Grouping with condition in RStudio - r

Question

Good morning everyone, I have a csv file (df2.csv) with several variables, as illustrated below (just for example):
CLASSE Variables Terms Number
1 DAT_1 20160701q 5
1 DAT_1 20160802q 2
1 DAT_1 20160901q 1
1 DAT_2 20161001q 1
1 DAT_2 20161201q 2
1 DAT_2 20170301q 3
2 DAT_1 20161001q 1
2 DAT_1 20161201q 2
2 DAT_1 20170301q 1
I want for each class (1 or 2 in this case), for each distinct date variable, if the number of individuals is less than 3, to group individuals with the next date. If I have a period of more than 3 individuals, in this case, I want to have a date like '20160701q-20160901q' instead of 20160701q and 20160901q separately. In this case, we group two dates or more to get a period of more than 3 individuals, and if the next date of the class has less than 3 individuals, we will group this date with the period before also.
I started whit this code
for (n in df2$CLASSE){
for (k in df2$Variables){
for (i in 1:nrow(df2)){
if (df2$Number[i]<3){
rempl_date=paste(df2$Terms[i],df2$Terms[i+1], sep="-")
df2$Terms[i]<-rempl_date
next
}
}
}
}
But it doesn't work, I want to have this one after grouping:
CLASSE Variables Terms Number
1 DAT_1 20160701q 5
1 DAT_1 20160802q-20160901q 3
1 DAT_2 20161001q-20161201q 3
1 DAT_2 20170301q 3
2 DAT_1 20161001q-20170301q 4
I don't know what I must change else if you can help me, I hope I was clear. Thanks in advance

Answer 1

We can use MESS::cumsumbinning function here to create groups until a threshold is reached.
library(dplyr)
thresh <- 3
temp <- df %>%
group_by(CLASSE, Variables,
group = MESS::cumsumbinning(Number, thresh)) %>%
summarise(Terms = if(n() > 1)
paste(first(Terms), last(Terms), sep = "-") else Terms,
Number = sum(Number)) %>%
select(-group)
This returns :
temp
# A tibble: 6 x 4
# Groups: CLASSE, Variables [3]
# CLASSE Variables Terms Number
# <int> <chr> <chr> <int>
#1 1 DAT_1 20160701q 5
#2 1 DAT_1 20160802q-20160901q 3
#3 1 DAT_2 20161001q-20161201q 3
#4 1 DAT_2 20170301q 3
#5 2 DAT_1 20161001q-20161201q 3
#6 2 DAT_1 20170301q 1
To combine the last row, we can do :
n <- nrow(temp)
if(temp$Number[n] < 3) {
temp$Terms[n-1] <- sub("-.*", paste0('-', temp$Terms[n]), temp$Terms[n -1])
temp$Number[n-1] <- sum(temp$Number[n-1], temp$Number[n])
temp <- temp[-n,]
}
# CLASSE Variables Terms Number
# <int> <chr> <chr> <int>
#1 1 DAT_1 20160701q 5
#2 1 DAT_1 20160802q-20160901q 3
#3 1 DAT_2 20161001q-20161201q 3
#4 1 DAT_2 20170301q 3
#5 2 DAT_1 20161001q-20170301q 4

Answer 2

Here is a base R solution:
define custom function for grouping
f <- function(v, th = 3) {
k <- 1
r <- c()
repeat {
if (length(v)==0) break
ind<-seq(head(which(cumsum(v)>=th),1))
if (sum(v)<2*th) {
r <- c(r,rep(k,length(v)))
v <- c()
} else {
r <- c(r,rep(k,length(ind)))
v <- v[-ind]
}
k <- k+1
}
r
}
then use aggregate + ave
dfout <- subset(aggregate(Terms~.,
within(within(df,grp <- ave(Number,Classe, Variables, FUN = f)),
Number <- ave(Number,Classe,Variables,grp,FUN = sum)),
c),
select = -grp)
format the dfout to the desired style by using order
dfout <- dfout[order(dfout$Classe,dfout$Variables),]
Output
> dfout
Classe Variables Number Terms
3 1 DAT_1 5 20160701q
4 1 DAT_1 3 20160802q, 20160901q
1 1 DAT_2 3 20161001q, 20161201q
5 1 DAT_2 3 20170301q
2 2 DAT_1 4 20161001q, 20161201q, 20170301q
DATA
df <- structure(list(Classe = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L),
Variables = c("DAT_1", "DAT_1", "DAT_1", "DAT_2", "DAT_2",
"DAT_2", "DAT_1", "DAT_1", "DAT_1"), Terms = c("20160701q",
"20160802q", "20160901q", "20161001q", "20161201q", "20170301q",
"20161001q", "20161201q", "20170301q"), Number = c(5L, 2L,
1L, 1L, 2L, 3L, 1L, 2L, 1L)), class = "data.frame", row.names = c(NA,
-9L))
UPDATE
If you want to concatenate the contents in Terms, try the code below
dfout <- subset(aggregate(Terms~.,
within(within(df,grp <- ave(Number,Classe, Variables, FUN = f)),
Number <- ave(Number,Classe,Variables,grp,FUN = sum)),
FUN = function(v) ifelse(length(v)==1,v,paste0(c(v[1],v[length(v)]),collapse = "-"))),
select = -grp)
dfout <- dfout[order(dfout$Classe,dfout$Variables),]
such that
> dfout
Classe Variables Number Terms
3 1 DAT_1 5 20160701q
4 1 DAT_1 3 20160802q-20160901q
1 1 DAT_2 3 20161001q-20161201q
5 1 DAT_2 3 20170301q
2 2 DAT_1 4 20161001q-20170301q

Answer 3

This is a quite cumbersome solution I've created that does what you asked. I'm sure it can be optimized or that functions from other packages can be used.
Explanations are inserted into the code
# new dataframe
df_new <- data.frame(
CLASSE = numeric(nrow(df2)),
Variables = character(nrow(df2)),
Terms = character(nrow(df2)),
Number = numeric(nrow(df2)),
stringsAsFactors = FALSE
)
# temporary dataframe
temp_df <- data.frame(
CLASSE = numeric(0),
Variables = character(0),
Terms = character(0),
Number = numeric(0),
stringsAsFactors = FALSE
)
temp_sum <- 0
present_row_temp_df <- 1
for (i in 1:nrow(df2)){
# if the row doesn't have to be grouped, just paste it in the new dataframe
if (df2$Number[i] >= 3){
df_new[i,] <- df2[i,]
next
}
# if the row has to be grouped, add it to a temporary dataframe
if (df2$Number[i] < 3){
temp_df[present_row_temp_df,] <- df2[i,]
temp_sum <- temp_sum + df2$Number[i]
present_row_temp_df <- present_row_temp_df + 1
# if the rows in the temporary dataframe need to be grouped now
if(temp_sum >= 3){
Terms_new <- paste(temp_df$Terms[1], temp_df$Terms[nrow(temp_df)], sep = "-")
Number_new <- sum(temp_df$Number)
df_new[i, c(1:3)] <- c(df2$CLASSE[i], df2$Variables[i], Terms_new)
df_new[i, 4] <- Number_new
# re-initialize temporary variables
temp_df <- data.frame(
CLASSE = numeric(0),
Variables = character(0),
Terms = character(0),
Number = numeric(0),
stringsAsFactors = FALSE
)
temp_sum <- 0
present_row_temp_df <- 1
}
# for the case in which the last row is not united with the previous rows
if (i == nrow(df2) & df2$Number[i] < 3){
Terms_new <- paste(stringr::str_extract(df_new$Terms[i-1], "^[^-]*"), df2$Terms[i], sep = "-")
Number_new <- df_new$Number[i-1] + df2$Number[i]
df_new[i, c(1:3)] <- c(df_new$CLASSE[i-1], df_new$Variables[i-1], Terms_new)
df_new[i, 4] <- Number_new
df_new[i-1,] <- c("0", "0", "0", 0)
}
}
}
# filter only relevant rows
df_new <- df_new[df_new$Number != 0,]
Result:
df_new
#CLASSE Variables Terms Number
# 1 DAT_1 20160701q 5
# 1 DAT_1 20160802q-20160901q 3
# 1 DAT_2 20161001q-20161201q 3
# 1 DAT_2 20170301q 3
# 2 DAT_1 20161001q-20170301q 4