ExtractThe most general way to subset a data frame by rows and/or columns is the base R Extract function, called by d[rows, columms], where d is the data frame. To use this function, for the rows parameter, pass the row names of the selected rows, the indices or actual names, or pass a logical statement that, when evaluated, results in these names. For the cols parameter, pass the column indices of the selected columns, or pass a list of variable names that reduces to these indices.
In the logical expression of rows, use the standard R operators.
| operator | meaning |
|---|---|
| & | and |
| | | or |
| ! | not |
| == | is equal to |
| != | is not equal to |
| %in% | is in a vector |
For the column specification, specify a variable range of contiguous variables with a colon, :.
ExtractWhen Extract evaluates the row or column specifications to obtain the indices, there are several limitations.
rows: Any reference to the variables in the data frame for this specification must contain the name of the data frame followed by a $. But this name has already been specified in the function call, and now is redundant, repeated for every variable reference.cols: Usually specified with a vector of variable names.rows and cols. Instead directly enter the conditions for both rows and columns, which can make the entire expression quite large.ExtractTo address the first two deficiencies, one possibility is the base R subset() function. To use Extract directly, lessR provides the function .() for obtaining the indices of selected rows and of selected columns. This function is only callable within the base R Extract function, with what R refers to as non-standard evaluation. That basically means that the annoying restrictions are removed, though in some advanced uses the .() may not apply.
The general form of the subsetting follows.
d[.(rows), .(columns)]That is, call the Extract function as before, but now wrap the row and column expressions with .().
To illustrate, use the Employee data set contained in lessR, here read into the d data frame.
##
## >>> Suggestions
## Details about your data, Enter: details() for d, or details(name)
##
## Data Types
## ------------------------------------------------------------
## character: Non-numeric data values
## integer: Numeric data values, integers only
## double: Numeric data values with decimal digits
## ------------------------------------------------------------
##
## Variable Missing Unique
## Name Type Values Values Values First and last values
## ------------------------------------------------------------------------------------------
## 1 Years integer 36 1 16 7 NA 15 ... 1 2 10
## 2 Gender character 37 0 2 M M M ... F F M
## 3 Dept character 36 1 5 ADMN SALE SALE ... MKTG SALE FINC
## 4 Salary double 37 0 37 53788.26 94494.58 ... 56508.32 57562.36
## 5 JobSat character 35 2 3 med low low ... high low high
## 6 Plan integer 37 0 3 1 1 3 ... 2 2 1
## 7 Pre integer 37 0 27 82 62 96 ... 83 59 80
## 8 Post integer 37 0 22 92 74 97 ... 90 71 87
## ------------------------------------------------------------------------------------------Subset the data frame by only listing observations with a Gender of “M” with scores on Post larger than 90. Only list columns for the variables in the range from Years to Salary, and Post. Referring back to the output of Read(), the variable range includes Years, Gender, Dept, and Salary.
## Years Gender Dept Salary Post
## Ritchie, Darnell 7 M ADMN 53788.26 92
## Hoang, Binh 15 M SALE 111074.86 97
## Pham, Scott 13 M SALE 81871.05 94
## Correll, Trevon 21 M SALE 134419.23 94
## Langston, Matthew 5 M SALE 49188.96 93
## Anderson, David 9 M ACCT 69547.60 91Following is the traditional R call to Extract for subsetting.
## Years Gender Dept Salary Post
## Ritchie, Darnell 7 M ADMN 53788.26 92
## Hoang, Binh 15 M SALE 111074.86 97
## Pham, Scott 13 M SALE 81871.05 94
## Correll, Trevon 21 M SALE 134419.23 94
## Langston, Matthew 5 M SALE 49188.96 93
## Anderson, David 9 M ACCT 69547.60 91To negate a row selection, add a ! to the beginning of the logical condition passed to .(), within the call to .(). To exclude the specified variables, place a -, in front of the call to .().
## Years Gender Post
## Wu, James NA M 74
## Jones, Alissa 5 F 62
## Downs, Deborah 7 F 86
## Afshari, Anbar 6 F 100
## Knox, Michael 18 M 84
## Campagna, Justin 8 M 84
## Kimball, Claire 8 F 92
## Cooper, Lindsay 4 F 91
## Saechao, Suzanne 8 F 100
## Tian, Fang 9 F 61
## Bellingar, Samantha 10 F 72
## Sheppard, Cory 14 M 73
## Kralik, Laura 10 F 71
## Skrotzki, Sara 18 F 61
## James, Leslie 18 F 70
## Osterman, Pascal 5 M 70
## Adib, Hassan 14 M 69
## Gvakharia, Kimberly 3 F 79
## Stanley, Grayson 9 M 73
## Link, Thomas 10 M 83
## Portlock, Ryan 13 M 73
## Stanley, Emma 3 F 84
## Singh, Niral 2 F 59
## Fulton, Scott 13 M 73
## Korhalkar, Jessica 2 F 87
## LaRoe, Maria 10 F 86
## Billing, Susan 4 F 90
## Capelle, Adam 24 M 81
## Hamide, Bita 1 F 90
## Anastasiou, Crystal 2 F 71
## Cassinelli, Anastis 10 M 87Can still provide the indices directly for one or both of the expressions as the base R Extract function is unmodified.
## Years Gender Dept Salary Post
## Ritchie, Darnell 7 M ADMN 53788.26 92
## Wu, James NA M SALE 94494.58 74
## Hoang, Binh 15 M SALE 111074.86 97## Years Gender Dept
## Ritchie, Darnell 7 M ADMN
## Hoang, Binh 15 M SALE
## Pham, Scott 13 M SALE
## Correll, Trevon 21 M SALE
## Langston, Matthew 5 M SALE
## Anderson, David 9 M ACCTTo enhance readability, store the specified row or column conditions as character strings. Each string must be named rows or cols. Because the entire expression is a character string, differentiate between single and double quotes as needed. For example, use single quotes within the string and double quotes to define the entire string, as illustrated next.
To subset, pass the respective character strings, rows and cols, to .(), respectively.
## Gender Dept Salary Post
## Hoang, Binh M SALE 111074.86 97
## Pham, Scott M SALE 81871.05 94
## Correll, Trevon M SALE 134419.23 94To negate, as with the literal expressions, use ! for the rows and - for the columns. Notice their placement.
## Years JobSat Plan Pre
## Ritchie, Darnell 7 med 1 82
## Wu, James NA low 1 62
## Jones, Alissa 5 <NA> 1 65
## Downs, Deborah 7 high 2 90
## Afshari, Anbar 6 high 2 100
## Knox, Michael 18 med 3 81
## Campagna, Justin 8 low 1 76
## Kimball, Claire 8 high 2 93
## Cooper, Lindsay 4 high 1 78
## Saechao, Suzanne 8 med 1 98
## Tian, Fang 9 med 2 60
## Bellingar, Samantha 10 med 1 67
## Sheppard, Cory 14 low 3 66
## Kralik, Laura 10 med 2 74
## Skrotzki, Sara 18 med 2 63
## James, Leslie 18 low 3 70
## Osterman, Pascal 5 high 3 69
## Adib, Hassan 14 med 2 71
## Gvakharia, Kimberly 3 med 2 83
## Stanley, Grayson 9 low 1 74
## Link, Thomas 10 low 1 83
## Portlock, Ryan 13 low 1 72
## Langston, Matthew 5 low 3 94
## Stanley, Emma 3 high 2 86
## Singh, Niral 2 high 2 59
## Anderson, David 9 low 1 94
## Fulton, Scott 13 low 1 72
## Korhalkar, Jessica 2 <NA> 2 74
## LaRoe, Maria 10 high 2 80
## Billing, Susan 4 med 2 91
## Capelle, Adam 24 med 2 83
## Hamide, Bita 1 high 2 83
## Anastasiou, Crystal 2 low 2 59
## Cassinelli, Anastis 10 high 1 80The variable Dept is missing for the fourth row of data.
## Years Gender Dept Salary JobSat Plan Pre Post
## Ritchie, Darnell 7 M ADMN 53788.26 med 1 82 92
## Wu, James NA M SALE 94494.58 low 1 62 74
## Hoang, Binh 15 M SALE 111074.86 low 3 96 97
## Jones, Alissa 5 F <NA> 53772.58 <NA> 1 65 62
## Downs, Deborah 7 F FINC 57139.90 high 2 90 86Here with the traditional use of Extract, specify rows of data only when the value of Dept is ADMN.
## Gender Dept Salary
## Ritchie, Darnell M ADMN 53788.26
## NA <NA> <NA> NA
## Afshari, Anbar F ADMN 69441.93
## James, Leslie F ADMN 122563.38
## Singh, Niral F ADMN 61055.44
## Billing, Susan F ADMN 72675.26
## Capelle, Adam M ADMN 108138.43The result provides what was requested, and also when Dept is <NA>, which is not requested. The requested value of ADMN is not the same as <NA>.
Use the .() function to obtain what is requested, rows of data in which the value of Dept is ADMN.
## Gender Dept Salary
## Ritchie, Darnell M ADMN 53788.26
## Afshari, Anbar F ADMN 69441.93
## James, Leslie F ADMN 122563.38
## Singh, Niral F ADMN 61055.44
## Billing, Susan F ADMN 72675.26
## Capelle, Adam M ADMN 108138.43If rows with the value of the variable missing are desired, then .() provides that information only when requested, such as with the base R function is.na().
## Gender Dept Salary
## Ritchie, Darnell M ADMN 53788.26
## Jones, Alissa F <NA> 53772.58
## Afshari, Anbar F ADMN 69441.93
## James, Leslie F ADMN 122563.38
## Singh, Niral F ADMN 61055.44
## Billing, Susan F ADMN 72675.26
## Capelle, Adam M ADMN 108138.43The function .() also provides for random selection of rows. To randomly select the specified number of rows from the data frame to subset, specify the random() function for the logical criterion of the rows. The value passed to random() can either be the actual number of rows to select, or the proportion of rows to select.
Here randomly select five rows of data from the d data frame.
## Years Gender Dept Salary
## Stanley, Grayson 9 M SALE 69624.87
## Fulton, Scott 13 M SALE 87785.51
## Hoang, Binh 15 M SALE 111074.86
## Anderson, David 9 M ACCT 69547.60
## Ritchie, Darnell 7 M ADMN 53788.26Here specify a proportion of rows to select.
## Years Gender Dept Salary
## Osterman, Pascal 5 M ACCT 49704.79
## Jones, Alissa 5 F <NA> 53772.58
## Sheppard, Cory 14 M FINC 95027.55
## LaRoe, Maria 10 F MKTG 61961.29