I have given a
a namend vector col:
col <- c(id = "CLUSTER", x = "LONGNUM", y = "LATNUM", n = "Severely.stunted.child.under.5.years..Total", pos = "Severely.stunted.child.under.5.years.Yes")
#a List of Dataframes with the the Elements of col as Columns and namend after a specific study area. (see deput below) The List Results contains 19 different files (different years)
a list of shapefiles of with 6 Elements (corresponding countries):
study_area <- c("Ethiopia", "Liberia", "Malawi", "Rwanda", "Uganda", "Zimbabwe")
Countries <- lapply(study_area, function(x){gisco_get_countries(country= x, resolution = 60 )})
Countries <- lapply(Countries, function(x) {as_Spatial(x, cast = TRUE, IDs = c("CNTR_NAME", "ISO§_CODE", "CNRT_ID", "NAME_ENGL", "FID"))})
names(Countries) <- study_area
I would like to preform the function from the prevR Library :
s.prevR(Results[[1]], col, Countries[[1]])
But actually for every element in the lists where the names fit:
I tried something like: Map(function(x, y) { as.prevR(x, col, y)}, Results, Countries)
But there it does (obviously) not match by names of x and y
dput( dput(Results[[1]][1:5,1:24])
structure(list(CLUSTER = c("", "1", "10", "100", "101"), Severely.stunted.child.under.5.years.No = c(3438,
8, 7, 9, 6), Severely.stunted.child.under.5.years.Yes = c(1047,
4, NA, 7, 1), Severely.stunted.child.under.5.years..Total = c(4485,
12, 7, 16, 7), Stunted.child.under.5.years.No = c(2531, 2, 7,
7, 5), Stunted.child.under.5.years.Yes = c(1954, 10, NA, 9, 2
), Stunted.child.under.5.years..Total = c(4485, 12, 7, 16, 7),
Severely.wasted.child.under.5.years.No = c(4295, 11, 7, 16,
7), Severely.wasted.child.under.5.years.Yes = c(190, 1, NA,
NA, NA), Severely.wasted.child.under.5.years..Total = c(4485,
12, 7, 16, 7), Wasted.child.under.5.years.No = c(3957, 10,
7, 16, 6), Wasted.child.under.5.years.Yes = c(528, 2, NA,
NA, 1), Wasted.child.under.5.years..Total = c(4485, 12, 7,
16, 7), Severely.underweight.child.under.5.years.No = c(4028,
10, 7, 12, 7), Severely.underweight.child.under.5.years.Yes = c(457,
2, NA, 4, NA), Severely.underweight.child.under.5.years..Total = c(4485,
12, 7, 16, 7), Underweight.child.under.5.years.No = c(3185,
7, 7, 12, 5), Underweight.child.under.5.years.Yes = c(1300,
5, NA, 4, 2), Underweight.child.under.5.years..Total = c(4485,
12, 7, 16, 7), LATNUM = c(NA, 10.889096, 5.323272, 8.830199,
10.806748), LONGNUM = c(NA, 37.269565, 39.556812, 40.72964,
39.7703), SurveyId = c("ET2005DHS", "ET2005DHS", "ET2005DHS",
"ET2005DHS", "ET2005DHS"), DHSC = c("ET", "ET", "ET", "ET",
"ET"), Country = c("Ethiopia", "Ethiopia", "Ethiopia", "Ethiopia",
"Ethiopia")), row.names = c(NA, 5L), class = "data.frame")
and Countries
dput(Countries[[1]])
new("SpatialPolygonsDataFrame", data = structure(list(CNTR_NAME = "Federal Democratic Republic of Ethiopia",
ISO3_CODE = "ETH", CNTR_ID = "ET", NAME_ENGL = "Ethiopia",
FID = "ET"), class = "data.frame", row.names = 1L), polygons = list(
new("Polygons", Polygons = list(new("Polygon", labpt = c(39.6420582930584,
8.63562315843106), area = 93.13026982, hole = FALSE, ringDir = 1L,
coords = structure(c(41.6307, 42.4043, 41.816, 41.8348,
42.9681, 42.7628, 42.9804, 43.9589, 45.6126, 46.9411,
47.8524, 45.6126, 45.4747, 45.2923, 44.9162, 43.4741,
42.8138, 41.9101, 41.2328, 40.708, 39.9305, 39.5667,
38.9731, 38.1026, 36.9621, 35.9477, 35.8294, 35.3235,
35.0325, 34.9588, 34.5428, 33.7557, 33.0448, 33.2485,
33.8204, 34.0937, 34.1132, 34.4181, 34.8021, 35.2153,
35.6227, 36.1342, 36.5603, 37.2972, 37.5268, 37.9201,
38.5391, 39.0217, 40.0851, 40.8941, 41.6307, 13.3913,
12.4686, 11.6292, 11.0448, 10.9974, 10.7159, 10.0644,
9.0545, 8.4674, 8.0224, 7.9151, 5.5657, 5.4241, 5.2367,
4.9368, 4.7993, 4.301, 3.9823, 3.9616, 4.2326, 3.8858,
3.5224, 3.5158, 3.6459, 4.3833, 4.62, 5.2367, 5.413,
5.8494, 6.4537, 6.7418, 7.6074, 7.899, 8.381, 8.4168,
8.6026, 9.4986, 10.6735, 10.8052, 11.9187, 12.5064, 12.8315,
14.2577, 14.3876, 14.2588, 14.8128, 14.4413, 14.5899,
14.5456, 14.0891, 13.3913), dim = c(51L, 2L)))), plotOrder = 1L,
labpt = c(39.6420582930584, 8.63562315843106), ID = "1",
area = 93.13026982)), plotOrder = 1L, bbox = structure(c(33.0448,
3.5158, 47.8524, 14.8128), dim = c(2L, 2L), dimnames = list(c("x",
"y"), c("min", "max"))), proj4string = new("CRS", projargs = "+proj=longlat +datum=WGS84 +no_defs"))
If the Countries names are all in the Results names and if 'Results' have duplicates for names, then we can make the Countries to have the same length by replicating based on the names of the 'Results'
Map(function(x, y) { as.prevR(x, col, y)}, Results, Countries[names(Results)])
Related
I want to give names to individual starplots in R
stars(norm_datas[, 1:12], full = TRUE,radius = TRUE,len = 1.0, key.loc = c(14,1), labels = abbreviate(case.names(norm_datas)),main = "Provision of Ecosystem services", draw.segments = TRUE, lwd = 0.25, lty = par("lty"), xpd = TRUE).
This is what I tried but it just labeled each star plot as 1, 2, 3.
Kindly help resolve.
structure(list(Type_Garden = c("AG", "AG", "AG"), Pollinators = c(10,
6, 5.5), Flower_abundance = c(384, 435, 499), Climate_regulation = c(1,
7, 2), Crop_area = c(34, 25, 10), Plant_diversity = c(22, 53,
41), Nitrogen_balance = c(0.95, 0.26, NA), Phosphorus_balance = c(0.24,
0.04, NA), Habitat_provision = c(1, 2, 0), Recreation_covid = c(1,
NA, NA), Aesthetic_appreciation = c(3, NA, NA), Reconnection_nature = c(4,
NA, NA), Mental_health = c(1, NA, NA), Physical_health = c(1,
NA, NA)), class = "data.frame", row.names = c(NA, -3L))
I merged nine plots together and I would like to group them based on different characteristics (A,B,C). Is there a simple way to add labels or annotations at the bottom of plots? When using cowplot or GridExtra i receive the following error:
In as_grob.default(plot) :
Cannot convert object of class list into a grob.
Sample data
list(list(stats = structure(c(43, 96.5, 297.5, 707.5, 778), .Dim = c(5L,
1L)), n = 36, conf = structure(c(136.603333333333, 458.396666666667
), .Dim = 2:1), out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(2, 10.5, 55.5, 102, 128), .Dim = c(5L,
1L)), n = 36, conf = structure(c(31.405, 79.595), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(1, 3, 5.5, 77, 88), .Dim = c(5L,
1L)), n = 36, conf = structure(c(-13.9866666666667, 24.9866666666667
), .Dim = 2:1), out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(531, 632.5, 701, 726.5, 786), .Dim = c(5L,
1L)), n = 36, conf = structure(c(676.246666666667, 725.753333333333
), .Dim = 2:1), out = c(485, 464, 446), group = c(1, 1, 1
), names = ""), list(stats = structure(c(104,
109.5, 113.5, 121, 125), .Dim = c(5L, 1L)), n = 36, conf = structure(c(110.471666666667,
116.528333333333), .Dim = 2:1), out = c(91, 91, 88, 84, 84,
79), group = c(1, 1, 1, 1, 1, 1), names = ""),
list(stats = structure(c(28, 53.5, 83.5, 88, 91), .Dim = c(5L,
1L)), n = 36, conf = structure(c(74.415, 92.585), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(80, 89, 102.5, 153, 236), .Dim = c(5L,
1L)), n = 36, conf = structure(c(85.6466666666667, 119.353333333333
), .Dim = 2:1), out = c(343, 318, 299, 257), group = c(1,
1, 1, 1), names = """"), list(stats = structure(c(7,
12, 22.5, 44, 72), .Dim = c(5L, 1L)), n = 36, conf = structure(c(14.0733333333333,
30.9266666666667), .Dim = 2:1), out = numeric(0), group = numeric(0),
names = ""), list(stats = structure(c(5,
5, 6, 12.5, 21), .Dim = c(5L, 1L)), n = 36, conf = structure(c(4.025,
7.975), .Dim = 2:1), out = numeric(0), group = numeric(0),
names = ""))
Many thanks
I agree with the idea of using ggplot2 graphics with facets, but given your plot objects, you could do something like this (to get you started). I used ggplotify instead of cowplot because I ran into trouble with the figure margins, but you might be able to fix that by changing the null device (not tested).
Edit:
Added individual labels and y axis labels, as well as outer margins. You might have to adjust some of that depending on the output size of your composite plot. This may show you how you could adjust those settings for individual plots. Still, using ggplot2 to generate the plots would make things quite a bit easier.
library(grid)
library(gridExtra)
library(ggplotify)
sdt <- list(list(stats = structure(c(43, 96.5, 297.5, 707.5, 778), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(136.603333333333, 458.396666666667), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(2, 10.5, 55.5, 102, 128), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(31.405, 79.595), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(1, 3, 5.5, 77, 88), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(-13.9866666666667, 24.9866666666667), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(531, 632.5, 701, 726.5, 786), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(676.246666666667, 725.753333333333), .Dim = 2:1),
out = c(485, 464, 446), group = c(1, 1, 1), names = ""),
list(stats = structure(c(104, 109.5, 113.5, 121, 125), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(110.471666666667, 116.528333333333), .Dim = 2:1),
out = c(91, 91, 88, 84, 84, 79), group = c(1, 1, 1, 1, 1, 1), names = ""),
list(stats = structure(c(28, 53.5, 83.5, 88, 91), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(74.415, 92.585), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(80, 89, 102.5, 153, 236), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(85.6466666666667, 119.353333333333), .Dim = 2:1),
out = c(343, 318, 299, 257), group = c(1,1, 1, 1), names = ""),
list(stats = structure(c(7, 12, 22.5, 44, 72), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(14.0733333333333, 30.9266666666667), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""),
list(stats = structure(c(5, 5, 6, 12.5, 21), .Dim = c(5L, 1L)),
n = 36, conf = structure(c(4.025, 7.975), .Dim = 2:1),
out = numeric(0), group = numeric(0), names = ""))
sublabels <- paste0(rep(LETTERS[1:3], each=3), 1:3)
gplts <- lapply(1:9, function(x) as.grob(function(y=sdt[[x]]) {
par(oma=c(0,3,0,3))
bxp(y, ylab="values", main=sublabels[x])}))
grid.arrange(rectGrob(gp=gpar(col="red")), rectGrob(gp=gpar(col="green")),
rectGrob(gp=gpar(col="yellow")), nrow=1, newpage =T)
vp <- viewport(.33/2,0.45, gp = gpar(col="red"))
grid.text("Group A",
y = .1, just = c("center", "bottom"),
gp = gpar(fontsize=20), vp = vp)
vp <- viewport(.5,.45, gp = gpar(col="green"))
grid.text("Group B",
y = .1, just = c("center", "bottom"),
gp = gpar(fontsize=20), vp = vp)
vp <- viewport(1-(.33/2),.45, gp = gpar(col="yellow"))
grid.text("Group C",
y = .1, just = c("center", "bottom"),
gp = gpar(fontsize=20), vp = vp)
grid.arrange(grobs=gplts, nrow=1, newpage=F)
Created on 2021-03-25 by the reprex package (v1.0.0)
Consider an example dataset where I have four polygons:
I want the colour of the polygon to be based on which group it is in, a legend showing what each colour means and a label over each polygon with it's house number.
The following line of code differentiates the colour by group:
plot(df$geometry, col=df$group)
Reproducible data from example above:
structure(list(shape = c("polygon 1", "polygon 2", "polygon 3",
"polygon 4"), geometry = structure(list(structure(list(structure(c(0,
1, 1, 0, 0, 1, 1, 0, 0, 1), .Dim = c(5L, 2L))), class = c("XY",
"POLYGON", "sfg"), precision = 0, bbox = structure(c(xmin = 0,
ymin = 0, xmax = 1, ymax = 1), class = "bbox"), crs = structure(list(
input = NA_character_, wkt = NA_character_), class = "crs"), n_empty =
0L),
structure(list(structure(c(2, 3, 3, 2, 2, 3, 3, 2, 2, 3), .Dim = c(5L,
2L))), class = c("XY", "POLYGON", "sfg"), precision = 0, bbox =
structure(c(xmin = 2,
ymin = 2, xmax = 3, ymax = 3), class = "bbox"), crs = structure(list(
input = NA_character_, wkt = NA_character_), class = "crs"),
n_empty = 0L),
structure(list(structure(c(4, 5, 5, 4, 4, 4, 4, 5, 5, 4), .Dim = c(5L,
2L))), class = c("XY", "POLYGON", "sfg"), precision = 0, bbox =
structure(c(xmin = 4,
ymin = 4, xmax = 5, ymax = 5), class = "bbox"), crs = structure(list(
input = NA_character_, wkt = NA_character_), class = "crs"),
n_empty = 0L),
structure(list(structure(c(6, 7, 7, 6, 6, 7, 7, 6, 6, 7), .Dim = c(5L,
2L))), class = c("XY", "POLYGON", "sfg"), precision = 0, bbox =
structure(c(xmin = 6,
ymin = 6, xmax = 7, ymax = 7), class = "bbox"), crs = structure(list(
input = NA_character_, wkt = NA_character_), class = "crs"),
n_empty = 0L)), precision = 0, bbox = structure(c(xmin = 0,
ymin = 0, xmax = 7, ymax = 7), class = "bbox"), crs = structure(list(
input = NA_character_, wkt = NA_character_), class = "crs"), n_empty =
0L, class = c("sfc_POLYGON",
"sfc")), group = c(1, 1, 2, 2), house = c(1, 2, 3, 4)), row.names =
c(NA,
4L), class = c("sf", "tbl_df", "tbl", "data.frame"), sf_column =
"geometry", agr = structure(c(shape = NA_integer_,
group = NA_integer_, house = NA_integer_), class = "factor", .Label =
c("constant",
"aggregate", "identity")))
I couldn't plot it using the basic plot(). However, I think I achieved your goal using the ggplot package in a much more readable way, as follows:
# setup environment
library(ggplot2)
# define a data frame with x and y coordinates and their groups
df = data.frame(
group = rep(1:2, each = 8),
house = rep(1:4, each = 4),
x = c(0, 1, 1, 0,
2, 3, 3, 2,
4, 5, 5, 4,
6, 7, 7, 6),
y = c(0, 0, 1, 1,
2, 2, 3, 3,
4, 4, 5, 5,
6, 6, 7, 7),
)
# create a data frame with text labels and their coordinates
lbl = df %>%
group_by(house) %>%
summarise(x = mean(x), y = max(y) + 0.5) %>%
unique()
# plot the houses using ggplot package
ggplot(df, aes(x, y)) +
geom_polygon(aes(fill = group, group = house), colour = 'black') +
labs(title = 'House Geometries', x = 'X', y = 'Y') +
scale_fill_manual(name = 'House', values = c('black', 'red')) +
annotate('text', x = lbl$x, y = lbl$y, label = lbl$house, size = 6) +
theme(plot.title = element_text(size = 19, face = 'bold', hjust = 0.5),
legend.title = element_text(size = 15, hjust = 0.5),
legend.text = element_text(size = 14, hjust = 0.5),
axis.title.x = element_text(size = 15),
axis.text.x = element_text(size = 14),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size = 14))
Here is the final plot:
By the way, I work with civil engineering and your problem was quite interesting to me...
Let me know if this is what you were planning to do.
I have a data frame of survey question responses. I would like to estimate Cohen's d effect sizes for each response using cohen.d from effsize.
Here are the first 6 rows of my data frame:
structure(list(id = c("HO1001", "HO1001", "HO1002", "HO1002",
"HO1003", "HO1003"), time = structure(c(1L, 2L, 1L, 2L, 1L, 2L
), .Label = c("0", "1"), class = "factor"), grit.distract = c(1,
1, 3, 2, 1, 2), grit.setback = c(5, 4, 3, 3, 4, 4), grit.obsess = c(3,
2, 2, 2, 3, 2), grit.work = c(4, 5, 3, 4, 5, 5), grit.goal = c(2,
3, 2, 1, 4, 4), grit.focus = c(3, 3, 3, 1, 2, 3), grit.finish = c(4,
4, 4, 4, 4, 3), grit.diligent = c(4, 4, 3, 4, 5, 4), grit.mean = c(3.25,
3.25, 2.875, 2.625, 3.5, 3.375)), class = c("tbl_df", "tbl",
"data.frame"), row.names = c(NA, -6L))
I successfully converted the df into wide format to use effsize on the summary statistics i.e. mean/total as follows:
structure(list(id = c("HO1001", "HO1002", "HO1003", "HO1004",
"HO1005", "HO1006"), pre = c(3.25, 2.875, 3.5, 2.25, NA, NA),
post = c(3.25, 2.625, 3.375, 2.5, 2.75, 2.875), change = c(0,
-0.25, -0.125, 0.25, NA, NA), highconf = structure(c(2L,
1L, 2L, 1L, NA, NA), .Label = c("0", "1"), class = "factor")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -6L)
cohen.d(grit.tot$pre, grit.tot$post, na.rm = T)
What I would like to do is calculate the effect sizes for each survey item i.e. grit distract, grit.setback, etc. between time 0 and time 1 (please no comments on my statistical methods). Given that I have plenty more data frames like this and don't want to do them all individually, I believe that I should use a function and a loop such as apply but I'm not sure how to construct this.
If I have understood your question this may help.
If your data frame from the first part of your questions is stored as dt running the following should give the cohen d for each survey item.
lapply(dt[c(-1,-2)],function(x) cohen.d(x ~ dt$time))
dt[c(-1,-2)] removes the ID column and the time column as you don't want to run the cohen d test on these.
I want to display a scatter plot of points from a csv table with ggplot2. The trick is that I'd like each point, or cross, to have a different colour according to their row number in the csv file (using RColorBrewer's spectral colours).
The dataset (dat) looks like this:
modu mnc eff
1 0.3080473 0 0.4420544
2 0.3110355 4 0.4633741
3 0.3334024 9 0.4653061
So I'd like row 1 to be very blue, row two to be a little less, row three to be kind of green, etc.
Here's my code so far:
library(ggplot2)
library(RColorBrewer)
dat <- structure(list(modu = c(0.30947265625, 0.3094921875, 0.32958984375,
0.33974609375, 0.33767578125, 0.3243359375, 0.33513671875, 0.3076171875,
0.3203125, 0.3205078125, 0.3220703125, 0.28994140625, 0.31181640625,
0.352421875, 0.31978515625, 0.29642578125, 0.34982421875, 0.3289453125,
0.30802734375, 0.31185546875, 0.3472265625, 0.303828125, 0.32279296875,
0.3165234375, 0.311328125, 0.33640625, 0.3140234375, 0.33515625,
0.34314453125, 0.33869140625), mnc = c(15, 9, 6, 0, 10, 12, 14,
9, 5, 11, 0, 15, 0, 2, 14, 13, 14, 17, 11, 12, 13, 6, 4, 0, 13,
7, 10, 12, 7, 13), eff = c(0.492448979591836, 0.49687074829932,
0.49421768707483, 0.478571428571428, 0.493537414965986, 0.493809523809524,
0.49891156462585, 0.499319727891156, 0.495102040816327, 0.492285714285714,
0.482312925170068, 0.498911564625851, 0.479931972789116, 0.492857142857143,
0.495238095238095, 0.49891156462585, 0.49530612244898, 0.495850340136055,
0.50156462585034, 0.496, 0.492897959183673, 0.487959183673469,
0.495605442176871, 0.47795918367347, 0.501360544217687, 0.497850340136054,
0.493496598639456, 0.493741496598639, 0.496734693877551, 0.499659863945578
)), .Names = c("modu", "mnc", "eff"), row.names = c(NA, 30L), class = "data.frame")
dat2 <- structure(list(modu = c(0.26541015625, 0.282734375, 0.28541015625,
0.29216796875, 0.293671875), mnc = c(0.16, 0.28, 0.28, 0.28,
0.28), eff = c(0.503877551020408, 0.504149659863946, 0.504625850340136,
0.505714285714286, 0.508503401360544)), .Names = c("modu", "mnc",
"eff"), row.names = c(NA, 5L), class = "data.frame")
dat$modu = dat$modu
dat$mnc = dat$mnc*50
dat$eff = dat$eff
dat2$modu = dat2$modu
dat2$mnc = dat2$mnc*50
dat2$eff = dat2$eff
res <- do.call(rbind, combn(1:3, 2, function(ii)
cbind(setNames(dat[,c(ii, setdiff(1:3, ii))], c("x", "y")),
var=paste(names(dat)[ii], collapse="/")), simplify=F))
ggplot(res, aes(x=x, y=y))+ geom_point(shape=4) +
facet_wrap(~ var, scales="free")
How should I go about doing this?
Thanks!
res <- do.call(rbind, combn(1:3, 2, function(ii)
cbind(row=seq(nrow(dat)),setNames(dat[,c(ii, setdiff(1:3, ii))], c("x", "y")),
var=paste(names(dat)[ii], collapse="/")), simplify=F))
ggplot(res, aes(x=x, y=y, color=row))+ geom_point(shape=4) +
scale_color_gradientn(colours=rev(brewer.pal(10,"Spectral")))+
facet_wrap(~ var, scales="free")