count_if  R Documentation 
These functions calculate count/sum/average/etc. on values that meet a
criterion that you specify. apply_if_*
apply custom functions. There
are different flavors of these functions: *_if
work on entire
dataset/matrix/vector, *_row_if
works on each row and *_col_if
works on each column.
count_if(criterion, ...) count_row_if(criterion, ...) count_col_if(criterion, ...) has(x, criterion) x %row_in% criterion x %has% criterion x %col_in% criterion sum_if(criterion, ..., data = NULL) sum_row_if(criterion, ..., data = NULL) sum_col_if(criterion, ..., data = NULL) mean_if(criterion, ..., data = NULL) mean_row_if(criterion, ..., data = NULL) mean_col_if(criterion, ..., data = NULL) sd_if(criterion, ..., data = NULL) sd_row_if(criterion, ..., data = NULL) sd_col_if(criterion, ..., data = NULL) median_if(criterion, ..., data = NULL) median_row_if(criterion, ..., data = NULL) median_col_if(criterion, ..., data = NULL) max_if(criterion, ..., data = NULL) max_row_if(criterion, ..., data = NULL) max_col_if(criterion, ..., data = NULL) min_if(criterion, ..., data = NULL) min_row_if(criterion, ..., data = NULL) min_col_if(criterion, ..., data = NULL) apply_row_if(fun, criterion, ..., data = NULL) apply_col_if(fun, criterion, ..., data = NULL)
criterion 
Vector with counted values or function. See details and examples. 
... 
Data on which criterion will be applied. Vector, matrix, data.frame, list. 
x 
Data on which criterion will be applied. Vector, matrix, data.frame, list. 
data 
Data on which function will be applied. Doesn't applicable to

fun 
Custom function that will be applied based on criterion. 
Possible type for criterion argument:
vector/single value All values in ...
which equal to the
elements of vector in the criteria will be used as function fun
argument.
function Values for which function gives TRUE will be used as
function fun
argument. There are some special functions for
convenience (e. g. gt(5)
is equivalent ">5" in spreadsheet)  see
criteria.
count*
and %in*%
never returns NA's. Other functions remove
NA's before calculations (as na.rm = TRUE
in base R functions).
Function criterion should return logical vector of same size and shape as its
argument. This function will be applied to each column of supplied data and
TRUE results will be used. There is asymmetrical behavior in *_row_if
and *_col_if
for function criterion: in both cases function criterion
will be applied columnwise.
*_if
return single value (vector of length 1).
*_row_if
returns vector for each row of supplied arguments.
*_col_if
returns vector for each column of supplied arguments.
%row_in%
/%col_in%
return logical vector  indicator of
presence of criterion in each row/column. %has%
is an alias for
%row_in%
.
set.seed(123) sheet1 = as.sheet( matrix(sample(c(1:10,NA), 30, replace = TRUE), 10) ) result = let(sheet1, # count 8 exact = count_row_if(8, V1, V2, V3), # count values greater than 8 greater = count_row_if(gt(8), V1, V2, V3), # count integer values between 5 and 8, e. g. 5, 6, 7, 8 integer_range = count_row_if(5:8, V1, V2, V3), # count values between 5 and 8 range = count_row_if(5 %thru% 8, V1, V2, V3), # count NA na = count_row_if(is.na, V1, V2, V3), # count notNA not_na = count_row_if(not_na, V1, V2, V3), # are there any 5 in each row? has_five = cbind(V1, V2, V3) %row_in% 5 ) print(result) mean_row_if(6, sheet1$V1, data = sheet1) median_row_if(gt(2), sheet1$V1, sheet1$V2, sheet1$V3) sd_row_if(5 %thru% 8, sheet1$V1, sheet1$V2, sheet1$V3) if_na(sheet1) = 5 # replace NA # custom apply apply_col_if(prod, gt(2), sheet1$V1, data = sheet1) # product of all elements by columns apply_row_if(prod, gt(2), sheet1$V1, data = sheet1) # product of all elements by rows # Examples borrowed from Microsoft Excel help for COUNTIF sheet1 = text_to_columns( " a b apples 32 oranges 54 peaches 75 apples 86 " ) count_if("apples", sheet1$a) # 2 count_if("apples", sheet1) # 2 with(sheet1, count_if("apples", a, b)) # 2 count_if(gt(55), sheet1$b) # greater than 55 = 2 count_if(ne(75), sheet1$b) # not equal 75 = 3 count_if(ge(32), sheet1$b) # greater than or equal 32 = 4 count_if(gt(32) & lt(86), sheet1$b) # 2 # count only integer values between 33 and 85 count_if(33:85, sheet1$b) # 2 # values with letters count_if(regex("^[Az]+$"), sheet1) # 4 # values that started on 'a' count_if(regex("^a"), sheet1) # 2 # count_row_if count_row_if(regex("^a"), sheet1) # c(1,0,0,1) sheet1 %row_in% 'apples' # c(TRUE,FALSE,FALSE,TRUE) # Some of Microsoft Excel examples for SUMIF/AVERAGEIF/etc sheet1 = text_to_columns( " property_value commission data 100000 7000 250000 200000 14000 300000 21000 400000 28000 " ) # Sum of commision for property value greater than 160000 with(sheet1, sum_if(gt(160000), property_value, data = commission)) # 63000 # Sum of property value greater than 160000 with(sheet1, sum_if(gt(160000), property_value)) # 900000 # Sum of commision for property value equals to 300000 with(sheet1, sum_if(300000, property_value, data = commission)) # 21000 # Sum of commision for property value greater than first value of data with(sheet1, sum_if(gt(data[1]), property_value, data = commission)) # 49000 sheet1 = text_to_columns( " category food sales Vegetables Tomatoes 2300 Vegetables Celery 5500 Fruits Oranges 800 NA Butter 400 Vegetables Carrots 4200 Fruits Apples 1200 " ) # Sum of sales for Fruits with(sheet1, sum_if("Fruits", category, data = sales)) # 2000 # Sum of sales for Vegetables with(sheet1, sum_if("Vegetables", category, data = sales)) # 12000 # Sum of sales for food which is ending on 'es' with(sheet1, sum_if(perl("es$"), food, data = sales)) # 4300 # Sum of sales for empty category with(sheet1, sum_if(NA, category, data = sales)) # 400 sheet1 = text_to_columns( " property_value commission data 100000 7000 250000 200000 14000 300000 21000 400000 28000 " ) # Commision average for comission less than 23000 with(sheet1, mean_if(lt(23000), commission)) # 14000 # Property value average for property value less than 95000 with(sheet1, mean_if(lt(95000), property_value)) # NaN # Commision average for property value greater than 250000 with(sheet1, mean_if(gt(250000), property_value, data = commission)) # 24500 sheet1 = text_to_columns( ' region profits East 45678 West 23789 North 4789 "South (New Office)" 0 MidWest 9678 ', quote = '"' ) # Mean profits for 'west' regions with(sheet1, mean_if(contains("West"), region, data = profits)) # 16733.5 # Mean profits for regions wich doesn't contain New Office with(sheet1, mean_if(not(contains("New Office")), region, data = profits)) # 18589 sheet1 = text_to_columns( " grade weight 89 1 93 2 96 2 85 3 91 1 88 1 " ) # Minimum grade for weight equals to 1 with(sheet1, min_if(1, weight, data = grade)) # 88 # Maximum grade for weight equals to 1 with(sheet1, max_if(1, weight, data = grade)) #91 # Example with offset sheet1 = text_to_columns( " weight grade 10 b 11 a 100 a 111 b 1 a 1 a " ) with(sheet1, min_if("a", grade[2:5], data = weight[1:4])) # 10
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