收集多組列我有一項在線調查的數(shù)據(jù)，在調查中，被調查者反復回答1到3次問題。調查軟件(Qualtrics)將這些數(shù)據(jù)記錄在多個列中，也就是說，調查中的Q3.2將包含列。Q3.2.1., Q3.2.2.，和Q3.2.3.:df <- data.frame(
id = 1:10,
time = as.Date('2009-01-01') + 0:9,
Q3.2.1. = rnorm(10, 0, 1),
Q3.2.2. = rnorm(10, 0, 1),
Q3.2.3. = rnorm(10, 0, 1),
Q3.3.1. = rnorm(10, 0, 1),
Q3.3.2. = rnorm(10, 0, 1),
Q3.3.3. = rnorm(10, 0, 1))# Sample data
id time Q3.2.1. Q3.2.2. Q3.2.3. Q3.3.1. Q3.3.2. Q3.3.3.1 1 2009-01-01 -0.2059165 -0.29177677 -0.7107192 1.52718069 -0.4484351 -1.215506002 2 2009-01-02 -0.1981136 -1.19813815 1.1750200 -0.40380049 -1.8376094 1.035884823 3 2009-01-03 0.3514795 -0.27425539 1.1171712 -1.02641801 -2.0646661 -0.35353058...我想將所有QN.N*列合并成整潔的單個QN.N列，最終得到如下的結果： id time loop_number Q3.2 Q3.31 1 2009-01-01 1 -0.20591649 1.527180692 2 2009-01-02 1 -0.19811357 -0.403800493 3 2009-01-03 1 0.35147949 -1.02641801...11 1 2009-01-01 2 -0.29177677 -0.448435112 2 2009-01-02 2 -1.19813815 -1.837609413 3 2009-01-03 2 -0.27425539 -2.0646661...21 1 2009-01-01 3 -0.71071921 -1.2155060022 2 2009-01-02 3 1.17501999 1.0358848223 3 2009-01-03 3 1.11717121 -0.35353058...這個tidyr庫有gather()函數(shù)，它對組合非常有用。一一組欄：得到的數(shù)據(jù)幀有30行，如預期的那樣(10個個體，每個循環(huán)3個循環(huán))。但是，收集第二組列不能正常工作-它成功地使這兩組合并的列正常工作。Q3.2和Q3.3，但最后為90行，而不是30行(10個個體的所有組合，Q3.2的3個循環(huán)，Q3.3的3個循環(huán)；實際數(shù)據(jù)中每組列的組合將大幅度增加)：df %>% gather(loop_number, Q3.2, starts_with("Q3.2")) %>%
gather(loop_number, Q3.3, starts_with("Q3.3")) %>%
mutate(loop_number = str_sub(loop_number,-2,-2))
id time loop_number Q3.2 Q3.31 1 2009-01-01 1 -0.20591649 1.527180692 2 2009-01-02 1 -0.19811357 -0.403800493 3 2009-01-03 1 0.35147949 -1.02641801...89 9 2009-01-09 3 -0.58581232 -0.1318702490 10 2009-01-10 3 -2.33393981 -0.48502131是否有一種方法可以使用多個調用gather()像這樣，在保持正確的行數(shù)的同時組合這樣的列的小子集？
3 回答
嚕嚕噠
TA貢獻1784條經(jīng)驗 獲得超7個贊
這種方法對我來說似乎很自然：
df %>%
gather(key, value, -id, -time) %>%
extract(key, c("question", "loop_number"), "(Q.\\..)\\.(.)") %>%
spread(question, value)
首先收集所有問題列，使用extract()分離成question和loop_number，然后spread()回到列中。
#> id time loop_number Q3.2 Q3.3
#> 1 1 2009-01-01 1 0.142259203 -0.35842736
#> 2 1 2009-01-01 2 0.061034802 0.79354061
#> 3 1 2009-01-01 3 -0.525686204 -0.67456611
#> 4 2 2009-01-02 1 -1.044461185 -1.19662936
#> 5 2 2009-01-02 2 0.393808163 0.42384717
Qyouu
TA貢獻1786條經(jīng)驗 獲得超11個贊
這可以用reshape..這是有可能的dplyr盡管如此。
colnames(df) <- gsub("\\.(.{2})$", "_\\1", colnames(df))
colnames(df)[2] <- "Date"
res <- reshape(df, idvar=c("id", "Date"), varying=3:8, direction="long", sep="_")
row.names(res) <- 1:nrow(res)
head(res)
# id Date time Q3.2 Q3.3
#1 1 2009-01-01 1 1.3709584 0.4554501
#2 2 2009-01-02 1 -0.5646982 0.7048373
#3 3 2009-01-03 1 0.3631284 1.0351035
#4 4 2009-01-04 1 0.6328626 -0.6089264
#5 5 2009-01-05 1 0.4042683 0.5049551
#6 6 2009-01-06 1 -0.1061245 -1.7170087
或使用dplyr
library(tidyr)
library(dplyr)
colnames(df) <- gsub("\\.(.{2})$", "_\\1", colnames(df))
df %>%
gather(loop_number, "Q3", starts_with("Q3")) %>%
separate(loop_number,c("L1", "L2"), sep="_") %>%
spread(L1, Q3) %>%
select(-L2) %>%
head()
# id time Q3.2 Q3.3
#1 1 2009-01-01 1.3709584 0.4554501
#2 1 2009-01-01 1.3048697 0.2059986
#3 1 2009-01-01 -0.3066386 0.3219253
#4 2 2009-01-02 -0.5646982 0.7048373
#5 2 2009-01-02 2.2866454 -0.3610573
#6 2 2009-01-02 -1.7813084 -0.7838389
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