max=aggregate(cbind(a$VALUE,Date=a$DATE) ~ format(a$DATE, "%m") + cut(a$CLASS, breaks=c(0,2,4,6,8,10,12,14)) , data = a, max)[-1]
max$DATE=as.Date(max$DATE, origin = "1970-01-01")
Sample Data :
DATE GRADE VALUE
2008-09-01 1 20
2008-09-02 2 30
2008-09-03 3 50
.
.
2008-09-30 2 75
.
.
2008-10-01 1 95
.
.
2008-11-01 4 90
.
.
2008-12-01 1 70
2008-12-02 2 40
2008-12-28 4 30
2008-12-29 1 40
2008-12-31 3 50
My Expected output according to above table for only first month is :
DATE GRADE VALUE
2008-09-30 (0,2] 75
2008-09-02 (2,4] 50
Output in my real data :
format(DATE, "%m")
1 09
2 10
3 11
4 12
5 09
6 10
7 11
cut(a$GRADE, breaks = c(0, 2, 4, 6, 8, 10, 12, 14)) value
1 (0,2] 0.30844444
2 (0,2] 1.00000000
3 (0,2] 1.00000000
4 (0,2] 0.73333333
5 (2,4] 0.16983488
6 (2,4] 0.09368000
7 (2,4] 0.10589335
Date
1 2008-09-30
2 2008-10-31
3 2008-11-28
4 2008-12-31
5 2008-09-30
6 2008-10-31
7 2008-11-28
The output is not according to the sample data , as the data is too big . A simple logic is that there are grades from 1 to 10 , so I want to find the highest value for a month in the corresponding grade groups . Eg : I need a highest value for each group (0,2],(0,4] etc
I used an aggregate condition with function max and two grouping it by two columns Date and Grade . Now when I run the code and display the value of max , I get 3 tables as output one after the other. Now I want to plot this output but i am not able to do that because of this .So how can i merge all these output ?
Try:
library(dplyr)
a %>%
group_by(MONTH=format(DATE, "%m"), GRADE=cut(GRADE, breaks=seq(0,14,by=2))) %>%
summarise_each(funs(max))
# MONTH GRADE DATE VALUE
#1 09 (0,2] 2008-09-30 75
#2 09 (2,4] 2008-09-03 50
#3 10 (0,2] 2008-10-01 95
#4 11 (2,4] 2008-11-01 90
#5 12 (0,2] 2008-12-29 70
#6 12 (2,4] 2008-12-31 50
Or using data.table
library(data.table)
setDT(a)[, list(DATE=max(DATE), VALUE=max(VALUE)),
by= list(MONTH=format(DATE, "%m"),
GRADE=cut(GRADE, breaks=seq(0,14, by=2)))]
# MONTH GRADE DATE VALUE
#1: 09 (0,2] 2008-09-30 75
#2: 09 (2,4] 2008-09-03 50
#3: 10 (0,2] 2008-10-01 95
#4: 11 (2,4] 2008-11-01 90
#5: 12 (0,2] 2008-12-29 70
#6: 12 (2,4] 2008-12-31 50
Or using aggregate
res <- transform(with(a,
aggregate(cbind(VALUE, DATE),
list(MONTH=format(DATE, "%m") ,GRADE=cut(GRADE, breaks=seq(0,14, by=2))), max)),
DATE=as.Date(DATE, origin="1970-01-01"))
res[order(res$MONTH),]
# MONTH GRADE VALUE DATE
#1 09 (0,2] 75 2008-09-30
#4 09 (2,4] 50 2008-09-03
#2 10 (0,2] 95 2008-10-01
#5 11 (2,4] 90 2008-11-01
#3 12 (0,2] 70 2008-12-29
#6 12 (2,4] 50 2008-12-31
data
a <- structure(list(DATE = structure(c(14123, 14124, 14125, 14152,
14153, 14184, 14214, 14215, 14241, 14242, 14244), class = "Date"),
GRADE = c(1L, 2L, 3L, 2L, 1L, 4L, 1L, 2L, 4L, 1L, 3L), VALUE = c(20L,
30L, 50L, 75L, 95L, 90L, 70L, 40L, 30L, 40L, 50L)), .Names = c("DATE",
"GRADE", "VALUE"), row.names = c(NA, -11L), class = "data.frame")
Update
If you want to include YEAR also in the grouping
library(dplyr)
a %>%
group_by(MONTH=format(DATE, "%m"), YEAR=format(DATE, "%Y"), GRADE=cut(GRADE, breaks=seq(0,14, by=2)))%>%
summarise_each(funs(max))
# MONTH YEAR GRADE DATE VALUE
#1 09 2008 (0,2] 2008-09-30 75
#2 09 2008 (2,4] 2008-09-03 50
#3 09 2009 (0,2] 2009-09-30 75
#4 09 2009 (2,4] 2009-09-03 50
#5 10 2008 (0,2] 2008-10-01 95
#6 10 2009 (0,2] 2009-10-01 95
#7 11 2008 (2,4] 2008-11-01 90
#8 11 2009 (2,4] 2009-11-01 90
#9 12 2008 (0,2] 2008-12-29 70
#10 12 2008 (2,4] 2008-12-31 50
#11 12 2009 (0,2] 2009-12-29 70
#12 12 2009 (2,4] 2009-12-31 50
data
a <- structure(list(DATE = structure(c(14123, 14124, 14125, 14152,
14153, 14184, 14214, 14215, 14241, 14242, 14244, 14488, 14489,
14490, 14517, 14518, 14549, 14579, 14580, 14606, 14607, 14609
), class = "Date"), GRADE = c(1L, 2L, 3L, 2L, 1L, 4L, 1L, 2L,
4L, 1L, 3L, 1L, 2L, 3L, 2L, 1L, 4L, 1L, 2L, 4L, 1L, 3L), VALUE = c(20L,
30L, 50L, 75L, 95L, 90L, 70L, 40L, 30L, 40L, 50L, 20L, 30L, 50L,
75L, 95L, 90L, 70L, 40L, 30L, 40L, 50L)), .Names = c("DATE",
"GRADE", "VALUE"), row.names = c("1", "2", "3", "4", "5", "6",
"7", "8", "9", "10", "11", "12", "21", "31", "41", "51", "61",
"71", "81", "91", "101", "111"), class = "data.frame")
Following code using base R may be helpful (using 'a' dataframe from akrun's answer):
xx = strsplit(as.character(a$DATE), '-')
a$month = sapply(strsplit(as.character(a$DATE), '-'),'[',2)
gradeCats = cut(a$GRADE, breaks = c(0, 2, 4, 6, 8, 10, 12, 14))
aggregate(VALUE~month+gradeCats, data= a, max)
month gradeCats VALUE
1 09 (0,2] 75
2 10 (0,2] 95
3 12 (0,2] 70
4 09 (2,4] 50
5 11 (2,4] 90
6 12 (2,4] 50
Related
My data look like the following example.
# A tibble: 18 x 4
DATE AUTHOR PRODUCT SALES
<dttm> <chr> <chr> <dbl>
1 2019-11-27 James B 80
2 2019-11-28 James B 100
3 2019-11-27 James A 80
4 2019-11-28 James A 100
5 2019-11-26 Frank B 70
6 2019-11-27 Frank B 75
7 2019-11-28 Frank B 65
8 2019-11-26 Frank A 70
9 2019-11-27 Frank A 75
10 2019-11-28 Frank A 65
11 2019-11-25 Mary A 100
12 2019-11-26 Mary A 80
13 2019-11-27 Mary A 95
14 2019-11-28 Mary A 110
15 2019-11-25 Mary B 100
16 2019-11-26 Mary B 80
17 2019-11-27 Mary B 95
18 2019-11-28 Mary B 110
I would like to add a "DIFF" column where the difference over day for SALES is calculated grouping by AUTHOR. My issues here are the following:
I have a different number of rows for every AUTHOR.
The same DATE could be repeated for some AUTHORS to report different information (in this example is PRODUCT), but the value for SALES will always remain the same, since it only depends on the DATE and the AUTHOR.
I have to keep every row in the dataset because every row contains specific information, so I can not just drop the rows where DATE is a duplicated.
Ideally I would implement the whole with a loop function in my script.
My desired outcome would be:
# A tibble: 18 x 4
DATE AUTHOR PRODUCT SALES DIFF
<dttm> <chr> <chr> <dbl>
1 2019-11-27 James B 80
2 2019-11-28 James B 100 20
3 2019-11-27 James A 80
4 2019-11-28 James A 100 20
5 2019-11-26 Frank B 70
6 2019-11-27 Frank B 75 5
7 2019-11-28 Frank B 65 -10
8 2019-11-26 Frank A 70
9 2019-11-27 Frank A 75 5
10 2019-11-28 Frank A 65 -10
11 2019-11-25 Mary A 100
12 2019-11-26 Mary A 80 -20
13 2019-11-27 Mary A 95 15
14 2019-11-28 Mary A 110 15
15 2019-11-25 Mary B 100
16 2019-11-26 Mary B 80 -20
17 2019-11-27 Mary B 95 15
18 2019-11-28 Mary B 110 15
I tried different things with dplyr and mutate but nothing seemed to work. Anyone has suggestions?
Thank you!
You could use lag to subtract previous value by group
library(dplyr)
df %>% group_by(AUTHOR, PRODUCT) %>% mutate(diff = SALES - lag(SALES))
# DATE AUTHOR PRODUCT SALES diff
# <fct> <fct> <fct> <int> <int>
# 1 2019-11-27 James B 80 NA
# 2 2019-11-28 James B 100 20
# 3 2019-11-27 James A 80 NA
# 4 2019-11-28 James A 100 20
# 5 2019-11-26 Frank B 70 NA
# 6 2019-11-27 Frank B 75 5
# 7 2019-11-28 Frank B 65 -10
# 8 2019-11-26 Frank A 70 NA
# 9 2019-11-27 Frank A 75 5
#10 2019-11-28 Frank A 65 -10
#11 2019-11-25 Mary A 100 NA
#12 2019-11-26 Mary A 80 -20
#13 2019-11-27 Mary A 95 15
#14 2019-11-28 Mary A 110 15
#15 2019-11-25 Mary B 100 NA
#16 2019-11-26 Mary B 80 -20
#17 2019-11-27 Mary B 95 15
#18 2019-11-28 Mary B 110 15
Or using diff
df %>% group_by(AUTHOR, PRODUCT) %>% mutate(diff = c(NA, diff(SALES)))
data
df <- structure(list(DATE = structure(c(3L, 4L, 3L, 4L, 2L, 3L, 4L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), .Label = c("2019-11-25",
"2019-11-26", "2019-11-27", "2019-11-28"), class = "factor"),
AUTHOR = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Frank",
"James", "Mary"), class = "factor"), PRODUCT = structure(c(2L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L), .Label = c("A", "B"), class = "factor"), SALES = c(80L,
100L, 80L, 100L, 70L, 75L, 65L, 70L, 75L, 65L, 100L, 80L,
95L, 110L, 100L, 80L, 95L, 110L)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18"))
We can use shift from data.table
library(data.table)
setDT(df)[, diff := SALES - shift(SALES), .(AUTHOR, PRODUCT)][]
I have a dataframe like this:
ID year age wage
1 2 1981 22 10000
2 2 1982 23 11000
3 2 1983 24 11500
4 2 1984 25 11000
5 2 1985 26 14000
6 2 1986 27 16000
7 2 1987 28 20000
8 2 1988 29 19000
9 2 1989 30 20000
10 2 1990 31 20000
11 2 1991 32 22000
12 2 1992 33 25000
13 2 1993 34 0
14 2 1994 35 NA
15 2 1995 36 0
16 2 1996 37 NA
17 2 1997 38 0
18 2 1998 39 NA
19 2 1999 40 0
20 2 2000 41 NA
21 2 2001 42 0
22 2 2002 43 NA
23 2 2003 44 0
24 2 2004 45 NA
25 2 2005 46 5500
26 2 2006 47 NA
27 2 2007 48 5000
28 2 2008 49 NA
29 2 2009 50 6000
30 2 2010 51 NA
31 2 2011 52 19000
32 2 2012 53 NA
33 2 2013 54 21000
34 2 2014 55 NA
35 2 2015 56 23000
36 3 1984 22 1300
37 3 1985 23 0
38 3 1986 24 1500
39 3 1987 25 1000
40 3 1988 26 0
I want to use an individual-specific regression of wage on age and age-squared to impute missing wage observations. I want to only impute when at least 5 non-missing observations are available.
As suggested by jay.sf, I tried the following but with fitted values:
df_imp <- do.call(rbind,
by(df, df$ID, function(x) {
IDs <- which(is.na(x$wage))
if (length(x$wage[- IDs]) >= 5) {
b <- lm(wage ~ poly(age, 2, raw=TRUE), x)$fitted.values
x$wage[IDs] <- with(x, b)[IDs]
}
return(x)
}))
I got the following results:
ID year age wage
36 2 1981 22 10000.000
37 2 1982 23 11000.000
38 2 1983 24 11500.000
39 2 1984 25 11000.000
40 2 1985 26 14000.000
41 2 1986 27 16000.000
42 2 1987 28 20000.000
43 2 1988 29 19000.000
44 2 1989 30 20000.000
45 2 1990 31 20000.000
46 2 1991 32 22000.000
47 2 1992 33 25000.000
48 2 1993 34 0.000
49 2 1994 35 7291.777
50 2 1995 36 0.000
51 2 1996 37 6779.133
52 2 1997 38 0.000
53 2 1998 39 7591.597
54 2 1999 40 0.000
55 2 2000 41 9729.168
56 2 2001 42 0.000
57 2 2002 43 13191.847
58 2 2003 44 0.000
59 2 2004 45 17979.633
60 2 2005 46 5500.000
61 2 2006 47 NA
62 2 2007 48 5000.000
63 2 2008 49 NA
64 2 2009 50 6000.000
65 2 2010 51 NA
66 2 2011 52 19000.000
67 2 2012 53 NA
68 2 2013 54 21000.000
69 2 2014 55 NA
70 2 2015 56 23000.000
You could use a simple if statement, without an else. Define an ID vector IDs that identifies missings, which you use to count them and to subset your Y column wage.
For this you can use by(), which splits your data similar to split() but you may apply a function; just rbind the result.
It's probably wiser to rather use the coefficients than the fitted values, because the latter also would be NA if your Y are NA. And you need to use raw=TRUE in the poly.
DF.imp <- do.call(rbind,
by(DF, DF$ID, function(x) {
IDs <- which(is.na(x$wage))
if (length(x$wage[- IDs]) >= 5) {
b <- lm(wage ~ poly(age, 2, raw=TRUE), x)$coefficients
x$wage[IDs] <- with(x, (b[1] + b[2]*age + b[3]*age^2))[IDs]
}
return(x)
}))
Note that I've slightly changed your example data, so that ID 3 also has missings, but less than 5 non-missings.
Result
DF.imp
# ID year age wage
# 2.1 2 1981 22 10000.000
# 2.2 2 1982 23 11000.000
# 2.3 2 1983 24 11500.000
# 2.4 2 1984 25 11000.000
# 2.5 2 1985 26 14000.000
# 2.6 2 1986 27 16000.000
# 2.7 2 1987 28 20000.000
# 2.8 2 1988 29 19000.000
# 2.9 2 1989 30 20000.000
# 2.10 2 1990 31 20000.000
# 2.11 2 1991 32 22000.000
# 2.12 2 1992 33 25000.000
# 2.13 2 1993 34 0.000
# 2.14 2 1994 35 7626.986
# 2.15 2 1995 36 0.000
# 2.16 2 1996 37 7039.387
# 2.17 2 1997 38 0.000
# 2.18 2 1998 39 6783.065
# 2.19 2 1999 40 0.000
# 2.20 2 2000 41 6858.020
# 2.21 2 2001 42 0.000
# 2.22 2 2002 43 7264.252
# 2.23 2 2003 44 0.000
# 2.24 2 2004 45 8001.761
# 2.25 2 2005 46 5500.000
# 2.26 2 2006 47 9070.546
# 2.27 2 2007 48 5000.000
# 2.28 2 2008 49 10470.609
# 2.29 2 2009 50 6000.000
# 2.30 2 2010 51 12201.948
# 2.31 2 2011 52 19000.000
# 2.32 2 2012 53 14264.565
# 2.33 2 2013 54 21000.000
# 2.34 2 2014 55 16658.458
# 2.35 2 2015 56 23000.000
# 3.36 3 1984 22 1300.000
# 3.37 3 1985 23 NA
# 3.38 3 1986 24 1500.000
# 3.39 3 1987 25 1000.000
# 3.40 3 1988 26 NA
Data
DF <- structure(list(ID = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L), year = c(1981L,
1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L,
1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 1984L, 1985L,
1986L, 1987L, 1988L), age = c(22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 56L, 22L, 23L, 24L, 25L, 26L), wage = c(10000L, 11000L,
11500L, 11000L, 14000L, 16000L, 20000L, 19000L, 20000L, 20000L,
22000L, 25000L, 0L, NA, 0L, NA, 0L, NA, 0L, NA, 0L, NA, 0L, NA,
5500L, NA, 5000L, NA, 6000L, NA, 19000L, NA, 21000L, NA, 23000L,
1300L, NA, 1500L, 1000L, NA)), row.names = c(NA, -40L), class = "data.frame")
I have a data table that repeats records. I would like to transpose the table but into the unique record names.
Below is a sample of the Data table:
V1 V2 id
ClientID 29 1
CheckID 201 1
PaymentAmount 256 1
Gross 301 1
Net 256 1
Invested 130 1
Invested 53 1
Invested 118 1
ClientID 31 2
CheckID 222 2
PaymentAmount 41 2
Gross 46 2
Net 41 2
Invested 46 2
ClientID 43 3
CheckID 310 3
PaymentAmount 41 3
Gross 46 3
Net 41 3
Invested 46 3
You can see from the table above that the record in X1 called "Investment" can occur more than once for a single ClientID. I'd like to transpose the data so that it looks as such:
ClientID CheckID PaymentAmount Gross Net Invested ID
29 201 256 301 256 130 1
29 201 256 301 256 53 1
29 201 256 301 256 118 1
31 222 41 46 41 46 2
43 310 41 46 41 46 3
43 310 41 46 41 48 3
any support is greatly appreciated!
We can create a sequence column grouped by the "V1", "id" column using data.table, then convert from 'long' to 'wide' format with dcast and replace the NA with the non-NA preceding values using na.locf from zoo.
library(data.table)
library(zoo)
setDT(df1)[, N:= 1:.N , by = .(V1, id)]
dcast(df1, id+N~V1, value.var="V2")[, lapply(.SD, na.locf),
by = id, .SDcols = CheckID:PaymentAmount]
# id CheckID ClientID Gross Invested Net PaymentAmount
#1: 1 201 29 301 130 256 256
#2: 1 201 29 301 53 256 256
#3: 1 201 29 301 118 256 256
#4: 2 222 31 46 46 41 41
#5: 3 310 43 46 46 41 41
data
df1 <- structure(list(V1 = c("ClientID", "CheckID", "PaymentAmount",
"Gross", "Net", "Invested", "Invested", "Invested", "ClientID",
"CheckID", "PaymentAmount", "Gross", "Net", "Invested", "ClientID",
"CheckID", "PaymentAmount", "Gross", "Net", "Invested"), V2 = c(29L,
201L, 256L, 301L, 256L, 130L, 53L, 118L, 31L, 222L, 41L, 46L,
41L, 46L, 43L, 310L, 41L, 46L, 41L, 46L), id = c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L)), .Names = c("V1", "V2", "id"), class = "data.frame",
row.names = c(NA, -20L))
Suppose I have the following dataframe:
dc tmin tmax cint wcmin wcmax wsmin wsmax gsmin gsmax wd rmin rmax cir lr
1: 24 -1 4 5 -5 -2 20 25 35 40 90 11.8 26.6 14.8 3
2: 41 -3 5 8 -8 -3 15 20 35 40 90 10.0 23.5 13.5 3
3: 48 0 5 5 -4 0 30 35 45 50 45 7.3 19.0 11.7 6
4: 50 0 5 5 -4 0 30 35 45 50 45 7.3 19.0 11.7 6
5: 52 3 5 2 -3 1 20 25 35 40 45 6.7 17.4 10.7 6
6: 57 -2 5 7 -6 -1 25 30 35 40 315 4.4 13.8 9.4 7
lc wc li yd yr nF factdcx
1: 1 3 TRUE 1 2010 2 24
2: 1 3 TRUE 1 2010 8 41
3: 2 3 TRUE 1 2010 0 48
4: 2 3 TRUE 1 2010 0 50
5: 2 3 TRUE 1 2010 0 52
6: 3 3 FALSE 1 2010 0 57
I'd like to turn it into a new dataframe like the following:
dc tmin tmax cint wcmin wcmax wsmin wsmax gsmin gsmax wd rmin rmax cir lr
1: 24 -1 4 5 -5 -2 20 25 35 40 90 11.8 26.6 14.8 3
2: 41 -3 5 8 -8 -3 15 20 35 40 90 10.0 23.5 13.5 3
3: 48 0 5 5 -4 0 30 35 45 50 45 7.3 19.0 11.7 6
4: 52 3 5 2 -3 1 20 25 35 40 45 6.7 17.4 10.7 6
5: 57 -2 5 7 -6 -1 25 30 35 40 315 4.4 13.8 9.4 7
lc wc li yd yr nF factdcx
1: 1 3 TRUE 1 2010 2 24
2: 1 3 TRUE 1 2010 8 41
3: 2 3 TRUE 1 2010 0 (sum of nF for 48 and 50, factdcx) 48
4: 2 3 TRUE 1 2010 0 52
5: 3 3 FALSE 1 2010 0 57
How can I do it? (Surely, the dataframe, abc, is much larger, but I want the sum of all categories of 48 and 50 and group it into a new category, say '48').
Many thanks!
> dput(head(abc1))
structure(list(dc = c(24L, 41L, 48L, 50L, 52L, 57L), tmin = c(-1L,
-3L, 0L, 0L, 3L, -2L), tmax = c(4L, 5L, 5L, 5L, 5L, 5L), cint = c(5L,
8L, 5L, 5L, 2L, 7L), wcmin = c(-5L, -8L, -4L, -4L, -3L, -6L),
wcmax = c(-2L, -3L, 0L, 0L, 1L, -1L), wsmin = c(20L, 15L,
30L, 30L, 20L, 25L), wsmax = c(25L, 20L, 35L, 35L, 25L, 30L
), gsmin = c(35L, 35L, 45L, 45L, 35L, 35L), gsmax = c(40L,
40L, 50L, 50L, 40L, 40L), wd = c(90L, 90L, 45L, 45L, 45L,
315L), rmin = c(11.8, 10, 7.3, 7.3, 6.7, 4.4), rmax = c(26.6,
23.5, 19, 19, 17.4, 13.8), cir = c(14.8, 13.5, 11.7, 11.7,
10.7, 9.4), lr = c(3L, 3L, 6L, 6L, 6L, 7L), lc = c(1L, 1L,
2L, 2L, 2L, 3L), wc = c(3L, 3L, 3L, 3L, 3L, 3L), li = c(TRUE,
TRUE, TRUE, TRUE, TRUE, FALSE), yd = c(1L, 1L, 1L, 1L, 1L,
1L), yr = c(2010L, 2010L, 2010L, 2010L, 2010L, 2010L), nF = c(2L,
8L, 0L, 0L, 0L, 0L), factdcx = structure(1:6, .Label = c("24",
"41", "48", "50", "52", "57", "70"), class = "factor")), .Names = c("dc",
"tmin", "tmax", "cint", "wcmin", "wcmax", "wsmin", "wsmax", "gsmin",
"gsmax", "wd", "rmin", "rmax", "cir", "lr", "lc", "wc", "li",
"yd", "yr", "nF", "factdcx"), class = c("data.table", "data.frame"
), row.names = c(NA, -6L), .internal.selfref = <pointer: 0x054b24a0>)
Still got a problem, sir/madam:
> head(abc1 (updated))
dc tmin tmax cint wcmin wcmax wsmin wsmax gsmin gsmax wd rmin rmax cir lr
1: 24 -1 4 5 -5 -2 20 25 35 40 90 11.8 26.6 14.8 3
2: 41 -3 5 8 -8 -3 15 20 35 40 90 10.0 23.5 13.5 3
3: 48 0 5 5 -4 0 30 35 45 50 45 7.3 19.0 11.7 6
4: 52 3 5 2 -3 1 20 25 35 40 45 6.7 17.4 10.7 6
5: 57 -2 5 7 -6 -1 25 30 35 40 315 4.4 13.8 9.4 7
6: 70 -2 3 5 -4 -1 20 25 30 35 360 3.6 10.2 6.6 7
lc wc li yd yr nF factdcx
1: 1 3 TRUE 1 2010 2 24
2: 1 3 TRUE 1 2010 8 41
3: 2 3 TRUE 1 2010 57 48
4: 2 3 TRUE 1 2010 0 52
5: 3 3 FALSE 1 2010 0 57
6: 3 2 TRUE 1 2010 1 70
The sum of nF was incorrect, it should be zero.
Try
library(data.table)
unique(setDT(df1)[, factdcx:= as.character(factdcx)][factdcx %chin%
c('48','50'), c('dc', 'factdcx', 'nF') := list('48', '48', sum(nF))])
# dc tmin tmax cint wcmin wcmax wsmin wsmax gsmin gsmax wd rmin rmax cir lr
#1: 24 -1 4 5 -5 -2 20 25 35 40 90 11.8 26.6 14.8 3
#2: 41 -3 5 8 -8 -3 15 20 35 40 90 10.0 23.5 13.5 3
#3: 48 0 5 5 -4 0 30 35 45 50 45 7.3 19.0 11.7 6
#4: 52 3 5 2 -3 1 20 25 35 40 45 6.7 17.4 10.7 6
#5: 57 -2 5 7 -6 -1 25 30 35 40 315 4.4 13.8 9.4 7
# lc wc li yd yr nF factdcx
#1: 1 3 TRUE 1 2010 2 24
#2: 1 3 TRUE 1 2010 8 41
#3: 2 3 TRUE 1 2010 0 48
#4: 2 3 TRUE 1 2010 0 52
#5: 3 3 FALSE 1 2010 0 57
For abc1,
res1 <- unique(setDT(abc1)[, factdcx:= as.character(factdcx)][factdcx %chin%
c('48','50'), c('dc', 'factdcx', 'nF') := list(48, '48', sum(nF))])
res1
# dc tmin tmax cint wcmin wcmax wsmin wsmax gsmin gsmax wd rmin rmax cir lr
#1: 24 -1 4 5 -5 -2 20 25 35 40 90 11.8 26.6 14.8 3
#2: 41 -3 5 8 -8 -3 15 20 35 40 90 10.0 23.5 13.5 3
#3: 48 0 5 5 -4 0 30 35 45 50 45 7.3 19.0 11.7 6
#4: 52 3 5 2 -3 1 20 25 35 40 45 6.7 17.4 10.7 6
#5: 57 -2 5 7 -6 -1 25 30 35 40 315 4.4 13.8 9.4 7
# lc wc li yd yr nF factdcx
#1: 1 3 TRUE 1 2010 2 24
#2: 1 3 TRUE 1 2010 8 41
#3: 2 3 TRUE 1 2010 0 48
#4: 2 3 TRUE 1 2010 0 52
#5: 3 3 FALSE 1 2010 0 57
data
df1 <- structure(list(dc = structure(1:6, .Label = c("24", "41",
"48",
"50", "52", "57"), class = "factor"), tmin = c(-1L, -3L, 0L,
0L, 3L, -2L), tmax = c(4L, 5L, 5L, 5L, 5L, 5L), cint = c(5L,
8L, 5L, 5L, 2L, 7L), wcmin = c(-5L, -8L, -4L, -4L, -3L, -6L),
wcmax = c(-2L, -3L, 0L, 0L, 1L, -1L), wsmin = c(20L, 15L,
30L, 30L, 20L, 25L), wsmax = c(25L, 20L, 35L, 35L, 25L, 30L
), gsmin = c(35L, 35L, 45L, 45L, 35L, 35L), gsmax = c(40L,
40L, 50L, 50L, 40L, 40L), wd = c(90L, 90L, 45L, 45L, 45L,
315L), rmin = c(11.8, 10, 7.3, 7.3, 6.7, 4.4), rmax = c(26.6,
23.5, 19, 19, 17.4, 13.8), cir = c(14.8, 13.5, 11.7, 11.7,
10.7, 9.4), lr = c(3L, 3L, 6L, 6L, 6L, 7L), lc = c(1L, 1L,
2L, 2L, 2L, 3L), wc = c(3L, 3L, 3L, 3L, 3L, 3L), li = c(TRUE,
TRUE, TRUE, TRUE, TRUE, FALSE), yd = c(1L, 1L, 1L, 1L, 1L,
1L), yr = c(2010L, 2010L, 2010L, 2010L, 2010L, 2010L), nF = c(2L,
8L, 0L, 0L, 0L, 0L), factdcx = structure(1:6, .Label = c("24",
"41", "48", "50", "52", "57"), class = "factor")), .Names = c("dc",
"tmin", "tmax", "cint", "wcmin", "wcmax", "wsmin", "wsmax", "gsmin",
"gsmax", "wd", "rmin", "rmax", "cir", "lr", "lc", "wc", "li",
"yd", "yr", "nF", "factdcx"), row.names = c("1:", "2:", "3:",
"4:", "5:", "6:"), class = "data.frame")
I have the following dataset (see for loading dataset below)
ID Date qty
1 ID25 2007-12-01 45
2 ID25 2008-01-01 26
3 ID25 2008-02-01 46
4 ID25 2008-03-01 0
5 ID25 2008-04-01 78
6 ID25 2008-05-01 65
7 ID25 2008-06-01 32
8 ID99 2008-02-01 99
9 ID99 2008-03-01 0
10 ID99 2008-04-01 99
And I would like to create a pivot table of that. I do that with the following command and that seems to be working fine:
pivottable <- xtabs(qty ~ ID + Date, table)
The output is the following:
ID 2007-12-01 2008-01-01 2008-02-01 2008-03-01 2008-04-01 2008-05-01 2008-06-01
ID25 45 26 46 0 78 65 32
ID99 0 0 99 0 99 0 0
However, for ID99 there are only values for 3 periods the rest is marked as '0'. I would like to display NA in the fields that have no values in the first table. I would like to get a table that looks as following:
ID 2007-12-01 2008-01-01 2008-02-01 2008-03-01 2008-04-01 2008-05-01 2008-06-01
ID25 45 26 46 0 78 65 32
ID99 NA NA 99 0 99 NA NA
Any suggestion on how to accomplish this?
Loading dataset:
table <- structure(list(ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L), .Label = c("ID25", "ID99"), class = "factor"), Date = structure(c(7L,
1L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L), .Label = c("01/01/2008",
"01/02/2008", "01/03/2008", "01/04/2008", "01/05/2008", "01/06/2008",
"01/12/2007"), class = "factor"), qty = c(45L, 26L, 46L, 0L,
78L, 65L, 32L, 99L, 0L, 99L)), .Names = c("ID", "Date", "qty"
), class = "data.frame", row.names = c(NA, -10L))
table$Date <- as.POSIXct(table$Date, format='%d/%m/%Y')
You could use xtabs twice to obtain the output you are looking for:
Create the table:
pivottable <- xtabs(qty ~ ID + Date, table)
Replace all zeros of non-existing combinations with NA:
pivottable[!xtabs( ~ ID + Date, table)] <- NA
The output:
Date
ID 2007-12-01 2008-01-01 2008-02-01 2008-03-01 2008-04-01 2008-05-01 2008-06-01
ID25 45 26 46 0 78 65 32
ID99 99 0 99
Note that NAs are not displayed. This is due to the print function for this class. But you could use unclass(pivottable) to achieve regular behavior of print.