Beginner R: Using Incident Data, Create a Series of New Dataframes with Sums of Categorical Variables -

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i have set of incident data following rough format:

incident #   date   year   state   criminal offense   location

155k incidents. want create new series of dataframes group ungrouped data (ie, opposite of first step in link: http://ww2.coastal.edu/kingw/statistics/r-tutorials/descriptive.html). want dataframes each year/each state totals of each categorical count each of last 2 columns above, "offense" , "location" (but there ever 1 row each year-state combination) 2 separate dataframes:

year   state   sum of criminal offense 1   sum of criminal offense 2   sum of crim 3

and

year   state   sum of location 1   sum of location 2   sum of location 3

the goal comparisons of incident counts state on time, or time-series predictions total incidents of crime type in state. ungrouped data? there resource or brief rules analyses/predictive approaches work best/most practically grouped versus ungrouped data?

here approach using table count entities , reshape put desired form.

fake data:

d <- data.frame(incident=1:4, year=c(1,1,2,2), state=c('al','mn','al','mn'),offense=c(1,1,1,2),location=c(1,2,2,2)) d ##   incident year state offense location ## 1        1    1    al       1        1 ## 2        2    1    mn       1        2 ## 3        3    2    al       1        2 ## 4        4    2    mn       2        2

locations:

dl <- as.data.frame(xtabs(~year+state+location, data=d)) # dl <- as.data.frame(table(year=d$year, state=d$state, location=d$location))  reshape(dl, direction='wide', timevar='location', idvar=c('year', 'state')) ##   year state freq.1 freq.2 ## 1    1    al      1      0 ## 2    2    al      0      1 ## 3    1    mn      0      1 ## 4    2    mn      0      1

offenses:

do <- as.data.frame(xtabs(~year+state+offense, data=d)) # <- as.data.frame(table(year=d$year, state=d$state, offense=d$offense))  reshape(do, direction='wide', timevar='offense', idvar=c('year', 'state')) ##   year state freq.1 freq.2 ## 1    1    al      1      0 ## 2    2    al      1      0 ## 3    1    mn      1      0 ## 4    2    mn      0      1

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