Question:

The root cause is the wrong data type.

The factor type has no sum method

#create a vector of class vector
factor_vector <- as.factor(c(1, 7, 12, 14, 15))

#attempt to find min value in the vector
sum(factor_vector)

Error in Summary.factor(1:5, na.rm = FALSE) : 
  ‘sum’ not meaningful for factors

Solution:
Convert to numeric values and use the as.numeric function.
mydata$value<-as.numeric(mydata$value)
is.numeric(mydata$value)

#convert factor vector to numeric vector and find the min value
new_vector <- as.numeric(as.character(factor_vector))
sum(new_vector)

#[1] 49

Complete error:

#create a vector of class vector
factor_vector <- as.factor(c(1, 7, 12, 14, 15))

#attempt to find min value in the vector
sum(factor_vector)

Error in Summary.factor(1:5, na.rm = FALSE) : 
  ‘sum’ not meaningful for factors

Other (the minimum value can be obtained for numeric value, string and date type)

Numeric value, string and date type can all be maximized. Similarly, the maximum value can be obtained.

numeric_vector <- c(1, 2, 12, 14)
max(numeric_vector)

#[1] 14

character_vector <- c("a", "b", "f")
max(character_vector)

#[1] "f"

date_vector <- as.Date(c("2019-01-01", "2019-03-05", "2019-03-04"))
max(date_vector)

#[1] "2019-03-05"

The R language is called R partly because of the names of the two R authors (Robert gentleman and Ross ihaka) and partly because of the influence of Bell Labs s language (called the dialect of s language).

R language is a mathematical programming language designed for mathematical researchers. It is mainly used for statistical analysis, drawing and data mining.

If you are a beginner of computer programs and are eager to understand the general programming of computers, R language is not an ideal choice. You can choose python, C or Java.

Both R language and C language are the research achievements of Bell Laboratories, but they have different emphasis areas. R language is an explanatory language for mathematical theory researchers, while C language is designed for computer software engineers.

R language is a language for interpretation and operation (different from the compilation and operation of C language). Its execution speed is much slower than that of C language, which is not conducive to optimization. However, it provides more abundant data structure operation at the syntax level and can easily output text and graphic information, so it is widely used in mathematics, especially in statistics

Error: ‘attrition’ is not an exported object from ‘namespace:rsample’

# Import package and library

# load required packages
library(rsample)
library(dplyr)
library(h2o)
library(DALEX)

# initialize h2o session
h2o.no_progress()
h2o.init()
##  Connection successful!
## 
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         4 hours 30 minutes 
##     H2O cluster timezone:       America/New_York 
##     H2O data parsing timezone:  UTC 
##     H2O cluster version:        3.18.0.11 
##     H2O cluster version age:    1 month and 17 days  
##     H2O cluster name:           H2O_started_from_R_bradboehmke_gny210 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   1.01 GB 
##     H2O cluster total cores:    4 
##     H2O cluster allowed cores:  4 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4 
##     R Version:                  R version 3.5.0 (2018-04-23)

#Data preprocessing and processing to h2o format;

Error: ‘attrition’ is not an exported object from ‘namespace:rsample’

#

# classification data
df <- rsample::attrition %>% 
  mutate_if(is.ordered, factor, ordered = FALSE) %>%
  mutate(Attrition = recode(Attrition, "Yes" = "1", "No" = "0") %>% factor(levels = c("1", "0")))

# convert to h2o object
df.h2o <- as.h2o(df)

# create train, validation, and test splits
set.seed(123)
splits <- h2o.splitFrame(df.h2o, ratios = c(.7, .15), destination_frames = c("train","valid","test"))
names(splits) <- c("train","valid","test")

# variable names for resonse & features
y <- "Attrition"
x <- setdiff(names(df), y) 

Solution:

You can use the attrition dataset of DALEX package directly;

Remove resample:

#

# classification data
df <- attrition %>% 
  mutate_if(is.ordered, factor, ordered = FALSE) %>%
  mutate(Attrition = recode(Attrition, "Yes" = "1", "No" = "0") %>% factor(levels = c("1", "0")))

# convert to h2o object
df.h2o <- as.h2o(df)

# create train, validation, and test splits
set.seed(123)
splits <- h2o.splitFrame(df.h2o, ratios = c(.7, .15), destination_frames = c("train","valid","test"))
names(splits) <- c("train","valid","test")

# variable names for resonse & features
y <- "Attrition"
x <- setdiff(names(df), y) 

Full Error Messages:

> # classification data
> df <- rsample::attrition %>% 
+     mutate_if(is.ordered, factor, ordered = FALSE) %>%
+     mutate(Attrition = recode(Attrition, "Yes" = "1", "No" = "0") %>% factor(levels = c("1", "0")))
Error: 'attrition' is not an exported object from 'namespace:rsample'
>