The number of Wynton users over time
users_over_time(file = NULL, since = "2017-01-01")Arguments
- file
A file with a single column of signup dates, or NULL. If NULL, then the Wynton LDAP server is queried.
- since
Drop signup dates prior to this date.
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 17
#> 2 2017-04-20 18
#> 3 2017-04-25 19
#> 4 2017-05-23 20
#> 5 2017-07-04 21
#> 6 2017-07-13 22
print(tail(signups))
#> # A tibble: 6 × 2
#> date total
#> <date> <int>
#> 1 2024-04-26 1281
#> 2 2024-04-27 1282
#> 3 2024-04-27 1283
#> 4 2024-05-01 1284
#> 5 2024-05-01 1285
#> 6 2024-05-01 1286
## 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: 8 × 4
#> # Groups: year [8]
#> year change total per
#> <chr> <int> <int> <date>
#> 1 2017 27 43 2017-12-12
#> 2 2018 35 78 2018-12-21
#> 3 2019 113 191 2019-12-18
#> 4 2020 153 344 2020-12-22
#> 5 2021 158 502 2021-12-18
#> 6 2022 236 738 2022-12-23
#> 7 2023 372 1110 2023-12-27
#> 8 2024 176 1286 2024-05-01
## 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: 84 × 5
#> # Groups: year, month [84]
#> year month change total per
#> <chr> <chr> <int> <int> <date>
#> 1 2017 02 1 17 2017-02-21
#> 2 2017 04 2 19 2017-04-25
#> 3 2017 05 1 20 2017-05-23
#> 4 2017 07 3 23 2017-07-19
#> 5 2017 08 3 26 2017-08-15
#> 6 2017 09 2 28 2017-09-22
#> 7 2017 10 5 33 2017-10-26
#> 8 2017 11 8 41 2017-11-30
#> 9 2017 12 2 43 2017-12-12
#> 10 2018 01 1 44 2018-01-16
#> 11 2018 02 6 50 2018-02-26
#> 12 2018 03 3 53 2018-03-29
#> 13 2018 05 4 57 2018-05-16
#> 14 2018 07 3 60 2018-07-20
#> 15 2018 08 9 69 2018-08-30
#> 16 2018 09 3 72 2018-09-07
#> 17 2018 10 2 74 2018-10-12
#> 18 2018 11 3 77 2018-11-30
#> 19 2018 12 1 78 2018-12-21
#> 20 2019 01 5 83 2019-01-17
#> 21 2019 02 10 93 2019-02-21
#> 22 2019 03 16 109 2019-03-22
#> 23 2019 04 13 122 2019-04-30
#> 24 2019 05 7 129 2019-05-28
#> 25 2019 06 6 135 2019-06-30
#> 26 2019 07 10 145 2019-07-29
#> 27 2019 08 6 151 2019-08-31
#> 28 2019 09 19 170 2019-09-28
#> 29 2019 10 7 177 2019-10-31
#> 30 2019 11 6 183 2019-11-26
#> 31 2019 12 8 191 2019-12-18
#> 32 2020 01 10 201 2020-01-29
#> 33 2020 02 11 212 2020-02-29
#> 34 2020 03 9 221 2020-03-25
#> 35 2020 04 14 235 2020-04-30
#> 36 2020 05 16 251 2020-05-29
#> 37 2020 06 5 256 2020-06-22
#> 38 2020 07 12 268 2020-07-29
#> 39 2020 08 20 288 2020-08-31
#> 40 2020 09 19 307 2020-09-29
#> 41 2020 10 15 322 2020-10-30
#> 42 2020 11 14 336 2020-11-19
#> 43 2020 12 8 344 2020-12-22
#> 44 2021 01 9 353 2021-01-28
#> 45 2021 02 13 366 2021-02-26
#> 46 2021 03 13 379 2021-03-25
#> 47 2021 04 9 388 2021-04-27
#> 48 2021 05 8 396 2021-05-21
#> 49 2021 06 12 408 2021-06-30
#> 50 2021 07 6 414 2021-07-27
#> 51 2021 08 19 433 2021-08-30
#> 52 2021 09 23 456 2021-09-30
#> 53 2021 10 18 474 2021-10-27
#> 54 2021 11 15 489 2021-11-25
#> 55 2021 12 13 502 2021-12-18
#> 56 2022 01 18 520 2022-01-30
#> 57 2022 02 13 533 2022-02-26
#> 58 2022 03 23 556 2022-03-31
#> 59 2022 04 17 573 2022-04-27
#> 60 2022 05 13 586 2022-05-27
#> 61 2022 06 20 606 2022-06-29
#> 62 2022 07 24 630 2022-07-27
#> 63 2022 08 27 657 2022-08-31
#> 64 2022 09 35 692 2022-09-24
#> 65 2022 10 19 711 2022-10-26
#> 66 2022 11 20 731 2022-11-29
#> 67 2022 12 7 738 2022-12-23
#> 68 2023 01 21 759 2023-01-31
#> 69 2023 02 17 776 2023-02-28
#> 70 2023 03 16 792 2023-03-24
#> 71 2023 04 17 809 2023-04-28
#> 72 2023 05 22 831 2023-05-13
#> 73 2023 06 41 872 2023-06-30
#> 74 2023 07 40 912 2023-07-29
#> 75 2023 08 41 953 2023-08-29
#> 76 2023 09 75 1028 2023-09-30
#> 77 2023 10 27 1055 2023-10-27
#> 78 2023 11 31 1086 2023-11-29
#> 79 2023 12 24 1110 2023-12-27
#> 80 2024 01 45 1155 2024-01-31
#> 81 2024 02 43 1198 2024-02-29
#> 82 2024 03 36 1234 2024-03-29
#> 83 2024 04 49 1283 2024-04-27
#> 84 2024 05 3 1286 2024-05-01
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)
}
