用 dcast 重塑长到宽
Reshape long to wide with dcast
我需要将数据框 csf_antibio（下面的示例）从长格式重塑为宽格式，按行分隔每个患者的所有值。
study_id,proanbx_dt,proanbx_tm,name,othername,route,dosage,units,doses,freq
CHLA_0001,2021-07-22,20:01:00,ceftriaxone,,IV,1250,mg,4,13
CHLA_0001,2021-07-22,20:19:00,metronidazole,,IV,250,mg,5,9
CHLA_0001,2021-07-22,23:17:00,vancomycin,,IV,350,mg,3,6
CHLA_0001,2021-08-09,19:34:00,cefazolin,,IV,738,mg,1,8
CHLA_0002,2020-12-18,0:30:00,cefepime,,IV,75,mg,5,8
CHLA_0002,2020-12-18,1:03:00,vancomycin,,IV,23,mg,4,13
CHLA_0002,2020-12-19,18:15:00,cefepime,,IV,60,mg,6,8
CHLA_0002,2020-12-20,4:18:00,vancomycin,,IV,24,mg,4,12
CHLA_0003,2021-04-20,15:17:00,meropenem,,IV,200,mg,2,1
CHLA_0003,2021-04-21,2:20:00,meropenem,,IV,400,mg,17,8
CHLA_0003,2021-04-22,14:16:00,Other,sulfamethoxazole-trimethoprim,IV,50,mg,9,12
我尝试了以下但没有成功：
csfmelt <- melt(csf_antibio, id.vars=1:1)
csf <- dcast(csfmelt, study_id ~ variable, value.var = "value", fun.aggregate = sum)
我希望最终的数据框每行都有每个研究 ID 和变量
study_id,proanbx_dt1,proanbx_tm1,name1,othername1,route1,dosage1,units1,doses1,freq1,proanbx_dt2,proanbx_tm2,name2,othername2,route2,dosage2,units2,doses2,freq2,proanbx_dt3,proanbx_tm3,name3,othername3,route3,dosage3,units3,doses3,freq3,proanbx_dt4,proanbx_tm4,name4,othername4,route4,dosage4,units4,doses4,freq4
CHLA_0001,2021-07-22,20:01:00,ceftriaxone,,IV,1250,mg,4,13, 2021-07-22,20:19:00,metronidazole,,IV,250,mg,5,9, 2021-07-22,23:17:00,vancomycin,,IV,350,mg,3,6,2021-08-09,19:34:00,cefazolin,,IV,738,mg,1,8
CHLA_0002,2020-12-18,0:30:00,cefepime,,IV,75,mg,5,8,2020-12-18,1:03:00,vancomycin,,IV,23,mg,4,13,2020-12-19,18:15:00,cefepime,,IV,60,mg,6,8,2020-12-20,4:18:00,vancomycin,,IV,24,mg,4,12,2021-04-20,15:17:00,meropenem,,IV,200,mg,2,1,2021-04-21,2:20:00,meropenem,,IV,400,mg,17,8,2021-04-22,14:16:00,Other,sulfamethoxazole-trimethoprim,IV,50,mg,9,12
提前致谢！
您想要的输出包含 dcast 无法自然推断的“数字”部分。我们可以使用 ave 相对容易地添加它（基础 R，当然这可以在 data.table 或 dplyr 分组中同样容易地完成）。
reshape2 和基础 R
csfmelt$num <- ave(seq(nrow(csfmelt)), csfmelt[c("study_id","variable")], FUN = seq_along)
head(csfmelt)
# study_id variable value num
# 1 CHLA_0001 proanbx_dt 2021-07-22 1
# 2 CHLA_0001 proanbx_dt 2021-07-22 2
# 3 CHLA_0001 proanbx_dt 2021-07-22 3
# 4 CHLA_0001 proanbx_dt 2021-08-09 4
# 5 CHLA_0002 proanbx_dt 2020-12-18 1
# 6 CHLA_0002 proanbx_dt 2020-12-18 2
csfwide <- reshape2::dcast(csfmelt, study_id ~ variable + num, value.var = "value")
csfwide
# study_id proanbx_dt_1 proanbx_dt_2 proanbx_dt_3 proanbx_dt_4 proanbx_tm_1 proanbx_tm_2 proanbx_tm_3 proanbx_tm_4 name_1 name_2 name_3 name_4 othername_1 othername_2 othername_3 othername_4 route_1 route_2 route_3 route_4 dosage_1 dosage_2 dosage_3 dosage_4 units_1 units_2 units_3 units_4 doses_1 doses_2 doses_3 doses_4 freq_1 freq_2 freq_3 freq_4
# 1 CHLA_0001 2021-07-22 2021-07-22 2021-07-22 2021-08-09 20:01:00 20:19:00 23:17:00 19:34:00 ceftriaxone metronidazole vancomycin cefazolin IV IV IV IV 1250 250 350 738 mg mg mg mg 4 5 3 1 13 9 6 8
# 2 CHLA_0002 2020-12-18 2020-12-18 2020-12-19 2020-12-20 0:30:00 1:03:00 18:15:00 4:18:00 cefepime vancomycin cefepime vancomycin IV IV IV IV 75 23 60 24 mg mg mg mg 5 4 6 4 8 13 8 12
# 3 CHLA_0003 2021-04-20 2021-04-21 2021-04-22 <NA> 15:17:00 2:20:00 14:16:00 <NA> meropenem meropenem Other <NA> sulfamethoxazole-trimethoprim <NA> IV IV IV <NA> 200 400 50 <NA> mg mg mg <NA> 2 17 9 <NA> 1 8 12 <NA>
列顺序不是您要求的，但可以稍微符合一下：
variables <- as.character(unique(csfmelt$variable))
sub(".*_", "", names(csfwide)[-(1:2)])
# [1] "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4"
sub("_[^_]*$", "", names(csfwide)[-(1:2)])
# [1] "proanbx_dt" "proanbx_dt" "proanbx_dt" "proanbx_tm" "proanbx_tm" "proanbx_tm" "proanbx_tm" "name" "name" "name" "name" "othername" "othername" "othername" "othername" "route" "route" "route" "route" "dosage"
# [21] "dosage" "dosage" "dosage" "units" "units" "units" "units" "doses" "doses" "doses" "doses" "freq" "freq" "freq" "freq"
nms <- names(csfwide)[-(1:2)]
newnms <- nms[order(sub(".*_", "", nms), match(nms, variables))]
csfwide2 <- subset(csfwide, select = c(names(csfwide)[1:2], newnms))
csfwide2
# study_id proanbx_dt_1 proanbx_tm_1 name_1 othername_1 route_1 dosage_1 units_1 doses_1 freq_1 proanbx_dt_2 proanbx_tm_2 name_2 othername_2 route_2 dosage_2 units_2 doses_2 freq_2 proanbx_dt_3 proanbx_tm_3 name_3 othername_3 route_3 dosage_3 units_3 doses_3 freq_3 proanbx_dt_4 proanbx_tm_4 name_4 othername_4 route_4 dosage_4 units_4 doses_4 freq_4
# 1 CHLA_0001 2021-07-22 20:01:00 ceftriaxone IV 1250 mg 4 13 2021-07-22 20:19:00 metronidazole IV 250 mg 5 9 2021-07-22 23:17:00 vancomycin IV 350 mg 3 6 2021-08-09 19:34:00 cefazolin IV 738 mg 1 8
# 2 CHLA_0002 2020-12-18 0:30:00 cefepime IV 75 mg 5 8 2020-12-18 1:03:00 vancomycin IV 23 mg 4 13 2020-12-19 18:15:00 cefepime IV 60 mg 6 8 2020-12-20 4:18:00 vancomycin IV 24 mg 4 12
# 3 CHLA_0003 2021-04-20 15:17:00 meropenem IV 200 mg 2 1 2021-04-21 2:20:00 meropenem IV 400 mg 17 8 2021-04-22 14:16:00 Other sulfamethoxazole-trimethoprim IV 50 mg 9 12 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
@r2evans 给了你一个很好的答案，但我在考虑你对日期和时间的评论。你没有提供你是如何收集这些数据的，所以我不能告诉你如何以这种方式导入它。但是，我确实在以下代码中转换了这些变量。话虽这么说，添加日期是没有意义的。我当时在想，经过的天数和时间量可能更符合您为那些特定变量寻找的内容。不幸的是，我无法弄清楚如何让它与 reshape2 一起工作。这使用 dplyr、tidyselect 和 hms。不过，您只需调用 dplyr，因为我已经附加了适用函数的包。 （不过，您需要安装软件包。）
我没有保留 name 和 othername 因为它们不是多个条目。
library(dplyr)
csf_antibio = read.table(header = T, sep = ",", text = "study_id,proanbx_dt,proanbx_tm,name,othername,route,dosage,units,doses,freq
CHLA_0001,2021-07-22,20:01:00,ceftriaxone,,IV,1250,mg,4,13
CHLA_0001,2021-07-22,20:19:00,metronidazole,,IV,250,mg,5,9
CHLA_0001,2021-07-22,23:17:00,vancomycin,,IV,350,mg,3,6
CHLA_0001,2021-08-09,19:34:00,cefazolin,,IV,738,mg,1,8
CHLA_0002,2020-12-18,0:30:00,cefepime,,IV,75,mg,5,8
CHLA_0002,2020-12-18,1:03:00,vancomycin,,IV,23,mg,4,13
CHLA_0002,2020-12-19,18:15:00,cefepime,,IV,60,mg,6,8
CHLA_0002,2020-12-20,4:18:00,vancomycin,,IV,24,mg,4,12
CHLA_0003,2021-04-20,15:17:00,meropenem,,IV,200,mg,2,1
CHLA_0003,2021-04-21,2:20:00,meropenem,,IV,400,mg,17,8
CHLA_0003,2021-04-22,14:16:00,Other,sulfamethoxazole-trimethoprim,IV,50,mg,9,12")
因为时间真正和日期挂钩，所以我写了一个处理时差的函数
timer <- function(df1){
maxtm = max(df1$proanbx_tm[df1$proanbx_dt == max(df1$proanbx_dt)]) %>% hms::as_hms()
mintm = min(df1$proanbx_tm[df1$proanbx_dt == min(df1$proanbx_dt)]) %>% hms::as_hms()
if(maxtm > mintm){
tmr = (maxtm - mintm) %>% hms::as_hms() # captures mult entries in the same day
} else if(mintm > maxtm) {
tmr = (maxtm - mintm) + hms::as_hms('24:00:00') # add a full day
} else { # only one entry or the time is identical in max/min
tmr = hms::as_hms('0')
}
return(tmr)
}
我将列名称收集到 return 列的原始顺序。
ordNames = names(csf_antibio) # collect names to return order to columns
# [1] "study_id" "proanbx_dt" "proanbx_tm" "name" "othername" "route"
# [7] "dosage" "units" "doses" "freq"
# names kept = ordNames[,c(1:3,6:10)]
求时间的和与差
csf2 <- csf_antibio %>%
mutate(proanbx_dt = as.Date(proanbx_dt), # convert to date
proanbx_tm = hms::as_hms(proanbx_tm)) %>% # convert to time
group_by(study_id) %>% # group by study
summarise(proanbx_tm = timer(.data), # difference in time
proanbx_dt = max(proanbx_dt) - min(proanbx_dt), # difference in days
across(tidyselect:::where(is.integer), sum),
units = "mg",
route = "IV") %>%
select(ordNames[c(1:3,6:10)])
head(csf2)
# # A tibble: 3 × 8
# study_id proanbx_dt proanbx_tm route dosage units doses freq
# <chr> <drtn> <time> <chr> <int> <chr> <int> <int>
# 1 CHLA_0001 18 days 23:33 IV 2588 mg 13 36
# 2 CHLA_0002 2 days 03:48 IV 182 mg 19 41
# 3 CHLA_0003 2 days 22:59 IV 650 mg 28 21
我需要将数据框 csf_antibio（下面的示例）从长格式重塑为宽格式，按行分隔每个患者的所有值。
study_id,proanbx_dt,proanbx_tm,name,othername,route,dosage,units,doses,freq
CHLA_0001,2021-07-22,20:01:00,ceftriaxone,,IV,1250,mg,4,13
CHLA_0001,2021-07-22,20:19:00,metronidazole,,IV,250,mg,5,9
CHLA_0001,2021-07-22,23:17:00,vancomycin,,IV,350,mg,3,6
CHLA_0001,2021-08-09,19:34:00,cefazolin,,IV,738,mg,1,8
CHLA_0002,2020-12-18,0:30:00,cefepime,,IV,75,mg,5,8
CHLA_0002,2020-12-18,1:03:00,vancomycin,,IV,23,mg,4,13
CHLA_0002,2020-12-19,18:15:00,cefepime,,IV,60,mg,6,8
CHLA_0002,2020-12-20,4:18:00,vancomycin,,IV,24,mg,4,12
CHLA_0003,2021-04-20,15:17:00,meropenem,,IV,200,mg,2,1
CHLA_0003,2021-04-21,2:20:00,meropenem,,IV,400,mg,17,8
CHLA_0003,2021-04-22,14:16:00,Other,sulfamethoxazole-trimethoprim,IV,50,mg,9,12
我尝试了以下但没有成功：
csfmelt <- melt(csf_antibio, id.vars=1:1)
csf <- dcast(csfmelt, study_id ~ variable, value.var = "value", fun.aggregate = sum)
我希望最终的数据框每行都有每个研究 ID 和变量
study_id,proanbx_dt1,proanbx_tm1,name1,othername1,route1,dosage1,units1,doses1,freq1,proanbx_dt2,proanbx_tm2,name2,othername2,route2,dosage2,units2,doses2,freq2,proanbx_dt3,proanbx_tm3,name3,othername3,route3,dosage3,units3,doses3,freq3,proanbx_dt4,proanbx_tm4,name4,othername4,route4,dosage4,units4,doses4,freq4
CHLA_0001,2021-07-22,20:01:00,ceftriaxone,,IV,1250,mg,4,13, 2021-07-22,20:19:00,metronidazole,,IV,250,mg,5,9, 2021-07-22,23:17:00,vancomycin,,IV,350,mg,3,6,2021-08-09,19:34:00,cefazolin,,IV,738,mg,1,8
CHLA_0002,2020-12-18,0:30:00,cefepime,,IV,75,mg,5,8,2020-12-18,1:03:00,vancomycin,,IV,23,mg,4,13,2020-12-19,18:15:00,cefepime,,IV,60,mg,6,8,2020-12-20,4:18:00,vancomycin,,IV,24,mg,4,12,2021-04-20,15:17:00,meropenem,,IV,200,mg,2,1,2021-04-21,2:20:00,meropenem,,IV,400,mg,17,8,2021-04-22,14:16:00,Other,sulfamethoxazole-trimethoprim,IV,50,mg,9,12
提前致谢！
您想要的输出包含 dcast 无法自然推断的“数字”部分。我们可以使用 ave 相对容易地添加它（基础 R，当然这可以在 data.table 或 dplyr 分组中同样容易地完成）。
reshape2 和基础 R
csfmelt$num <- ave(seq(nrow(csfmelt)), csfmelt[c("study_id","variable")], FUN = seq_along)
head(csfmelt)
# study_id variable value num
# 1 CHLA_0001 proanbx_dt 2021-07-22 1
# 2 CHLA_0001 proanbx_dt 2021-07-22 2
# 3 CHLA_0001 proanbx_dt 2021-07-22 3
# 4 CHLA_0001 proanbx_dt 2021-08-09 4
# 5 CHLA_0002 proanbx_dt 2020-12-18 1
# 6 CHLA_0002 proanbx_dt 2020-12-18 2
csfwide <- reshape2::dcast(csfmelt, study_id ~ variable + num, value.var = "value")
csfwide
# study_id proanbx_dt_1 proanbx_dt_2 proanbx_dt_3 proanbx_dt_4 proanbx_tm_1 proanbx_tm_2 proanbx_tm_3 proanbx_tm_4 name_1 name_2 name_3 name_4 othername_1 othername_2 othername_3 othername_4 route_1 route_2 route_3 route_4 dosage_1 dosage_2 dosage_3 dosage_4 units_1 units_2 units_3 units_4 doses_1 doses_2 doses_3 doses_4 freq_1 freq_2 freq_3 freq_4
# 1 CHLA_0001 2021-07-22 2021-07-22 2021-07-22 2021-08-09 20:01:00 20:19:00 23:17:00 19:34:00 ceftriaxone metronidazole vancomycin cefazolin IV IV IV IV 1250 250 350 738 mg mg mg mg 4 5 3 1 13 9 6 8
# 2 CHLA_0002 2020-12-18 2020-12-18 2020-12-19 2020-12-20 0:30:00 1:03:00 18:15:00 4:18:00 cefepime vancomycin cefepime vancomycin IV IV IV IV 75 23 60 24 mg mg mg mg 5 4 6 4 8 13 8 12
# 3 CHLA_0003 2021-04-20 2021-04-21 2021-04-22 <NA> 15:17:00 2:20:00 14:16:00 <NA> meropenem meropenem Other <NA> sulfamethoxazole-trimethoprim <NA> IV IV IV <NA> 200 400 50 <NA> mg mg mg <NA> 2 17 9 <NA> 1 8 12 <NA>
列顺序不是您要求的，但可以稍微符合一下：
variables <- as.character(unique(csfmelt$variable))
sub(".*_", "", names(csfwide)[-(1:2)])
# [1] "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4" "1" "2" "3" "4"
sub("_[^_]*$", "", names(csfwide)[-(1:2)])
# [1] "proanbx_dt" "proanbx_dt" "proanbx_dt" "proanbx_tm" "proanbx_tm" "proanbx_tm" "proanbx_tm" "name" "name" "name" "name" "othername" "othername" "othername" "othername" "route" "route" "route" "route" "dosage"
# [21] "dosage" "dosage" "dosage" "units" "units" "units" "units" "doses" "doses" "doses" "doses" "freq" "freq" "freq" "freq"
nms <- names(csfwide)[-(1:2)]
newnms <- nms[order(sub(".*_", "", nms), match(nms, variables))]
csfwide2 <- subset(csfwide, select = c(names(csfwide)[1:2], newnms))
csfwide2
# study_id proanbx_dt_1 proanbx_tm_1 name_1 othername_1 route_1 dosage_1 units_1 doses_1 freq_1 proanbx_dt_2 proanbx_tm_2 name_2 othername_2 route_2 dosage_2 units_2 doses_2 freq_2 proanbx_dt_3 proanbx_tm_3 name_3 othername_3 route_3 dosage_3 units_3 doses_3 freq_3 proanbx_dt_4 proanbx_tm_4 name_4 othername_4 route_4 dosage_4 units_4 doses_4 freq_4
# 1 CHLA_0001 2021-07-22 20:01:00 ceftriaxone IV 1250 mg 4 13 2021-07-22 20:19:00 metronidazole IV 250 mg 5 9 2021-07-22 23:17:00 vancomycin IV 350 mg 3 6 2021-08-09 19:34:00 cefazolin IV 738 mg 1 8
# 2 CHLA_0002 2020-12-18 0:30:00 cefepime IV 75 mg 5 8 2020-12-18 1:03:00 vancomycin IV 23 mg 4 13 2020-12-19 18:15:00 cefepime IV 60 mg 6 8 2020-12-20 4:18:00 vancomycin IV 24 mg 4 12
# 3 CHLA_0003 2021-04-20 15:17:00 meropenem IV 200 mg 2 1 2021-04-21 2:20:00 meropenem IV 400 mg 17 8 2021-04-22 14:16:00 Other sulfamethoxazole-trimethoprim IV 50 mg 9 12 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
@r2evans 给了你一个很好的答案，但我在考虑你对日期和时间的评论。你没有提供你是如何收集这些数据的，所以我不能告诉你如何以这种方式导入它。但是，我确实在以下代码中转换了这些变量。话虽这么说，添加日期是没有意义的。我当时在想，经过的天数和时间量可能更符合您为那些特定变量寻找的内容。不幸的是，我无法弄清楚如何让它与 reshape2 一起工作。这使用 dplyr、tidyselect 和 hms。不过，您只需调用 dplyr，因为我已经附加了适用函数的包。 （不过，您需要安装软件包。）
我没有保留 name 和 othername 因为它们不是多个条目。
library(dplyr)
csf_antibio = read.table(header = T, sep = ",", text = "study_id,proanbx_dt,proanbx_tm,name,othername,route,dosage,units,doses,freq
CHLA_0001,2021-07-22,20:01:00,ceftriaxone,,IV,1250,mg,4,13
CHLA_0001,2021-07-22,20:19:00,metronidazole,,IV,250,mg,5,9
CHLA_0001,2021-07-22,23:17:00,vancomycin,,IV,350,mg,3,6
CHLA_0001,2021-08-09,19:34:00,cefazolin,,IV,738,mg,1,8
CHLA_0002,2020-12-18,0:30:00,cefepime,,IV,75,mg,5,8
CHLA_0002,2020-12-18,1:03:00,vancomycin,,IV,23,mg,4,13
CHLA_0002,2020-12-19,18:15:00,cefepime,,IV,60,mg,6,8
CHLA_0002,2020-12-20,4:18:00,vancomycin,,IV,24,mg,4,12
CHLA_0003,2021-04-20,15:17:00,meropenem,,IV,200,mg,2,1
CHLA_0003,2021-04-21,2:20:00,meropenem,,IV,400,mg,17,8
CHLA_0003,2021-04-22,14:16:00,Other,sulfamethoxazole-trimethoprim,IV,50,mg,9,12")
因为时间真正和日期挂钩，所以我写了一个处理时差的函数
timer <- function(df1){
maxtm = max(df1$proanbx_tm[df1$proanbx_dt == max(df1$proanbx_dt)]) %>% hms::as_hms()
mintm = min(df1$proanbx_tm[df1$proanbx_dt == min(df1$proanbx_dt)]) %>% hms::as_hms()
if(maxtm > mintm){
tmr = (maxtm - mintm) %>% hms::as_hms() # captures mult entries in the same day
} else if(mintm > maxtm) {
tmr = (maxtm - mintm) + hms::as_hms('24:00:00') # add a full day
} else { # only one entry or the time is identical in max/min
tmr = hms::as_hms('0')
}
return(tmr)
}
我将列名称收集到 return 列的原始顺序。
ordNames = names(csf_antibio) # collect names to return order to columns
# [1] "study_id" "proanbx_dt" "proanbx_tm" "name" "othername" "route"
# [7] "dosage" "units" "doses" "freq"
# names kept = ordNames[,c(1:3,6:10)]
求时间的和与差
csf2 <- csf_antibio %>%
mutate(proanbx_dt = as.Date(proanbx_dt), # convert to date
proanbx_tm = hms::as_hms(proanbx_tm)) %>% # convert to time
group_by(study_id) %>% # group by study
summarise(proanbx_tm = timer(.data), # difference in time
proanbx_dt = max(proanbx_dt) - min(proanbx_dt), # difference in days
across(tidyselect:::where(is.integer), sum),
units = "mg",
route = "IV") %>%
select(ordNames[c(1:3,6:10)])
head(csf2)
# # A tibble: 3 × 8
# study_id proanbx_dt proanbx_tm route dosage units doses freq
# <chr> <drtn> <time> <chr> <int> <chr> <int> <int>
# 1 CHLA_0001 18 days 23:33 IV 2588 mg 13 36
# 2 CHLA_0002 2 days 03:48 IV 182 mg 19 41
# 3 CHLA_0003 2 days 22:59 IV 650 mg 28 21