我正在R中使用以下数据框。
dput：
structure(list(uid = c("K-1", "K-1",
"K-2", "K-3", "K-4", "K-5",
"K-6", "K-7", "K-8", "K-9",
"K-10", "K-11", "K-12", "K-13",
"K-14"), Date = c("2020-03-16 12:11:33", "2020-03-16 12:11:33",
"2020-03-16 06:13:55", "2020-03-16 10:03:43", "2020-03-16 12:37:09",
"2020-03-16 06:40:24", "2020-03-16 09:46:45", "2020-03-16 12:07:44",
"2020-03-16 14:09:51", "2020-03-16 09:19:23", "2020-03-16 09:07:37",
"2020-03-16 11:48:34", "2020-03-16 06:23:24", "2020-03-16 04:39:03",
"2020-03-16 04:59:13"), batch_no = c(7, 7, 8, 9, 9, 8,
7, 6, 7, 9, 8, 8, 7, 7, 7), marking = c("S1", "S1", "S2",
"SE_hold1", "SD_hold1", "SD_hold2", "S3", "S3", "", "SA_hold3", "S1", "S1", "S2",
"S3", "S3"), seq = c("FRD",
"FHL", NA, NA, NA, NA, NA, NA, "ABC", NA, NA, NA, NA, "DEF", NA)), .Names = c("uid",
"Date", "batch_no", "marking",
"seq"), row.names = c(NA, 15L), class = "data.frame")
uid Date batch_no marking seq
K-1 16/03/2020 12:11:33 7 S1 FRD
K-1 16/03/2020 12:11:33 7 S1 FHL
K-2 16/03/2020 12:11:33 8 SE_hold1 ABC
K-3 16/03/2020 12:11:33 9 SD_hold2 DEF
K-4 16/03/2020 12:11:33 8 S1 XYZ
K-5 16/03/2020 12:11:33 NA ABC
K-6 16/03/2020 12:11:33 7 ZZZ
K-7 16/03/2020 12:11:33 NA S2 NA
K-8 16/03/2020 12:11:33 6 S3 FRD
seq列将具有包括NA在内的八个唯一值，并非所有8个值都可用于每天的日期。batch_no将有六个唯一值，包括NA和空白，这不是所有六个值都可用于每天的日期。marking列将具有〜25个唯一值，但需要将带有后缀_hold#的值视为Hold，之后将有六个唯一值，包括空白和NA 。要求是按以下顺序合并dcast数据框，以具有用于分析的单个视图摘要。
我想在代码中将所有唯一值保持静态，这样，如果特定值在特定日期不可用，我将在汇总表中得到0或-。
所需的输出：
seq count percentage Marking count Percentage batch_no count Percentage
FRD 1 12.50% S1 2 25.00% 6 1 12.50%
FHL 1 12.50% S2 1 12.50% 7 2 25.00%
ABC 2 25.00% S3 1 12.50% 8 2 25.00%
DEF 1 12.50% Hold 2 25.00% 9 1 12.50%
XYZ 1 12.50% NA 1 12.50% NA 1 12.50%
ZZZ 1 12.50% (Blank) 1 12.50% (Blank) 1 12.50%
FRD 1 12.50% - - - - - -
NA 1 12.50% - - - - - -
(Blank) 0 0.00% - - - - - -
Total 8 112.50% - 8 100.00% - 8 100.00%
对于seq，我们有％> 100，因为对值uid和FRD重复计算了相同的FHL。那是公认的情况。在Total中将只有uid个不同的计数。
我正在使用SO的下述代码，但无法获得所需的输出。
df = df_original %>%
mutate(marking = if_else(str_detect(marking,"hold"),"Hold", marking)) %>%
mutate_at(vars(c("seq", "batch_no", "marking")), forcats::fct_explicit_na, na_level = "(Blank)")
## You Need to do something similar with vectors of the possible values
df_combinations = purrr::cross_df(list(seq = df$seq %>% unique(),
batch_no = df$batch_no %>% unique(),
marking = df$marking %>% unique()))
df_all_combination = df_combinations %>%
left_join(df, by = c("seq", "batch_no", "marking")) %>%
group_by(seq, batch_no, marking) %>%
summarise(count = n())
答案 0 :(得分：2)
Base R解决方案（请注意：我不确定我是否完全理解您的问题）：
# Function to summarise each of the vectors required: summariser => function
summariser <- function(vec) {
within(unique(data.frame(
vec = vec,
counter = as.numeric(ifelse(is.na(vec), sum(is.na(vec)),
ave(vec, vec, FUN = length))), stringsAsFactors = FALSE
)),
{
perc = paste0(round(counter / sum(counter) * 100, 2), "%")
})
}
# Vectors to summarise: vecs_to_summarise => character vector
vecs_to_summarise <- c("seq", "marking", "batch_no")
# Create an empty list in order to allocate some memory: df_list => list
df_list <- vector("list", length(vecs_to_summarise))
# Apply the summariser function to each of the vectors required: df_list => list of dfs
df_list <- lapply(df[,vecs_to_summarise], summariser)
# Rename the vectors of each data.frame in the list: df_list => list of dfs:
df_list <- lapply(seq_along(df_list), function(i) {
names(df_list[[i]]) <- gsub("_vec", "",
paste(names(df_list[i]), names(df_list[[i]]), sep = "_"))
return(df_list[[i]])
})
# Determine the number of rows of the maximum data.frame: numeric scalar
max_df_length <- max(sapply(df_list, nrow))
# Extend each data.frame to be the same length (pad with NAs if necessary): df_list => list
df_list <- lapply(seq_along(df_list), function(i){
y <- data.frame(df_list[[i]][rep(seq_len(nrow(df_list[[1]])), each = 1),])
y[1:(nrow(y)),] <- NA
y <- y[1:(max_df_length - nrow(df_list[[i]])),]
if(length(y) > 0){
x <- data.frame(rbind(df_list[[i]], y)[1:max_df_length,])
}else{
x <- data.frame(df_list[[i]][1:max_df_length,])
}
return(x)
}
)
# Bind the data.frames in the list into a single df: analysed_df => data.frame
analysed_df <- do.call("cbind", df_list)
数据：
df <- structure(list(uid = c("K-1", "K-1", "K-2", "K-3", "K-4", "K-5",
"K-6", "K-7", "K-8"), Date = structure(c(1584321093, 1584321093,
1584321093, 1584321093, 1584321093, 1584321093, 1584321093, 1584321093,
1584321093), class = c("POSIXct", "POSIXt"), tzone = ""), batch_no = c(7L,
7L, 8L, 9L, 8L, NA, 7L, NA, 6L), marking = c("S1", "S1", "SE_hold1",
"SD_hold2", "S1", NA, NA, "S2", "S3"), seq = c("FRD", "FHL",
"ABC", "DEF", "XYZ", "ABC", "ZZZ", NA, "FRD")), row.names = c(NA,
-9L), class = "data.frame")