NAs introduced by coercion when labeling breaks in cut function

Question

I am having issues labeling the breaks (or intervals) when using the cut function.

Here are some sample data (the actual dataset has over 22,000 entries ranging from 1899-2014 within a dataframe with a total of 12 vectors):

x <- c(1899,1900,2001,2012,1999,1943,1944,1950,1988,1981,1988,1997,2014)

I'd like to categorize these by decade (eg. 1890s includes 1890 through 1899). I am able to make the breaks using the following code:

decade_vector <- cut(x, breaks = c(1889,1899,1909,1919,1929,1939,1949,
                                   1959,1969,1979,1989,1999,2009,2019))

However, when I go to label these breaks using the following code:

decade_vector <- cut(x, breaks = c(1889,1899,1909,1919,1929,1939,1949,
                                   1959,1969,1979,1989,1999,2009,2019), 
                     labels = c("1890s","1900s","1910s","1920s","1930s",
                                "1940s","1950s","1960s","1970s","1980s",
                                "1990s","2000s","2010s"), 
                     ordered=TRUE))

I get:

Warning message:
In is.factor(x) : NAs introduced by coercion

I created a dataframe and wrote it to csv, and when I look at it I don't see any NAs, but the labels are not there.

Can someone let me know what I'm doing wrong? I'd like to get various stats by decade, but I can't seem to figure out what I am missing here.

Any help would be super appreciated!

Thanks!

Answer 1

As the others already stated in the comments, your "NAs introduced by coercion" is not reproducible. But let me just give you a hint on how to make the code more "scalable" and readable:

x <- c(1890, 1899,1900,2001,2012,1999,1943,1944,1950,1988,1981,1988,1997,2014)
brk <- seq(1890, 2020, by=10) # breaks
cut(x, breaks=brk, right=FALSE, labels=paste(brk[-length(brk)], "s", sep=""), ordered=TRUE)
## [1] 1890s 1890s 1900s 2000s 2010s 1990s 1940s 1940s 1950s 1980s 1980s 1980s 1990s 2010s
## Levels: 1890s < 1900s < 1910s < 1920s < 1930s < 1940s < 1950s < 1960s < 1970s < 1980s < 1990s < 2000s < 2010s