How to change display format of long variable?

will be helpful for those in need I have long message string. Each parent message is separated by a symbol # and child message separated by a symbol ^. , You almost done it, check out some modifications:

angular.module('app', []).controller('MyCtrl', function($scope) {
  $scope.messages = "Message1#Message2#Message3#Message4^Message41^Message42#Message5^Message51^Message52^Message53^Message54^Message55";
})

<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.2.23/angular.min.js">
</script>

<ul ng-app='app' ng-controller="MyCtrl">
  <li ng-repeat="message in messages.split('#')">    
    {{message.split('^')[0]}}
    <ul>
      <li ng-repeat="msg in message.split('^').splice(1)">
        {{msg}}
      </li>
    </ul>
  </li>
</ul>

should help you out I am making calls to a database using a package (brapi) that return the necessary data in long format. The api treats certain ID variables as measured variables which is problematic because my analysis scripts need them as ID variables. I can't modify the call to the database in such a way that it will do it for me and I do not want to just reshape2::cast and then melt the entire thing because there are hundreds of variables which would make it expensive. Ideally, I am looking for a way to do this with reshape2, plyr, or the like. , I'll replicate the problem using your approach of using mtcars:

df <- head(mtcars) # get a small chunk to work with

df <- df %>% 
  mutate(id = paste0("id", row_number())) # create an id variable

# use tidyr::gather() to "melt" the data:
df_long <- df %>%
  gather()

# Here's the problem area. Focus in on the last '10':
df_long %>% tail(10)

    key value
63 carb     1
64 carb     1
65 carb     2
66 carb     1
67   id   id1
68   id   id2
69   id   id3
70   id   id4
71   id   id5
72   id   id6

# get the id strings, store in `ids`
ids <- df_long %>%
  filter(key == "id") %>%
  select(value) %>%
  .[[1]]

df_long <- df_long %>%
  mutate(newid = rep(ids, length(unique(.$key)))) %>%
  filter(key != "id") %>%
  select(newid, key, value)

head(df_long)

  newid key value
1   id1 mpg    21
2   id2 mpg    21
3   id3 mpg  22.8
4   id4 mpg  21.4
5   id5 mpg  18.7
6   id6 mpg  18.1

hope this fix your issue I am trying to reshape a pandas dataframe with the following long format: , First check columns names:

print (smallstack.columns.tolist())
['ISO3', 'Indicator', 'Year', 'Value']

df = smallstack.set_index(['ISO3', 'Indicator', 'Year'])['Value'].unstack([1,2])
print (df)
Indicator Pop. density               Pupil-teacher ratio            
Year              2003   2004   2005                2003  2004  2005
ISO3                                                                
FRA              113,6  114,5  115,4                18,6  18,6  18,6
USA               31,7   32,0   32,3                14,8  14,2  14,1

smallstack['Value'] = smallstack['Value'].str.replace(',','.').astype(float)
smallstack.pivot_table(index='ISO3', columns=['Indicator', 'Year'], values='Value')

this one helps. I have a monthly time series data (1987-2017) for 20 station. I want to convert the long format data to wide format data such that all the data covering 20 station are in one data frame. , A solution using data.table

DT <- as.data.table(df)

#filling all ne NAs for optical rasons
DT[, c("stn1", "stn2")  := .(sample(1:100, 30), sample(1:100, 30))]

dcast(DT, Month ~ Year, value.var = c("stn1", "stn2")) %>%
  melt(id.vars = 1, variable.name = "year") %>%
  dcast(year ~ Month, value.var = "value") -> DT2

        year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1: stn1_1987  98   5  89  84  75  36   4  34  26  78  33  28
2: stn1_1988  67  74  40  63   9  19  79  61  66  93  47  62
3: stn1_1989  68  29   7  46  54  87  NA  NA  NA  NA  NA  NA
4: stn2_1987  31  61  74  89  46  54  70  80  84   6  96  32
5: stn2_1988  75  71  11  99  20   7  77  13  52  14   2  41
6: stn2_1989  83  22  97  43  59  15  NA  NA  NA  NA  NA  NA

DT2[, c("stn", "year") := tstrsplit(year, "_", fixed=TRUE)]

   year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec  stn
1: 1987  98   5  89  84  75  36   4  34  26  78  33  28 stn1
2: 1988  67  74  40  63   9  19  79  61  66  93  47  62 stn1
3: 1989  68  29   7  46  54  87  NA  NA  NA  NA  NA  NA stn1
4: 1987  31  61  74  89  46  54  70  80  84   6  96  32 stn2
5: 1988  75  71  11  99  20   7  77  13  52  14   2  41 stn2
6: 1989  83  22  97  43  59  15  NA  NA  NA  NA  NA  NA stn2