跨多列的序列中的行均值

Question

我有 2017 年到 2019 年和 2022 年各州失业率的月度数据。我想获得每个州的年平均失业率。

有没有办法使用序列或 for 循环来获取从第 2 列开始的每 12 列的平均值？那么对于 2022 年，它只会是三列（1 月到 3 月）的平均值

我目前有以下内容，但这非常低效。尤其是当我开始处理更大的数据集时。

编辑

# Edit: Below shows unemployment rates for year 2017 (not showing 2018-2022)

> df[2:13]
   Jan.2017 Feb.2017 Mar.2017 Apr.2017 May.2017 Jun.2017 Jul.2017 Aug.2017 Sep.2017 Oct.2017 Nov.2017 Dec.2017
1       5.5      5.2      5.0      4.8      4.6      4.4      4.3      4.2      4.1      4.0      4.0      4.0
2       6.6      6.6      6.5      6.5      6.5      6.5      6.5      6.5      6.5      6.5      6.5      6.4
3       5.2      5.2      5.1      5.0      5.0      4.9      4.9      4.8      4.9      4.9      4.9      4.9
4       3.8      3.7      3.7      3.7      3.7      3.7      3.7      3.7      3.8      3.8      3.8      3.8

# I am using the below to get the average from Jan2017-Dec2017, Jan 2018-Dec 2018 etc.

df$x2017 <- rowMeans(df[ , c(2:13)], na.rm=TRUE)
df$x2018 <- rowMeans(df[ , c(14:25)], na.rm=TRUE)
df$x2019 <- rowMeans(df[ , c(26:37)], na.rm=TRUE)
df$x2021 <- rowMeans(df[ , c(38:49)], na.rm=TRUE)
df$x2022 <- rowMeans(df[ , c(50:52)], na.rm=TRUE)

# output
  State x2017 x2018 x2019 x2021 x2022
1               Alabama   8.0   7.2   6.6   6.1   5.9
2                Alaska   7.2   7.0   6.6   6.3   6.5
3               Arizona   8.3   7.7   6.7   6.0   5.6
4              Arkansas   7.2   6.9   5.7   4.7   4.0

我只是在寻找可以减少错误可能性的方法，告诉它要获取哪些列的平均值。

如有需要，补上df

state = c("Alabama", "Alaska", "Arizona")
Jan2017 = c(1:3)
Feb2017 = c(4:6)
Jan2018 = c(7:9)
Feb2018 = c(10:12)
Jan2019 = c(13:15)
Feb2019 = c(16:18)
df3=data.frame(state,Jan2017,Feb2017,Jan2018,Feb2018,Jan2019,Feb2019)

> df3
    state Jan2017 Feb2017 Jan2018 Feb2018 Jan2019 Feb2019
1 Alabama       1       4       7      10      13      16
2  Alaska       2       5       8      11      14      17
3 Arizona       3       6       9      12      15      18

Answer 1

这是一个 tidyverse 解决方案，其中包含旋转和汇总：

library(dplyr)
library(tidyr)

df3 %>% 
  pivot_longer(-state) %>% 
  mutate(helper = parse_number(name)) %>% 
  group_by(state, helper) %>% 
  mutate(mean = mean(value, na.rm=TRUE)) %>% 
  pivot_wider(names_from = helper,
              values_from = mean) %>% 
  group_by(state) %>% 
  summarise(across(-c(name, value), mean, na.rm = TRUE), .groups = 'drop')

       state   `2017` `2018` `2019`
  <chr>    <dbl>  <dbl>  <dbl>
1 Alabama    2.5    8.5   14.5
2 Alaska     3.5    9.5   15.5
3 Arizona    4.5   10.5   16.5

Answer 2

cbind(df3[1], sapply(split.default(df3[-1], sub("\D+", "", names(df3)[-1])), rowMeans))

    state 2017 2018 2019
1 Alabama  2.5  8.5 14.5
2  Alaska  3.5  9.5 15.5
3 Arizona  4.5 10.5 16.5

Answer 3

这是另一个略有不同的 tidyverse 选项：

library(tidyverse)

df3 %>%
  pivot_longer(
    cols = -state,
    names_to = c(NA, ".value"),
    names_pattern = "(.*)(\d{4})"
  ) %>%
  group_by(state) %>% 
  summarize(across(everything(), mean, na.rm = TRUE))

输出

  state   `2017` `2018` `2019`
  <chr>    <dbl>  <dbl>  <dbl>
1 Alabama    2.5    8.5   14.5
2 Alaska     3.5    9.5   15.5
3 Arizona    4.5   10.5   16.5

跨多列的序列中的行均值

Row mean in a sequence across multiple columns

r

dataframe

for-loop

sequence

mean