how to get quick summary of count in data.table - r

Question

This is a part of feature engineering that summarizes each ID depending on column called Col. The same preprocess will be applied to the testing set. Since the data set is large, data.table based solution may be more preferred.
Training Input:
ID Col
A M
A M
A M
B K
B M
Expected output for above training input:
ID Col_M Col_K
A 3 0 # A has 3 M in Col and 0 K in Col
B 1 1
Above is for processing training data. For testing dataset, if requires to mapping over Col_M, Col_K, meaning, if other value like S appearing in Col, it will be ignored.
Testing Input:
ID Col
C M
C S
Expected output for above testing input:
ID Col_M Col_K
C 1 0 # A has 1 M in Col and 0 K in Col. S value is ignored

Answer 1

A possible data.table implementation could be first filter by c("M", "K"), then add these level (in case they aren't present like in your second case), then running dcast while specifying drop = FALSE, fill = 0L (for the cases when one of the desired levels is missing) while specifying fun = length (in order to count).
Testing on both data sets
library(data.table)
### First example
df <- fread("ID Col
A M
A M
A M
B K
B M")
dcast(df[Col %in% c("M", "K")], # Work only with c("M", "K")
ID ~ factor(Col, levels = union(unique(Col), c("M", "K"))), # Add missing levels
drop = FALSE, # Keep missing levels in output
fill = 0L, # Fill missing values with zeroes instead of NAs
fun = length) # Count. you can also specify 'value.var'
# ID M K
# 1: A 3 0
# 2: B 1 1
### Second example
df <- fread("ID Col
C M
C S")
dcast(df[Col %in% c("M", "K")],
ID ~ factor(Col, levels = union(unique(Col), c("M", "K"))),
drop = FALSE,
fill = 0L,
fun = length)
# ID M K
# 1: C 1 0

Answer 2

I am not sure how big is your data and how flexible the expected code should be, but I have this:
zz = '
ID Col
A M
A M
A M
B K
B M
'
df <- read.table(text = zz, header = TRUE)
col = as.data.frame(table(df))
out <- reshape(col, idvar = "ID",
timevar = "Col", direction = "wide")
out
which gives you:
> out
ID Freq.K Freq.M
1 A 0 3
2 B 1 1
And for the second data frame:
yy = '
ID Col
C M
C S
'
df1 <- read.table(text = yy, header = TRUE)
col1 = as.data.frame(table(df1))
out1 <- reshape(col1, idvar = "ID",
timevar = "Col", direction = "wide")
out1
you get:
> out1
ID Freq.M Freq.S
1 C 1 1
Then just merge them together and delete the redundant:
ss = merge(out1, out, all.y = T, all.x = T)
ss
ID Freq.M Freq.S Freq.K
1 C 1 1 NA
2 A 3 NA 0
3 B 1 NA 1

Answer 3

> library(data.table)
> dt=NULL
> dt$ID=c("A","A","A","B","B")
> dt$Col=c("M","M","M","K","M")
> dt=data.frame(dt)
> dt=data.table(dt)
> dt
ID Col
1: A M
2: A M
3: A M
4: B K
5: B M
> a=dt[Col=="M",sum(.N),ID]
> b=dt[Col=="K",sum(.N),ID]
> a
ID V1
1: A 3
2: B 1
> b
ID V1
1: B 1
> setkey(a,ID)
> setkey(b,ID)
> m=b[a]
> m
ID V1 i.V1
1: A NA 3
2: B 1 1
> names(m)=c("ID","Col_K","Col_M")
> m
ID Col_K Col_M
1: A NA 3
2: B 1 1