[英]Order by month and year in R data frame
I have cleaned and ordered my data by date, which looks like below: 我按日期清理并订购了我的数据,如下所示:
df1 <- data.frame(matrix(vector(),ncol=4, nrow = 3))
colnames(df1) <- c("Date","A","B","C")
df1[1,] <- c("2000-01-30","0","1","0")
df1[2,] <- c("2000-01-31","2","0","3")
df1[3,] <- c("2000-02-29","1","2","1")
df1[4,] <- c("2000-03-31","2","1","3")
df1
Date A B C
1 2000-01-30 0 1 0
2 2000-01-31 2 0 3
3 2000-02-29 1 2 1
4 2000-03-31 2 1 3
However, I want to drop the day and order the data by month and year so the data will look like: 但是,我想删除当天按月和年订购数据,以便数据如下所示:
Date A B C
1 2000-01 2 1 3
3 2000-02 1 2 1
4 2000-03 2 1 3
I tried to use as.yearmon
from zoo
df2 <- as.yearmon(df1$Date, "%b-%y")
and it returns NA
. 我试图从zoo
df2 <- as.yearmon(df1$Date, "%b-%y")
使用as.yearmon
并返回NA
。 Thank you in advance for your generous help! 提前感谢您的慷慨帮助!
Here's a way to get the sum of the values for each column within each combination of Year-Month: 这是一种获取Year-Month的每个组合中每列的值总和的方法:
library(zoo)
library(dplyr)
# Convert non-date columns to numeric
df1[,-1] = lapply(df1[,-1], as.numeric)
df1 %>% mutate(Date = as.yearmon(Date)) %>%
group_by(Date) %>%
summarise_each(funs(sum))
Or, even shorter: 或者,甚至更短:
df1 %>%
group_by(Date=as.yearmon(Date)) %>%
summarise_each(funs(sum))
Date ABC 1 Jan 2000 2 1 3 2 Feb 2000 1 2 1 3 Mar 2000 2 1 3
Add the number of rows for each group: 添加每个组的行数:
df1 %>% group_by(Date=as.yearmon(Date)) %>% summarise_each(funs(sum)) %>% bind_cols(df1 %>% count(d=as.yearmon(Date)) %>% select(-d))
Multiple summary functions: 多个汇总功能:
df1 %>% group_by(Date=as.yearmon(Date)) %>% summarise_each(funs(sum(.), mean(.))) %>% bind_cols(df1 %>% count(d=as.yearmon(Date)) %>% select(-d))
Date A_sum B_sum C_sum A_mean B_mean C_mean n 1 Jan 2000 2 1 3 1 0.5 1.5 2 2 Feb 2000 1 2 1 1 2.0 1.0 1 3 Mar 2000 2 1 3 2 1.0 3.0 1
Your Date
column is a character vector, when it needs to be a Date type vector. 当Date
列需要是Date类型向量时,它是一个字符向量。 So: 所以:
df1$Date <- as.Date(df1$Date)
df1$Date <- as.yearmon(df1$Date)
Date A B C
1 Jan 2000 0 1 0
2 Jan 2000 2 0 3
3 Feb 2000 1 2 1
4 Mar 2000 2 1 3
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