counting occurrence of strings across multiple columns in R [duplicate] - r

Question

This question already has answers here:
Reshaping multiple sets of measurement columns (wide format) into single columns (long format)
(8 answers)
Closed 5 years ago.
I have a dataset in R which looks like the following (only relevant columns shown). It has sex disaggregated data on what crops respondents wanted more information about and how much of a priority this crop for them.
sex wantcropinfo1 priority1 wantcropinfo2 priority2
m wheat high eggplant medium
m rice low cabbage high
m rice high
f eggplant medium
f cotton low
...
I want to be able to (a) count the total occurrences of each crop across all the wantcropinfoX columns; and (b) get the same count but sort them by priority; and (c) do the same thing but disaggregated by sex.
(a) output should look like this:
crop count
wheat 1
eggplant 2
rice 2
...
(b) output should look like this:
crop countm countf
wheat 1 0
eggplant 1 1
rice 2 0
...
(c) should look like this:
crop high_m med_m low_m high_f med_f low_f
wheat 1 0 0 0 0 0
eggplant 0 1 0 0 1 0
rice 1 0 1 0 0 0
...
I'm a bit of an R newbie and the manuals are slightly bewildering. I've googled a lot but couldn't find anything that was quite like this even though it seems like a fairly common thing one might want to do. Similar questions on stackoverflow seemed to be asking something a bit different.

Answer 1

We can use melt from data.table to convert from 'wide' to 'long' format. It can take multiple measure columns.
library(data.table)
dM <- melt(setDT(df1), measure = patterns("^want", "priority"),
value.name = c("crop", "priority"))[crop!='']
In the 'long' format, we get the 3 expected results by either grouping by 'crop' and get the number of rows or convert to 'wide' with dcast specifying the fun.aggregate as length.
dM[,.(count= .N) , crop]
# crop count
#1: wheat 1
#2: rice 2
#3: eggplant 2
#4: cotton 1
#5: cabbage 1
dcast(dM, crop~sex, value.var='sex', length)
# crop f m
#1: cabbage 0 1
#2: cotton 1 0
#3: eggplant 1 1
#4: rice 0 2
#5: wheat 0 1
dcast(dM, crop~priority+sex, value.var='priority', length)
# crop high_m low_f low_m medium_f medium_m
#1: cabbage 1 0 0 0 0
#2: cotton 0 1 0 0 0
#3: eggplant 0 0 0 1 1
#4: rice 1 0 1 0 0
#5: wheat 1 0 0 0 0

Answer 2

Use ddply function in the plyr package.
The structure of how you use this function is the following:
ddply(dataframe,.(var1,var2,...), summarize, function)
In this case you might want to do the follow for:
a) ddply(df,.(wantcropinfo1),summarize,count=length(wantcropinfo1))
b)ddply(df,.(wantcropinfo1,priority),summarize,count=length(wantcropinfo1))
c) ddply(df,.(wantcropinfo1,priority,sex),summarize,count=length(wantcropinfo1))
Note that the output will not have the same structure you mention in your question but the information will be the same. For the mentioned structure use the table function