R: Put rows in columns and use N / A for missing values

Question

I have a dataframe that looks something like this.

NUM <- c("45", "45", "45", "45", "48", "50", "66", "66", "66", "68")
Type <- c("A", "F", "C", "B", "D", "A", "E", "C", "F", "D")
Points <- c(9.2,60.8,22.9,1012.7,18.7,11.1,67.2,63.1,16.7,58.4)

df1 <- data.frame(NUM,Type,Points)

df1:

+-----+------+--------+
| NUM | TYPE | Points |
+-----+------+--------+
|  45 | A    | 9.2    |
|  45 | F    | 60.8   |
|  45 | C    | 22.9   |
|  45 | B    | 1012.7 |
|  48 | D    | 18.7   |
|  50 | A    | 11.1   |
|  66 | E    | 67.2   |
|  66 | C    | 63.1   |
|  66 | F    | 16.7   |
|  65 | D    | 58.4   |
+-----+------+--------+

I am trying to get an output that takes rows in a type column to convert it to separate columns.

Output Required:

+-----+----------+----------+----------+----------+----------+----------+
| NUM | Points.A | Points.B | Points.C | Points.D | Points.E | Points.F |
+-----+----------+----------+----------+----------+----------+----------+
|  45 | 9.2      | 1012.7   | 22.9     | N/A      | N/A      | 60.8     |
|  48 | N/A      | N/A      | N/A      | 18.7     | N/A      | N/A      |
|  50 | 11.1     | N/A      | N/A      | N/A      | N/A      | N/A      |
|  66 | N/A      | N/A      | 63.1     | N/A      | 67.2     | 16.7     |
|  65 | N/A      | N/A      | N/A      | N/A      | 58.4     | N/A      |
+-----+----------+----------+----------+----------+----------+----------+

I tried to use melt (df1), but I did it wrong, since the values ​​in the strings are NUM values, not dots. Please let me know how I can solve this.

Answer 1

You are looking for a basic “long” for a “broad” conversion process.

In the R database, you can use the notorious reshape. The syntax for this data type is pretty simple:

reshape(df1, direction = "wide", idvar = "NUM", timevar = "Type")
#    NUM Points.A Points.F Points.C Points.B Points.D Points.E
# 1   45      9.2     60.8     22.9   1012.7       NA       NA
# 5   48       NA       NA       NA       NA     18.7       NA
# 6   50     11.1       NA       NA       NA       NA       NA
# 7   66       NA     16.7     63.1       NA       NA     67.2
# 10  68       NA       NA       NA       NA     58.4       NA

"tidyr", reshape2, . :

> library(tidyr)
> spread(df1, Type, Points)

Answer 2

dcast

library(reshape2)
dcast(df1, NUM~paste0('Points.',Type), value.var='Points')

data.table dcast .

library(data.table)#v1.9.5+
dcast(setDT(df1), NUM~paste0('Points.',Type), value.var='Points')