英文:
How can I recode scores in a set columns based on the scores on another set of columns with related names?
问题
我有96个不同刺激的识别数据。我有a) 他们是否认出它，b) 他们对自己认出它的信心程度。例如，alligator_recognition，sheep_recognition，worm_recognition，alligator_recog_confidence，sheep_recog_confidence，worm_recog_confidence。我想要将数据分割，如果他们认出了它（在_recognition字段中编码为1），并且他们在信心上评分较高（在_recog_confidence字段上>7），适用于每个刺激。所以，如果在alligator_recognition上得分为1且在alligator_recog_confidence上得分超过7，我想要将alligator分割出来，对所有96个刺激进行相同操作。有没有关于如何高效实现这个目标的想法？
英文:
So I have recognition data for 96 different stimulus. I have a) whether they recognized it b) how confident they are that they recognized it. e.g., alligator_recognition, sheep_recognition, worm_recognition, alligator_recog_cofidence, sheep_recog_confidence, worm_recog_confidence.
I want to subset the data if they recognized it (coded 1 for _recognition) and if they rated it high on confidence (>7 on _recog_confidence) for each of the stimulus. So subset alligator if they scored a 1 on alligator_recognition and above 7 on alliator_recog_confidence for all the 96 stimulus. Any ideas on how I can do this efficiently?
I can subset the data for either of them using the grep function or subset each stimulus (1 for alligator_recognition and alligator_recog_confidence) and do it for each of the 96 then try to score them before merging them altogether but hoping for a more efficient way?
alligator_recognition <- c(1,1,2,2,2,2,2,2,2)alligator_recog_confidence <- c(7,9,11,1,10,5,9,8,8)sheep_recognition <- c(2,2,1,2,2,1,2,2,2)sheep_recog_confidence <- c(3,8,1,2,9,3,8,11,5)worm_recognition <- c(2,2,1,2,2,1,2,1,1)worm_recog_confidence <- c(9,9,11,1,10,6,8,11,2)data <- data.frame(alligator_recognition, alligator_recog_confidence,sheep_recognition, sheep_recog_confidence, worm_recognition,worm_recog_confidence)
答案1
得分: 3
以下是代码的翻译部分：
可能的第一步是将您的数据转换为长格式，如下所示：
然后，您可以更容易地进行子集筛选：
英文:
Probably a good first step is to pivot your data to long, like so:
library(dplyr)library(tidyr)data_long <-data %>%mutate(stimulus = row_number()) %>%pivot_longer(-stimulus,names_pattern = "(.*)_(recognition|recog_confidence)",names_to = c("species", ".value"),names_transform = list(species = factor))# # A tibble: 27 × 4# stimulus species recognition recog_confidence# <int> <fct> <dbl> <dbl># 1 1 alligator 1 7# 2 1 sheep 2 3# 3 1 worm 2 9# 4 2 alligator 1 9# 5 2 sheep 2 8# 6 2 worm 2 9# 7 3 alligator 2 11# 8 3 sheep 1 1# 9 3 worm 1 11# 10 4 alligator 2 1# # … with 17 more rows# # ℹ Use `print(n = ...)` to see more rows
Then, you can subset more easily:
data_long %>%filter(recognition == 1, recog_confidence >= 7) %>%split(.$species)# $alligator# # A tibble: 2 × 4# stimulus species recognition recog_confidence# <int> <fct> <dbl> <dbl># 1 1 alligator 1 7# 2 2 alligator 1 9## $sheep# # A tibble: 0 × 4# # … with 4 variables: stimulus <int>, species <fct>, recognition <dbl>,# # recog_confidence <dbl># # ℹ Use `colnames()` to see all variable names## $worm# # A tibble: 2 × 4# stimulus species recognition recog_confidence# <int> <fct> <dbl> <dbl># 1 3 worm 1 11# 2 8 worm 1 11</details># 答案2**得分**: 3以下是您要翻译的代码部分：也许，我们通过列名前缀，即物种，来拆分数据并循环遍历列表以过滤每个动物```Rlibrary(dplyr)library(stringr)library(purrr)split.default(data, str_remove(names(data), "_.*")) %>%map(~ .x %>% filter(pick(1)[[1]] == 1, pick(2)[[1]] >= 7))
-output
$alligatoralligator_recognition alligator_recog_confidence1 1 72 1 9$sheep[1] sheep_recognition sheep_recog_confidence<0 rows> (or 0-length row.names)$wormworm_recognition worm_recog_confidence1 1 112 1 11
英文:
Perhaps, we split the data by the prefix of the column name i.e. species and loop over the list to filter each animal
library(dplyr)library(stringr)library(purrr)split.default(data, str_remove(names(data), "_.*")) |>map(~ .x %>% filter(pick(1)[[1]] == 1, pick(2)[[1]] >= 7))
-output
$alligatoralligator_recognition alligator_recog_confidence1 1 72 1 9$sheep[1] sheep_recognition sheep_recog_confidence<0 rows> (or 0-length row.names)$wormworm_recognition worm_recog_confidence1 1 112 1 11
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