The goal of rcohort is to perform cohort analysis with R. This package
build on top of data.table hence it should bring good speed!
rcohort use data.table version 1.14.3, which is not available on
CRAN yet. Therefore, you need to update data.table to the development
version first to be able to use rcohort. You can install the
development version of rcohort from
GitHub with:
# install.packages("devtools")
data.table::update.dev.pkg()
devtools::install_github("vohai611/rcohort")This is a basic example which shows you how to execute cohort analysis
with rcohort
df = readRDS("example-data/online-retail.rds")This is a typical dataset of an online retailer (Source)
head(df)#> InvoiceNo StockCode Description Quantity
#> 1 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6
#> 2 536365 71053 WHITE METAL LANTERN 6
#> 3 536365 84406B CREAM CUPID HEARTS COAT HANGER 8
#> 4 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
#> 5 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6
#> 6 536365 22752 SET 7 BABUSHKA NESTING BOXES 2
#> InvoiceDate UnitPrice CustomerID Country
#> 1 2010-12-01 08:26:00 2.55 17850 United Kingdom
#> 2 2010-12-01 08:26:00 3.39 17850 United Kingdom
#> 3 2010-12-01 08:26:00 2.75 17850 United Kingdom
#> 4 2010-12-01 08:26:00 3.39 17850 United Kingdom
#> 5 2010-12-01 08:26:00 3.39 17850 United Kingdom
#> 6 2010-12-01 08:26:00 7.65 17850 United Kingdom
cohort_count() perform cohort analysis by counting the appearance of
customer in each time period. In particular, Customer are grouped by the
first time they appear in the dataset. We can choose the rounded time to
every “month”, “week”, “quarter” or “year”.
library(rcohort)
df1 = df |>
cohort_count(.id_col = CustomerID,
.time_col = InvoiceDate,
time_unit = "month",
percent_form = TRUE)
head(df1, 5)#> cohort month_1 month_2 month_3 month_4 month_5 month_6 month_7
#> <fctr> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2010-12 100 36.61017 32.31638 38.41808 36.27119 39.77401 36.27119
#> 2: 2011-1 100 22.06235 26.61871 23.02158 32.13429 28.77698 24.70024
#> 3: 2011-2 100 18.68421 18.68421 28.42105 27.10526 24.73684 25.26316
#> 4: 2011-3 100 15.04425 25.22124 19.91150 22.34513 16.81416 26.76991
#> 5: 2011-4 100 21.33333 20.33333 21.00000 19.66667 22.66667 21.66667
#> month_8 month_9 month_10 month_11 month_12 month_13
#> <num> <num> <num> <num> <num> <num>
#> 1: 34.91525 35.367232 39.548023 37.401130 50.28249 26.55367
#> 2: 24.22062 29.976019 32.613909 36.450839 11.75060 NA
#> 3: 27.89474 24.736842 30.526316 6.842105 NA NA
#> 4: 23.00885 27.876106 8.628319 NA NA NA
#> 5: 26.00000 7.333333 NA NA NA NA
To include group of all customer, use all_group = TRUE
df2 = df |>
cohort_count(.id_col = CustomerID,
.time_col = InvoiceDate,
time_unit = "month",
all_group = TRUE)
head(df2, 5)#> cohort month_1 month_2 month_3 month_4 month_5 month_6 month_7
#> <fctr> <num> <num> <num> <num> <num> <num> <num>
#> 1: All 100 22.49885 21.71508 21.36929 20.86215 20.23974 18.53389
#> 2: 2010-12 100 36.61017 32.31638 38.41808 36.27119 39.77401 36.27119
#> 3: 2011-1 100 22.06235 26.61871 23.02158 32.13429 28.77698 24.70024
#> 4: 2011-2 100 18.68421 18.68421 28.42105 27.10526 24.73684 25.26316
#> 5: 2011-3 100 15.04425 25.22124 19.91150 22.34513 16.81416 26.76991
#> month_8 month_9 month_10 month_11 month_12 month_13
#> <num> <num> <num> <num> <num> <num>
#> 1: 16.71277 15.67543 14.776395 11.733518 11.38774 5.417243
#> 2: 34.91525 35.36723 39.548023 37.401130 50.28249 26.553672
#> 3: 24.22062 29.97602 32.613909 36.450839 11.75060 NA
#> 4: 27.89474 24.73684 30.526316 6.842105 NA NA
#> 5: 23.00885 27.87611 8.628319 NA NA NA
Beside counting the appearance of customers, you can summarise cohort performance over time. For example, we can observe the change of the revenue over time.
df$Revenue = df$Quantity * df$UnitPrice
cohort_df = df |>
cohort_summarise(.id_col = CustomerID,
.time_col = InvoiceDate,
.summarise_col = Revenue,.fn = "sum", time_unit = "month",
percent_form = TRUE)
head(cohort_df,5)#> cohort month_1 month_2 month_3 month_4 month_5 month_6 month_7
#> <fctr> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2010-12 100 48.23310 40.83110 52.92684 35.69106 58.77760 54.85507
#> 2: 2011-1 100 18.80918 21.57796 24.41528 27.66441 28.84777 23.91052
#> 3: 2011-2 100 18.36528 26.01103 30.50350 25.38724 21.64081 31.43163
#> 4: 2011-3 100 15.02446 29.51843 21.40082 25.81963 20.00341 32.44839
#> 5: 2011-4 100 24.13938 20.55304 19.92556 21.56698 24.75693 23.49695
#> month_8 month_9 month_10 month_11 month_12 month_13
#> <num> <num> <num> <num> <num> <num>
#> 1: 54.26513 57.92582 82.55669 79.634983 89.713834 32.44257
#> 2: 24.79123 24.56407 38.13134 42.250680 9.025974 NA
#> 3: 39.49033 35.01918 40.98179 6.702973 NA NA
#> 4: 35.49956 35.62045 6.43176 NA NA NA
#> 5: 28.05360 5.20451 NA NA NA NA
We can also quick visualize cohort data by calling auto_plot
cohort_df |> auto_plot("tile")