I have a data frame like,
2015-01-30 1 Fri
2015-01-30 2 Sat
2015-02-01 3 Sun
2015-02-02 1 Mon
2015-02-03 1
Convert to date and use the %W
format to get a week number...
df <- read.csv(textConnection("2015-01-30, 1, Fri,
2015-01-30, 2, Sat,
2015-02-01, 3, Sun,
2015-02-02, 1, Mon,
2015-02-03, 1, Tue,
2015-02-04, 1, Wed,
2015-02-05, 1, Thu,
2015-02-06, 1, Fri,
2015-02-07, 1, Sat,
2015-02-08, 1, Sun"), header=F, stringsAsFactors=F)
names(df) <- c("date", "something", "day")
df$date <- as.Date(df$date, format="%Y-%m-%d")
df$week <- format(df$date, "%W")
aggregate(df$something, list(df$week), sum)
Wit dplyr
and lubridate
is this really easy thanks to the function isoweek
my.df <- read.table(header=FALSE, text=
'2015-01-30 1 Fri
2015-01-30 2 Sat
2015-02-01 3 Sun
2015-02-02 1 Mon
2015-02-03 1 Tue
2015-02-04 1 Wed
2015-02-05 1 Thu
2015-02-06 1 Fri
2015-02-07 1 Sat
2015-02-08 1 Sun')
my.df %>% mutate(week = isoweek(V1)) %>% group_by(week) %>% summarise(sum(V2))
or a bit shorter
my.df %>% group_by(isoweek(V1)) %>% summarise(sum(V2))
This should work. I've called the dataframe m
and named the columns possibly different to yours.
library(plyr) # install.packages("plyr")
colnames(m) = c("Date", "count","Day")
start = as.Date("2015-01-26")
m$Week <- floor(unclass(as.Date(m$Date) - as.Date(start)) / 7) + 1
m$Week = as.numeric(m$Week)
m %>% group_by(Week) %>% summarise(count = sum(count))
The library plyr is great for data manipulation, but it's just a rough hack to get the week number in.