#obs每月的中位数图。 在r-studio中每月在第二轴上

Question

I have a data.frame consisting of 2 variables with each 2.5 million obs.

str(values)
data.frame':    2529905 obs. of  2 variables:
 $ Date : Factor w/ 498 levels "1977-11","1978-06",..: 108 60 12 108 58 108 132 188 51 60     ...
$ Value: num  223000 171528 110269 426000 172436 ...
> head(values)
 Date    Value
1 2003-01 223000.0
2 1999-01 171528.0
3 1992-01 110268.6
4 2003-01 426000.0
5 1998-11 172436.5
6 2003-01 334000.0

I wanted to make a data.frame with the median per date:

library(plyr)
medianperdate = ddply(values, .(Date), summarize, median_value = median(Value))

> str(medianperdate)
'data.frame':   498 obs. of  2 variables:
 $ Date        : Factor w/ 498 levels "1977-11","1978-06",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ median_value: num  106638 84948 85084 75725 88487 ...
> head(medianperdate)
     Date median_value
1 1977-11    106638.35
2 1978-06     84947.65
3 1985-07     85083.79
4 1986-05     75724.58
5 1986-11     88487.14
6 1986-12     98697.20

But what I want, is an extra column which counts the observations per month (eg. 2003-01, the data used would be object "values"

And another extra column where I define which class house it is:

a : < 200 000 
b : < 300 000 & > 200 000
c : < 300 000 & > 2000000

I will continuetrying this but because I am already stuck for a couple of hours I will appreciate help very much!!

If it is not clear, what I can understand. The following testdataframe presents how I would like my dataframe to look like

> testdf
Year MedianValue HouseClass #Observations
1 1999-1      200000          B           501
2 1999-2      150000          A           664
3 1999-3      250000          C           555

Answer 1

Like my answer to your previous question 0

library(data.table)
dt <- data.table(df)


dt2 <- dt[,list(
   medianvalue = median(value),
   obs = .N
   ),
   by = "Date"
]

dt2[,HouseClass := "c"]
dt2[obs < 300000,HouseClass := "b"]
dt2[obs < 200000,HouseClass := "a"]

Answer 2

You can write functions in the apply and apply like functions (which includes the plyr functions). It would look something like this:

ddply(values, .(Date), .fun = function(x) {
  median <- median(x)
  value <- ifelse(median < 200000, 'A', ifelse(median < 300000, 'B', 'C'))
  n <- length(x)
  return(c(median, value, n))
})