How to Coerce a List Object to Type 'Double'

How to coerce a list object to type 'double'

If you want to convert all elements of a to a single numeric vector and length(a) is greater than 1 (OK, even if it is of length 1), you could unlist the object first and then convert.

as.numeric(unlist(a))
# [1]  10  38  66 101 129 185 283 374

Bear in mind that there aren't any quality controls here. Also, X$Days a mighty odd name.

coerce a list object to type double

To apply the coercion on a subset of multiple columns, lapply is the right way. That yields a list, that you want to assign back to the subset columns.

as.numeric.factor <- function(f) as.numeric(levels(f))[f]    
# as.numeric.factor <- function(f) as.numeric(as.character(f))  ## alternatively

d[,2:3] <- lapply(d[,2:3], as.numeric.factor)

str(d)
# 'data.frame': 3 obs. of  3 variables:
# $ var1: chr  "ab" "ac" "ad"
# $ var2: num  1.1 2.6 0
# $ var3: num  1 5.4 0

If you want a new object you may use the lapply with replace.

fg <- replace(d, 2:3, lapply(d[,2:3], as.numeric.factor))
str(fg)
# 'data.frame': 3 obs. of  3 variables:
# $ var1: chr  "ab" "ac" "ad"
# $ var2: num  1.1 2.6 0
# $ var3: num  1 5.4 0

---

Data

d <- structure(list(var1 = c("ab", "ac", "ad"), var2 = structure(c(2L, 
3L, 1L), .Label = c("0", "1.1", "2.6"), class = "factor"), var3 = structure(c(2L, 
3L, 1L), .Label = c("0", "1", "5.4"), class = "factor")), row.names = c(NA, 
-3L), class = "data.frame")

data.frame - object cannot be coerced to type 'double'

Assuming you have all numeric columns except for the first column, you can lapply like

data_stand[-1] <- lapply(data_stand[-1], coeff_var)

Or with dplyr

library(dplyr)
data_stand %>% mutate_if(is.numeric, coeff_var)

Error: (list) object cannot be coerced to type 'double' with multiple variables

We can do:

res<-sapply(iris[,-5], as.numeric)
attr(res,"dimnames") <- NULL

Or as @markus suggests simply:

unname(as.matrix(iris[,1:4]))

Result:

       [,1] [,2] [,3] [,4]
  [1,]  5.1  3.5  1.4  0.2
  [2,]  4.9  3.0  1.4  0.2
  [3,]  4.7  3.2  1.3  0.2
  [4,]  4.6  3.1  1.5  0.2

Unusual (list) object cannot be coerced to type 'double' error when conductin KNN alogrithm

You need to use as.matrix as I suggested in my comments above. Here's why:

str(matrix(iris,byrow=T,ncol=5))

As you can see this produces a list.

List of 5
 $ : num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ : num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
 - attr(*, "dim")= int [1:2] 1 5

as.matrix on the other hand produces a matrix.
Now why the error anyways?
From ?knn we can see that it accepts matrices or dataframes:

train

matrix or data frame of training set cases.
test

matrix or data frame of test set cases.
A vector will be interpreted as a row vector for a single case

This explains why we have the error:

Error in knn(train, test, clas, k = kk, prob = TRUE) : (list) object cannot be coerced to type 'double'

The safe thing to do is to either use as.data.frame or as.matrix

How to coerce a list object to type 'integer', Error in R

It looks like one of your independent variables is a string or factor variable, and all need to be numeric. See my toy dataset below. I get the same error when including all the variables; however, when I take var4 out (where variables are strings), it works).

If you want to use the variable, you could convert the string variable to a factor, then convert to the factor to a numeric variable (which will capture the underlying values of the factor).

library(naivebayes)
#data <- read.csv(file.choose(),header = T) 
data <- data.frame(admit = sample(100, x=c(F,T), prob=c(.5,.5), replace=T),
           var1 = sample(100, x=1:4, replace=T),
           var2 = sample(100, x=1:3, replace=T),
           var3 = sample(100, x=1:3, replace=T),
           var4 = sample(100, x=c("s1", "s2"), replace=T))

str(data)
set.seed(1234)
splitData <- sample(2,nrow(data),replace = T,prob = c(0.8,0.2))
train<-data[splitData == 1,]
test <- data[splitData == 2,]

# Doesn't work
mdl <- naive_bayes(admit ~ .,data = train)
predicted <- predict(mdl, train, type = 'prob')

# Works
mdl <- naive_bayes(admit ~ var1 + var2 + var3,data = train)
predicted <- predict(mdl, train, type = 'prob')

# Convert string to factor then numeric
train$var4 <- as.numeric(as.factor(train$var4))

mdl <- naive_bayes(admit ~ .,data = train)
predicted <- predict(mdl, train, type = 'prob')

exact_extract throws error Error in .num_expected_args(fun) : 'list' object cannot be coerced to type 'double'

It turns out that I am just an idiot. exact_extract() expects the functions to be quoted because they are run internally and not taken from the external functions. This solved the issue entirely:

##Loading Necessary Packages##
library(exactextractr)
library(sf)
library(spData)
library(raster)
library(tmap)

## Getting the State of Oregon from the us_states and projecting it into Oregon Statewide Lambert (EPSG 2992)  
data("us_states")
OR<-st_transform(us_states[us_states$NAME=="Oregon",], st_crs("EPSG:2992"))

##Making a fake classified raster with the same extent and projection as the state of Oregon
FAKE<-raster(ext=extent(OR), res=1000, crs=crs(OR))
values(FAKE)<-sample(c(1:4), ncell(FAKE),replace=TRUE)

#Map to demonstrate that polygon overlays the raster
tmap_mode("plot")
tm_shape(FAKE)+
  tm_raster()+
tm_shape(OR)+
  tm_polygons(border.col="black", alpha=0)

test<-exact_extract(FAKE, OR, fun="count", summarize_df=TRUE)

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