"Renaming factors" section does not reflect true results of running these commands

[rkmeade]

Hi Maintainers!

A quick comment on the "Renaming factors" section, which does not work on my console the same way the episode says that it should.

Beginning with the first command, plot(surveys$sex), my console plots the ~1700 missing values as their own column (which is not reflected on the plot in the episode). I believe this is because instead of NA, it recognizes a third category of values, designated "".

In the next set of commands:
sex <- surveys$sex
levels(sex)

The lesson says the output should be:
[1] "F" "M"

This is what I get:
[1] "" "F" "M"

In the next code block, a new category for missing values is added:
sex <- addNA(sex)
levels(sex)

The lesson says the output should be:
[1] "F" "M" NA

My output now has two equivalents of missing values:
[1] "" "F" "M" NA

I believe all downstream errors can be remediated by running this before the initial plot command:
levels(sex)[1] <- NA

I hope this is helpful!

-- Rachel

[Teebusch]

Hi @rkmeade, thank you for raising this issue. Using the code from the lesson, it runs as expected. See reproducible example below. However, I can replicate your issue by using read.csv() (base R) instead of read_csv() (tidyverse, used in the lesson). This is an easy to make mistake that has been brought up a few times (e.g., #710). We could probably do a better job at preventing this.

Correct output, using read_csv()

## Loading the survey data
# modified slightly, for reprex to work
library(tidyverse)
surveys <- read_csv("https://ndownloader.figshare.com/files/2292169")
#> 
#> -- Column specification --------------------------------------------------------
#> cols(
#>   record_id = col_double(),
#>   month = col_double(),
#>   day = col_double(),
#>   year = col_double(),
#>   plot_id = col_double(),
#>   species_id = col_character(),
#>   sex = col_character(),
#>   hindfoot_length = col_double(),
#>   weight = col_double(),
#>   genus = col_character(),
#>   species = col_character(),
#>   taxa = col_character(),
#>   plot_type = col_character()
#> )

# ...

## Factors
surveys$sex <- factor(surveys$sex)

# ...

### Renaming factors

plot(surveys$sex)

sex <- surveys$sex
levels(sex)
#> [1] "F" "M"
sex <- addNA(sex)
levels(sex)
#> [1] "F" "M" NA
head(sex)
#> [1] M    M    <NA> <NA> <NA> <NA>
#> Levels: F M <NA>
levels(sex)[3] <- "undetermined"
levels(sex)
#> [1] "F"            "M"            "undetermined"
head(sex)
#> [1] M            M            undetermined undetermined undetermined
#> [6] undetermined
#> Levels: F M undetermined
plot(sex)

---

Unexpected output, using read.csv()

## Loading the survey data
# modified slightly, for reprex to work
library(tidyverse)
surveys <- read.csv("https://ndownloader.figshare.com/files/2292169")

# ...

## Factors
surveys$sex <- factor(surveys$sex)

# ...

### Renaming factors

plot(surveys$sex)

sex <- surveys$sex
levels(sex)
#> [1] ""  "F" "M"
sex <- addNA(sex)
levels(sex)
#> [1] ""  "F" "M" NA
head(sex)
#> [1] M M        
#> Levels:  F M <NA>
levels(sex)[3] <- "undetermined"
levels(sex)
#> [1] ""             "F"            "undetermined"
head(sex)
#> [1] undetermined undetermined                                       
#> [6]             
#> Levels:  F undetermined
plot(sex)

^{Created on 2021-07-05 by the reprex package (v2.0.0)}

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