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# 2-Way Frequency Table
attach(meganslaw)The following objects are masked from meganslaw (pos = 3):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 4):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 5):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 6):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 7):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 12):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 13):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 14):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 15):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 16):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 17):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 18):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 21):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, yearmegtable <- table(meganslaw$state,meganslaw$ucr.code) # A will be rows, B will be columns
megtable # print table
Other Sex_Offense Theft Violent
CO 19007 10097 337233 79761
IA 23928 8991 379848 119569
ID 10902 6208 152867 55779
MA 34014 5619 340288 130038
MI 134395 38464 1017996 376108
SC 115282 22319 365916 457504
TX 32383 8791 269712 105562
UT 13520 7883 169363 52890
VA 31446 10859 328526 150450
VT 1751 1134 36562 7260margin.table(megtable, 1) # A frequencies (summed over B)
CO IA ID MA MI SC TX UT VA
446098 532336 225756 509959 1566963 961021 416448 243656 521281
VT
46707 margin.table(megtable, 2) # B frequencies (summed over A)
Other Sex_Offense Theft Violent
416628 120365 3398311 1534921 prop.table(megtable) # cell percentages
Other Sex_Offense Theft Violent
CO 0.0034746286 0.0018458107 0.0616488353 0.0145809359
IA 0.0043742259 0.0016436253 0.0694391913 0.0218581503
ID 0.0019929710 0.0011348711 0.0279452856 0.0101968383
MA 0.0062180258 0.0010271972 0.0622073132 0.0237719655
MI 0.0245684592 0.0070315206 0.1860976468 0.0687554899
SC 0.0210744531 0.0040800881 0.0668923125 0.0836353166
TX 0.0059198662 0.0016070637 0.0493054673 0.0192975609
UT 0.0024715620 0.0014410742 0.0309608837 0.0096687065
VA 0.0057485752 0.0019851103 0.0600571274 0.0275034391
VT 0.0003200965 0.0002073041 0.0066838201 0.0013271849prop.table(megtable, 1) # row percentages
Other Sex_Offense Theft Violent
CO 0.04260723 0.02263404 0.75596169 0.17879704
IA 0.04494905 0.01688971 0.71354934 0.22461190
ID 0.04829108 0.02749872 0.67713372 0.24707649
MA 0.06669948 0.01101853 0.66728502 0.25499697
MI 0.08576782 0.02454685 0.64966180 0.24002354
SC 0.11995784 0.02322426 0.38075755 0.47606036
TX 0.07776001 0.02110948 0.64764869 0.25348183
UT 0.05548807 0.03235299 0.69509062 0.21706833
VA 0.06032447 0.02083138 0.63022823 0.28861593
VT 0.03748903 0.02427902 0.78279487 0.15543709prop.table(megtable, 2) # column percentages
Other Sex_Offense Theft Violent
CO 0.045621034 0.083886512 0.099235473 0.051964238
IA 0.057432530 0.074697794 0.111775526 0.077899123
ID 0.026167228 0.051576455 0.044983228 0.036339981
MA 0.081641176 0.046683006 0.100134449 0.084719670
MI 0.322577935 0.319561334 0.299559399 0.245034109
SC 0.276702478 0.185427658 0.107675842 0.298063549
TX 0.077726413 0.073036182 0.079366485 0.068773572
UT 0.032451011 0.065492460 0.049837405 0.034457799
VA 0.075477404 0.090217256 0.096673318 0.098018074
VT 0.004202790 0.009421343 0.010758874 0.004729885# 2-Way Frequency Table
attach(meganslaw)The following objects are masked from meganslaw (pos = 3):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 4):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 5):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 6):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 7):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 8):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 13):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 14):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 15):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 16):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 17):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 18):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 19):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, year
The following objects are masked from meganslaw (pos = 22):
age_of_victim, ages, crime, Date, Fed_Law, incident_date,
incident_number, is.minor, is.svu, meglaw, month,
month_number, originating_agency_identifier, race_of_victim,
remove, sex_of_victim, state, timebin, type_of_victim,
ucr.code, yearmegtable <- table(meganslaw$year,meganslaw$ucr.code) # A will be rows, B will be columns
megtable # print table
Other Sex_Offense Theft Violent
1992 990 682 15691 5561
1993 2433 1618 32456 12458
1994 2939 1783 43068 15140
1995 5426 2903 78271 24947
1996 20333 6526 223317 72238
1997 56119 16527 507289 216442
1998 77791 23840 654148 300140
1999 87935 26063 684305 321164
2000 86578 23965 669405 320221
2001 57834 12971 374152 188538
2002 18250 3487 116209 58072margin.table(megtable, 1) # A frequencies (summed over B)
1992 1993 1994 1995 1996 1997 1998 1999 2000
22924 48965 62930 111547 322414 796377 1055919 1119467 1100169
2001 2002
633495 196018 margin.table(megtable, 2) # B frequencies (summed over A)
Other Sex_Offense Theft Violent
416628 120365 3398311 1534921 prop.table(megtable) # cell percentages
Other Sex_Offense Theft Violent
1992 0.0001809798 0.0001246749 0.0028684378 0.0010165944
1993 0.0004447715 0.0002957831 0.0059332112 0.0022774200
1994 0.0005372722 0.0003259464 0.0078731679 0.0027677107
1995 0.0009919153 0.0005306912 0.0143085522 0.0045605071
1996 0.0037170317 0.0011930039 0.0408240977 0.0132056725
1997 0.0102589930 0.0030212651 0.0927364048 0.0395672938
1998 0.0142208044 0.0043581388 0.1195833809 0.0548679442
1999 0.0160752071 0.0047645207 0.1250963169 0.0587112962
2000 0.0158271369 0.0043809898 0.1223724801 0.0585389084
2001 0.0105725084 0.0023712005 0.0683979178 0.0344662240
2002 0.0033362430 0.0006374509 0.0212439159 0.0106160167prop.table(megtable, 1) # row percentages
Other Sex_Offense Theft Violent
1992 0.04318618 0.02975048 0.68447915 0.24258419
1993 0.04968855 0.03304401 0.66284080 0.25442663
1994 0.04670269 0.02833307 0.68437947 0.24058478
1995 0.04864317 0.02602490 0.70168628 0.22364564
1996 0.06306488 0.02024106 0.69264052 0.22405355
1997 0.07046788 0.02075273 0.63699605 0.27178334
1998 0.07367137 0.02257749 0.61950585 0.28424529
1999 0.07855077 0.02328162 0.61127751 0.28689010
2000 0.07869518 0.02178302 0.60845652 0.29106528
2001 0.09129354 0.02047530 0.59061555 0.29761561
2002 0.09310369 0.01778918 0.59284862 0.29625851prop.table(megtable, 2) # column percentages
Other Sex_Offense Theft Violent
1992 0.002376221 0.005666099 0.004617294 0.003622988
1993 0.005839742 0.013442446 0.009550627 0.008116379
1994 0.007054255 0.014813276 0.012673354 0.009863700
1995 0.013023609 0.024118307 0.023032324 0.016252954
1996 0.048803729 0.054218419 0.065714115 0.047063008
1997 0.134698100 0.137307357 0.149276803 0.141011818
1998 0.186715727 0.198064221 0.192492094 0.195541008
1999 0.211063587 0.216533045 0.201366208 0.209238130
2000 0.207806484 0.199102729 0.196981677 0.208623766
2001 0.138814482 0.107763885 0.110099399 0.122832380
2002 0.043804065 0.028970216 0.034196105 0.037833869# 3-Way Frequency Table
lawtable <- table(meganslaw$state,meganslaw$ucr.code, meganslaw$year)
ftable(lawtable) 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
CO Other 0 0 0 0 0 3836 4242 3871 4479 2579 0
Sex_Offense 0 0 0 0 0 2268 2470 2187 2144 1028 0
Theft 0 0 0 0 0 79430 82590 68530 71917 34766 0
Violent 0 0 0 0 0 17193 19687 17015 17468 8398 0
IA Other 0 0 0 0 2382 4758 4524 4695 4838 2731 0
Sex_Offense 0 0 0 0 776 1605 1780 2027 1855 948 0
Theft 0 0 0 0 39637 74991 76487 76961 76574 35198 0
Violent 0 0 0 0 11165 23058 23421 24684 24942 12299 0
ID Other 990 2433 2809 3157 1513 0 0 0 0 0 0
Sex_Offense 682 1618 1620 1507 781 0 0 0 0 0 0
Theft 15691 32456 39614 44820 20286 0 0 0 0 0 0
Violent 5561 12458 14494 15918 7348 0 0 0 0 0 0
MA Other 0 0 0 0 0 1405 4596 5377 6501 9296 6839
Sex_Offense 0 0 0 0 0 212 798 945 1147 1442 1075
Theft 0 0 0 0 0 18994 53166 60816 67298 80442 59572
Violent 0 0 0 0 0 7114 20478 22900 26143 31950 21453
MI Other 0 0 0 0 12657 24422 27360 28440 32539 8977 0
Sex_Offense 0 0 0 0 2979 5460 8387 8742 9991 2905 0
Theft 0 0 0 0 112290 192668 215922 203841 241837 51438 0
Violent 0 0 0 0 38202 65936 77514 77191 95012 22253 0
SC Other 0 0 0 0 0 13948 22389 24286 22772 23929 7958
Sex_Offense 0 0 0 0 0 3391 4605 4691 3912 4120 1600
Theft 0 0 0 0 0 43953 70315 66830 64487 90221 30110
Violent 0 0 0 0 0 67643 97432 94481 89933 81939 26076
TX Other 0 0 0 0 0 1224 4980 6218 6186 10322 3453
Sex_Offense 0 0 0 0 0 431 1562 1605 1853 2528 812
Theft 0 0 0 0 0 11606 42175 52998 54319 82087 26527
Violent 0 0 0 0 0 3985 16494 19994 22847 31699 10543
UT Other 0 0 0 1467 1791 2716 3254 3580 712 0 0
Sex_Offense 0 0 0 958 1273 1494 1775 2002 381 0 0
Theft 0 0 0 22239 27255 35336 37069 39883 7581 0 0
Violent 0 0 0 5987 7690 10856 12405 13229 2723 0 0
VA Other 0 0 0 490 1817 3611 5823 11154 8551 0 0
Sex_Offense 0 0 0 190 564 1544 2184 3695 2682 0 0
Theft 0 0 0 5152 19314 45750 64902 108016 85392 0 0
Violent 0 0 0 1658 6875 19803 30615 50346 41153 0 0
VT Other 0 0 130 312 173 199 623 314 0 0 0
Sex_Offense 0 0 163 248 153 122 279 169 0 0 0
Theft 0 0 3454 6060 4535 4561 11522 6430 0 0 0
Violent 0 0 646 1384 958 854 2094 1324 0 0 0lawtable <- table(meganslaw$is.minor,meganslaw$ucr.code, meganslaw$year)
ftable(lawtable) 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
0 Other 867 2130 2590 4904 18444 51326 71109 80050 79069 52643 16598
Sex_Offense 149 269 281 598 1631 4731 6353 6930 6720 3552 1017
Theft 14365 29772 39691 71997 207677 476801 617603 646839 634170 356861 111128
Violent 4169 9104 11076 18814 55486 171691 239871 256954 256733 152332 46609
1 Other 123 303 349 522 1889 4793 6682 7885 7509 5191 1652
Sex_Offense 533 1349 1502 2305 4895 11796 17487 19133 17245 9419 2470
Theft 1326 2684 3377 6274 15640 30488 36545 37466 35235 17291 5081
Violent 1392 3354 4064 6133 16752 44751 60269 64210 63488 36206 11463lawtable <- table(meganslaw$is.minor,meganslaw$ucr.code, meganslaw$meglaw)
ftable(lawtable) 0 1
0 Other 121964 257766
Sex_Offense 10472 21759
Theft 1033527 2173377
Violent 408324 814515
1 Other 11670 25228
Sex_Offense 26930 61204
Theft 64771 126636
Violent 104778 207304#Table ignores missing values. To include NA as a category in counts, include the table option exclude=NULL if the variable is a vector. If the variable is a factor you have to create a new factor using newfactor <- factor(oldfactor, exclude=NULL).
xtabsfunction (formula = ~., data = parent.frame(), subset, sparse = FALSE,
na.action, exclude = c(NA, NaN), drop.unused.levels = FALSE)
{
if (missing(formula) && missing(data))
stop("must supply either 'formula' or 'data'")
if (!missing(formula)) {
formula <- as.formula(formula)
if (!inherits(formula, "formula"))
stop("'formula' missing or incorrect")
}
if (any(attr(terms(formula, data = data), "order") > 1))
stop("interactions are not allowed")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m$... <- m$exclude <- m$drop.unused.levels <- m$sparse <- NULL
m[[1L]] <- quote(stats::model.frame)
mf <- eval(m, parent.frame())
if (length(formula) == 2L) {
by <- mf
y <- NULL
}
else {
i <- attr(attr(mf, "terms"), "response")
by <- mf[-i]
y <- mf[[i]]
}
has.exclude <- !missing(exclude)
by <- lapply(by, function(u) {
if (!is.factor(u))
u <- factor(u, exclude = exclude)
else if (has.exclude)
u <- factor(as.character(u), levels = setdiff(levels(u),
exclude), exclude = NULL)
u[, drop = drop.unused.levels]
})
if (!sparse) {
x <- if (is.null(y))
do.call("table", by)
else if (NCOL(y) == 1L)
tapply(y, by, sum)
else {
z <- lapply(as.data.frame(y), tapply, by, sum)
array(unlist(z), dim = c(dim(z[[1L]]), length(z)),
dimnames = c(dimnames(z[[1L]]), list(names(z))))
}
x[is.na(x)] <- 0L
class(x) <- c("xtabs", "table")
attr(x, "call") <- match.call()
x
}
else {
if (length(by) != 2L)
stop(gettextf("%s applies only to two-way tables",
"xtabs(*, sparse=TRUE)"), domain = NA)
if (is.null(tryCatch(loadNamespace("Matrix"), error = function(e) NULL)))
stop(gettextf("%s needs package 'Matrix' correctly installed",
"xtabs(*, sparse=TRUE)"), domain = NA)
if (length(i.ex <- unique(unlist(lapply(by, function(f) which(is.na(f)))))))
by <- lapply(by, `[`, -i.ex)
rows <- by[[1L]]
cols <- by[[2L]]
rl <- levels(rows)
cl <- levels(cols)
if (is.null(y))
y <- rep.int(1, length(rows))
as(new("dgTMatrix", i = as.integer(rows) - 1L, j = as.integer(cols) -
1L, x = as.double(y), Dim = c(length(rl), length(cl)),
Dimnames = list(rl, cl)), "CsparseMatrix")
}
}
<bytecode: 0x1a9d1eb20>
<environment: namespace:stats>#The xtabs( ) function allows you to create crosstabulations using formula style input.
# 3-Way Frequency Table
#mytable <- xtabs(~A+B+c, data=mydata)
#ftable(mytable) # print table
#summary(mytable) # chi-square test of indepedenceinstall.packages("gmodels")Error in install.packages : Updating loaded packageslibrary(gmodels)
CrossTable(meganslaw$meglaw, meganslaw$ucr.code)
Cell Contents
|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
Total Observations in Table: 5470225
| meganslaw$ucr.code
meganslaw$meglaw | Other | Sex_Offense | Theft | Violent | Row Total |
-----------------|-------------|-------------|-------------|-------------|-------------|
0 | 133634 | 37402 | 1098298 | 513102 | 1782436 |
| 33.151 | 84.283 | 73.455 | 335.740 | |
| 0.075 | 0.021 | 0.616 | 0.288 | 0.326 |
| 0.321 | 0.311 | 0.323 | 0.334 | |
| 0.024 | 0.007 | 0.201 | 0.094 | |
-----------------|-------------|-------------|-------------|-------------|-------------|
1 | 282994 | 82963 | 2300013 | 1021819 | 3687789 |
| 16.023 | 40.737 | 35.503 | 162.275 | |
| 0.077 | 0.022 | 0.624 | 0.277 | 0.674 |
| 0.679 | 0.689 | 0.677 | 0.666 | |
| 0.052 | 0.015 | 0.420 | 0.187 | |
-----------------|-------------|-------------|-------------|-------------|-------------|
Column Total | 416628 | 120365 | 3398311 | 1534921 | 5470225 |
| 0.076 | 0.022 | 0.621 | 0.281 | |
-----------------|-------------|-------------|-------------|-------------|-------------|
install.packages("gmodels") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 71885 100 71885 0 0 64311 0 0:00:01 0:00:01 --:--:-- 64355
The downloaded binary packages are in
/var/folders/qx/twjwptxx76d7vl1m4l50_1z00000gn/T//RtmpJEwnbr/downloaded_packagesAdd a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).