Adding NULL when no variable data

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Below is a sample DF that illustrates the issue that I am having. I am having an issue with a group not having a value for all variables so R is not returning anything for it. That is, in the data below R returns:

Course   Gender  n
English1 Female  1
English1 Male    3
English2 Female  2
English2 Male    1
English2 Unknown 1
English3 Female  3
English3 Unknown 1

df1 <- data.frame("Course"=c("English1", "English1", "English1", "English1", 
                             "English2", "English2", "English2", "English2", 
                             "English3", "English3", "English3", "English3"),  
                  Gender=c("Male", "Female", "Male", "Male", "Male", "Female", 
                           "Unknown", "Female", "Female", "Female", "Female", 
                           "Unknown"),  Grade=c("A", "A", "C", "D", "D", "A", "B", 
                                                "C", "B", "D", "A", "C"))
library(dplyr)
df1 %>% group_by(Course, Gender) %>% count

What I am trying to do is return a Null or 0 when there are not counts of the Gender within the Course group. I would like the data to return this (I tagged the new rows with *):

Course   Gender  n
English1 Female  1
English1 Male    3
English1 Unknown 0*
English2 Female  2
English2 Male    1
English2 Unknown 1
English3 Female  3
English3 Male    0*
English3 Unknown 1

The reason that I need this is because I need to have identical groups (three genders for each course) for an rMarkdown output. Any help is greatly appreciated

Actually, a dplyr solution has already been solved here using the complete function after the count function in your code. You choose the fill=list(value=0) option for filling those missing rows with the values you need, but it could be any other.

Note, you have to ungroup first or you will be doing this operation once per group, thus duplicating your rows.

This is pretty straightforward now and more adjusted to the way you are expressing your needs:

    df1 %>%
     group_by(Course,Gender) %>%
     count %>% 
     ungroup() %>%
     complete(Course,Gender,fill=list(n=0))



 # A tibble: 9 x 3
  Course   Gender      n
  <fct>    <fct>   <dbl>
1 English1 Female      1
2 English1 Male        3
3 English1 Unknown     0
4 English2 Female      2
5 English2 Male        1
6 English2 Unknown     1
7 English3 Female      3
8 English3 Male        0
9 English3 Unknown     1

The Null Variable, For example, suppose the variable U has not been defined in the current IDL Thinking of a null variable in this way makes it easy to add variables to an array� For example, in one DATA step a new variable, NewAirCost, was created to contain the value of the airfare plus the new $10 tax: NewAirCost = AirCost + 10; You can also decide to change the value of an existing variable rather than create a new variable.

data.frame(xtabs(a~Gender+Course,cbind(a=1,df1)))[c(2,1,3)]
    Course  Gender Freq
1 English1  Female    1
2 English1    Male    3
3 English1 Unknown    0
4 English2  Female    2
5 English2    Male    1
6 English2 Unknown    1
7 English3  Female    3
8 English3    Male    0
9 English3 Unknown    1

If you do not care about the ordering then:

data.frame(xtabs(Grade~.,cbind(Grade=1,df1)))

NULL - Manual, Casting a variable to null using (unset) $var will not remove the variable or unset its value. It will only return a NULL value. add a note. User Contributed Notes 5� Thanks Ken. Actually there is no need to replace 0s with nulls. The text version of the formula returns an empty string for the nulls which converts to null when the data type is changed to integer. I just find it weird that this extra step is necessary when using an Access data source as opposed to Excel (have not tested with SQL yet).

As of dplyr 0.8.0, you can just add .drop = FALSE to the statement:

df1 %>% 
  group_by(Course, Gender, .drop = FALSE) %>% 
  count

Output:

# A tibble: 9 x 3
# Groups:   Course, Gender [9]
  Course   Gender      n
  <fct>    <fct>   <int>
1 English1 Female      1
2 English1 Male        3
3 English1 Unknown     0
4 English2 Female      2
5 English2 Male        1
6 English2 Unknown     1
7 English3 Female      3
8 English3 Male        0
9 English3 Unknown     1

Note that this can be simplified and still works also if you just use count alone:

df1 %>% count(Course, Gender, .drop = FALSE)

# A tibble: 9 x 3
  Course   Gender      n
  <fct>    <fct>   <int>
1 English1 Female      1
2 English1 Male        3
3 English1 Unknown     0
4 English2 Female      2
5 English2 Male        1
6 English2 Unknown     1
7 English3 Female      3
8 English3 Male        0
9 English3 Unknown     1

A quick and thorough guide to 'null': what it is, and how you should , Here we declare a variable of type String and with the identifier name that points You can also think of it as the absence of data or simply no data. the results are not yet available), or if we want to add specific data for each� You can change the null values to a coded value that means null (like -999 or something); something that would not be mistaken as a valid value in the data set and is not zero (since as you say, that's misleading). You'd make sure in your classification that value had its own class with no other values (can be done on the symbology tab).

null, The value null represents the intentional absence of any object value. Polski � Portugu�s (do Brasil) � Русский � Українська � 中文 (简体) � 正體中文 (繁體) � Add a translation Instead, null expresses a lack of identification, indicating that a variable points to no object. Update compatibility data on GitHub� If the nature of the data requires that all data be present to save the record, you can handle null values at the table level. Simply set the field's Required property to Yes and bypass the

Null and Undefined, JavaScript - Variable � JavaScript - Operators � JavaScript - Data Types JavaScript includes two additional primitive type values - null and undefined, that A variable or an object has an undefined value when no value is assigned before using it. function Sum(val1, val2) { var result = val1 + val2; } var result = Sum(5,� Here, the variable has the same 5 variables in both data frames as we have not done any insertion/removal to the variable/column of the data frame. Adding Multiple Observations/Rows To R Data Frame Adding single observations one by one is a repetitive, time-consuming, as well as, a boring task.

VBA Data Types - Null, This keyword cannot be assigned to variables of other data types. The Null data type can be assigned explicitly to indicate that the variable has no data. A Null� Understanding the Limitations of Data in NOT NULL Columns. Before any changes are made to your table, it’s important to briefly go over what data can (and cannot) be specified within an existing column that you wish to alter to NOT NULL, ensuring that no row is allowed to have a NULL value in that column.

Comments
  • See tidyr::complete().
  • group_by(Course, Gender, .drop = FALSE) solves this if you're using dplyr 0.8.0 or higher