Creating cohort tables from event data is complicated and requires several lines of code. The cohorts package lets users convert data frames to cohort tables in both long and wide formats with simple functions. Users may choose between day and month level cohorts.
You can install the released version of cohorts from CRAN with:
install.packages("cohorts")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("PeerChristensen/cohorts")In this example, we use a dataset consisting of customer IDs and invoice dates.
library(cohorts)
head(online_cohorts)
#> CustomerID InvoiceDate
#> 1 17850 2010-12-01
#> 2 13047 2010-12-01
#> 3 12583 2010-12-01
#> 4 13748 2010-12-01
#> 5 15100 2010-12-01
#> 6 15291 2010-12-01We can then turn this into a cohort table where each customer ID is tracked from the first invoice month until the last month in the period.
online_cohorts %>%
cohort_table_month(CustomerID, InvoiceDate)
#> # A tibble: 13 x 14
#> cohort `Dec 2010` `Jan 2011` `Feb 2011` `Mar 2011` `Apr 2011` `May 2011`
#> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 1423 622 522 666 539 655
#> 2 2 NA 514 127 163 132 201
#> 3 3 NA NA 451 122 97 135
#> 4 4 NA NA NA 509 106 155
#> 5 5 NA NA NA NA 362 98
#> 6 6 NA NA NA NA NA 336
#> 7 7 NA NA NA NA NA NA
#> 8 8 NA NA NA NA NA NA
#> 9 9 NA NA NA NA NA NA
#> 10 10 NA NA NA NA NA NA
#> 11 11 NA NA NA NA NA NA
#> 12 12 NA NA NA NA NA NA
#> 13 13 NA NA NA NA NA NA
#> # … with 7 more variables: Jun 2011 <int>, Jul 2011 <int>, Aug 2011 <int>,
#> # Sep 2011 <int>, Oct 2011 <int>, Nov 2011 <int>, Dec 2011 <int>If we need to track activity on a daily basis, we can instead use the
cohort_table_month() function.
gamelaunch %>%
cohort_table_day(userid, eventDate)
#> # A tibble: 31 x 32
#> cohort `2016-04-27` `2016-04-28` `2016-04-29` `2016-04-30` `2016-05-01`
#> <int> <int> <int> <int> <int> <int>
#> 1 1 192 66 55 46 46
#> 2 2 NA 399 118 97 85
#> 3 3 NA NA 740 209 184
#> 4 4 NA NA NA 773 225
#> 5 5 NA NA NA NA 807
#> 6 6 NA NA NA NA NA
#> 7 7 NA NA NA NA NA
#> 8 8 NA NA NA NA NA
#> 9 9 NA NA NA NA NA
#> 10 10 NA NA NA NA NA
#> # … with 21 more rows, and 26 more variables: 2016-05-02 <int>,
#> # 2016-05-03 <int>, 2016-05-04 <int>, 2016-05-05 <int>, 2016-05-06 <int>,
#> # 2016-05-07 <int>, 2016-05-08 <int>, 2016-05-09 <int>, 2016-05-10 <int>,
#> # 2016-05-11 <int>, 2016-05-12 <int>, 2016-05-13 <int>, 2016-05-14 <int>,
#> # 2016-05-15 <int>, 2016-05-16 <int>, 2016-05-17 <int>, 2016-05-18 <int>,
#> # 2016-05-19 <int>, 2016-05-20 <int>, 2016-05-21 <int>, 2016-05-22 <int>,
#> # 2016-05-23 <int>, 2016-05-24 <int>, 2016-05-25 <int>, 2016-05-26 <int>,
#> # 2016-05-27 <int>In order to see the percent of remaining customers in subsequent
periods, we can pipe the above code into the cohort_table_pct()
function.
gamelaunch %>%
cohort_table_day(userid, eventDate) %>%
cohort_table_pct(decimals = 1)
#> # A tibble: 31 x 32
#> cohort `2016-04-27` `2016-04-28` `2016-04-29` `2016-04-30` `2016-05-01`
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 100 34.4 28.6 24 24
#> 2 2 NA 100 29.6 24.3 21.3
#> 3 3 NA NA 100 28.2 24.9
#> 4 4 NA NA NA 100 29.1
#> 5 5 NA NA NA NA 100
#> 6 6 NA NA NA NA NA
#> 7 7 NA NA NA NA NA
#> 8 8 NA NA NA NA NA
#> 9 9 NA NA NA NA NA
#> 10 10 NA NA NA NA NA
#> # … with 21 more rows, and 26 more variables: 2016-05-02 <dbl>,
#> # 2016-05-03 <dbl>, 2016-05-04 <dbl>, 2016-05-05 <dbl>, 2016-05-06 <dbl>,
#> # 2016-05-07 <dbl>, 2016-05-08 <dbl>, 2016-05-09 <dbl>, 2016-05-10 <dbl>,
#> # 2016-05-11 <dbl>, 2016-05-12 <dbl>, 2016-05-13 <dbl>, 2016-05-14 <dbl>,
#> # 2016-05-15 <dbl>, 2016-05-16 <dbl>, 2016-05-17 <dbl>, 2016-05-18 <dbl>,
#> # 2016-05-19 <dbl>, 2016-05-20 <dbl>, 2016-05-21 <dbl>, 2016-05-22 <dbl>,
#> # 2016-05-23 <dbl>, 2016-05-24 <dbl>, 2016-05-25 <dbl>, 2016-05-26 <dbl>,
#> # 2016-05-27 <dbl>Another option is to shift cohort tables left. Here, we align cohorts such that date columns are replaced by time periods, i.e. t0, t1, t2 etc.
To left-shift a cohort table, we can use the shift_left() function.
gamelaunch %>%
cohort_table_day(userid, eventDate) %>%
shift_left()
#> # A tibble: 31 x 32
#> cohort t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 t10
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 192 66 55 46 46 46 45 33 35 32 27
#> 2 2 399 118 97 85 83 77 62 74 63 52 59
#> 3 3 740 209 184 153 139 129 116 98 95 89 84
#> 4 4 773 225 177 153 132 124 107 115 114 86 89
#> 5 5 807 228 181 153 131 131 129 104 86 99 86
#> 6 6 648 188 150 126 119 105 85 74 72 59 59
#> 7 7 539 167 129 114 113 102 86 90 75 74 73
#> 8 8 527 143 125 92 73 76 81 63 61 55 55
#> 9 9 531 154 116 113 100 94 89 72 69 63 66
#> 10 10 251 74 58 51 42 42 50 41 42 40 32
#> # … with 21 more rows, and 20 more variables: t11 <dbl>, t12 <dbl>, t13 <dbl>,
#> # t14 <dbl>, t15 <dbl>, t16 <dbl>, t17 <dbl>, t18 <dbl>, t19 <dbl>,
#> # t20 <dbl>, t21 <dbl>, t22 <dbl>, t23 <dbl>, t24 <dbl>, t25 <dbl>,
#> # t26 <dbl>, t27 <dbl>, t28 <dbl>, t29 <dbl>, t30 <dbl>We can also get the raw numbers as percentages.
gamelaunch %>%
cohort_table_day(userid, eventDate) %>%
shift_left_pct()
#> # A tibble: 31 x 32
#> cohort t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 t10
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 100 34.4 28.6 24 24 24 23.4 17.2 18.2 16.7 14.1
#> 2 2 100 29.6 24.3 21.3 20.8 19.3 15.5 18.5 15.8 13 14.8
#> 3 3 100 28.2 24.9 20.7 18.8 17.4 15.7 13.2 12.8 12 11.4
#> 4 4 100 29.1 22.9 19.8 17.1 16 13.8 14.9 14.7 11.1 11.5
#> 5 5 100 28.3 22.4 19 16.2 16.2 16 12.9 10.7 12.3 10.7
#> 6 6 100 29 23.1 19.4 18.4 16.2 13.1 11.4 11.1 9.1 9.1
#> 7 7 100 31 23.9 21.2 21 18.9 16 16.7 13.9 13.7 13.5
#> 8 8 100 27.1 23.7 17.5 13.9 14.4 15.4 12 11.6 10.4 10.4
#> 9 9 100 29 21.8 21.3 18.8 17.7 16.8 13.6 13 11.9 12.4
#> 10 10 100 29.5 23.1 20.3 16.7 16.7 19.9 16.3 16.7 15.9 12.7
#> # … with 21 more rows, and 20 more variables: t11 <dbl>, t12 <dbl>, t13 <dbl>,
#> # t14 <dbl>, t15 <dbl>, t16 <dbl>, t17 <dbl>, t18 <dbl>, t19 <dbl>,
#> # t20 <dbl>, t21 <dbl>, t22 <dbl>, t23 <dbl>, t24 <dbl>, t25 <dbl>,
#> # t26 <dbl>, t27 <dbl>, t28 <dbl>, t29 <dbl>, t30 <dbl>To visualize the data, we can turn a cohort table into long format and create a line plot.
In this example, we select only the first seven cohorts.
library(tidyverse)
gamelaunch_long <- gamelaunch %>%
cohort_table_day(userid, eventDate) %>%
shift_left_pct() %>%
pivot_longer(-cohort) %>%
mutate(time = as.numeric(str_remove(name,"t")))
gamelaunch_long %>%
filter(value > 0, cohort <= 7, time > 0) %>%
ggplot(aes(time, value, colour = factor(cohort), group = cohort)) +
geom_line(size = 1.5) +
geom_point(size = 1.5) +
theme_light()
Another way to plot a cohort table is by means of tiles. In this case we provide the percentages and colour the tiles accordingly.
gamelaunch_long %>%
filter(time > 0, value > 0) %>%
ggplot(aes(time, reorder(cohort, desc(cohort)))) +
geom_raster(aes(fill = log(value))) +
coord_equal(ratio = 1) +
geom_text(aes(label = glue::glue("{round(value,0)}%")), size = 2, color = "snow") +
scale_fill_gradient(guide = F) +
theme_minimal() +
theme(panel.grid = element_blank(),
panel.border = element_blank()) +
labs(y= "cohort")
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