如何计算 r 中李克特类型响应的累积比例? [英] How to calculate cumulative proportion of Likert-type responses in r?
问题描述
我正在尝试提供我的李克特式(5 分)调查结果的基本摘要.我知道我们如何使用聚合函数跨子组使用基本数学函数.例如，我可以生成子组中每个项目的均值，但我不知道如何获得所有项目中超过 2 个可能响应的出现百分比.
I am trying to provide a basic summary of my Likert-type (5-point) survey results. I know how we can use basic mathematical functions across subgroups using the aggregate function. For instance, I can produce means of each item across subgroups but I do not know how to get a percentage of occurrence of more than 2 possible responses across all items.
我一直使用 SPSS 来汇总子组中每个项目的正面响应(例如 4 和 5)的比例.因此，结果是，我收到了按子组细分的每个项目的积极响应(好感度)百分比.
I have always used SPSS to aggregate the proportion of positive responses (say 4 and 5) for each item across subgroups. So, as a result, I received percentages of positive responses (favorability) for each item broken down by subgroups.
### What I can produce
aggregate(dataset[items], by=subgroup, FUN=mean)
### What I am trying to produce
aggregate(datase[items], by=subgroup, FUN=[proportion of 4 and 5 choices on each item])
推荐答案
考虑结合 aggregate 和 ave(保持与行数相同的行数的内联聚合函数)输入).具体来说，使用 aggregate 计算每个子组的 Likert 值(使用公式样式更容易阅读，cbind 重命名列)，然后计算每个计数与整个子组计数的比率与ave的比例.
Consider a combination of aggregate and ave (the inline aggregation function which keeps same number of rows as input). Specifically, count the likert values per subgroup with aggregate (using formula style for easier read with cbind to rename column), then calculate the ratio of each count by entire subgroup count for proportion percentage with ave.
agg_df <- aggregate(cbind(count=some_num_col) ~ likert + subgroup, dataset, FUN=length)
agg_df$prop <- with(agg_df, count / ave(count, subgroup, FUN=sum))
agg_df
<小时>
使用随机种子数据进行演示(将替换为 OP 的数据).下面假设 likert 为长格式，但可以从宽格式重新调整:
To demonstrate with random, seeded data (to be replaced with OP's data). Below assumes likert in long format but can be reshaped from wide:
数据
set.seed(8302019)
dataset <- data.frame(
subgroup = sample(c("sas", "stata", "spss", "python", "r", "julia"), 500, replace=TRUE),
likert = sample(1:5, 500, replace=TRUE),
some_num_col = 1
)
head(dataset, 20)
# subgroup likert some_num_col
# 1 julia 5 1
# 2 python 1 1
# 3 spss 5 1
# 4 sas 1 1
# 5 sas 4 1
# 6 spss 2 1
# 7 r 5 1
# 8 r 5 1
# 9 r 1 1
# 10 spss 3 1
# 11 spss 4 1
# 12 sas 3 1
# 13 spss 5 1
# 14 spss 1 1
# 15 spss 2 1
# 16 sas 4 1
# 17 r 2 1
# 18 sas 4 1
# 19 sas 4 1
# 20 spss 1 1
按子组的比例
agg_df <- aggregate(cbind(count=some_num_col) ~ likert + subgroup, dataset, FUN=length)
agg_df$prop <- with(agg_df, count / ave(count, subgroup, FUN=sum))
agg_df
# likert subgroup count prop
# 1 1 julia 21 0.2359551
# 2 2 julia 16 0.1797753
# 3 3 julia 18 0.2022472
# 4 4 julia 17 0.1910112
# 5 5 julia 17 0.1910112
# 6 1 python 14 0.1891892
# 7 2 python 16 0.2162162
# 8 3 python 16 0.2162162
# 9 4 python 16 0.2162162
# 10 5 python 12 0.1621622
# 11 1 r 20 0.2061856
# 12 2 r 19 0.1958763
# 13 3 r 26 0.2680412
# 14 4 r 17 0.1752577
# 15 5 r 15 0.1546392
# 16 1 sas 18 0.1956522
# 17 2 sas 16 0.1739130
# 18 3 sas 24 0.2608696
# 19 4 sas 18 0.1956522
# 20 5 sas 16 0.1739130
# 21 1 spss 13 0.1688312
# 22 2 spss 22 0.2857143
# 23 3 spss 15 0.1948052
# 24 4 spss 16 0.2077922
# 25 5 spss 11 0.1428571
# 26 1 stata 17 0.2394366
# 27 2 stata 8 0.1126761
# 28 3 stata 16 0.2253521
# 29 4 stata 12 0.1690141
# 30 5 stata 18 0.2535211
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