dplyr data manipulation

library(data.table)
library(reshape2)
library(dplyr)
library(tidyr)
library(magrittr)
dat = data.table(ID = rep(LETTERS[1:2], 4:3),
  location = c('TAI', 'JP', 'CH', 'KOE', 'JP', 'GOK', 'TA'),
  year = c(2012, 2013, 2014, 2011, 2011, 2015, 2012),
  sex = rep(c("F", "M"), 4:3))
## 1
dat %>% group_by(ID, sex) %>%
  mutate(location_name = paste0("location_", 1:n())) %>%
  select(-year) %>% spread(location_name, location, fill = "")
#   ID sex location_1 location_2 location_3 location_4
# 1  A   F        TAI         JP         CH        KOE
# 2  B   M         JP        GOK         TA
## 2
dat %>% spread(year, location, fill="")
## dcast.data.table(dat, ID + sex ~ year, value.var = "location")
#    ID sex 2011 2012 2013 2014 2015
# 1:  A   F  KOE  TAI   JP   CH
# 2:  B   M   JP   TA            GOK
## 3
dat %>% spread(year, location, fill="") %>%
  setnames(as.character(2011:2015), paste0("year_", 2011:2015)) %>%
  mutate(location = paste(year_2011, year_2012,year_2013,year_2014,year_2015, sep = ",")) %>%
  mutate(location = gsub(',+', ',',gsub(',$', '', location))) %>%
  select(ID, sex, location)
#    ID sex       location
# 1:  A   F  KOE,TAI,JP,CH
# 2:  B   M      JP,TA,GOK
## 3 (method 2)
dat3 = dat %>% mutate(ones = "c1") %>% select(-year) %>%
  dcast.data.table(ID + sex ~ ones,
    fun.aggregate = function(x){paste(x, collapse = ',')},
    value.var = "location") %>% setnames("c1", "location")
dat3
#    ID sex      location
# 1:  A   F TAI,JP,CH,KOE
# 2:  B   M     JP,GOK,TA

## 3 (method 2 with order of year)
dat3_2 = dat %>% mutate(ones = "c1") %>%
  arrange(ID, year) %>% select(-year) %>%
  dcast.data.table(ID + sex ~ ones,
    fun.aggregate = function(x){paste(x, collapse = ',')},
    value.var = "location") %>% setnames("c1", "location")
dat3_2
#    ID sex      location
# 1:  A   F KOE,TAI,JP,CH
# 2:  B   M     JP,TA,GOK