(This article was first published on

When plotting time series data, you might want to bin the values so that each data point corresponds to the sum for a given month or week. This post will show an easy way to use **Mollie's Research Blog**, and kindly contributed to R-bloggers)*cut*and

**ggplot2**'s

*stat_summary*to plot month totals in R without needing to reorganize the data into a second data frame.

Let's start with a simple sample data set with a series of dates and quantities:

library(ggplot2)

library(scales)

# load data:

log <- data.frame(Date = c("2013/05/25","2013/05/28","2013/05/31","2013/06/01","2013/06/02","2013/06/05","2013/06/07"),

Quantity = c(9,1,15,4,5,17,18))

log

str(log)

> log

Date Quantity

1 2013/05/25 9

2 2013/05/28 1

3 2013/05/31 15

4 2013/06/01 4

5 2013/06/02 5

6 2013/06/05 17

7 2013/06/07 18

> str(log)

'data.frame': 7 obs. of 2 variables:

$ Date : Factor w/ 7 levels "2013/05/25","2013/05/28",..: 1 2 3 4 5 6 7

$ Quantity: num 9 1 15 4 5 17 18

Next, if the date data is not already in a date format, we'll need to convert it to date format:

# convert date variable from factor to date format:

log$Date <- as.Date(log$Date,

"%Y/%m/%d") # tabulate all the options here

str(log)

> str(log)

'data.frame': 7 obs. of 2 variables:

$ Date : Date, format: "2013-05-25" "2013-05-28" ...

$ Quantity: num 9 1 15 4 5 17 18

Next we need to create variables stating the week and month of each observation. For week,

*cut*has an option that allows you to break weeks as you'd like, beginning weeks on either Sunday or Monday.

# create variables of the week and month of each observation:

log$Month <- as.Date(cut(log$Date,

breaks = "month"))

log$Week <- as.Date(cut(log$Date,

breaks = "week",

start.on.monday = FALSE)) # changes weekly break point to Sunday

log

> log

Date Quantity Month Week

1 2013-05-25 9 2013-05-01 2013-05-19

2 2013-05-28 1 2013-05-01 2013-05-26

3 2013-05-31 15 2013-05-01 2013-05-26

4 2013-06-01 4 2013-06-01 2013-05-26

5 2013-06-02 5 2013-06-01 2013-06-02

6 2013-06-05 17 2013-06-01 2013-06-02

7 2013-06-07 18 2013-06-01 2013-06-02

Finally, we can create either a line or bar plot of the data by month and by week, using

*stat_summary*to sum up the values associated with each week or month:

# graph by month:

ggplot(data = log,

aes(Month, Quantity)) +

stat_summary(fun.y = sum, # adds up all observations for the month

geom = "bar") + # or "line"

scale_x_date(

labels = date_format("%Y-%m"),

breaks = "1 month") # custom x-axis labels

Time series plot, binned by month |

# graph by week:

ggplot(data = log,

aes(Week, Quantity)) +

stat_summary(fun.y = sum, # adds up all observations for the week

geom = "bar") + # or "line"

scale_x_date(

labels = date_format("%Y-%m-%d"),

breaks = "1 week") # custom x-axis labels

Time series plot, totaled by week |

The full code is available in a gist.

### References

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