Question

I need to turn this

id  |  amount |  day
---------------------
 A  |   10    |  0 
 A  |   54    |  8
 A  |   23    |  18
 A  |   43    |  28
 A  |   87    |  51
 B  |   34    |  0
 B  |   76    |  1 
 B  |   12    |  7

into this

id | a1 | a2 | a3 | a4 | a5 | d1 | d2 | d3 | d4 | d5 
--------------------------------------------------------
 A | 10 | 54 | 23 | 43 | 87 | 0  |  8 | 18 | 28 | 51
 B | 34 | 76 | 12 | 0  |  0 | 0  |  1 |  7 | 0  | 0

ie. transpose/cast the rows of the df to an unknown number of columns by id, putting zeros where there would be empty values because of the inequal length.

I’ve experimented with

df <- data.frame(id=c('A','A','A','A','A','B','B','B'),    amount=c(10,54,23,43,87,34,76,12), day=c(0,8,18,28,51,0,1,7))
library(reshape2)
x <- dcast(df, id ~ day, mean, value = 'amount')

but it’s not quite right. How do I do it?

Answer 1

Use base R reshape() after introducing a “time” variable:

df$time <- ave(as.numeric(as.character(df$id)), df$id, FUN = seq_along)
df
#   id amount day time
# 1  A     10   0    1
# 2  A     54   8    2
# 3  A     23  18    3
# 4  A     43  28    4
# 5  A     87  51    5
# 6  B     34   0    1
# 7  B     76   1    2
# 8  B     12   7    3
reshape(df, direction = "wide", idvar="id", timevar="time")
#   id amount.1 day.1 amount.2 day.2 amount.3 day.3 amount.4 day.4 amount.5 day.5
# 1  A       10     0       54     8       23    18       43    28       87    51
# 6  B       34     0       76     1       12     7       NA    NA       NA    NA

Optional steps:

1. Reorganize column order:

   df2 <- df2[c("id", 
                grep("amount", names(df2), value=TRUE), 
                grep("day", names(df2), value = TRUE))]
   

2. Replace NA with 0:

   df2[is.na(df2)] <- 0
   df2
   #   id amount.1 amount.2 amount.3 amount.4 amount.5 day.1 day.2 day.3 day.4 day.5
   # 1  A       10       54       23       43       87     0     8    18    28    51
   # 6  B       34       76       12        0        0     0     1     7     0     0