[英]Calculate proportion (percent) for each column of a dataset
I'm trying to calculate the proportion (percent) of categories in each column of a dataset.我正在尝试计算数据集每一列中类别的比例(百分比)。
Example data:示例数据:
df <- data.frame(
"Size" = c("Y","N","N","Y","Y"),
"Type" = c("N","N","N","Y","N"),
"Age" = c("N","Y","N","Y","N"),
"Sex"=c("N","N","N","N","N")
)
df
Data produces a table like this:数据生成如下表格:
Size Type Age Sex
1 Y N N N
2 N N Y N
3 N N N N
4 Y Y Y N
5 Y N N N
I've tried using prop.table to calculate proportion for one category:我尝试使用 prop.table 来计算一个类别的比例:
prop.table(table(df$Size))
This works, but only calculates the percent of Y or N answers for one column.这有效,但仅计算一列的 Y 或 N 答案的百分比。 This dataset is quite large, so I'd like to calculate the proportion for each category at once.这个数据集非常大,所以我想一次计算每个类别的比例。
My goal is to have a table that shows the proportion of "yes" answers for each column.我的目标是有一个表格,显示每列“是”答案的比例。
Like this:像这样:
Proportion Y
Size 0.60
Type 0.20
Age 0.40
Sex 0.00
I am relatively new to R, so any help would be appreciated!我对 R 比较陌生,因此我们将不胜感激!
One way in base R would be to use apply
column-wise on a logical vector基础 R 的一种方法是在逻辑向量上按列apply
apply(df == "Y", 2, mean)
#Size Type Age Sex
# 0.6 0.2 0.4 0.0
A simpler version with colSums
. colSums
的更简单版本。
colMeans(df == "Y")
A dplyr approach: dplyr 方法:
library(dplyr)
df %>% summarise_all(~mean(.=="Y"))
If you have more than one group:如果您有多个组:
df1 = data.frame(class="A",df)
df2 = data.frame(class="B",df)
#make df2 different
df2$Size<- rep("Y",5)
newdf = rbind(df1,df2)
newdf %>% group_by(class) %>% summarise_all(~mean(.=="Y"))
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