Merging multiple rows into single row - r

Question

I've some problems with my data frame in R.
My data frame looks something like this:
ID TIME DAY URL_NAME VALUE TIME_SPEND
1 12:15 Monday HOME 4 30
1 13:15 Tuesday CUSTOMERS 5 21
1 15:00 Thursday PLANTS 8 8
1 16:21 Friday MANAGEMENT 1 6
....
So, I want to write the rows, containing the same "ID" into one single row.
Looking something like this:
ID TIME DAY URL_NAME VALUE TIME_SPEND TIME1 DAY1 URL_NAME1 VALUE1 TIME_SPEND1 TIME2 DAY2 URL_NAME2 VALUE2 TIME_SPEND2 TIME3 DAY3 URL_NAME3 VALUE3 TIME_SPEND3
1 12:15 Monday HOME 4 30 13:15 Tuesday CUSTOMERS 5 21 15:00 Thursday PLANTS 8 8 16:21 Friday MANAGEMENT 1 6
My second problem is, that there are about 1.500.00 unique IDs and i would like to do this for the whole data frame.
I did not find any solution fitting to my problem.
I would be happy about any solutions or links to handle my problem.

Answer 1

I'd recommend using dcast from the "data.table" package, which would allow you to reshape multiple measure variables at once.
Example:
library(data.table)
as.data.table(mydf)[, dcast(.SD, ID ~ rowid(ID), value.var = names(mydf)[-1])]
# ID TIME_1 TIME_2 TIME_3 DAY_1 DAY_2 DAY_3 URL_NAME_1 URL_NAME_2 URL_NAME_3 VALUE_1 VALUE_2
# 1: 1 12:15 13:15 15:00 Monday Tuesday Thursday HOME CUSTOMERS PLANTS 4 5
# 2: 2 14:15 10:19 NA Tuesday Monday NA CUSTOMERS CUSTOMERS NA 2 9
# VALUE_3 TIME_SPEND_1 TIME_SPEND_2 TIME_SPEND_3
# 1: 8 30 19 40
# 2: NA 21 8 NA
Here's the sample data used:
mydf <- data.frame(
ID = c(1, 1, 1, 2, 2),
TIME = c("12:15", "13:15", "15:00", "14:15", "10:19"),
DAY = c("Monday", "Tuesday", "Thursday", "Tuesday", "Monday"),
URL_NAME = c("HOME", "CUSTOMERS", "PLANTS", "CUSTOMERS", "CUSTOMERS"),
VALUE = c(4, 5, 8, 2, 9),
TIME_SPEND = c(30, 19, 40, 21, 8)
)
mydf
# ID TIME DAY URL_NAME VALUE TIME_SPEND
# 1 1 12:15 Monday HOME 4 30
# 2 1 13:15 Tuesday CUSTOMERS 5 19
# 3 1 15:00 Thursday PLANTS 8 40
# 4 2 14:15 Tuesday CUSTOMERS 2 21
# 5 2 10:19 Monday CUSTOMERS 9 8

Answer 2

Try this tidyverse solution which will produce an output close to what you want. You can group by TIME then create a sequential id that will identify the future columns. After that reshape to long (pivot_longer()) combine the variable name with the id and then reshape to wide (pivot_wider()). Here's the code where I have used a dataset of my own,
df1 <- data.frame(Components = c(rep("ABC",5),rep("BCD",5)),
Size = c(sample(1:100,5),sample(45:100,5)),
Age = c(sample(1:100,5),sample(45:100,5)))
For the above-generated data set, the following code piece is the solution:
library(tidyverse)
#Code
newdf <- df1 %>% group_by(Components) %>% mutate(id=row_number()) %>%
pivot_longer(-c(Components,id)) %>%
mutate(name=paste0(name,'.',id)) %>% select(-id) %>%
pivot_wider(names_from = name,values_from=value)
OUTPUT would look like:
# A tibble: 2 x 11
# Groups: Components [2]
Components Size.1 Age.1 Size.2 Age.2 Size.3 Age.3 Size.4 Age.4 Size.5 Age.5
<fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 ABC 23 94 52 89 15 25 76 38 33 99
2 BCD 59 62 55 81 81 61 80 83 97 68
ALTERNATIVE SOLUTION:
We could use unite to unite the columns and then use pivot_wider
library(dplyr)
library(tidyr)
library(data.table)
df1 %>%
mutate(rn = rowid(Components)) %>%
pivot_longer(cols = Size:Age) %>%
unite(name, name, rn, sep=".") %>%
pivot_wider(names_from = name, values_from = value)
OUTPUT would look like:
# A tibble: 2 x 11
# Components Size.1 Age.1 Size.2 Age.2 Size.3 Age.3 Size.4 Age.4 Size.5 Age.5
# <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#1 ABC 11 16 79 57 70 2 80 6 91 24
#2 BCD 67 81 63 77 48 73 52 100 49 76
Both the solutions were #Duck's Duck's Profile URL and #akrun's Akrun's Profile URL brainchild. Thanks a tonne to them.