Q:

Reshape data from long to wide format - more than one variable

I’m trying to reshape my data from long to wide formula using the dcast function from reshape2. 
The objective is to use different variables in the value.var parameter but R doesn't let me use more than one value in it.
Is there any other way I could fix it? I've looked at other similar questions but I haven't been able to find a similar examples.
Here is my current dataset:
+---------+------+--------+--------------+------------+
| Country | Year | Growth | Unemployment | Population |
+---------+------+--------+--------------+------------+
| A       | 2015 |      2 |          8.3 |         40 |
| B       | 2015 |      3 |          9.2 |         32 |
| C       | 2015 |    2.5 |          9.1 |         30 |
| D       | 2015 |    1.5 |          6.1 |         27 |
| A       | 2016 |      4 |          8.1 |         42 |
| B       | 2016 |    3.5 |            9 |       32.5 |
| C       | 2016 |    3.7 |            9 |         31 |
| D       | 2016 |    3.1 |          5.3 |         29 |
| A       | 2017 |    4.5 |          8.1 |       42.5 |
| B       | 2017 |    4.4 |          8.4 |         33 |
| C       | 2017 |    4.3 |          8.5 |         30 |
| D       | 2017 |    4.2 |          5.2 |         30 |
+---------+------+--------+--------------+------------+

My objective is to pass year column to the rest of the columns (growth, unemployment and population). I’m using the below dcast function.
data_wide <- dcast(world, country  ~ year,
     value.var=c("Growth","Unemployment","Population"))

Ideal outcome
+---------+-------------+-------------------+-----------------+-------------+-------------------+-----------------+
| Country | Growth_2015 | Unemployment_2015 | Population_2015 | Growth_2016 | Unemployment_2016 | Population_2016 |
+---------+-------------+-------------------+-----------------+-------------+-------------------+-----------------+
| A       |           2 |               8.3 |              40 |           4 |               8.1 |              42 |
| B       |           3 |               9.2 |              32 |         3.5 |                 9 |            32.5 |
| C       |         2.5 |               9.1 |              30 |         3.7 |                 9 |              31 |
| D       |         1.5 |               6.1 |              27 |         3.1 |               5.3 |              29 |
+---------+-------------+-------------------+-----------------+-------------+-------------------+-----------------+

A:

If you're not married to a dcast solution, I personally find tidyr easier.
library(tidyr)
df <- df %>% 
     gather(key, value, -Country, -Year) %>%  
     unite(new.col, c(key, Year)) %>%   
     spread(new.col, value) 

Result
  Country Growth_2015 Growth_2016 Growth_2017 Population_2015 Population_2016 Population_2017 Unemployment_2015 Unemployment_2016 Unemployment_2017
1       A         2.0         4.0         4.5              40            42.0            42.5               8.3               8.1               8.1
2       B         3.0         3.5         4.4              32            32.5            33.0               9.2               9.0               8.4
3       C         2.5         3.7         4.3              30            31.0            30.0               9.1               9.0               8.5
4       D         1.5         3.1         4.2              27            29.0            30.0               6.1               5.3               5.2

This works by 
Stacking all values into one column...
Combining variable name and year columns into a single column...
The new column is then spread into wide format

A:

The dcast() statement given by the OP works almost perfect with the recent versions of the data.table package as these allow for multiple measure variables to be used with dcast() and melt():
library(data.table)   # CRAN version 1.10.4
setDT(world)   # coerce to data.table
data_wide <- dcast(world, Country ~ Year, 
                   value.var = c("Growth", "Unemployment", "Population"))

data_wide
#   Country Growth_2015 Growth_2016 Growth_2017 Unemployment_2015 Unemployment_2016 Unemployment_2017 Population_2015
#1:       A         2.0         4.0         4.5               8.3               8.1               8.1              40
#2:       B         3.0         3.5         4.4               9.2               9.0               8.4              32
#3:       C         2.5         3.7         4.3               9.1               9.0               8.5              30
#4:       D         1.5         3.1         4.2               6.1               5.3               5.2              27
#   Population_2016 Population_2017
1:            42.0            42.5
2:            32.5            33.0
3:            31.0            30.0
4:            29.0            30.0

This is the same result as the tidyr solution.

However, the OP has requested a specific column order for his ideal solution where the different measure variables of each year are grouped together.
If the proper order of columns is important, there are two ways to achieve this. The first approach is to reorder the columns appropriately using setcolorder():
new_ord <- CJ(world$Year, c("Growth","Unemployment","Population"), 
              sorted = FALSE, unique = TRUE)[, paste(V2, V1, sep = "_")]
setcolorder(data_wide, c("Country", new_ord))

data_wide
#   Country Growth_2015 Unemployment_2015 Population_2015 Growth_2016 Unemployment_2016 Population_2016 Growth_2017
#1:       A         2.0               8.3              40         4.0               8.1            42.0         4.5
#2:       B         3.0               9.2              32         3.5               9.0            32.5         4.4
#3:       C         2.5               9.1              30         3.7               9.0            31.0         4.3
#4:       D         1.5               6.1              27         3.1               5.3            29.0         4.2
#   Unemployment_2017 Population_2017
#1:               8.1            42.5
#2:               8.4            33.0
#3:               8.5            30.0
#4:               5.2            30.0

Note the the cross join function  CJ() is used to create the cross product of the vectors.

The other approach to achieve the desired column order is to melt and recast:
molten <- melt(world, id.vars = c("Country", "Year"))
dcast(molten, Country ~ Year + variable)
#   Country 2015_Growth 2015_Unemployment 2015_Population 2016_Growth 2016_Unemployment 2016_Population 2017_Growth
#1:       A         2.0               8.3              40         4.0               8.1            42.0         4.5
#2:       B         3.0               9.2              32         3.5               9.0            32.5         4.4
#3:       C         2.5               9.1              30         3.7               9.0            31.0         4.3
#4:       D         1.5               6.1              27         3.1               5.3            29.0         4.2
#   2017_Unemployment 2017_Population
#1:               8.1            42.5
#2:               8.4            33.0
#3:               8.5            30.0
#4:               5.2            30.0

