MarkLogic server has a large number of statistical functions built in that the rfml package exposes.

Field level functions

The built in functions are primary on field level, one or more fields that can be used as inputs. All functions will return a value.

Currently Pearson correlation, Covariance, Population Covariance, Variance, Population variance, Standard Deviation, Population Standard Deviation, Median, Mean, Sum, Max and Min are implemented.

Example of field level functions:

library(rfml)

mlLocal <- ml.connect()
# Create a ml.data.frame based on iris, you can upload the iris data set using as.ml.data.frame
mlIris <- ml.data.frame(mlLocal, collection = "iris")

# Pearson correlation
cor(mlIris$Sepal.Length, mlIris$Petal.Length)
# ==>
# [1] 0.8717538

# Covariance
cov(mlIris$Sepal.Length, mlIris$Petal.Length)
# ==>
# [1] 1.274315

# Variance
var(mlIris$Sepal.Length)
# ==>
# [1] 0.6856935

# Standard derivation
sd(mlIris$Sepal.Length)
# ==>
# [1] 0.8280661

ml.data.frame level functions

In addition to the built in field level functions there is a number of statistical functions that take a ml.data.frame object as input. These functions are as well executed on the server side and only the result are returned to the client.

Currently summary and Pearson Correlation are implemented.

Example using summary:

library(rfml)

mlLocal <- ml.connect()
# Create a ml.data.frame based on iris, you can upload the iris data set using as.ml.data.frame
mlIris <- ml.data.frame(mlLocal, collection = "iris")

summary(mlIris)
# ====>
# Sepal.Length     Sepal.Width    Petal.Length     Petal.Width       Species    
# Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100   setosa    :50  
# 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.550   1st Qu.:0.300   versicolor:50  
# Median :5.800   Median :3.000   Median :4.350   Median :1.300   virginica :50  
# Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199                  
# 3rd Qu.:6.400   3rd Qu.:3.350   3rd Qu.:5.100   3rd Qu.:1.800                  
# Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500

Example using correlation:

library(rfml)

mlLocal <- ml.connect()
# Create a ml.data.frame based on iris, you can upload the iris data set using as.ml.data.frame
mlIris <- ml.data.frame(mlLocal, collection = "iris")

cor(mlIris)
# ====>
#                Sepal.Length Sepal.Width Petal.Length Petal.Width
# Sepal.Length    1.0000000  -0.1175698    0.8717538   0.8179411
# Sepal.Width    -0.1175698   1.0000000   -0.4284401  -0.3661259
# Petal.Length    0.8717538  -0.4284401    1.0000000   0.9628654
# Petal.Width     0.8179411  -0.3661259    0.9628654   1.0000000

For more examples how how to use the package see the help.