I am trying to convert a data.frame to a daisy matrix from the CRAN's Cluster package in R. I have a dataset of 13109 observations with 9 categorical variables.
I got two types of errors about NAs being introduced by coercion and no missing arguments to min/max. Why am I getting this error?
I do not have any NA values in the data.frame. Here's information on my dataset:
> str(df4)
'data.frame': 13109 obs. of 9 variables:
$ Age : chr "55-64" "55-64" "55-64" "55-64" ...
$ Gender : chr "Female" "Female" "Male" "Male" ...
$ HouseholdIncome : chr "50k-75k" "150k-175k" "150k-175k" "150k-175k" ...
$ MaritalStatus : chr "Single" "Married" "Married" "Married" ...
$ PresenceofChildren: chr "No" "Yes" "Yes" "Yes" ...
$ HomeOwnerStatus : chr "Own" "Rent" "Rent" "Rent" ...
$ HomeMarketValue : chr "350k-500k" "500k-1mm" "500k-1mm" "500k-1mm" ...
$ Occupation : chr "White Collar Worker" "Professional" "Professional" "Professional" ...
$ Education : chr "Completed High School" "Completed College" "Completed College" "Completed College" ...
Here's proof that the NA values where coerced: I tryed performing the PAM clustering function, but got an error saying NA values not being allowed.
>library(cluster)
>#Create dissimilarity matrix
>#Gower coefficient for finding distance between mixed variable
>daisy4 <- daisy(df4, metric = "gower", type = list(ordratio = c(1:9)))
> warnings()
Warning messages:
1: In data.matrix(x) : NAs introduced by coercion
2: In data.matrix(x) : NAs introduced by coercion
3: In data.matrix(x) : NAs introduced by coercion
4: In data.matrix(x) : NAs introduced by coercion
5: In data.matrix(x) : NAs introduced by coercion
6: In data.matrix(x) : NAs introduced by coercion
7: In data.matrix(x) : NAs introduced by coercion
8: In data.matrix(x) : NAs introduced by coercion
9: In data.matrix(x) : NAs introduced by coercion
10: In min(x) : no non-missing arguments to min; returning Inf
11: In max(x) : no non-missing arguments to max; returning -Inf
12: In min(x) : no non-missing arguments to min; returning Inf
13: In max(x) : no non-missing arguments to max; returning -Inf
14: In min(x) : no non-missing arguments to min; returning Inf
15: In max(x) : no non-missing arguments to max; returning -Inf
16: In min(x) : no non-missing arguments to min; returning Inf
17: In max(x) : no non-missing arguments to max; returning -Inf
18: In min(x) : no non-missing arguments to min; returning Inf
19: In max(x) : no non-missing arguments to max; returning -Inf
20: In min(x) : no non-missing arguments to min; returning Inf
21: In max(x) : no non-missing arguments to max; returning -Inf
22: In min(x) : no non-missing arguments to min; returning Inf
23: In max(x) : no non-missing arguments to max; returning -Inf
24: In min(x) : no non-missing arguments to min; returning Inf
25: In max(x) : no non-missing arguments to max; returning -Inf
26: In min(x) : no non-missing arguments to min; returning Inf
27: In max(x) : no non-missing arguments to max; returning -Inf
28: In min(x) : no non-missing arguments to min; returning Inf
29: In max(x) : no non-missing arguments to max; returning -Inf
> k4answers <- pam(daisy4, 3, diss = TRUE)
Error in pam(daisy4, 3, diss = TRUE) :
NA values in the dissimilarity matrix not allowed.
Please let me know if I can provide more information.
EDIT: I solved my error. I read in the .csv file as a character. That's why it worked with the other dataset. Here's where I went wrong:
#Load Data
Store4 <- read.csv("/Users/scdavis/Documents/Work/Data/Client4.csv",
na.strings = "", stringsAsFactors=FALSE, head = TRUE)
Solution:
#Load Data
Store4 <- read.csv("/Users/scdavis/Documents/Work/Data/Client4.csv",
na.strings = "", head = TRUE)
Read the data in as factor variables instead of characters.
#Load Data
Store4 <- read.csv("/Users/scdavis/Documents/Work/Data/Client4.csv",
na.strings = "", head = TRUE)
I had this solution in before and created an error.
#Load Data
Store4 <- read.csv("/Users/scdavis/Documents/Work/Data/Client4.csv",
na.strings = "", stringsAsFactors=FALSE, head = TRUE)