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IF then do end equivalent in r... EDIT: in dplyr


IF then do end equivalent in r... EDIT: in dplyr

By : user2951091
Date : November 18 2020, 01:01 AM
wish helps you Imagine a vector animals <- c("dog", "wolf", "cat"); animals <- as.data.frame(animals)
code :
animals <- data.frame(animals=c("dog", "wolf", "cat"))
library("dplyr")
animals %>% mutate(dogs=ifelse(animals %in% c("dog","wolf"),
                                  "dog",
                                  "cat"))
library("car")
animals %>% mutate(dogs=recode(animals,"c('dog','wolf')='dog'"))


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ngGrid - edit a row on button click (ASP.NET Gridview edit equivalent)

ngGrid - edit a row on button click (ASP.NET Gridview edit equivalent)


By : somethinghere
Date : March 29 2020, 07:55 AM
wish of those help It seems like there's no straightforward way to do this, I had to add a cell template and set it to edit in the [Edit] button click. Following is what I did:
code :
   vm.holidayProperties.childHolidayGridOptions = {
            data: "holidayProperties.selectedHoliday.childHolidays",
            rowHeight: 35,
            enableCellSelection: false,
            enableRowSelection: true,
            multiSelect: false,
            columnDefs: [
                { field: 'holidayId', displayName: localize.getLocalizedString('_HolidayId_'), visible: false },
                { field: 'location.locationName', displayName: localize.getLocalizedString('_LocationName_'), width: "15%" },
                { field: 'holidayName', displayName: localize.getLocalizedString('_Holidayname_'), width: "15%" },
                {
                  field: 'isAllDay', displayName: localize.getLocalizedString('_IsAllday_'), width: "10%",
                  cellTemplate: '<input type="checkbox" ng-model="row.entity.isAllDay" ng-change="holidayProperties.setEndDateDisabled()" ng-disabled="!row.editable">'
                },
                {
                    field: '', displayName: localize.getLocalizedString('_Action_'), width: "10%",
                    cellTemplate: '<button ng-show="!row.editable" ng-click="holidayProperties.setRowEditable(row)"><span class="glyphicon glyphicon-pencil"></span></button>' +
                                  '<button ng-show="row.editable" ng-click="holidayProperties.reset(row)"><span class="glyphicon glyphicon-arrow-left"></span></button>'
                }
            ],
            enablePaging: true,
            showFooter: true,
            showFilter: true,
            pagingOptions: { pageSizes: [10, 20, 30], pageSize: 10, currentPage: 1 },
            totalServerItems: 'holidayProperties.selectedHoliday.childHolidays.length',
        };


        vm.holidayProperties.setRowEditable = function (row) {
            row.editable = true;
        }

        vm.holidayProperties.reset = function (row) {
            clientcontext.rejectChangesForEntity(row.entity);
            row.editable = false;
        }
What is a dplyr equivalent for using which function?

What is a dplyr equivalent for using which function?


By : Noorodin Kiani
Date : March 29 2020, 07:55 AM
hop of those help? I have a dataframe 1: , This worked for me:
code :
df1 <- setNames(df1, "x") # rename column to `x` for brevity
df1 %>% rowwise() %>%
 mutate(test = df2[grep(x,df2)[1]])
# Source: local data frame [20 x 2]
# Groups: <by row>

# # A tibble: 20 × 2
#          x                  test
#      <int>                 <chr>
# 1  1094000 ts.01094000.crest.csv
# 2  1094000 ts.01094000.crest.csv
# 3  1094000 ts.01094000.crest.csv
dplyr equivalent to DF[DF==X] <- Y

dplyr equivalent to DF[DF==X] <- Y


By : Guizi
Date : March 29 2020, 07:55 AM
To fix this issue 1) replace Try this. It only requires magrittr although dplyr imports the relevant part of magrittr so it will work with dplyr too:
code :
df %>% replace(. == 2, 10)
   A  B  C
1  1 10 -1
2 10  3  0
3  3  4  1
4  4  5 10
5  5  6  3
df <- df %>% replace(. == 2, 10)
df %>% replace(. == 2, 10) -> df
df %<>% replace(. == 2, 10)
df %>% { 10 * (. == 2) + . * (. != 2) }
Dplyr equivalent of SUM over PARTITION BY

Dplyr equivalent of SUM over PARTITION BY


By : user2368672
Date : March 29 2020, 07:55 AM
hop of those help? I'm sure this question has been asked before, but I can't find the answer. , Using dplyr -
code :
df %>%
    group_by(group) %>%
    mutate(group_sum = sum(value))
Pandas equivalent to dplyr dot

Pandas equivalent to dplyr dot


By : user3629951
Date : March 29 2020, 07:55 AM
hop of those help? In Pandas, run the chain of two assign calls but do so in any way that does not rely on original data frame context such as with DataFrame.apply call. Below uses a list comprehension equivalent across index values:
code :
test_df = pd.DataFrame({'group': np.repeat([1,2,3],3), 'data': np.arange(1,10)})

(
   test_df.assign(min_of_max = lambda x: [np.min(x["data"].loc[(x["data"] > x["data"].iloc[i]) &
                                                               (x["group"] == x["group"].iloc[i])]
                                                ) for i in test_df.index.values])
          .assign(min_of_max_2 = lambda x: [np.min(x["min_of_max"].loc[(x["min_of_max"] > x["min_of_max"].iloc[i]) &
                                                                       (x["group"] == x["group"].iloc[i])]
                                                  ) for i in test_df.index.values])
)

#    group  data  min_of_max  min_of_max_2
# 0      1     1         2.0           3.0
# 1      1     2         3.0           NaN
# 2      1     3         NaN           NaN
# 3      2     4         5.0           6.0
# 4      2     5         6.0           NaN
# 5      2     6         NaN           NaN
# 6      3     7         8.0           9.0
# 7      3     8         9.0           NaN
# 8      3     9         NaN           NaN
test_df <- data.frame(group = rep(c(1,2,3), each = 3), data = c(1:9))

test_df %>%
  group_by(group) %>%
  mutate(min_of_max = map_dbl(data, ~data[data > .x] %>% min()),
         min_of_max_2 = map_dbl(min_of_max, ~min_of_max[min_of_max > .x] %>% min()))

# # A tibble: 9 x 4
# # Groups:   group [3]
#   group  data min_of_max min_of_max_2
#   <dbl> <int>      <dbl>        <dbl>
# 1     1     1          2            3
# 2     1     2          3          Inf
# 3     1     3        Inf          Inf
# 4     2     4          5            6
# 5     2     5          6          Inf
# 6     2     6        Inf          Inf
# 7     3     7          8            9
# 8     3     8          9          Inf
# 9     3     9        Inf          Inf
test_df = pd.DataFrame({'group': np.repeat([1,2,3],3), 'data': np.arange(1,10)})

test_df.assign(min_of_max = lambda x: [np.min(x["data"].loc[(x["data"] > x["data"].iloc[i]) &
                                                            (x["group"] == x["group"].iloc[i])]
                                             ) for i in test_df.index.values],
               min_of_max_2 = lambda x: [np.min(x["min_of_max"].loc[(x["min_of_max"] > x["min_of_max"].iloc[i]) &
                                                                    (x["group"] == x["group"].iloc[i])]
                                               ) for i in test_df.index.values])

#    group  data  min_of_max  min_of_max_2
# 0      1     1         2.0           3.0
# 1      1     2         3.0           NaN
# 2      1     3         NaN           NaN
# 3      2     4         5.0           6.0
# 4      2     5         6.0           NaN
# 5      2     6         NaN           NaN
# 6      3     7         8.0           9.0
# 7      3     8         9.0           NaN
# 8      3     9         NaN           NaN
test_df <- within(test_df, {      
  min_of_max <- sapply(1:nrow(test_df), 
                       function(i) min(data[data > data[i] & 
                                            group == group[i]]))

  min_of_max_2 <- sapply(1:nrow(test_df), 
                         function(i) min(min_of_max[min_of_max > min_of_max[i] & 
                                                    group == group[i]]))      
})

test_df[c("group", "data", "min_of_max", "min_of_max_2")]

#   group data min_of_max min_of_max_2
# 1     1    1          2            3
# 2     1    2          3          Inf
# 3     1    3        Inf          Inf
# 4     2    4          5            6
# 5     2    5          6          Inf
# 6     2    6        Inf          Inf
# 7     3    7          8            9
# 8     3    8          9          Inf
# 9     3    9        Inf          Inf
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