 3 2017 05 1 17 2017-05-23
#> 4 2017 07 1 18 2017-07-04
#> 5 2017 08 2 20 2017-08-11
#> 6 2017 09 2 22 2017-09-22
#> 7 2017 10 6 28 2017-10-27
#> 8 2017 11 9 37 2017-11-30
#> 9 2017 12 2 39 2017-12-12
#> 10 2018 01 1 40 2018-01-16
#> 11 2018 02 6 46 2018-02-26
#> 12 2018 03 3 49 2018-03-29
#> 13 2018 04 1 50 2018-04-10
#> 14 2018 05 2 52 2018-05-14
#> 15 2018 06 1 53 2018-06-12
#> 16 2018 07 3 56 2018-07-20
#> 17 2018 08 5 61 2018-08-30
#> 18 2018 09 3 64 2018-09-07
#> 19 2018 10 2 66 2018-10-12
#> 20 2018 11 5 71 2018-11-30
#> 21 2019 01 3 74 2019-01-17
#> 22 2019 02 9 83 2019-02-21
#> 23 2019 03 16 99 2019-03-22
#> 24 2019 04 14 113 2019-04-30
#> 25 2019 05 7 120 2019-05-20
#> 26 2019 06 6 126 2019-06-30
#> 27 2019 07 7 133 2019-07-29
#> 28 2019 08 5 138 2019-08-31
#> 29 2019 09 18 156 2019-09-28
#> 30 2019 10 7 163 2019-10-31
#> 31 2019 11 5 168 2019-11-26
#> 32 2019 12 7 175 2019-12-18
#> 33 2020 01 6 181 2020-01-29
#> 34 2020 02 8 189 2020-02-29
#> 35 2020 03 8 197 2020-03-25
#> 36 2020 04 10 207 2020-04-30
#> 37 2020 05 11 218 2020-05-28
#> 38 2020 06 2 220 2020-06-19
#> 39 2020 07 11 231 2020-07-29
#> 40 2020 08 19 250 2020-08-31
#> 41 2020 09 16 266 2020-09-29
#> 42 2020 10 12 278 2020-10-30
#> 43 2020 11 14 292 2020-11-19
#> 44 2020 12 7 299 2020-12-22
#> 45 2021 01 10 309 2021-01-28
#> 46 2021 02 12 321 2021-02-26
#> 47 2021 03 9 330 2021-03-25
#> 48 2021 04 9 339 2021-04-27
#> 49 2021 05 6 345 2021-05-21
#> 50 2021 06 11 356 2021-06-30
#> 51 2021 07 6 362 2021-07-27
#> 52 2021 08 14 376 2021-08-30
#> 53 2021 09 22 398 2021-09-30
#> 54 2021 10 16 414 2021-10-27
#> 55 2021 11 13 427 2021-11-25
#> 56 2021 12 12 439 2021-12-18
#> 57 2022 01 19 458 2022-01-30
#> 58 2022 02 14 472 2022-02-26
#> 59 2022 03 24 496 2022-03-31
#> 60 2022 04 12 508 2022-04-27
#> 61 2022 05 11 519 2022-05-27
#> 62 2022 06 9 528 2022-06-25
#> 63 2022 07 17 545 2022-07-27
#> 64 2022 08 20 565 2022-08-31
#> 65 2022 09 33 598 2022-09-24
#> 66 2022 10 20 618 2022-10-26
#> 67 2022 11 18 636 2022-11-29
#> 68 2022 12 4 640 2022-12-21
#> 69 2023 01 19 659 2023-01-31
#> 70 2023 02 15 674 2023-02-28
#> 71 2023 03 16 690 2023-03-24
#> 72 2023 04 17 707 2023-04-28
#> 73 2023 05 11 718 2023-05-10
#> 74 2023 06 19 737 2023-06-30
#> 75 2023 07 21 758 2023-07-29
#> 76 2023 08 18 776 2023-08-23
#> 77 2023 09 44 820 2023-09-30
#> 78 2023 10 19 839 2023-10-25
#> 79 2023 11 19 858 2023-11-29
#> 80 2023 12 13 871 2023-12-27
#> 81 2024 01 40 911 2024-01-31
#> 82 2024 02 40 951 2024-02-29
#> 83 2024 03 31 982 2024-03-29
#> 84 2024 04 44 1026 2024-04-27
#> 85 2024 05 26 1052 2024-05-31
#> 86 2024 06 33 1085 2024-06-28
#> 87 2024 07 41 1126 2024-07-31
#> 88 2024 08 35 1161 2024-08-31
#> 89 2024 09 75 1236 2024-09-28
#> 90 2024 10 50 1286 2024-10-31
#> 91 2024 11 32 1318 2024-11-28
#> 92 2024 12 45 1363 2024-12-28
#> 93 2025 01 32 1395 2025-01-18
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)
}
