R语言入门：如何将数据列中yes/no转换为1、2数值

Hey there! Let's work through this together. You've already made a solid start loading your data, and now we just need to turn those "yes"/"no" values in the glucose column into numerical 1s and 2s (yes=1, no=2). Here are three beginner-friendly methods to get this done:

Method 1: Use ifelse() (most straightforward for new R users) #

This function checks each value in the column and replaces it based on your condition—super easy to follow:

# First, confirm your data is loaded and column names are set correctly
cell <- read.table(file="tetrahymena.txt", header=TRUE, sep="\t", dec = ".")
colnames(cell) <- c("glucose","conc","diameter")

# Convert glucose values directly
cell$glucose <- ifelse(cell$glucose == "yes", 1, 2)

The logic here is simple: if the value is "yes", swap it for 1; every other value (which should be "no" in your dataset) gets swapped for 2.

Method 2: Convert via factors #

Since "yes"/"no" are categorical values, turning them into a factor first then converting to numbers works well—just make sure you set the level order to get the right numbers:

# Load and rename your data as before
cell <- read.table(file="tetrahymena.txt", header=TRUE, sep="\t", dec = ".")
colnames(cell) <- c("glucose","conc","diameter")

# Create a factor with "yes" as the first level (so it becomes 1)
cell$glucose <- factor(cell$glucose, levels = c("yes", "no"))
# Convert the factor to numeric values
cell$glucose <- as.numeric(cell$glucose)

Pro tip: Always specify the levels parameter! If you skip it, R might sort values alphabetically (meaning "no" would become 1, which isn't what you want).

Method 3: Use dplyr (for tidyverse-focused workflows) #

If you plan to do more data cleaning later, the dplyr package makes this step clean and readable:

# Install dplyr first if you haven't: install.packages("dplyr")
library(dplyr)

# Load, rename, and convert in one streamlined pipeline
cell <- read.table(file="tetrahymena.txt", header=TRUE, sep="\t", dec = ".") %>%
  rename(glucose = 1, conc = 2, diameter = 3) %>% # Alternative to colnames()
  mutate(glucose = case_when(
    glucose == "yes" ~ 1,
    glucose == "no" ~ 2
  ))

case_when() lets you write out each condition explicitly, which is great if you ever need to handle more than two categories down the line.

Quick check to confirm it worked #

After running any of these methods, use summary(cell) or table(cell$glucose) to double-check that your glucose column now has 1s and 2s instead of "yes"/"no".

内容的提问来源于stack exchange，提问作者V.Heik