count_if: Count/sum/average/other functions on values that meet a criterion

Description

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.

Usage

count_if(criterion, ...)
count_row_if(criterion, ...)
count_col_if(criterion, ...)
x %row_in% 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)

Arguments

criterion

Vector with counted values, logical vector/matrix or function. See details and examples.

...

Data on which criterion will be applied. Vector, matrix, data.frame, list. Shorter arguments will be recycled.

data

Data on which function will be applied. Doesn't applicable to count_*_if functions. If omitted then function will be applied on the ... argument.

fun

Custom function that will be applied based on criterion.

Value

*_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.

Details

Possible type for criterion argument:

vector/single value All values in ... which equal to elements of vector in 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.
logical vector/matrix/data.frame Values for which element of criterion equals to TRUE will be used as function fun argument. Logical vector will be recycled across all columns of ...data. If criteria is logical matrix/data.frame then column from this matrix/data.frame will be used for corresponding column/element of ...data. Note that this kind of criterion doesn't use ... so ... can be used instead of data argument.

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.

Examples

Run this code

# NOT RUN {
set.seed(123)
dfs = as.data.frame(
       matrix(sample(c(1:10,NA), 30, replace = TRUE), 10)
)

result  = modify(dfs, {
             # 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 not-NA 
             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   
         })  
result
 
mean_row_if(6, dfs$V1, data = dfs)
median_row_if(gt(2), dfs$V1, dfs$V2, dfs$V3) 
sd_row_if(5 %thru% 8, dfs$V1, dfs$V2, dfs$V3)
 
if_na(dfs) = 5 # replace NA 

# custom apply
apply_col_if(prod, gt(2), dfs$V1, data = dfs) # product of all elements by columns
apply_row_if(prod, gt(2), dfs$V1, data = dfs) # product of all elements by rows
 
# Examples borrowed from Microsoft Excel help for COUNTIF
df1 = data.frame(
    a = c("apples",   "oranges",     "peaches",     "apples"),
    b = c(32, 54, 75, 86)
)

count_if("apples", df1$a) # 2

count_if("apples", df1) # 2

with(df1, count_if("apples", a, b)) # 2

count_if(gt(55), df1$b) # greater than 55 = 2

count_if(ne(75), df1$b) # not equal 75 = 3

count_if(ge(32), df1$b) # greater than or equal 32 = 4

count_if(gt(32) & lt(86), df1$b) # 2

# count only integer values between 33 and 85
count_if(33:85, df1$b) # 2

# values with letters
count_if(regex("^[A-z]+$"), df1) # 4

# values that started on 'a'
count_if(regex("^a"), df1) # 2

# count_row_if
count_row_if(regex("^a"), df1) # c(1,0,0,1)

df1 %row_in% 'apples'  # c(TRUE,FALSE,FALSE,TRUE)

# Some of Microsoft Excel examples for SUMIF/AVERAGEIF/etc 
dfs = read.csv(
    text = "
    property_value,commission,data
    100000,7000,250000
    200000,14000,	
    300000,21000,	
    400000,28000,"
)

# Sum of commision for property value greater than 160000
with(dfs, sum_if(gt(160000), property_value, data = commission)) # 63000
    
# Sum of property value greater than 160000
with(dfs, sum_if(gt(160000), property_value)) # 900000

# Sum of commision for property value equals to 300000
with(dfs, sum_if(300000, property_value, data = commission)) # 21000
    
# Sum of commision for property value greater than first value of data
with(dfs, sum_if(gt(data[1]), property_value, data = commission)) # 49000
    

dfs = data.frame(
    category = c("Vegetables", "Vegetables", "Fruits", "", "Vegetables", "Fruits"),
    food = c("Tomatoes", "Celery", "Oranges", "Butter", "Carrots", "Apples"),
    sales = c(2300, 5500, 800, 400, 4200, 1200),
    stringsAsFactors = FALSE
)

# Sum of sales for Fruits
with(dfs, sum_if("Fruits", category, data = sales)) # 2000

# Sum of sales for Vegetables    
with(dfs, sum_if("Vegetables", category, data = sales)) # 12000

# Sum of sales for food which is ending on 'es' 
with(dfs, sum_if(perl("es$"), food, data = sales)) # 4300

# Sum of sales for empty category
with(dfs, sum_if("", category, data = sales))  # 400


dfs = read.csv(
    text = "
    property_value,commission,data
    100000,7000,250000
    200000,14000,	
    300000,21000,	
    400000,28000,"
)

# Commision average for comission less than 23000
with(dfs, mean_if(lt(23000), commission)) # 14000


# Property value average for property value less than 95000
with(dfs, mean_if(lt(95000), property_value)) #  NaN

# Commision average for property value greater than 250000
with(dfs, mean_if(gt(250000), property_value, data = commission)) # 24500


dfs = data.frame(
    region = c("East", "West", "North", "South (New Office)",  "MidWest"),
    profits = c(45678, 23789, -4789, 0, 9678),
    stringsAsFactors = FALSE
)


# Mean profits for 'west' regions
with(dfs, mean_if(fixed("West"), region, data = profits)) # 16733.5


# Mean profits for regions wich doesn't contain New Office
with(dfs, mean_if(!fixed("(New Office)"), region, data = profits))  # 18589


dfs = read.csv(
    text = '
    grade,weight 
    89,1
    93,2
    96,2
    85,3
    91,1
    88,1'
    ,stringsAsFactors = FALSE
)

# Minimum grade for weight equals to 1
with(dfs, min_if(1, weight, data = grade)) # 88


# Maximum grade for weight equals to 1
with(dfs, max_if(1, weight, data = grade)) #91


# Example with offset
dfs = read.csv(
    text = '
    weight,grade 
    10,b
    11,a
    100,a
    111,b
    1,a
    1,a'
    ,stringsAsFactors = FALSE
)

with(dfs, min_if("a", grade[2:5], data = weight[1:4])) # 10


# }