count number of rows in a data frame in R based on group [duplicate]

Answer 1

Just for completion the data.table solution:

library(data.table)

mydf <- structure(list(ID = c(110L, 111L, 121L, 131L, 141L), 
                       MONTH.YEAR = c("JAN. 2012", "JAN. 2012", 
                                      "FEB. 2012", "FEB. 2012", 
                                      "MAR. 2012"), 
                       VALUE = c(1000L, 2000L, 3000L, 4000L, 5000L)), 
                  .Names = c("ID", "MONTH.YEAR", "VALUE"), 
                  class = "data.frame", row.names = c(NA, -5L))

setDT(mydf)
mydf[, .(`Number of rows` = .N), by = MONTH.YEAR]

   MONTH.YEAR Number of rows
1:  JAN. 2012              2
2:  FEB. 2012              2
3:  MAR. 2012              1

Answer 2

The count() function in plyr does what you want:

library(plyr)

count(mydf, "MONTH-YEAR")

Answer 3

Suppose we have a df_data data frame as below

> df_data
   ID MONTH-YEAR VALUE
1 110   JAN.2012  1000
2 111   JAN.2012  2000
3 121   FEB.2012  3000
4 131   FEB.2012  4000
5 141   MAR.2012  5000

To count number of rows in df_data grouped by MONTH-YEAR column, you can use:

> summary(df_data$`MONTH-YEAR`)

FEB.2012 JAN.2012 MAR.2012 
   2        2        1 

summary function will create a table from the factor argument, then create a vector for the result (line 7 & 8)

Answer 4

Here's an example that shows how table(.) (or, more closely matching your desired output, data.frame(table(.)) does what it sounds like you are asking for.

Note also how to share reproducible sample data in a way that others can copy and paste into their session.

Here's the (reproducible) sample data:

mydf <- structure(list(ID = c(110L, 111L, 121L, 131L, 141L), 
                       MONTH.YEAR = c("JAN. 2012", "JAN. 2012", 
                                      "FEB. 2012", "FEB. 2012", 
                                      "MAR. 2012"), 
                       VALUE = c(1000L, 2000L, 3000L, 4000L, 5000L)), 
                  .Names = c("ID", "MONTH.YEAR", "VALUE"), 
                  class = "data.frame", row.names = c(NA, -5L))

mydf
#    ID MONTH.YEAR VALUE
# 1 110  JAN. 2012  1000
# 2 111  JAN. 2012  2000
# 3 121  FEB. 2012  3000
# 4 131  FEB. 2012  4000
# 5 141  MAR. 2012  5000

Here's the calculation of the number of rows per group, in two output display formats:

table(mydf$MONTH.YEAR)
# 
# FEB. 2012 JAN. 2012 MAR. 2012 
#         2         2         1

data.frame(table(mydf$MONTH.YEAR))
#        Var1 Freq
# 1 FEB. 2012    2
# 2 JAN. 2012    2
# 3 MAR. 2012    1

Answer 5

Here is another way of using aggregate to count rows by group:

my.data <- read.table(text = '
    month.year    my.cov
      Jan.2000     apple
      Jan.2000      pear
      Jan.2000     peach
      Jan.2001     apple
      Jan.2001     peach
      Feb.2002      pear
', header = TRUE, stringsAsFactors = FALSE, na.strings = NA)

rows.per.group  <- aggregate(rep(1, length(my.data$month.year)),
                             by=list(my.data$month.year), sum)
rows.per.group

#    Group.1 x
# 1 Feb.2002 1
# 2 Jan.2000 3
# 3 Jan.2001 2

Answer 6

Try using the count function in dplyr:

library(dplyr)
dat1_frame %>% 
    count(MONTH.YEAR)

I am not sure how you got MONTH-YEAR as a variable name. My R version does not allow for such a variable name, so I replaced it with MONTH.YEAR.

As a side note, the mistake in your code was that dat1_frame %.% group_by(MONTH-YEAR) without a summarise function returns the original data frame without any modifications. So, you want to use

dat1_frame %>%
    group_by(MONTH.YEAR) %>%
    summarise(count=n())