horizontal and vertical join count in r dataframe - r

Question

having a dataframe with sales per customer and months.
df <-
data.frame(
stringsAsFactors = FALSE,
date = c("jan","jan","jan","jan",
"jan","jan","jan","feb","feb","feb","feb","feb",
"feb","feb"),
customer = c("john","john","john","Mary",
"Mary","Mary","Mary","Robert","Robert","Mary",
"john","john","Robert","Robert"),
product = c("a","b","d","a","b","c",
"d","a","b","c","a","c","c","d")
date customer product
1 jan john a
2 jan john b
3 jan john d
4 jan Mary a
5 jan Mary b
6 jan Mary c
7 jan Mary d
8 feb Robert a
9 feb Robert b
10 feb Mary c
11 feb john a
12 feb john c
13 feb Robert c
14 feb Robert d
I need to summarize how many times the same customer is present across months and products.
Expected result:
date a b c d same cust
jan 2 2 1 2 0
feb 2 1 2 0 1
same cust 1 0 1 0

Answer 1

A possible solution:
library(tidyverse)
df <-
data.frame(
stringsAsFactors = FALSE,
date = c("jan","jan","jan","jan",
"jan","jan","jan","feb","feb","feb","feb","feb",
"feb","feb"),
customer = c("john","john","john","Mary",
"Mary","Mary","Mary","Robert","Robert","Mary",
"john","john","Robert","Robert"),
product = c("a","b","d","a","b","c",
"d","a","b","c","a","c","c","d"))
df %>%
pivot_wider(date,names_from=product,values_from=customer,values_fn=length)%>%
bind_cols(SCust = table(df$customer, df$date) %>% apply(2, \(x) sum(x>=2))) %>%
bind_rows(c(tibble(date="SCust"),
table(df$customer, df$product) %>% apply(2, \(x) sum(x>=2))))
#> # A tibble: 3 × 6
#> date a b d c SCust
#> <chr> <int> <int> <int> <int> <int>
#> 1 jan 2 2 2 1 2
#> 2 feb 2 1 1 3 2
#> 3 SCust 1 0 0 1 NA

Answer 2

I don't know about the marginals, but for the main table
library(reshape2)
dcast(
df,
date~product,
function(x){length(unique(x))},
value.var="customer"
)
date a b c d
1 feb 2 1 3 1
2 jan 2 2 1 2

Answer 3

You can try
library(tidyverse)
df %>%
pivot_wider(names_from = product, values_from = customer, values_fn = n_distinct) %>%
bind_rows(
df %>%
count(product, customer) %>%
group_by(product) %>%
summarise(n=sum(n-1),
date = "all") %>%
pivot_wider(names_from = product,values_from=n ))
# A tibble: 3 x 5
date a b d c
<chr> <dbl> <dbl> <dbl> <dbl>
1 jan 2 2 2 1
2 feb 2 1 1 3
3 all 1 0 0 1

Answer 4

dt <- data.frame(stringsAsFactors = FALSE,
date = c("jan","jan","jan","jan", "jan","jan","jan","feb","feb","feb","feb","feb","feb","feb"),
customer = c("john","john","john","Mary", "Mary","Mary","Mary","Robert","Robert","Mary","john","john","Robert","Robert"),
product = c("a","b","d","a","b","c","d","a","b","c","a","c","c","d")
)
library(data.table)
setDT(dt)
setorder(dt, product)
rbindlist(list(
dcast(dt[, .(value = .N), by = .(date, product)], date ~ product),
transpose(dt[, .(same_cust_row = .N - length(unique(customer))), by = .(product)], make.names = "product", keep.names = "date")
))
# date a b c d
# 1: feb 2 1 3 1
# 2: jan 2 2 1 2
# 3: same_cust_row 1 0 1 0

Answer 5

Do you need the "detail" data, or just the summary ("same cust") data?
library(dplyr)
library(tidyr)
library(purrr)
# by month / same customer bought in both months
df %>% pivot_wider(names_from = product, values_from = date, values_fn = length) %>%
select(-customer) %>%
map( ~ sum(.x==2))
$a
[1] 1
$b
[1] 0
$d
[1] 0
$c
[1] 1
# by month / same customer bought all (4) products
z <- df %>% pivot_wider(names_from = date, values_from = product, values_fn = length) %>%
select(-customer) %>%
map( ~ sum(.x==4))
$jan
[1] NA
$feb
[1] 1