Data reshaping with conditions

Hi sri,
As your problem involves a few logical steps, I found it easier to
approach it in a stepwise way. Perhaps there are more elegant ways to
accomplish this.

svdat<-read.table(text="Count id name type
117 335 sally A
19 335 sally A
167 335 sally B
18 340 susan A
56 340 susan A
22 340 susan B
53 340 susan B
135 351 lee A
114 351 lee A
84 351 lee A
80 351 lee A
19 351 lee A
8 351 lee A
21 351 lee A
88 351 lee B
111 351 lee B
46 351 lee B
108 351 lee B",header=TRUE)
# you can also do this with other reshape functions
library(prettyR)
svdatstr<-stretch_df(svdat,"id",c("Count","type"))
count_ind<-grep("Count",names(svdatstr))
type_ind<-grep("type",names(svdatstr))
svdatstr$maxA<-NA
svdatstr$maxB<-NA
svdatstr$x<-NA
svdatstr$y<-NA
for(row in 1:nrow(svdatstr)) {
 svdatstr[row,"maxA"]<-

max(svdatstr[row,count_ind[as.logical(match(svdatstr[1,type_ind],"A",0))]])
 svdatstr[row,"maxB"]<-

max(svdatstr[row,count_ind[as.logical(match(svdatstr[1,type_ind],"B",0))]])
 svdatstr[row,"x"]<-svdatstr[row,"maxA"] < svdatstr[row,"maxB"]
 svdatstr[row,"y"]<-!svdatstr[row,"x"]
}
svdatstr

You can then just extract the columns that you need.

Jim


On Wed, Apr 20, 2016 at 3:03 PM, sri vathsan <srivibish at gmail.com> wrote:

Dear All,

I am trying to reshape the data with some conditions. A small part of the
data looks like below. Like this there will be more data with repeating

ID.

Count id name type
117 335 sally A
19 335 sally A
167 335 sally B
18 340 susan A
56 340 susan A
22 340 susan B
53 340 susan B
135 351 lee A
114 351 lee A
84 351 lee A
80 351 lee A
19 351 lee A
8 351 lee A
21 351 lee A
88 351 lee B
111 351 lee B
46 351 lee B
108 351 lee B

From the above data I am expecting an output like below.

id name type count_of_B Max of count B     x               y
335 sally B 167 167 117,19      NA
340 susan B 22,53 53 18              56
351 lee B 88,111,46,108  111 84,80,19,8,2   135,114

Where, the column x and column y are:

x = Count_A_less_than_max of (Count type B)
y = Count_A_higher_than_max of (Count type B).

*1)* I tried with dplyr with the following code for the initial step to

get

the values for each column.
*2)*  I thought to transpose the columns which has the unique ID alone.

I tried with the following code and I am struck with the intial step
itself. The code is executed but higher and lower value of A is not

coming.

Expected_output= data %>%
  group_by(id, Type) %>%
  mutate(Count_of_B = paste(unlist(count[Type=="B"]), collapse = ","))%>%
  mutate(Max_of_count_B = ifelse(Type == "B", max(count[Type ==
"B"]),max(count[Type == "A"]))) %>%
  mutate(count_type_A_lesser = ifelse
(Type=="B",(paste(unlist(count[Type=="A"]) < Max_of_count_B[Type=="B"],
collapse = ",")), "NA"))%>%
  mutate(count_type_A_higher =
ifelse(Type=="B",(paste(unlist(count[Type=="A"]) >
Max_of_count_B[Type=="B"], collapse = ",")), "NA"))

I hope I make my point clear. Please bare with the code, as I am new to
this.

Regards,
sri

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