Creating new column with values from multiple other columns - r

I hope someone can help me with this one!
I have the following dataset and want to create a new column where the values of aver1, aver2 and aver3 are represented.
I tried it with rowSums but this did not work for me because when i put na.rm = TRUE also those rows who have only empty columns have 0 as their sum and I can not differentiate these from the ones that actually do have 0 as their value.
What I have:
count
aver1.
aver2.
aver3.
X
NA
1
NA
Y
1
NA
NA
X
NA
NA
0
What I want:
count
aver1.
aver2.
aver3.
aver_all
X
NA
1
NA
1
Y
1
NA
NA
1
X
NA
NA
0
0
the dput output:
structure(list(count = c(0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,
0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,
1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0,
1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
1), start = c(NA, NA, NA, 5, NA, NA, NA, NA, 1, NA, NA, NA, NA,
1, 1, 1, NA, NA, 9, NA, NA, NA, 3, 4, NA, 11, 1, NA, NA, 1, NA,
NA, NA, 6, NA, NA, 5, NA, 5, NA, NA, NA, NA, NA, 1, NA, 3, NA,
NA, 3, 1, NA, 13, NA, 0, NA, NA, NA, NA, 1, NA, NA, NA, 12, 1,
NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, NA, 1, NA, NA, NA, NA,
2, NA, 2, NA, NA, NA, 2, NA, NA, 1, NA, 3, NA, 3, NA, NA, NA,
NA, 10, NA, 1, NA, 0, 0, 1, 1, NA, NA, NA, NA, NA, 1, NA, 2,
7, NA, 1, NA, NA, 3, NA, 2, 6, NA, 3, NA, 1, 8, 1, NA, 1, NA,
NA, 0, NA, 0, 1, NA, NA, NA, NA, 3, NA, 0, NA, NA, NA, 1, NA,
NA, 0, NA, NA, NA, NA, NA, 2, NA, NA, 0, NA, NA, NA, NA, NA,
NA, 1, NA, 4), aver1 = c(NA, NA, NA, 0.5, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0.166666666666667, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.133333333333333, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, 0.266666666666667, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0.566666666666667, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 0.266666666666667, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), aver2 = c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.333333333333333, 0.416666666666667, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.25, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.916666666666667,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.472222222222222,
NA, NA, NA, NA, NA, NA, 0.388888888888889, NA, NA, NA, 0.0833333333333333,
NA, NA, NA, NA, 0.0555555555555556, NA, 0.111111111111111, NA,
NA, NA, NA, NA, NA, NA, NA, 0.305555555555556, NA, 0.861111111111111,
NA, NA, NA, NA, NA, NA, NA, NA, 0.194444444444444, NA, NA, NA,
NA, NA, 0.611111111111111, NA, NA, NA, NA, 0, NA, 1, NA, 0.694444444444444,
NA, NA, NA, NA, 0.5, NA, 1, NA, NA, NA, NA, NA, 0.0277777777777778,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.138888888888889,
NA, NA, 0.583333333333333, NA, NA, NA, NA, NA, NA, 0.194444444444444,
NA, NA), aver3 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 0.514285714285714,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 0.0285714285714286, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 1, 0.214285714285714, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.0142857142857143, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0.614285714285714, NA, NA, NA, NA, 0.371428571428571,
NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA,
NA, NA, NA, 0.9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.0571428571428571,
NA, NA, 0.128571428571429, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 0.1)), row.names = c(NA, -170L
), class = c("tbl_df", "tbl", "data.frame"))


This is an example that allows you to sum your selected variables from your data-frame (let's call this data-frame: 'df').
df$aver_all <- apply(df[, c("aver1", "aver2", "aver3")], 1, function(x) sum(x, na.rm=TRUE))
It will add 0s to rows where there are only NAs for aver1-2-3.
The next code will replace by NAs, the rows with full NAs.
df$aver_all <- apply(df[, c("aver1", "aver2", "aver3")], 1, function(x) ifelse(FALSE %in% is.na(x), sum(x, na.rm=TRUE), NA))


Given that you have said that you also have rows where all column values are NAs, I will create an additional row in your dataset that fulfills this condition:
dataset <- tibble(count = c("X", "Y", "X", "Z"), aver1. = c(NA, 1, NA, NA),
aver2. = c(1, NA, NA, NA), aver3. = c(NA, NA, 0, NA))
You can use the conditional case_when (https://dplyr.tidyverse.org/reference/case_when.html), which will allow you to set values depending on the conditions you choose for each row. In this case, you could use:
dataset$aver_all <- case_when(is.na(aver1.) & is.na(aver2.) & is.na(aver3.) ~ NA_real_,
aver1. | aver2. | aver3. ~ 1,
TRUE ~ 0)
Here the first condition sets rows where all values are NA to NA, the second sets a 1 if at least one of the three values of a row is a 1; and finally if none of these conditions is satisfied, a 0 is set.

Related

Periodic Patterns Identification in R

I want to identify temporal patterns in a time series.
structure(list(ID = c("a", "b", "c", "d", "e", "f", "g", "h",
"i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u",
"v", "w", "x"), `2016/01` = c(1, NA, NA, 1, NA, NA, 1, NA, NA,
1, NA, 1, 1, 1, NA, 1, NA, NA, 1, NA, NA, 1, NA, NA), `2016/02` = c(NA,
1, NA, NA, 1, NA, NA, 1, NA, NA, 1, 1, 1, NA, 1, NA, 1, NA, NA,
1, NA, NA, 1, NA), `2016/03` = c(NA, NA, 1, NA, NA, 1, NA, NA,
1, 1, NA, 1, 1, 1, NA, NA, NA, 1, NA, NA, 1, NA, NA, 1), `2016/04` = c(NA,
NA, NA, 1, NA, NA, NA, NA, NA, NA, 1, 1, 1, NA, 1, NA, NA, NA,
1, NA, NA, NA, NA, NA), `2016/05` = c(NA, NA, NA, NA, 1, NA,
NA, NA, NA, 1, NA, 1, 1, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA,
NA), `2016/06` = c(NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 1,
1, 1, NA, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA), `2016/07` = c(NA,
NA, NA, 1, NA, NA, 1, NA, NA, 1, NA, 1, 1, 1, NA, 1, NA, NA,
1, NA, NA, NA, NA, NA), `2016/08` = c(NA, NA, NA, NA, 1, NA,
NA, 1, NA, NA, 1, 1, 1, NA, 1, NA, 1, NA, NA, 1, NA, NA, NA,
NA), `2016/09` = c(NA, NA, NA, NA, NA, 1, NA, NA, 1, 1, NA, 1,
1, 1, NA, NA, NA, 1, NA, NA, 1, NA, NA, NA), `2016/10` = c(NA,
NA, NA, 1, NA, NA, NA, NA, NA, NA, 1, 1, 1, NA, 1, NA, NA, NA,
1, NA, NA, NA, NA, NA), `2016/11` = c(NA, NA, NA, NA, 1, NA,
NA, NA, NA, 1, NA, 1, 1, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA,
NA), `2016/12` = c(NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 1,
1, 1, NA, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA), `2017/01` = c(1,
NA, NA, 1, NA, NA, 1, NA, NA, 1, NA, 1, 1, 1, NA, 1, NA, NA,
1, NA, NA, 1, NA, NA), `2017/02` = c(NA, 1, NA, NA, 1, NA, NA,
1, NA, NA, 1, 1, 1, NA, 1, NA, 1, NA, NA, 1, NA, NA, 1, NA),
`2017/03` = c(NA, NA, 1, NA, NA, 1, NA, NA, 1, 1, NA, 1,
1, 1, NA, NA, NA, 1, NA, NA, 1, NA, NA, 1), `2017/04` = c(NA,
NA, NA, 1, NA, NA, NA, NA, NA, NA, 1, 1, 1, NA, 1, NA, NA,
NA, 1, NA, NA, NA, NA, NA), `2017/05` = c(NA, NA, NA, NA,
1, NA, NA, NA, NA, 1, NA, 1, 1, 1, NA, NA, NA, NA, NA, 1,
NA, NA, NA, NA), `2017/06` = c(NA, NA, NA, NA, NA, 1, NA,
NA, NA, NA, 1, 1, 1, NA, 1, NA, NA, NA, NA, NA, 1, NA, NA,
NA), `2017/07` = c(NA, NA, NA, 1, NA, NA, 1, NA, NA, 1, NA,
1, 1, 1, NA, 1, NA, NA, 1, NA, NA, NA, NA, NA), `2017/08` = c(NA,
NA, NA, NA, 1, NA, NA, 1, NA, NA, 1, 1, 1, NA, 1, NA, 1,
NA, NA, 1, NA, NA, NA, NA), `2017/09` = c(NA, NA, NA, NA,
NA, 1, NA, NA, NA, 1, NA, 1, 1, 1, NA, NA, NA, NA, NA, NA,
1, NA, NA, NA), `2017/10` = c(NA, NA, NA, 1, NA, NA, NA,
NA, NA, NA, 1, 1, 1, NA, 1, NA, NA, NA, 1, NA, NA, NA, NA,
NA), `2017/11` = c(NA, NA, NA, NA, 1, NA, NA, NA, NA, 1,
NA, 1, 1, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA), `2017/12` = c(1,
NA, NA, NA, NA, 1, NA, NA, NA, NA, 1, 1, 1, NA, 1, NA, NA,
NA, NA, NA, 1, 1, NA, NA), `2018/01` = c(NA, 1, NA, 1, NA,
NA, 1, NA, NA, 1, NA, 1, 1, 1, NA, 1, NA, NA, 1, NA, NA,
NA, 1, NA), `2018/02` = c(NA, NA, 1, NA, 1, NA, NA, 1, NA,
NA, 1, 1, 1, NA, 1, NA, 1, NA, NA, 1, NA, NA, NA, 1), `2018/03` = c(NA,
NA, NA, NA, NA, 1, NA, NA, 1, 1, NA, 1, 1, 1, NA, NA, NA,
1, NA, NA, 1, NA, NA, NA), `2018/04` = c(NA, NA, NA, 1, NA,
NA, NA, NA, NA, NA, 1, 1, 1, NA, 1, NA, NA, NA, 1, NA, NA,
NA, NA, NA), `2018/05` = c(NA, NA, NA, NA, 1, NA, NA, NA,
NA, 1, NA, 1, 1, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA
), `2018/06` = c(NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 1,
1, 1, NA, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA), `2018/07` = c(NA,
NA, NA, 1, NA, NA, 1, NA, NA, 1, NA, 1, 1, 1, NA, 1, NA,
NA, 1, NA, NA, NA, NA, NA), `2018/08` = c(NA, NA, NA, NA,
1, NA, NA, 1, NA, NA, 1, 1, 1, NA, 1, NA, 1, NA, NA, 1, NA,
NA, NA, NA), `2018/09` = c(NA, NA, NA, NA, NA, 1, NA, NA,
1, 1, NA, 1, 1, 1, NA, NA, NA, 1, NA, NA, 1, NA, NA, NA),
`2018/10` = c(NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, 1, 1,
1, NA, 1, NA, NA, NA, 1, NA, NA, NA, NA, NA), `2018/11` = c(NA,
NA, NA, NA, 1, NA, NA, NA, NA, 1, NA, 1, 1, 1, NA, NA, NA,
NA, NA, 1, NA, NA, NA, NA), `2018/12` = c(NA, NA, NA, NA,
NA, 1, NA, NA, NA, NA, 1, 1, 1, NA, 1, NA, NA, NA, NA, NA,
1, NA, NA, NA)), row.names = c(NA, -24L), class = c("tbl_df",
"tbl", "data.frame"))
In the upper data frame individual:
List item
a has the same pattern as v
b has the same pattern as w
c has the same pattern as x
In the upper data frame individuals a, b, c, v, w and x have the same frequency - yearly.
The are some other cases as bimensal, quarterly and semestral.
My objective is to identify all this cases and classify all individuals with a time pattern.
I suppose that the package arulesSequences can be useful.
Can you help me please?

I think a good start would be a full hierarchical clustering:
library(gplots)
library(dendsort)
# data preparation
dm <- matrix( as.numeric(!is.na(dat[,-1])), nrow=nrow(dat[,-1]) )
rownames(dm) <- dat$ID
colnames(dm) <- colnames(dat[,-1])
heatmap.2( dm, trace="none", hclustfun=function(x){
dendsort(hclust(x, method="single"), type="average")
}, col=c("grey90","darkblue") )
Clearly visible are all time dependent connections through the columns.
I included dendsort to bring similar clusters together to make ID related patterns more obvious.
Also, only plotting the row-cluster lets you visualize the temporal patterns better.
heatmap.2( dm, trace="none", Colv=NA, dendrogram="row",
hclustfun=function(x){ dendsort(hclust(x, method="single"),
type="average") }, col=c("grey90","darkblue") )
Adding a summary and k-means for comparison:
hierarchical cluster
dis <- dist(dm, method="euclidean")
hc <- hclust(dis, method="single")
# choose the height where to cut
# lower means more fine grained cluster, less member per cluster
cutree(hc, h=4)
a b c d e f g h i j k l m n o p q r s t u v w x
1 2 1 3 2 4 1 2 1 5 6 7 7 5 6 1 2 1 3 2 4 1 2 1
# higher h means larger clusters, i.e. more member per cluster
cutree(hc, h=5)
a b c d e f g h i j k l m n o p q r s t u v w x
1 2 1 1 2 1 1 2 1 1 2 3 3 1 2 1 2 1 1 2 1 1 2 1
k-means
# pre-defining k=6, has to be rerun to change k
km <- kmeans(dm, 6, algorithm="Hartigan-Wong")
km$cluster
a b c d e f g h i j k l m n o p q r s t u v w x
2 5 2 6 5 4 2 5 4 3 1 1 1 3 1 2 5 4 6 5 4 2 5 2

How to plot an igraph object on a vector map in R

I use the igraph and sf packages.
I have an igraph object whose vertices have spatial coordinates geo_dist_graph.
The vertices names and coordinates look like this:
grid_grid <-
structure(list(coords.x1 = c(15.504078, 15.704078, 15.904078,
15.104078, 15.304078, 15.504078, 15.704078, 15.104078, 15.304078,
15.704078, 14.904078, 14.304078, 13.904078, 14.704078, 13.704078,
14.104078, 14.704078, 14.904078, 13.704078, 13.904078, 14.704078,
13.704078, 13.904078, 14.304078),
coords.x2 = c(43.835623, 43.835623,
43.835623, 44.035623, 44.035623, 44.035623, 44.035623, 44.235623,
44.235623, 44.235623, 44.435623, 44.635623, 44.835623, 44.835623,
45.035623, 45.035623, 45.035623, 45.035623, 45.235623, 45.235623,
45.235623, 45.435623, 45.435623, 45.435623),
g9.nodes = c(27,
28, 29, 40, 41, 42, 43, 55, 56, 58, 69, 81, 94, 98, 108, 110,
113, 114, 123, 124, 128, 138, 139, 141)),
class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24"
))
The graph is from a simple squared adjacency matrix:
geo_dist_graph <-
structure(c(NA, 1, 1, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 1, NA, NA, NA,
1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1, 1, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA,
NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 1, NA, 1, NA, NA, 1, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA,
1, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1, 1, 1, NA, NA, 1, NA, NA, NA, 1, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA,
NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1, 1, 1, NA, 1, NA, 1, 1, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, NA,
1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, 1, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 1, 1,
NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1, 1, NA, NA, NA, NA, 1, 1, NA, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 1, NA,
NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1, 1, NA, 1, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 1,
NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 1, NA, NA, 1, NA, NA, NA, 1, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA,
NA, 1, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1, NA, 1, 1, NA, NA, 1, NA, NA, 1, 1, 1, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 1, 1, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1,
1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, NA, 1, NA),
.Dim = c(24L,
24L))
colnames(geo_dist_graph) <- grid_grid$g9.nodes
row.names(geo_dist_graph) <- grid_grid$g9.nodes
geo_dist_graph <- graph_from_adjacency_matrix(geo_dist_graph, mode = "upper", diag = F)
The spatial coordinates where attched this way:
V(geo_dist_graph)$x <-
grid_grid$coords.x1[match(V(geo_dist_graph)$name, grid_grid$g9.nodes)]
V(geo_dist_graph)$y <-
grid_grid$coords.x2[match(V(geo_dist_graph)$name, grid_grid$g9.nodes)]
The graph is correclty plotted in space when using the plot function. But when I try to add a basemap like this plot(map_crop_sp, add = T), the map doesn't show behind the graph, but there is no error message.
The map is vector map, don't know if it's important. Here is the code used to create it.
map <- st_read("ne_10m_coastline/ne_10m_coastline.shp")
map_crop <- st_crop(map, xmin = 13.304078, ymin = 43.635623, xmax = 16.503846, ymax = 45.60185)
map_crop_sp <- as(map_crop, Class = "Spatial")

Answer
Since the igraph should be on top of the map, I plot it second. I also added rescale = F:
plot(map_crop_sp)
plot(geo_dist_graph, add = T, rescale = F)
Rationale
I typed ?plot.igraph. From there, I found ?igraph.plotting. It seems that plotting an igraph object rescales it (plot(..., rescale = TRUE):
Logical constant, whether to rescale the coordinates to the [-1,1]x-1,1 interval. This parameter is not implemented for tkplot.
Defaults to TRUE, the layout will be rescaled.

extracting information from excel into lists in R

hello all i have this datasset :
> dput(test1)
structure(list(startdate = c("2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-01", "2019-11-05", "2019-11-15",
"2019-11-16", "2019-11-17", "2019-11-18", "2019-11-19", "2019-11-20",
"2019-11-21", NA), id = c("POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL62", "POL63", "POL64", "POL65",
"POL66", "POL67", "POL68", "POL69", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL62", "POL63", "POL64", "POL65",
"POL66", "POL67", "POL68", NA), m0_9 = c(NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98,
33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), m10_19 = c(NA,
NA, NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65,
3, 98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), m20_29 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA,
NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA,
NA, NA, NA, NA, NA), m30_39 = c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), m40_49 = c(32, 34, NA, NA,
NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), m50_59 = c(NA,
NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA,
7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), m60_69 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9,
1, 65, 3, 98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA), m70 = c(NA, NA, NA, NA, NA, NA, 32,
34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), f0_9 = c(32, 34, NA,
NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), f10_19 = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA, 55,
3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), f20_29 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA), f30_39 = c(NA, NA, NA, 32, 34, NA, NA, NA,
NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), f40_49 = c(NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA,
55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA), f50_59 = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA,
55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), f60_69 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA,
55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), f70 = c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA)), row.names = c(NA, -50L), class = c("tbl_df",
"tbl", "data.frame"))
I would like to create a list called ageCat. This list should contain a number of lists. The number of lists is the amount of age categories. Then for each age category i would like to extract the following info startAge, endAge, maleCount,femaleCount, totalCount.
Additionaly, i want only to sum up only individuals that have the same id and start date. For now i have written this:
create list of age
createLists <- function(startdate, id){
testFiltered = test1[policyid == id & start == startdate]
ageGroup <- vector("list", length == 8)
names(ageGroup) <- as.character(seq_along(ageGroup))
for(ageCat in seq_along(ageGroup)){
ageGroup[[ageCat]] <- getAgeInfo(testFiltered, ageCat)
}
getAgeInfo <- function(testFiltered, ageCat){
start =
end =
nomales =
nofemales =
}
ageGroup <- list(startAge = start,
endAge = end ,
maleCount = nomales ,
femaleCount = nofemales)
}
I have hard coded the length of the vecor ageGroup. How can i do this without hard coding it, aka. to look up how many columns with age categories I have for each gender?
Secondly, how can i extract the information startAge, endAge, maleCount,femaleCount, totalCount

Instead of working with lists I suggest to convert your data.frame to long format, getting rid of missing values and extracting sex and age. A `tidyverse´ approach might look like this:
library(dplyr)
library(tidyr)
library(tibble)
df <- tibble(
startdate = c(
"2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06", "2019-11-06",
"2019-11-06", "2019-11-06", "2019-11-06", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27", "2019-11-27",
"2019-11-27", "2019-11-27", "2019-11-01", "2019-11-05", "2019-11-15",
"2019-11-16", "2019-11-17", "2019-11-18", "2019-11-19", "2019-11-20",
"2019-11-21", NA
),
id = c(
"POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL62", "POL63", "POL64", "POL65",
"POL66", "POL67", "POL68", "POL69", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL55", "POL56", "POL57", "POL58",
"POL59", "POL60", "POL61", "POL62", "POL63", "POL64", "POL65",
"POL66", "POL67", "POL68", NA
),
m0_9 = c(
NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98,
33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
),
m10_19 = c(
NA,
NA, NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65,
3, 98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
),
m20_29 = c(
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA,
NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA,
NA, NA, NA, NA, NA
),
m30_39 = c(
NA, NA, NA, NA, NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA
),
m40_49 = c(
32, 34, NA, NA,
NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
),
m50_59 = c(
NA,
NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA,
7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), m60_69 = c(
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9,
1, 65, 3, 98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA
), m70 = c(
NA, NA, NA, NA, NA, NA, 32,
34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), f0_9 = c(
32, 34, NA,
NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), f10_19 = c(
NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA, 55,
3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), f20_29 = c(
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA
), f30_39 = c(
NA, NA, NA, 32, 34, NA, NA, NA,
NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA
), f40_49 = c(
NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA,
55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA
), f50_59 = c(
NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA,
55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), f60_69 = c(
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32, 34, NA, NA, NA, NA,
55, 3, NA, NA, NA, 7, 9, 1, 65, 3, 98, 33, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA
), f70 = c(
NA, NA, NA, NA, NA, NA, NA, NA,
NA, 32, 34, NA, NA, NA, NA, 55, 3, NA, NA, NA, 7, 9, 1, 65, 3,
98, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA
)
)
# Convert to tidy data frame
df_age <- df %>%
gather(age_sex, count, -startdate, -id) %>%
filter(!is.na(count)) %>%
extract(age_sex, into = c("sex", "start_age", "end_age"), regex = "(m|f)(\\d+)_?(\\d+)?", remove = FALSE) %>%
mutate(ageg = paste0(start_age, "_", end_age))
df_age
#> # A tibble: 187 x 8
#> startdate id age_sex sex start_age end_age count ageg
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 2019-11-27 POL55 m0_9 m 0 9 32 0_9
#> 2 2019-11-27 POL56 m0_9 m 0 9 34 0_9
#> 3 2019-11-27 POL61 m0_9 m 0 9 55 0_9
#> 4 2019-11-27 POL55 m0_9 m 0 9 3 0_9
#> 5 2019-11-27 POL59 m0_9 m 0 9 7 0_9
#> 6 2019-11-27 POL60 m0_9 m 0 9 9 0_9
#> 7 2019-11-27 POL61 m0_9 m 0 9 1 0_9
#> 8 2019-11-27 POL55 m0_9 m 0 9 65 0_9
#> 9 2019-11-27 POL56 m0_9 m 0 9 3 0_9
#> 10 2019-11-27 POL57 m0_9 m 0 9 98 0_9
#> # ... with 177 more rows
# df back to nested list by startdate and ageg
df_list <- df_age %>%
# Count by startdate, ageg, start_age, end_age, sex
count(startdate, ageg, start_age, end_age, sex, wt = count) %>%
# male and female counts back in columns
spread(sex, n, fill = 0) %>%
# split by startdate
split(.$startdate) %>%
# ... and split each startdate list by ageg
lapply(function(x) split(x, x$ageg))
Created on 2020-03-10 by the reprex package (v0.3.0)

ggplot2 over multiple pages

I have generated some partial dependence plots.
library(mlr)
plots = plotPartialDependence(pd)
The output looks like this:
class(plots)
[1] "gg" "ggplot"
pd
Per default, this function is printing the plots on one page. However, since there are many plots I can´t see any details. Is there a way to print the plots on multiple pages (i.e. 4 plots per page e.g. nrows = 2/ncols = 2)?
I have read some posts on this topic but they are dealing with plots which were generated by the user, not by a function. So, this doesn't helped me.
Here is small example of 5 features.
structure(list(data = structure(list(review_count = c(73.1921519112757,
72.9381584023148, 72.9381584023148, 72.9381584023148, 72.9381584023148,
63.1251979284659, 63.1251979284659, 63.1251979284659, 63.1251979284659,
63.1251979284659, 45.1564179015755, 45.1564179015755, 45.1564179015755,
45.1564179015755, 45.1564179015755, 70.3673395995618, 70.3673395995618,
70.3673395995618, 70.3673395995618, 70.3673395995618, 62.363785022433,
61.3743337919256, 61.3743337919256, 61.3743337919256, 61.3743337919256,
64.3075754021323, 64.3075754021323, 64.3075754021323, 64.3075754021323,
64.3075754021323, 58.1782568771601, 58.1716123314153, 58.1716123314153,
58.1716123314153, 58.1716123314153, 95.6300994996321, 95.6300994996321,
95.6300994996321, 95.6300994996321, 95.6300994996321, 65.8695037727425,
66.679524424974, 66.679524424974, 66.679524424974, 66.679524424974,
43.4884670405162, 43.4884670405162, 43.4884670405162, 43.4884670405162,
43.4884670405162), diveyTrue = c(0, 0.111111111111111, 0.222222222222222,
0.333333333333333, 0.444444444444444, 0.555555555555556, 0.666666666666667,
0.777777777777778, 0.888888888888889, 1, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), dinnerTrue = c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 0, 0.111111111111111, 0.222222222222222, 0.333333333333333,
0.444444444444444, 0.555555555555556, 0.666666666666667, 0.777777777777778,
0.888888888888889, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), BikeParkingTrue = c(NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0.111111111111111,
0.222222222222222, 0.333333333333333, 0.444444444444444, 0.555555555555556,
0.666666666666667, 0.777777777777778, 0.888888888888889, 1, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), latenightTrue = c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0, 0.111111111111111, 0.222222222222222,
0.333333333333333, 0.444444444444444, 0.555555555555556, 0.666666666666667,
0.777777777777778, 0.888888888888889, 1, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), NoiseLevelquiet = c(NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, 0.111111111111111, 0.222222222222222, 0.333333333333333,
0.444444444444444, 0.555555555555556, 0.666666666666667, 0.777777777777778,
0.888888888888889, 1)), row.names = c(NA, -50L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x0000000002521ef0>),
task.desc = structure(list(id = "dat", type = "regr", target = "review_count",
size = 9943L, n.feat = c(numerics = 79L, factors = 0L,
ordered = 0L, functionals = 0L), has.missings = TRUE,
has.weights = FALSE, has.blocking = FALSE, has.coordinates = FALSE), class = c("RegrTaskDesc",
"SupervisedTaskDesc", "TaskDesc")), target = c("diveyTrue",
"dinnerTrue", "BikeParkingTrue", "latenightTrue", "NoiseLevelquiet"
), features = c("diveyTrue", "dinnerTrue", "BikeParkingTrue",
"latenightTrue", "NoiseLevelquiet"), derivative = FALSE,
interaction = FALSE, individual = FALSE), class = "PartialDependenceData")

How to further format forest Plots in R, from the metafor package?

I'm quite new to R and have been struggling with properly formatting a forest plot I've created.
When I click the "zoom" option in R to open the graph in a new window, it looks as such:
Forest Plot Currently
My main goal is to get the forest plot as compact as possible, i.e. publication quality/style. I currently have wayyyy too much white space in my plot. I think it has something to do with me messing around with the par() function, and now have no clue how to revert to defaults.
#Metafor library
library(metafor)
#ReadXL library to import excel sheet
library(readxl)
#Name the data sheet from the excel file
ACDF<- read_excel("outpatient_ACDF_meta_analysis.xlsx")
#View the data sheet with view(ACDF)
par(mar=c(20,1,1,1))
#This below measures with risk ratios. If you want to measure odds ratios, use argument measure=OR
returnop <- escalc(measure="OR", ai=op_return_OR, bi=op_no_return_OR, ci=ip_return_OR, di=ip_no_return_OR, data=ACDF)
#Generate a Random Effects Model
REmodel<-rma(yi=yi, vi=vi, data=returnop, slab=paste(Author, Year, sep=", "), method="REML")
#Generate a forest plot of the data
forest(REmodel, xlim=c(-17, 6),
ilab=cbind(ACDF$op_return_OR, ACDF$op_no_return_OR, ACDF$ip_return_OR, ACDF$ip_no_return_OR),
ilab.xpos=c(-9.5,-8,-6,-4.5), cex=.75, ylim=c(-1, 27),
psize=1)
### add column headings to the plot
text(c(-9.5,-8,-6,-4.5), 26, c("Return+", "Return-", "Return+", "Return-"))
text(c(-8.75,-5.25), 27, c("Outpatient", "Inpatient"))
text(-16, 26, "Study", pos=4)
text(6, 26, "Log Odds Ratio [95% CI]", pos=2)
I'm not 100% as to how to provide my data otherwise, but I used the dput function to provide as follows. Apologies for the N/As, still fleshing out the data for the future.
structure(list(Study = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), Author = c("Stieber", "Villavicencio",
"Lied", "Liu", "Garringer", "Joseffer", "Trahan", "Lied", "Sheperd",
"Talley", "Martin", "McGirt", "Adamson", "Fu", "Arshi", "Khanna",
"McClelland", "Purger", "McLellend2", NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Year = c(2005, 2007,
2007, 2009, 2010, 2010, 2011, 2012, 2012, 2013, 2015, 2015, 2016,
2017, 2017, 2017, 2017, 2017, 2017, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), op_return_OR = c(NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1, 3, 2, 16, 257, 7, NA, 5, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), op_no_return_OR = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
596, 769, 992, 4581, 958, 1749, NA, 3120, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_return_OR = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 8, 9, 2, 257, 2034, 12, NA,
200, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), ip_no_return_OR = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 589, 641, 482, 16171, 8930, 1744, NA, 46312, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), op_death = c(NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, 1, NA,
1, 0, NA, 2, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA), op_no_death = c(NA, NA, NA, 45, NA,
NA, NA, NA, NA, NA, 596, NA, 993, 4597, NA, 1754, NA, 3125, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), ip_death = c(NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, 0, NA,
0, 42, NA, 2, NA, 20, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), ip_no_death = c(NA, NA, NA, 64,
NA, NA, NA, NA, NA, NA, 597, NA, 484, 16386, NA, 1754, NA, 46492,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
2979.79797979798), op_thrombo = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, NA, NA, 8, 20, 4, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_thrombo = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 597, NA, NA, 4589, 1195,
1752, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), ip_thrombo = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 2, NA, NA, 67, 150, 4, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_thrombo = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 595, NA, NA, 16361, 10814,
1752, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), op_stroke = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, NA, NA, 2, 12, 0, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_stroke = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 597, NA, NA, 4595, 1203,
1756, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), ip_stroke = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 2, NA, NA, 14, 132, 0, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_stroke = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 595, NA, NA, 16414, 10832,
1756, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), op_dysphagia = c(NA, NA, NA, 0, NA, NA,
NA, NA, NA, NA, NA, NA, 11, NA, NA, NA, NA, 2, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_dysphagia = c(NA,
NA, NA, 45, NA, NA, NA, NA, NA, NA, NA, NA, 618, NA, NA, NA,
NA, 49, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), ip_dysphagia = c(NA, NA, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, NA, 59, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_dysphagia = c(NA,
NA, NA, 63, NA, NA, NA, NA, NA, NA, NA, NA, 273, NA, NA, NA,
NA, 2917, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), op_hematoma = c(NA, NA, NA, 0, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, 1, 4, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_hematoma = c(NA,
NA, NA, 45, NA, NA, NA, NA, NA, NA, NA, NA, 629, NA, NA, NA,
2015, 47, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), ip_hematoma = c(NA, NA, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, 273, 65, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_hematoma = c(NA,
NA, NA, 63, NA, NA, NA, NA, NA, NA, NA, NA, 273, NA, NA, NA,
7791, 1713, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA)), .Names = c("Study", "Author", "Year", "op_return_OR",
"op_no_return_OR", "ip_return_OR", "ip_no_return_OR", "op_death",
"op_no_death", "ip_death", "ip_no_death", "op_thrombo", "op_no_thrombo",
"ip_thrombo", "ip_no_thrombo", "op_stroke", "op_no_stroke", "ip_stroke",
"ip_no_stroke", "op_dysphagia", "op_no_dysphagia", "ip_dysphagia",
"ip_no_dysphagia", "op_hematoma", "op_no_hematoma", "ip_hematoma",
"ip_no_hematoma"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-35L))

The par option looks ok to me. I changed the ylim option and modified the y location and size of some of the header text as below:
#Generate a forest plot of the data
forest(REmodel, xlim=c(-17, 6),
ylim=c(-1, 10),
ilab=cbind(ACDF$op_return_OR, ACDF$op_no_return_OR, ACDF$ip_return_OR,
ACDF$ip_no_return_OR),
ilab.xpos=c(-9.5,-8,-6,-4.5), cex=.75,
psize=1)
### add column headings to the plot
text(c(-9.5,-8,-6,-4.5), 8.5, c("Return+", "Return-", "Return+", "Return-"),
cex = 0.65)
text(c(-8.75,-5.25), 9.5, c("Outpatient", "Inpatient"))
text(-17, 8.5, "Study", pos=4)
text(6, 8.5, "Log Odds Ratio [95% CI]", pos=2)
This gives the following plot:

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