From c351cab657897229e1d6b826a53399bca129b489 Mon Sep 17 00:00:00 2001 From: Hank Hester Date: Fri, 15 Jan 2021 02:30:42 -0800 Subject: Remove binary gender example from R (#4082) --- r.html.markdown | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) (limited to 'r.html.markdown') diff --git a/r.html.markdown b/r.html.markdown index 3e855602..79af40ce 100644 --- a/r.html.markdown +++ b/r.html.markdown @@ -255,16 +255,16 @@ c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE # FACTORS # The factor class is for categorical data -# Factors can be ordered (like childrens' grade levels) or unordered (like gender) -factor(c("female", "female", "male", NA, "female")) -# female female male female -# Levels: female male +# Factors can be ordered (like childrens' grade levels) or unordered (like colors) +factor(c("blue", "blue", "green", NA, "blue")) +# blue blue green blue +# Levels: blue green # The "levels" are the values the categorical data can take # Note that missing data does not enter the levels -levels(factor(c("male", "male", "female", NA, "female"))) # "female" "male" +levels(factor(c("green", "green", "blue", NA, "blue"))) # "blue" "green" # If a factor vector has length 1, its levels will have length 1, too -length(factor("male")) # 1 -length(levels(factor("male"))) # 1 +length(factor("green")) # 1 +length(levels(factor("green"))) # 1 # Factors are commonly seen in data frames, a data structure we will cover later data(infert) # "Infertility after Spontaneous and Induced Abortion" levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs" -- cgit v1.2.3