How to spread data and combine into same row by same id in r

Question

I have a data looks like this:

Id Time       Type location  count  no_activities      id 
1  2014/10/11  A   level1      20     10              1
2  2014/10/11  B   level1      20     10              2
3  2014/10/11  C   level1      10      9              3
4  2014/10/11  A   level2      5      12              4
5  2014/10/11  B   level2      6       5              5
6  2014/10/11  C   level2      7       8              6
7  2014/10/11  A   level3      7       8              7
8  2014/10/11  B   level3      9       5              8
9  2014/10/11  C   level3      20     23              9
...
...
     Time       Type location count  no_activities      id 
101  2014/11/27  A   level33     20     10              101
102  2014/11/27  B   level33     30     10              102
103  2014/11/27  C   level33     10      9              103
104  2014/11/27  A   level34     10     12              104
105  2014/11/27  B   level34     16      5              105
106  2014/11/27  C   level34     5       8              106
107  2014/11/27  A   level35     3       8              107
108  2014/11/27  B   level35     1       5              108
109  2014/11/27  C   level35     10     23              109

After grouping them by date and location, I want to spread the "Type" column and combine the "count" of the same group(based on time and location) into the same row, like this:

Id Time        location     A  B  C 
1  2014/10/11   level1     20 20 10
4  2014/10/11   level2      5  6  7
7  2014/10/11   level3      7  9 20
10 2014/10/11   level4     ...
13 2014/10/11   level5     ...
16 2014/10/11   level6      
19 2014/10/11   level7         
22 2014/10/11   level8   
25 2014/10/11   level9      
... 

I have read some similar questions but none seem to be solving my problem in this case. I tried these code:

df %>% dplyr::group_by(Time, location) %>% summarise_all(funs(toString(na.omit(.)))) 

which managed to group the observations by date and level but replaced whatever is missing in the columns with NA rather than combining the rows of same time and level into one:

Id  Time        location  count  no_activities id    A   B  C
1  2014/10/11   level1      20     10         1    20  NA NA
2  2014/10/11   level1      20     10         2    NA  20 NA
3  2014/10/11   level1      10      9         3    NA  NA 10
4  2014/10/11   level2      5      12         4     5  NA NA
5  2014/10/11   level2      6       5         5    NA   6 NA
6  2014/10/11   level2      7       8         6    NA  NA  7
7  2014/10/11   level3      7       8         7     7  NA NA  
8  2014/10/11   level3      9       5         8    NA   9 NA 
9  2014/10/11   level3     20      23         9    NA  NA 20
... 

I also tried

df %>%  reshape(df, v.names= c("A", "B", "C"), idvar=Id, timevar ="Time", direction="wide")

and

df %>%
group_by(Time, location) %>%
  spread(Type, count)

none of them worked. Any help is appreciated. Thanks.

Answer 1

Further to the discussion in the comments above, do you mean something like this?

df %>%
    group_by(Time, location) %>%
    select(Time, location, Type, count) %>%
    spread(Type, count) %>%
    ungroup()
## A tibble: 6 x 5
#  Time       location     A     B     C
#  <fct>      <fct>    <int> <int> <int>
#1 2014/10/11 level1      20    20    10
#2 2014/10/11 level2       5     6     7
#3 2014/10/11 level3       7     9    20
#4 2014/11/27 level33     20    30    10
#5 2014/11/27 level34     10    16     5
#6 2014/11/27 level35      3     1    10

---

Sample data

df <- read.table(text =
"Id Time       Type location  count  no_activities      id
1  '2014/10/11'  A   level1      20     10              1
2  '2014/10/11'  B   level1      20     10              2
3  '2014/10/11'  C   level1      10      9              3
4  '2014/10/11'  A   level2      5      12              4
5  '2014/10/11'  B   level2      6       5              5
6  '2014/10/11'  C   level2      7       8              6
7  '2014/10/11'  A   level3      7       8              7
8  '2014/10/11'  B   level3      9       5              8
9  '2014/10/11'  C   level3      20     23              9
101  '2014/11/27'  A   level33     20     10              101
102  '2014/11/27'  B   level33     30     10              102
103  '2014/11/27'  C   level33     10      9              103
104  '2014/11/27'  A   level34     10     12              104
105  '2014/11/27'  B   level34     16      5              105
106  '2014/11/27'  C   level34     5       8              106
107  '2014/11/27'  A   level35     3       8              107
108  '2014/11/27'  B   level35     1       5              108
109  '2014/11/27'  C   level35     10     23              109", header = T)