Count and summarise ID's each day while creating a new column that shows the accumulated ID'sHow to simulate customer walk-ins for a given period in a fast food chain in RCreate multiple matrices from 2 bigger ones in RProbability of what product will be purchased in repeat ordersEcho-Effect-Metric Networkk-Nearest Neighbours with time series data - how to obtain whole-time-period estimatorsConvert from many sub-tables to a single tidy dataframeLook for previous date in dataframe that has certain column category in RWhat is the difference between Missing at Random and Missing not at Random data?R summarise with conditionplot the histogram of purchases
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Count and summarise ID's each day while creating a new column that shows the accumulated ID's
How to simulate customer walk-ins for a given period in a fast food chain in RCreate multiple matrices from 2 bigger ones in RProbability of what product will be purchased in repeat ordersEcho-Effect-Metric Networkk-Nearest Neighbours with time series data - how to obtain whole-time-period estimatorsConvert from many sub-tables to a single tidy dataframeLook for previous date in dataframe that has certain column category in RWhat is the difference between Missing at Random and Missing not at Random data?R summarise with conditionplot the histogram of purchases
$begingroup$
I have two column, first one being the ID of a customer and second one being the Date of purchase.
ID Date
1 2017-01-17
2 2017-01-17
3 2017-01-17
4 2017-01-17
5 2017-01-17
1 2017-01-17
7 2017-01-17
1 2017-01-17
9 2017-01-18
2 2017-01-18
3 2017-01-18
5 2017-01-18
1 2017-01-18
2 2017-01-18
I would like to summarise the Purchases made by a Customer at one day and create a third column that shows the amount of purchases for the customer on that date.
r
edited 15 hours ago
Stephen Rauch♦
1,52551330
asked Apr 6 at 11:54
JelleManneJelleManne
112
$endgroup$
add a comment |
$begingroup$
I have two column, first one being the ID of a customer and second one being the Date of purchase.
ID Date
1 2017-01-17
2 2017-01-17
3 2017-01-17
4 2017-01-17
5 2017-01-17
1 2017-01-17
7 2017-01-17
1 2017-01-17
9 2017-01-18
2 2017-01-18
3 2017-01-18
5 2017-01-18
1 2017-01-18
2 2017-01-18
I would like to summarise the Purchases made by a Customer at one day and create a third column that shows the amount of purchases for the customer on that date.
r
edited 15 hours ago
Stephen Rauch♦
1,52551330
asked Apr 6 at 11:54
JelleManneJelleManne
112
$endgroup$
add a comment |
$begingroup$
I have two column, first one being the ID of a customer and second one being the Date of purchase.
ID Date
1 2017-01-17
2 2017-01-17
3 2017-01-17
4 2017-01-17
5 2017-01-17
1 2017-01-17
7 2017-01-17
1 2017-01-17
9 2017-01-18
2 2017-01-18
3 2017-01-18
5 2017-01-18
1 2017-01-18
2 2017-01-18
I would like to summarise the Purchases made by a Customer at one day and create a third column that shows the amount of purchases for the customer on that date.
r
edited 15 hours ago
Stephen Rauch♦
1,52551330
asked Apr 6 at 11:54
JelleManneJelleManne
112
$endgroup$
I have two column, first one being the ID of a customer and second one being the Date of purchase.
ID Date
1 2017-01-17
2 2017-01-17
3 2017-01-17
4 2017-01-17
5 2017-01-17
1 2017-01-17
7 2017-01-17
1 2017-01-17
9 2017-01-18
2 2017-01-18
3 2017-01-18
5 2017-01-18
1 2017-01-18
2 2017-01-18
I would like to summarise the Purchases made by a Customer at one day and create a third column that shows the amount of purchases for the customer on that date.
r
r
edited 15 hours ago
Stephen Rauch♦
1,52551330
asked Apr 6 at 11:54
JelleManneJelleManne
112
edited 15 hours ago
Stephen Rauch♦
1,52551330
asked Apr 6 at 11:54
JelleManneJelleManne
112
edited 15 hours ago
Stephen Rauch♦
1,52551330
edited 15 hours ago
Stephen Rauch♦
1,52551330
edited 15 hours ago
Stephen Rauch♦
1,52551330
1,52551330
asked Apr 6 at 11:54
JelleManneJelleManne
112
asked Apr 6 at 11:54
JelleManneJelleManne
112
asked Apr 6 at 11:54
JelleManneJelleManne
112
112
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
This is a base solution for your problem:
aggregate(paste(ID , Date) ~ ID + Date, data = df, FUN = length)
there are many more solutions like any of the ones below, using dplyr:
library(dplyr)
df %>% group_by(ID, Date) %>% summarise(PurchaseCount = n())
df %>% group_by(ID, Date) %>% tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% count(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_count(name="PurchaseCount")
or by using data.table package:
library(data.table)
setDT(df)[, PurchaseCount:=.N, by = list(ID, Date)]
or using sqldf package:
library(sqldf)
sqldf("SELECT ID, Date, COUNT(*) as PurchaseCount
FROM df
GROUP BY Date, ID")
or plyr:
plyr::count(df, c('ID','Date'))
I personally prefer data.table as it is directly writes to the dataframe and often is time efficient. aggregate is also favorable when you wanna avoid loading new libraries. dplyr generally make your code more legible as it uses pipingpersonal opinion.
answered 19 hours ago
M-MM-M
15010
$endgroup$
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
This is a base solution for your problem:
aggregate(paste(ID , Date) ~ ID + Date, data = df, FUN = length)
there are many more solutions like any of the ones below, using dplyr:
library(dplyr)
df %>% group_by(ID, Date) %>% summarise(PurchaseCount = n())
df %>% group_by(ID, Date) %>% tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% count(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_count(name="PurchaseCount")
or by using data.table package:
library(data.table)
setDT(df)[, PurchaseCount:=.N, by = list(ID, Date)]
or using sqldf package:
library(sqldf)
sqldf("SELECT ID, Date, COUNT(*) as PurchaseCount
FROM df
GROUP BY Date, ID")
or plyr:
plyr::count(df, c('ID','Date'))
I personally prefer data.table as it is directly writes to the dataframe and often is time efficient. aggregate is also favorable when you wanna avoid loading new libraries. dplyr generally make your code more legible as it uses pipingpersonal opinion.
answered 19 hours ago
M-MM-M
15010
$endgroup$
add a comment |
$begingroup$
This is a base solution for your problem:
aggregate(paste(ID , Date) ~ ID + Date, data = df, FUN = length)
there are many more solutions like any of the ones below, using dplyr:
library(dplyr)
df %>% group_by(ID, Date) %>% summarise(PurchaseCount = n())
df %>% group_by(ID, Date) %>% tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% count(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_count(name="PurchaseCount")
or by using data.table package:
library(data.table)
setDT(df)[, PurchaseCount:=.N, by = list(ID, Date)]
or using sqldf package:
library(sqldf)
sqldf("SELECT ID, Date, COUNT(*) as PurchaseCount
FROM df
GROUP BY Date, ID")
or plyr:
plyr::count(df, c('ID','Date'))
I personally prefer data.table as it is directly writes to the dataframe and often is time efficient. aggregate is also favorable when you wanna avoid loading new libraries. dplyr generally make your code more legible as it uses pipingpersonal opinion.
answered 19 hours ago
M-MM-M
15010
$endgroup$
add a comment |
$begingroup$
This is a base solution for your problem:
aggregate(paste(ID , Date) ~ ID + Date, data = df, FUN = length)
there are many more solutions like any of the ones below, using dplyr:
library(dplyr)
df %>% group_by(ID, Date) %>% summarise(PurchaseCount = n())
df %>% group_by(ID, Date) %>% tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% count(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_count(name="PurchaseCount")
or by using data.table package:
library(data.table)
setDT(df)[, PurchaseCount:=.N, by = list(ID, Date)]
or using sqldf package:
library(sqldf)
sqldf("SELECT ID, Date, COUNT(*) as PurchaseCount
FROM df
GROUP BY Date, ID")
or plyr:
plyr::count(df, c('ID','Date'))
I personally prefer data.table as it is directly writes to the dataframe and often is time efficient. aggregate is also favorable when you wanna avoid loading new libraries. dplyr generally make your code more legible as it uses pipingpersonal opinion.
answered 19 hours ago
M-MM-M
15010
$endgroup$
This is a base solution for your problem:
aggregate(paste(ID , Date) ~ ID + Date, data = df, FUN = length)
there are many more solutions like any of the ones below, using dplyr:
library(dplyr)
df %>% group_by(ID, Date) %>% summarise(PurchaseCount = n())
df %>% group_by(ID, Date) %>% tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% count(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_tally(name="PurchaseCount")
df %>% group_by(ID, Date) %>% add_count(name="PurchaseCount")
or by using data.table package:
library(data.table)
setDT(df)[, PurchaseCount:=.N, by = list(ID, Date)]
or using sqldf package:
library(sqldf)
sqldf("SELECT ID, Date, COUNT(*) as PurchaseCount
FROM df
GROUP BY Date, ID")
or plyr:
plyr::count(df, c('ID','Date'))
I personally prefer data.table as it is directly writes to the dataframe and often is time efficient. aggregate is also favorable when you wanna avoid loading new libraries. dplyr generally make your code more legible as it uses pipingpersonal opinion.
answered 19 hours ago
M-MM-M
15010
answered 19 hours ago
M-MM-M
15010
answered 19 hours ago
M-MM-M
15010
answered 19 hours ago
M-MM-M
15010
15010
add a comment |
add a comment |
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