假设这样的数据结构:
ID testA_wave1 testA_wave2 testA_wave3 testB_wave1 testB_wave2 testB_wave3
1 1 3 2 3 6 5 3
2 2 4 4 4 3 6 6
3 3 10 2 1 4 4 4
4 4 5 3 12 2 7 4
5 5 5 3 9 2 4 2
6 6 10 0 2 6 6 5
7 7 6 8 4 6 8 3
8 8 1 5 4 5 6 0
9 9 3 2 7 8 4 4
10 10 4 9 5 11 8 8
我想要实现的是分别为每个测试计算一个配对的t检验(在这种情况下，这意味着testA和testB，但实际上，我有更多的测试)。我想这样做，以便将给定测试的第一波与同一测试的所有其他后续波进行比较(在testA的情况下，意味着testA_wave1与testA_wave2以及testA_wave1与testA_wave3)。
这样，我就可以实现它:
df %>%
gather(variable, value, -ID) %>%
mutate(wave_ID = paste0("wave", parse_number(variable)),
variable = ifelse(grepl("testA", variable), "testA",
ifelse(grepl("testB", variable), "testB", NA_character_))) %>%
group_by(wave_ID, variable) %>%
summarise(value = list(value)) %>%
spread(wave_ID, value) %>%
group_by(variable) %>%
mutate(p_value_w1w2 = t.test(unlist(wave1), unlist(wave2), paired = TRUE)$p.value,
p_value_w1w3 = t.test(unlist(wave1), unlist(wave3), paired = TRUE)$p.value) %>%
select(variable, matches("(p_value)"))
variable p_value_w1w2 p_value_w1w3
<chr> <dbl> <dbl>
1 testA 0.664 0.921
2 testB 0.146 0.418
但是，我希望看到不同/更优雅的解决方案，它们给出相似的结果。我主要是在寻找
dplyr/tidyverse解决方案，但是如果有一种完全不同的方法来实现它，我并不反对。样本数据:
set.seed(123)
df <- data.frame(ID = 1:20,
testA_wave1 = round(rnorm(20, 5, 3), 0),
testA_wave2 = round(rnorm(20, 5, 3), 0),
testA_wave3 = round(rnorm(20, 5, 3), 0),
testB_wave1 = round(rnorm(20, 5, 3), 0),
testB_wave2 = round(rnorm(20, 5, 3), 0),
testB_wave3 = round(rnorm(20, 5, 3), 0))
最佳答案
这是一种实现方法，使用了很多purrr。
library("tidyverse")
set.seed(123)
df <- tibble(
ID = 1:20,
testA_wave1 = round(rnorm(20, 5, 3), 0),
testA_wave2 = round(rnorm(20, 5, 3), 0),
testA_wave3 = round(rnorm(20, 5, 3), 0),
testB_wave1 = round(rnorm(20, 5, 3), 0),
testB_wave2 = round(rnorm(20, 5, 3), 0),
testB_wave3 = round(rnorm(20, 5, 3), 0)
)
pvalues <- df %>%
# From wide tibble to long tibble
gather(test, value, -ID) %>%
separate(test, c("test", "wave")) %>%
# Not stricly necessary; will order the waves alphabetically instead
mutate(wave = parse_number(wave)) %>%
inner_join(., ., by = c("ID", "test")) %>%
# If there are two waves w1 and w2,
# we end up with pairs (w1, w1), (w1, w2), (w2, w1) and (w2, w2),
# so filter out to keep the pairing (w1, w2) only
filter(wave.x == 1, wave.x < wave.y) %>%
nest(ID, value.x, value.y) %>%
mutate(pvalue = data %>%
# Perform the test
map(~t.test(.$value.x, .$value.y, paired = TRUE)) %>%
map(broom::tidy) %>%
# Also not strictly necessary; you might want to keep all
# information about the test: estimate, statistic, etc.
map_dbl(pluck, "p.value"))
pvalues
#> # A tibble: 4 x 5
#> test wave.x wave.y data pvalue
#> <chr> <dbl> <dbl> <list> <dbl>
#> 1 testA 1 2 <tibble [20 x 3]> 0.664
#> 2 testA 1 3 <tibble [20 x 3]> 0.921
#> 3 testB 1 2 <tibble [20 x 3]> 0.146
#> 4 testB 1 3 <tibble [20 x 3]> 0.418
pvalues %>%
# Drop the data in order to pivot the table
select(- data) %>%
unite("waves", wave.x, wave.y, sep = ":") %>%
spread(waves, pvalue)
#> # A tibble: 2 x 3
#> test `1:2` `1:3`
#> <chr> <dbl> <dbl>
#> 1 testA 0.664 0.921
#> 2 testB 0.146 0.418
由reprex package(v0.2.1)创建于2019-03-08