Working with lists
Tad Dallas
What are lists?
Lists in R are collections of objects that can be of any mix of types (or all the same type). They can be useful when dealing with multiple data.frames that each correspond to a different unit of study (note that we solved this before by considering a single data.frame with a column corresponding to country). Lists tend to be useful in my research when I’m simulating ecological dynamics, where each list item can be the results of a single simulation, or when I’m writing functions to return data that is not really structured as a data.frame. Before we go too far into use cases, lets refresh on how we form and index list objects.
lst <- list(runif(100), data.frame(x=runif(100), y=runif(100)), letters[1:10], 'a')
# index single list elements
lst[[1]]## [1] 0.90704945 0.77652364 0.90730792 0.18234435 0.16822273 0.17585736
## [7] 0.74447123 0.99180202 0.52654958 0.69324169 0.85301198 0.53240988
## [13] 0.86396784 0.18200349 0.65977339 0.37382909 0.66034647 0.33585775
## [19] 0.94423552 0.47108947 0.64014545 0.14299730 0.83758049 0.09136402
## [25] 0.83603546 0.65112385 0.79237058 0.04926343 0.60975753 0.81184443
## [31] 0.71950415 0.34576116 0.69708172 0.71480971 0.43852277 0.91306516
## [37] 0.33709835 0.64484189 0.23874983 0.03005836 0.85905594 0.62504449
## [43] 0.46302086 0.85274002 0.66683303 0.25209227 0.57347857 0.94172352
## [49] 0.60845584 0.87653416 0.80555945 0.12586274 0.10692094 0.65519174
## [55] 0.53719319 0.77821136 0.52197263 0.77467111 0.19578390 0.13651637
## [61] 0.94467557 0.40883423 0.80625228 0.88294754 0.93449914 0.54107256
## [67] 0.73536660 0.57453253 0.56101505 0.58099235 0.79263289 0.02114419
## [73] 0.45113483 0.16046333 0.55647937 0.06394116 0.65338370 0.76742226
## [79] 0.52071744 0.51420978 0.19473846 0.27844844 0.66024552 0.61151509
## [85] 0.27119185 0.61293326 0.43017132 0.88117462 0.73549543 0.86919798
## [91] 0.18498700 0.44791807 0.98227175 0.07176038 0.04772194 0.24734227
## [97] 0.64038550 0.32221512 0.27069574 0.05654550## x y
## 1 0.9711248630 0.163197247
## 2 0.7570663751 0.270849837
## 3 0.9294406015 0.210958205
## 4 0.5364000814 0.623438141
## 5 0.1478103679 0.343618301
## 6 0.0776882314 0.984681295
## 7 0.5223464179 0.342049163
## 8 0.9478134413 0.532526935
## 9 0.4807977404 0.229018181
## 10 0.5042717066 0.051131312
## 11 0.9878702404 0.765741721
## 12 0.7547797987 0.830578280
## 13 0.8516403732 0.307384243
## 14 0.2377978710 0.905092597
## 15 0.0147246015 0.078250922
## 16 0.7972173975 0.879985108
## 17 0.4303299964 0.071435214
## 18 0.3909151922 0.870034968
## 19 0.4469986777 0.765329620
## 20 0.4262235162 0.557005505
## 21 0.8393820038 0.066578333
## 22 0.4331469364 0.152505867
## 23 0.9786027721 0.345341266
## 24 0.4867427261 0.028881846
## 25 0.0536901939 0.827337408
## 26 0.6377427704 0.006246690
## 27 0.4677336896 0.434778638
## 28 0.6986140336 0.943243005
## 29 0.7970075202 0.483888759
## 30 0.2506599759 0.633864311
## 31 0.9226017850 0.800301642
## 32 0.0025038649 0.626590540
## 33 0.9791661468 0.822802631
## 34 0.2556128427 0.353439753
## 35 0.8965716977 0.228846215
## 36 0.2377629776 0.150140253
## 37 0.9181363718 0.576929338
## 38 0.0227344930 0.007893186
## 39 0.4970574481 0.189509989
## 40 0.3314644506 0.987734135
## 41 0.0939055309 0.860263986
## 42 0.1094001110 0.587914045
## 43 0.1992902977 0.598895860
## 44 0.2197697689 0.416871586
## 45 0.7620014367 0.099557628
## 46 0.9508141740 0.400835748
## 47 0.3105879214 0.265780705
## 48 0.0001503848 0.693386884
## 49 0.4055376027 0.848887042
## 50 0.9087191643 0.941244133
## 51 0.0330890527 0.147223553
## 52 0.2824787481 0.503735947
## 53 0.2147192874 0.401763608
## 54 0.3223300606 0.810525108
## 55 0.3014176236 0.598305455
## 56 0.7976211212 0.103607750
## 57 0.3001318870 0.305545563
## 58 0.3210178553 0.788012243
## 59 0.8012628120 0.759338537
## 60 0.0374469769 0.079603650
## 61 0.7665565868 0.920609603
## 62 0.1607759397 0.527411877
## 63 0.2617601696 0.625022524
## 64 0.9111487148 0.950359337
## 65 0.1428027758 0.912524596
## 66 0.1014903188 0.319150291
## 67 0.2758274928 0.215314365
## 68 0.0643036880 0.675780897
## 69 0.8788718681 0.240681072
## 70 0.0586141017 0.576686268
## 71 0.4017654506 0.493577742
## 72 0.2588958980 0.311832946
## 73 0.5271591337 0.301856225
## 74 0.4924153527 0.197087383
## 75 0.2448883604 0.659983148
## 76 0.6887018394 0.949690158
## 77 0.5053683585 0.956632456
## 78 0.0644460099 0.852933277
## 79 0.6971812530 0.268945243
## 80 0.9590696895 0.466347856
## 81 0.4175283399 0.473655002
## 82 0.6887016196 0.727598679
## 83 0.1077271374 0.787842710
## 84 0.7235806210 0.396830022
## 85 0.9123932591 0.420076610
## 86 0.0309946393 0.261360365
## 87 0.7591738373 0.875259334
## 88 0.0739150322 0.109421942
## 89 0.7174504709 0.278115505
## 90 0.5633311914 0.118831564
## 91 0.6141034598 0.823268995
## 92 0.1190678899 0.442317624
## 93 0.9592662000 0.242461875
## 94 0.6394779414 0.102035899
## 95 0.1984358446 0.086960612
## 96 0.8283020621 0.926463483
## 97 0.9358594120 0.329809410
## 98 0.7497626538 0.500541312
## 99 0.4889136362 0.807810561
## 100 0.1254644105 0.396967957## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"## [1] "a"## [[1]]
## [1] 0.90704945 0.77652364 0.90730792 0.18234435 0.16822273 0.17585736
## [7] 0.74447123 0.99180202 0.52654958 0.69324169 0.85301198 0.53240988
## [13] 0.86396784 0.18200349 0.65977339 0.37382909 0.66034647 0.33585775
## [19] 0.94423552 0.47108947 0.64014545 0.14299730 0.83758049 0.09136402
## [25] 0.83603546 0.65112385 0.79237058 0.04926343 0.60975753 0.81184443
## [31] 0.71950415 0.34576116 0.69708172 0.71480971 0.43852277 0.91306516
## [37] 0.33709835 0.64484189 0.23874983 0.03005836 0.85905594 0.62504449
## [43] 0.46302086 0.85274002 0.66683303 0.25209227 0.57347857 0.94172352
## [49] 0.60845584 0.87653416 0.80555945 0.12586274 0.10692094 0.65519174
## [55] 0.53719319 0.77821136 0.52197263 0.77467111 0.19578390 0.13651637
## [61] 0.94467557 0.40883423 0.80625228 0.88294754 0.93449914 0.54107256
## [67] 0.73536660 0.57453253 0.56101505 0.58099235 0.79263289 0.02114419
## [73] 0.45113483 0.16046333 0.55647937 0.06394116 0.65338370 0.76742226
## [79] 0.52071744 0.51420978 0.19473846 0.27844844 0.66024552 0.61151509
## [85] 0.27119185 0.61293326 0.43017132 0.88117462 0.73549543 0.86919798
## [91] 0.18498700 0.44791807 0.98227175 0.07176038 0.04772194 0.24734227
## [97] 0.64038550 0.32221512 0.27069574 0.05654550
##
## [[2]]
## x y
## 1 0.9711248630 0.163197247
## 2 0.7570663751 0.270849837
## 3 0.9294406015 0.210958205
## 4 0.5364000814 0.623438141
## 5 0.1478103679 0.343618301
## 6 0.0776882314 0.984681295
## 7 0.5223464179 0.342049163
## 8 0.9478134413 0.532526935
## 9 0.4807977404 0.229018181
## 10 0.5042717066 0.051131312
## 11 0.9878702404 0.765741721
## 12 0.7547797987 0.830578280
## 13 0.8516403732 0.307384243
## 14 0.2377978710 0.905092597
## 15 0.0147246015 0.078250922
## 16 0.7972173975 0.879985108
## 17 0.4303299964 0.071435214
## 18 0.3909151922 0.870034968
## 19 0.4469986777 0.765329620
## 20 0.4262235162 0.557005505
## 21 0.8393820038 0.066578333
## 22 0.4331469364 0.152505867
## 23 0.9786027721 0.345341266
## 24 0.4867427261 0.028881846
## 25 0.0536901939 0.827337408
## 26 0.6377427704 0.006246690
## 27 0.4677336896 0.434778638
## 28 0.6986140336 0.943243005
## 29 0.7970075202 0.483888759
## 30 0.2506599759 0.633864311
## 31 0.9226017850 0.800301642
## 32 0.0025038649 0.626590540
## 33 0.9791661468 0.822802631
## 34 0.2556128427 0.353439753
## 35 0.8965716977 0.228846215
## 36 0.2377629776 0.150140253
## 37 0.9181363718 0.576929338
## 38 0.0227344930 0.007893186
## 39 0.4970574481 0.189509989
## 40 0.3314644506 0.987734135
## 41 0.0939055309 0.860263986
## 42 0.1094001110 0.587914045
## 43 0.1992902977 0.598895860
## 44 0.2197697689 0.416871586
## 45 0.7620014367 0.099557628
## 46 0.9508141740 0.400835748
## 47 0.3105879214 0.265780705
## 48 0.0001503848 0.693386884
## 49 0.4055376027 0.848887042
## 50 0.9087191643 0.941244133
## 51 0.0330890527 0.147223553
## 52 0.2824787481 0.503735947
## 53 0.2147192874 0.401763608
## 54 0.3223300606 0.810525108
## 55 0.3014176236 0.598305455
## 56 0.7976211212 0.103607750
## 57 0.3001318870 0.305545563
## 58 0.3210178553 0.788012243
## 59 0.8012628120 0.759338537
## 60 0.0374469769 0.079603650
## 61 0.7665565868 0.920609603
## 62 0.1607759397 0.527411877
## 63 0.2617601696 0.625022524
## 64 0.9111487148 0.950359337
## 65 0.1428027758 0.912524596
## 66 0.1014903188 0.319150291
## 67 0.2758274928 0.215314365
## 68 0.0643036880 0.675780897
## 69 0.8788718681 0.240681072
## 70 0.0586141017 0.576686268
## 71 0.4017654506 0.493577742
## 72 0.2588958980 0.311832946
## 73 0.5271591337 0.301856225
## 74 0.4924153527 0.197087383
## 75 0.2448883604 0.659983148
## 76 0.6887018394 0.949690158
## 77 0.5053683585 0.956632456
## 78 0.0644460099 0.852933277
## 79 0.6971812530 0.268945243
## 80 0.9590696895 0.466347856
## 81 0.4175283399 0.473655002
## 82 0.6887016196 0.727598679
## 83 0.1077271374 0.787842710
## 84 0.7235806210 0.396830022
## 85 0.9123932591 0.420076610
## 86 0.0309946393 0.261360365
## 87 0.7591738373 0.875259334
## 88 0.0739150322 0.109421942
## 89 0.7174504709 0.278115505
## 90 0.5633311914 0.118831564
## 91 0.6141034598 0.823268995
## 92 0.1190678899 0.442317624
## 93 0.9592662000 0.242461875
## 94 0.6394779414 0.102035899
## 95 0.1984358446 0.086960612
## 96 0.8283020621 0.926463483
## 97 0.9358594120 0.329809410
## 98 0.7497626538 0.500541312
## 99 0.4889136362 0.807810561
## 100 0.1254644105 0.396967957Let’s think about how we might use lists. For one, many functions in
R output data in list format. For instance, working with network data in
R through igraph, most things are lists. Let’s explore
this, both as a way to introduce lists and to talk about how to analyze
network data in R.
## Class 'igraph' hidden list of 10
## $ : num 100
## $ : logi FALSE
## $ : num [1:200] 4 5 7 10 10 13 13 16 17 19 ...
## $ : num [1:200] 2 3 5 3 5 6 8 3 11 0 ...
## $ : num [1:200] 0 1 2 3 4 5 6 7 8 9 ...
## $ : num [1:200] 9 19 94 133 144 12 46 56 90 134 ...
## $ : num [1:101] 0 0 0 0 0 1 2 2 3 3 ...
## $ : num [1:101] 0 5 10 12 19 22 24 30 34 39 ...
## $ :List of 4
## ..$ : num [1:3] 1 0 1
## ..$ :List of 4
## .. ..$ name : chr "Erdos-Renyi (gnm) graph"
## .. ..$ type : chr "gnm"
## .. ..$ loops: logi FALSE
## .. ..$ m : num 200
## ..$ : list()
## ..$ : list()
## $ :<environment: 0x557c08cae6d0>Recall when we introduced the plot function, and I said
that packages build in functionality such that some base functions will
work with more complex objects (the igraph object above is
a list). Try it here.
Nice. That’s neat. We can also write a wrapper function (we will not go over function writing now, but will save that for the coming weeks), which can be useful across multiple projects. This is a function I routinely use for visualizing networks in a prettier way.
#' @param g graph object
#' @param colz
#' @param nodeColor
#' @param nodeSize
#'
#' @return a graph plot
plotGraph <- function(g, lay=layout_nicely(g), colz='dodgerblue'){
plot(g, layout=lay, edge.width= E(g)$weight, vertex.size=10, directed=FALSE,
vertex.color= colz,
vertex.label=NA)
}
plotGraph(g)
But let’s get back to lists. We’ve seen that igraph
graph objects are lists, but also many of the outputs of functions
within igraph are lists (or even lists of lists!). I will
not defend nested lists as being all that useful, but we will see them
in a couple of weeks when we talk about APIs and spatial data.
So one of the functions built into igraph is the
sir function. This is a function which runs a model on the
network known as the Susceptible-Infected-Recovered model (or SIR for
short), which aims to capture how diseases spread through
populations.
\[\begin{align} \frac{dS}{dt} & = -\beta SI \\ \frac{dI}{dt} & = \beta SI - \gamma I \\ \frac{dR}{dt} & = \gamma I \end{align}\]
The default behavior of the function (?sir) will run 100
simulations of the SIR model on a graph object that you provide to the
function, and store the output as a list.
## [1] "list"## [1] "sir"## $times
## [1] 0.00000000 0.03404459 0.13798784 0.77099134
##
## $NS
## [1] 99 98 98 98
##
## $NI
## [1] 1 2 1 0
##
## $NR
## [1] 0 0 1 2## [1] 0.00000000 0.03404459 0.13798784 0.77099134## [1] 0.00000000 0.03404459 0.13798784 0.77099134And just to go back to plotting for a quick second,
igraph has written functionality into the sir
class to work well with the base R plot function.
Neat, right?
But back to lists. Let’s work through the rest of working with lists
through some exercises. Given the simulations above (sims
list) …
Calculate the mean number of infected individuals for each simulation
Find the time associated with the maximum number of infected nodes
Calculate the fraction of all simulations in which fewer than 5 nodes are infected
apply statements
How did we approach the above questions? You almost certaintly used a
for loop, right? This is perfectly fine, but there is
another way. apply statements allow you to take a function
and run it over all elements of a vector, columns/rows of a matrix, or a
list.
apply statements typically have a prefix which gives
information about what type of data it works well with. For instance,
working with lists, we will use lapply. The base
apply function is to work with matrices, where we want to
apply a function to every row or column of a matrix (e.g.,
apply(matrix, 2, sum) is the same as
colSums(matrix)). We will go over more examples of
apply statements at some point, but for now we will focus
on lapply and our problem of working with lists.
And here we hit an issue. lapply statements take a
function argument, where the function needs to take the list object as
an argument and then does something with it. So we’ll have to learn a
bare minimum of function writing now. Let’s use a problem above as a
motivating example, where we try to calculate the mean number of
infected individuals for each simulation.
meanInfections <- function(x){
# if we consider the mean infecteds as the mean of the infected vector
return(mean(x$NI))
# if we consider the mean infecteds as the mean number of nodes infected
# return((x$NS[1]+1)-tail(x$NS,1))
}
meanInfs <- lapply(sims, meanInfections)
str(meanInfs)## List of 100
## $ : num 1
## $ : num 23.6
## $ : num 0.5
## $ : num 21.2
## $ : num 0.5
## $ : num 20.8
## $ : num 11.5
## $ : num 22.9
## $ : num 19.5
## $ : num 20.8
## $ : num 0.5
## $ : num 20.1
## $ : num 10.9
## $ : num 1
## $ : num 16.2
## $ : num 22.8
## $ : num 18.2
## $ : num 15.2
## $ : num 15.9
## $ : num 12.3
## $ : num 1
## $ : num 22.5
## $ : num 8.91
## $ : num 12.3
## $ : num 16.2
## $ : num 22.7
## $ : num 1
## $ : num 0.5
## $ : num 1
## $ : num 1
## $ : num 0.5
## $ : num 24.1
## $ : num 18.8
## $ : num 1
## $ : num 1.17
## $ : num 23.7
## $ : num 19
## $ : num 1
## $ : num 16.1
## $ : num 0.5
## $ : num 17.1
## $ : num 0.5
## $ : num 23.2
## $ : num 15.9
## $ : num 19.8
## $ : num 23.4
## $ : num 15.9
## $ : num 19.9
## $ : num 14.5
## $ : num 17
## $ : num 19.8
## $ : num 17.1
## $ : num 16.5
## $ : num 25.7
## $ : num 0.5
## $ : num 15.5
## $ : num 17.8
## $ : num 19.7
## $ : num 1
## $ : num 0.5
## $ : num 12.9
## $ : num 1
## $ : num 12.6
## $ : num 0.5
## $ : num 21
## $ : num 28.3
## $ : num 19.6
## $ : num 1
## $ : num 1.5
## $ : num 14.3
## $ : num 14
## $ : num 18.6
## $ : num 23.5
## $ : num 0.5
## $ : num 0.5
## $ : num 17
## $ : num 19.2
## $ : num 22.5
## $ : num 1
## $ : num 10.6
## $ : num 17.9
## $ : num 0.5
## $ : num 1
## $ : num 1
## $ : num 0.5
## $ : num 16.3
## $ : num 19.7
## $ : num 14.1
## $ : num 1
## $ : num 26.2
## $ : num 0.5
## $ : num 18.2
## $ : num 22
## $ : num 23.1
## $ : num 15.6
## $ : num 24
## $ : num 18.7
## $ : num 18
## $ : num 0.5
## [list output truncated]lapply is nice in that if we give it a list object, it
gives us a list object back. This makes analytical pipelines that deal
with lists pretty straightforward, but if the output is a single value,
we may want this to be a vector instead of a list.
## [1] 1.000000 23.614943 0.500000 21.212644 0.500000 20.833333 11.484375
## [8] 22.897436 19.538961 20.790698 0.500000 20.054217 10.897260 1.000000
## [15] 16.234940 22.837349 18.157895 15.224138 15.879747 12.291045 1.000000
## [22] 22.475904 8.909091 12.330986 16.222222 22.737500 1.000000 0.500000
## [29] 1.000000 1.000000 0.500000 24.139535 18.837349 1.000000 1.166667
## [36] 23.656627 18.967532 1.000000 16.143836 0.500000 17.100000 0.500000
## [43] 23.178161 15.949275 19.791139 23.353659 15.922535 19.937500 14.475000
## [50] 16.969136 19.837838 17.089041 16.524691 25.651163 0.500000 15.536145
## [57] 17.750000 19.740964 1.000000 0.500000 12.913793 1.000000 12.636986
## [64] 0.500000 20.994505 28.264706 19.591954 1.000000 1.500000 14.270492
## [71] 14.033333 18.597561 23.547059 0.500000 0.500000 16.962500 19.212644
## [78] 22.487342 1.000000 10.597222 17.858025 0.500000 1.000000 1.000000
## [85] 0.500000 16.305195 19.746753 14.057377 1.000000 26.241176 0.500000
## [92] 18.200000 21.969136 23.133333 15.550000 24.012500 18.731884 18.013158
## [99] 0.500000 24.861446sapply statements are essentially just
lapply statements that simplify the result to a vector.
This is useful when the output of the function is a single value, and
not so useful when function returns multiple values.
A side note: some people will criticize for
loops in R, and say “just use apply, it’s faster”. It’s not, really.
Write however you feel comfortable. For awhile, apply
statements were super confusing to me, so I tended to use
for loops instead. After more work in, I shifted and tend
to use apply statements when it fits, as they are less code
and are more intuitive to me for many situations.
Let’s practice a bit.
Calculate the maximum number of infected inviduals at any time in the
sims list using the apply approach.
What is the mean duration (the total time the epidemic took before it
stopped) across all the epidemics in sims?
plyr apply functionality tweaks
XYply statements as nice wrappers to more classic
apply statements. Here, X and Y
can take values of ‘a’, ‘l’, or ‘d’, depending on the input or output
data structure desired. For instance, if we have a list that we would
like to apply over and return a data.frame, we would use
ldply, where the l is claiming that the input
is a list object, and the d is claiming that the output
should be formatted as a data.frame. Other examples of this syntax would
be adply, ddply, laply,
aaply, etc. etc.
Below, I provide an example of the aXply syntax (e.g., adply, alply, aaply).
arr <- array(1:27, c(3,3,3))
rownames(arr) = c("Curly", "Larry", "Moe")
colnames(arr) = c("Groucho", "Harpo", "Zeppo")
dimnames(arr)[[3]] = c("Bart", "Lisa", "Maggie")
arr## , , Bart
##
## Groucho Harpo Zeppo
## Curly 1 4 7
## Larry 2 5 8
## Moe 3 6 9
##
## , , Lisa
##
## Groucho Harpo Zeppo
## Curly 10 13 16
## Larry 11 14 17
## Moe 12 15 18
##
## , , Maggie
##
## Groucho Harpo Zeppo
## Curly 19 22 25
## Larry 20 23 26
## Moe 21 24 27Arrays are something that we did not introduce when we talked about
R basics, and that is because they really are not used
too often. Think of matrix. It has two dimensions (x and y), so
it can be viewed as a rectangle of data. Arrays simply add more
dimensions. In the example above, there is another dimension, forming a
data cube (in the rectangle analogy).
We can use plyr functionality to operate on this array
and return different forms. For instance, aaply takes an
array and returns a simplified array (here a vector).
## Curly Larry Moe
## 117 126 135We can change one letter and now return a data.frame containing two
columns. This is also a good time to point out the flexibility of the
XYply statements to different margins. Margins (denoted as
.margins argument in R, asks along which axis
you would like to operate on the array. If we set .margins=1, this
corresponds to a row-wise operation, so we calculate the sum across the
array for Curly, Larry, and Moe. If we change this to .margins=2, we
operate on columns, and will return sums for Groucho, Harpo, and Zeppo.
And if we use .margins=3, we will return sums for Bart, Lisa, and
Maggie.
## X1 V1
## 1 Curly 117
## 2 Larry 126
## 3 Moe 135## X1 V1
## 1 Groucho 99
## 2 Harpo 126
## 3 Zeppo 153## X1 V1
## 1 Bart 45
## 2 Lisa 126
## 3 Maggie 207Finally, we can return a list object. In this use case, this is not super helpful, but in other use cases the list output is pretty helpful.
## $`1`
## [1] 117
##
## $`2`
## [1] 126
##
## $`3`
## [1] 135
##
## attr(,"split_type")
## [1] "array"
## attr(,"split_labels")
## X1
## 1 Curly
## 2 Larry
## 3 MoeA pitch for plyr::ldply. I really like this function, as
I often find myself with lists of similar structures that I want to
operate on and get a single clean object back. I will not go into an
example, but this is a pretty useful function (though all the utility is
basically contained in vapply).
Finally, you may wonder why am I pushing apply statements so hard. It
has nothing to do with speed, and only a bit to do with code clarity.
The main advantage is understanding the programmatic nature of apply
statements (which will be similar but less chronological than a for
loop), and many parallel computing packages have their own little
versions of apply statements ready to go (e.g.,
parallel::mclapply, parallel::parLapply,
parallel::clusterApplyLB).
Let’s do one practice problem to showcase the utility of
ldply specifically.
Calculate the correlation between number of infections and time for
each simulation, reporting the estimate, p-value, and confidence
intervals around the estimate. (you will use cor.test to do
this, whose output is a list object as well)
A note about do.call and Reduce
While a bit opaque, these functions are pretty useful in a variety of
situations. Speaking of data manipulation functions that are useful but
a bit conceptually difficult, do.call and
Reduce are solid base R functions.
do.call is a way of calling the same function
recursively on multiple objects, and may have similar output to
Reduce, which is also a way to recursively apply a
function.
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[2]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[3]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[4]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[5]]
## [1] 1 2 3 4 5 6 7 8 9 10#this makes a single rbind call with each element of the list as an argument
str(do.call(rbind, lst))## int [1:5, 1:10] 1 1 1 1 1 2 2 2 2 2 ...## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## init 1 2 3 4 5 6 7 8 9 10
## 1 2 3 4 5 6 7 8 9 10
## 1 2 3 4 5 6 7 8 9 10
## 1 2 3 4 5 6 7 8 9 10
## 1 2 3 4 5 6 7 8 9 10sessionInfo
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] geodata_0.5-8 terra_1.7-29 maps_3.4.1 gbm_2.1.8.1 igraph_1.4.3
## [6] dplyr_1.1.2 plyr_1.8.8 DBI_1.1.3 rgbif_3.7.7 jsonlite_1.8.5
## [11] httr_1.4.6 rmarkdown_2.11 fastmap_1.1.1
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.3 xfun_0.29 ggplot2_3.4.2 lattice_0.21-8
## [5] vctrs_0.6.2 tools_4.3.0 generics_0.1.2 parallel_4.3.0
## [9] curl_4.3.3 tibble_3.2.1 fansi_1.0.2 RSQLite_2.3.1
## [13] highr_0.9 blob_1.2.4 pkgconfig_2.0.3 Matrix_1.5-1
## [17] data.table_1.14.6 dbplyr_2.3.2 lifecycle_1.0.3 compiler_4.3.0
## [21] stringr_1.5.0 munsell_0.5.0 codetools_0.2-19 htmltools_0.5.2
## [25] yaml_2.3.6 lazyeval_0.2.2 pillar_1.9.0 jquerylib_0.1.4
## [29] whisker_0.4.1 cachem_1.0.8 viridis_0.6.3 tidyselect_1.2.0
## [33] digest_0.6.31 stringi_1.7.12 purrr_1.0.1 splines_4.3.0
## [37] grid_4.3.0 colorspace_2.1-0 cli_3.6.1 magrittr_2.0.2
## [41] triebeard_0.4.1 survival_3.5-3 crul_1.4.0 utf8_1.2.2
## [45] withr_2.5.0 scales_1.2.1 bit64_4.0.5 oai_0.4.0
## [49] bit_4.0.5 gridExtra_2.3 memoise_2.0.1 evaluate_0.15
## [53] knitr_1.37 viridisLite_0.4.2 rlang_1.1.1 urltools_1.7.3
## [57] Rcpp_1.0.10 glue_1.6.2 httpcode_0.3.0 xml2_1.3.4
## [61] R6_2.5.1