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How to filter data frame by categorical variable in R?
To filter data frame by categorical variable in R, we can follow the below steps −
Use inbuilt data sets or create a new data set and look at top few rows in the data set.
Then, look at the bottom few rows in the data set.
Check the data structure.
Filter the data by categorical column using split function.
Use inbuilt data set
Let’s consider CO2 data set in base R −
data(CO2) head(CO2,10)
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
Grouped Data: uptake ~ conc | Plant Plant Type Treatment conc uptake 1 Qn1 Quebec nonchilled 95 16.0 2 Qn1 Quebec nonchilled 175 30.4 3 Qn1 Quebec nonchilled 250 34.8 4 Qn1 Quebec nonchilled 350 37.2 5 Qn1 Quebec nonchilled 500 35.3 6 Qn1 Quebec nonchilled 675 39.2 7 Qn1 Quebec nonchilled 1000 39.7 8 Qn2 Quebec nonchilled 95 13.6 9 Qn2 Quebec nonchilled 175 27.3 10 Qn2 Quebec nonchilled 250 37.1
Look at few bottom rows
Use tail function to look at some bottom rows in CO2 data −
data(CO2) tail(CO2,10)
Output
Grouped Data: uptake ~ conc | Plant Plant Type Treatment conc uptake 75 Mc2 Mississippi chilled 500 12.5 76 Mc2 Mississippi chilled 675 13.7 77 Mc2 Mississippi chilled 1000 14.4 78 Mc3 Mississippi chilled 95 10.6 79 Mc3 Mississippi chilled 175 18.0 80 Mc3 Mississippi chilled 250 17.9 81 Mc3 Mississippi chilled 350 17.9 82 Mc3 Mississippi chilled 500 17.9 83 Mc3 Mississippi chilled 675 18.9 84 Mc3 Mississippi chilled 1000 19.9
Check the data structure
Use str function to check the data structure of data in CO2 −
Example
data(CO2) str(CO2)
Output
Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 84 obs. of 5 variables: $ Plant : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ... $ Type : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ... $ Treatment: Factor w/ 2 levels "nonchilled","chilled": 1 1 1 1 1 1 1 1 1 1 ... $ conc : num 95 175 250 350 500 675 1000 95 175 250 ... $ uptake : num 16 30.4 34.8 37.2 35.3 39.2 39.7 13.6 27.3 37.1 ... - attr(*, "formula")=Class 'formula' language uptake ~ conc | Plant .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> - attr(*, "outer")=Class 'formula' language ~Treatment * Type .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> - attr(*, "labels")=List of 2 ..$ x: chr "Ambient carbon dioxide concentration" ..$ y: chr "CO2 uptake rate" - attr(*, "units")=List of 2 ..$ x: chr "(uL/L)" ..$ y: chr "(umol/m^2 s)"
Filter data frame by categorical column
Using split function to filter the data frame CO2 based on Type column −
data(CO2) split(CO2,CO2$Type)
Output
$Quebec Grouped Data: uptake ~ conc | Plant Plant Type Treatment conc uptake 1 Qn1 Quebec nonchilled 95 16.0 2 Qn1 Quebec nonchilled 175 30.4 3 Qn1 Quebec nonchilled 250 34.8 4 Qn1 Quebec nonchilled 350 37.2 5 Qn1 Quebec nonchilled 500 35.3 6 Qn1 Quebec nonchilled 675 39.2 7 Qn1 Quebec nonchilled 1000 39.7 8 Qn2 Quebec nonchilled 95 13.6 9 Qn2 Quebec nonchilled 175 27.3 10 Qn2 Quebec nonchilled 250 37.1 11 Qn2 Quebec nonchilled 350 41.8 12 Qn2 Quebec nonchilled 500 40.6 13 Qn2 Quebec nonchilled 675 41.4 14 Qn2 Quebec nonchilled 1000 44.3 15 Qn3 Quebec nonchilled 95 16.2 16 Qn3 Quebec nonchilled 175 32.4 17 Qn3 Quebec nonchilled 250 40.3 18 Qn3 Quebec nonchilled 350 42.1 19 Qn3 Quebec nonchilled 500 42.9 20 Qn3 Quebec nonchilled 675 43.9 21 Qn3 Quebec nonchilled 1000 45.5 22 Qc1 Quebec chilled 95 14.2 23 Qc1 Quebec chilled 175 24.1 24 Qc1 Quebec chilled 250 30.3 25 Qc1 Quebec chilled 350 34.6 26 Qc1 Quebec chilled 500 32.5 27 Qc1 Quebec chilled 675 35.4 28 Qc1 Quebec chilled 1000 38.7 29 Qc2 Quebec chilled 95 9.3 30 Qc2 Quebec chilled 175 27.3 31 Qc2 Quebec chilled 250 35.0 32 Qc2 Quebec chilled 350 38.8 33 Qc2 Quebec chilled 500 38.6 34 Qc2 Quebec chilled 675 37.5 35 Qc2 Quebec chilled 1000 42.4 36 Qc3 Quebec chilled 95 15.1 37 Qc3 Quebec chilled 175 21.0 38 Qc3 Quebec chilled 250 38.1 39 Qc3 Quebec chilled 350 34.0 40 Qc3 Quebec chilled 500 38.9 41 Qc3 Quebec chilled 675 39.6 42 Qc3 Quebec chilled 1000 41.4 $Mississippi Grouped Data: uptake ~ conc | Plant Plant Type Treatment conc uptake 43 Mn1 Mississippi nonchilled 95 10.6 44 Mn1 Mississippi nonchilled 175 19.2 45 Mn1 Mississippi nonchilled 250 26.2 46 Mn1 Mississippi nonchilled 350 30.0 47 Mn1 Mississippi nonchilled 500 30.9 48 Mn1 Mississippi nonchilled 675 32.4 49 Mn1 Mississippi nonchilled 1000 35.5 50 Mn2 Mississippi nonchilled 95 12.0 51 Mn2 Mississippi nonchilled 175 22.0 52 Mn2 Mississippi nonchilled 250 30.6 53 Mn2 Mississippi nonchilled 350 31.8 54 Mn2 Mississippi nonchilled 500 32.4 55 Mn2 Mississippi nonchilled 675 31.1 56 Mn2 Mississippi nonchilled 1000 31.5 57 Mn3 Mississippi nonchilled 95 11.3 58 Mn3 Mississippi nonchilled 175 19.4 59 Mn3 Mississippi nonchilled 250 25.8 60 Mn3 Mississippi nonchilled 350 27.9 61 Mn3 Mississippi nonchilled 500 28.5 62 Mn3 Mississippi nonchilled 675 28.1 63 Mn3 Mississippi nonchilled 1000 27.8 64 Mc1 Mississippi chilled 95 10.5 65 Mc1 Mississippi chilled 175 14.9 66 Mc1 Mississippi chilled 250 18.1 67 Mc1 Mississippi chilled 350 18.9 68 Mc1 Mississippi chilled 500 19.5 69 Mc1 Mississippi chilled 675 22.2 70 Mc1 Mississippi chilled 1000 21.9 71 Mc2 Mississippi chilled 95 7.7 72 Mc2 Mississippi chilled 175 11.4 73 Mc2 Mississippi chilled 250 12.3 74 Mc2 Mississippi chilled 350 13.0 75 Mc2 Mississippi chilled 500 12.5 76 Mc2 Mississippi chilled 675 13.7 77 Mc2 Mississippi chilled 1000 14.4 78 Mc3 Mississippi chilled 95 10.6 79 Mc3 Mississippi chilled 175 18.0 80 Mc3 Mississippi chilled 250 17.9 81 Mc3 Mississippi chilled 350 17.9 82 Mc3 Mississippi chilled 500 17.9 83 Mc3 Mississippi chilled 675 18.9 84 Mc3 Mississippi chilled 1000 19.9
Updated on: 14-Aug-2021
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