PCA x must be numeric in R-CodePudding

I have a dataset like this called df

head(df[, 1:3])

ratio	P	T	H	S	p1	p2	PM10	CO2	B	G	Month	Year
0.5	89	-7	98	133	0	40	50	30	3	20	1	2019
0.5	55	4	43	43	30	30	40	32	1	15	1	2019
0.85	75	4	63	43	30	30	42	32	1	18	1	2019

I would like to do a principal component analysis to reduced number of variables for regression analysis. I gave that code

library(factoextra)
df.pca <- prcomp(df, scale = TRUE)

But I got this error message and for that reason I was not able to continue

Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric

What I am doing wrong?

CodePudding user response：

prcomp() will assume that every column in the object you are passing to it should be used in the analysis. You'll need to drop any non-numeric columns, as well as any numeric columns that should not be used in the PCA.

library(factoextra)

# Example data
df <- data.frame(
  x = letters,
  y1 = rbinom(26,1,0.5),
  y2 = rnorm(26),
  y3 = 1:26,
  id = 1:26
)

# Reproduce your error
prcomp(df)
#> Error in colMeans(x, na.rm = TRUE): 'x' must be numeric

# Remove all non-numeric columns
df_nums <- df[sapply(df, is.numeric)]

# Conduct PCA - works but ID column is in there!
prcomp(df_nums, scale = TRUE)
#> Standard deviations (1, .., p=4):
#> [1] 1.445005e 00 1.039765e 00 9.115092e-01 1.333315e-16
#> 
#> Rotation (n x k) = (4 x 4):
#>            PC1        PC2        PC3           PC4
#> y1  0.27215111 -0.5512026 -0.7887391  0.000000e 00
#> y2  0.07384194 -0.8052981  0.5882536  4.715914e-16
#> y3 -0.67841033 -0.1543868 -0.1261909 -7.071068e-01
#> id -0.67841033 -0.1543868 -0.1261909  7.071068e-01

# Remove ID
df_nums$id <- NULL

# Conduct PCA without ID - success!
prcomp(df_nums, scale = TRUE)
#> Standard deviations (1, .., p=3):
#> [1] 1.1253120 0.9854030 0.8733006
#> 
#> Rotation (n x k) = (3 x 3):
#>           PC1         PC2        PC3
#> y1 -0.6856024  0.05340108 -0.7260149
#> y2 -0.4219813 -0.84181344  0.3365738
#> y3  0.5931957 -0.53712052 -0.5996836