To animate stroke-dashoffset I am aware of using CSS #keyframes to move the stroke-dashoffset of a SVG path. However, because I want to size the SVG with background-size: cover, I am unable to target the individual elements inside the SVG since it's being referenced as a background-image in CSS.
Is there a way to use SVG's built-in <animate /> tags to animate stroke-dashoffset?
Lion head animation example
Animation of drawing lines from zero to maximum value is implemented by changing the stroke-dashoffset from maximum to zero.
attributeName="stroke-dashoffset"
begin="0.1s;f1.end+0.4s"
values="2037;0;2037"
dur="15s"
calcMode="linear"
/>
A second animation has been added - filling with a color that starts after the animation of drawing lines is completed.
<animate id="f1"
attributeName="fill"
begin="p1.end+0.4s"
dur="10s"
values="#FCFCFC;#A9A9A9;black;#A9A9A9;#FCFCFC"
/>
Drawing and erasing lines is accomplished using the attribute:
values="2037;0;2037"
.txt {
font-size:1.2em;
color:gray;
}
h1 {
text-align: center;
}
.lion {
padding:0.25em;
margin-left:-1.5em;
float:left;
}
<body>
<h1>Lion</h1>
<div class="lion">
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"
width="200" height="200" viewBox="-30 85 600 600"
style="border:0px dotted red;">
<title>The animation is drawing lines</title>
<g transform="scale(0.85) ">
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19.9-22.6 3.3-6.1 4.9-12.5 6.5-19 1.4-5.7 2-8.5 2.9-17.4 1.2-11.6 1.3-15.6 0.6-26-1.1-16.1-1-14.3-1.9-21.4-1.3-10-2.5-20.1-4.9-30-2-8.6-4.1-16.9-7.8-25.3-0.3-0.8-0.8-1.5-1.1-1.5zm-44.1 5.9c-13.8 0-14.7 0.1-15.2 0.9-0.7 1 0.2 15.1 0.5 22.7 0.3 8 0.9 16.1 1.4 24.1 0.6 8.7 1.2 17.3 2.2 26 0.8 7.4 1.4 14.8 3.1 22 1.5 6.7 3.8 13.2 6.3 19.6 1.2 3.1 3.3 8.3 4.3 9.1 1-0.9 3.1-6 4.3-9.1 2.5-6.4 4.8-12.9 6.3-19.6 1.7-7.2 2.3-14.6 3.1-22 1-8.6 1.5-17.3 2.2-26 0.6-8 1.1-16.1 1.4-24.1 0.3-7.6 1.2-21.7 0.5-22.7-0.5-0.8-1.4-0.8-15.2-0.9-1.3 0.1-2 0.1-2.6 0.1-0.7 0-1.3 0-2.6-0.1z">
<animate id="p1"
attributeName="stroke-dashoffset"
begin="0.1s;f1.end+0.4s"
values="2037;0;2037"
dur="15s"
calcMode="linear"
/>
<animate id="f1"
attributeName="fill"
begin="p1.end+0.4s"
dur="10s"
values="#FCFCFC;#A9A9A9;black;#A9A9A9;#FCFCFC"
/>
</path>
</g>
</svg>
</div>
<div class="txt">
<p> The lion (Panthera leo) is a species in the family Felidae; it is a muscular, deep-chested cat with a short, rounded head, a reduced neck and round ears, and a hairy tuft at the end of its tail. The lion is sexually dimorphic; males are larger than females with a typical weight range of 150 to 250 kg (330 to 550 lb) for males and 120 to 182 kg (265 to 400 lb) for females. Male lions have a prominent mane, which is the most recognisable feature of the species. A lion pride consists of a few adult males, related females and cubs. Groups of female lions typically hunt together, preying mostly on large ungulates. The species is an apex and keystone predator, although they scavenge when opportunities occur. Some lions have been known to hunt humans, although the species typically does not.</p>
<p>Typically, the lion inhabits grasslands and savannas but is absent in dense forests. It is usually more diurnal than other big cats, but when persecuted it adapts to being active at night and at twilight. In the Pleistocene, the lion ranged throughout Eurasia, Africa and North America but today it has been reduced to fragmented populations in Sub-Saharan Africa and one critically endangered population in western India. It has been listed as Vulnerable on the IUCN Red List since 1996 because populations in African countries have declined by about 43% since the early 1990s. Lion populations are untenable outside designated protected areas. Although the cause of the decline is not fully understood, habitat loss and conflicts with humans are the greatest causes for concern.</p>
<p>One of the most widely recognised animal symbols in human culture, the lion has been extensively depicted in sculptures and paintings, on national flags, and in contemporary films and literature. Lions have been kept in menageries since the time of the Roman Empire and have been a key species sought for exhibition in zoological gardens across the world since the late 18th century. Cultural depictions of lions were prominent in the Upper Paleolithic period; carvings and paintings from the Lascaux and Chauvet Caves in France have been dated to 17,000 years ago, and depictions have occurred in virtually all ancient and medieval cultures that coincided with the lion's former and current ranges.is not fully understood, habitat loss and conflicts with humans are the greatest causes for concern. </p>
wikipedia.org
</div>
</body>
I found the answer. You insert the <animate /> tag within the path.
<path stroke-dashoffset="200" stroke-dasharray="200 30" stroke-width="2" stroke="#333" d="...">
<animate attributeName="stroke-dashoffset" values="0 2000" dur="5s" repeatCount="indefinite" />
</path>
Related
Truck location coordinates>
X[Now] Y[Now]
A 5.4 15.4
B 8.3 9.0
C 6.6 5.2
D 6.5 13.5
E 15.0 1.9
Load location coordinates> print(Bcd)
Pick-up-X Pick-up-Y Drop-off-X Drop-off-Y
1 18.3 0.5 4.0 13.9
2 11.1 0.1 17.1 18.9
3 20.0 8.9 18.4 7.4
4 4.4 18.2 8.6 15.0
5 12.7 2.9 4.0 0.7
6 5.2 10.7 16.9 18.9
7 18.5 19.0 4.8 9.5
8 8.2 17.3 0.6 4.6
9 11.5 0.5 3.4 11.4
10 2.1 11.3 11.4 0.1
I have two tables with the same rows, but they each have different columns. Is it possible to merge them together? I am using the kabbleExtra package to output tables.
18-24 25-34 35-44 45-54 55-64 65-74 75+ Total
Democrat 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3
Republican 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0
Other 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7
Total 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0
White Latino Asian African-American Other Total
Democrat 25.2 22.4 10.0 2.2 5.2 65.1
Republican 14.4 7.2 2.8 0.4 2.0 26.8
Other 2.5 4.1 0.9 0.0 0.6 8.1
Total 42.2 33.7 13.7 2.6 7.8 100.0
The expected output should look like this:
Ethnicity Age
White Latino Asian African-American Other Total 18-24 25-34 35-44 45-54 55-64 65-74 75+ Total
Democrat 25.2 22.4 10.0 2.2 5.2 65.1 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3
Republican 14.4 7.2 2.8 0.4 2.0 26.8 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0
Other 2.5 4.1 0.9 0.0 0.6 8.1 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7
Total 42.2 33.7 13.7 2.6 7.8 100.0 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0
We can merge the tables using dplyr:
> df1 %>% rownames_to_column('party') %>%
+ inner_join(df2 %>% rownames_to_column('party'), by = 'party') %>% column_to_rownames('party')
18-24 25-34 35-44 45-54 55-64 65-74 75+ Total.x White Latino Asian African-American Other Total.y
Democrat 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3 25.2 22.4 10.0 2.2 5.2 65.1
Republican 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0 14.4 7.2 2.8 0.4 2.0 26.8
Other 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7 2.5 4.1 0.9 0.0 0.6 8.1
Total 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0 42.2 33.7 13.7 2.6 7.8 100.0
Data used:
> df1
18-24 25-34 35-44 45-54 55-64 65-74 75+ Total
Democrat 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3
Republican 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0
Other 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7
Total 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0
> df2
White Latino Asian African-American Other Total
Democrat 25.2 22.4 10.0 2.2 5.2 65.1
Republican 14.4 7.2 2.8 0.4 2.0 26.8
Other 2.5 4.1 0.9 0.0 0.6 8.1
Total 42.2 33.7 13.7 2.6 7.8 100.0
>
I have two lists of different sizes. One list (named * trees * ) is composed of phylogenetic trees (class phylo) and the second list (named * data_values*) is composed of numeric values.
The tips names of each phylogenetic tree of the list * tree* match with the names of each element inside of the list of values. But the list data_values is composed of a greater number of elements than the tips of each tree.
library(phytools)
library(ape)
#original tree:
tree_original = rtree(12, tip.label = paste0("species", LETTERS[1:12]))
##list of trees:
nodes = 14:23
trees = lapply(nodes,extract.clade,phy=tree_orignal)
names(trees) <- paste0("", 14:23)
data_values <- list()
for (i in 1:17) { data_values[[paste0('species', LETTERS[i])]] <- round(rnorm(10, 5, 4), 1) }
I would like to match both lists (trees and data_values) using species as an index to have a data frame for each tree (see example below). I can do this operation for each tree of the list trees individually but, as my list of species is much bigger than this example, I would like to know if I can do this operation (below) for the all list of trees and not run tree by tree, like this:
tree14 = data_values[match(trees$`14`$tip.label, names(data_values))]
tree14 = llply(tree14, function(x) sapply(x, as.numeric))
tree14_df = ldply(tree14, .fun=identity) **I will need each result as a data.frame**
.id 1 2 3 4 5 6 7 8 9 10
1 speciesE -0.5 3.4 2.0 5.3 3.7 8.2 3.5 -2.0 3.1 10.2
2 speciesL 6.8 4.3 7.1 5.5 4.9 2.5 0.3 -3.8 4.1 6.4
3 speciesA 2.5 2.5 9.6 10.6 2.2 7.1 4.1 4.4 6.0 6.7
4 speciesI -3.5 7.2 6.8 2.8 7.5 8.9 13.4 13.1 1.8 5.5
5 speciesC 4.3 2.2 10.0 7.4 4.4 8.3 -0.7 3.6 9.2 6.3
6 speciesH 6.3 6.1 2.2 4.6 7.4 7.3 2.9 0.6 3.0 5.2
7 speciesB 8.3 1.7 -0.1 4.5 9.4 -0.2 7.5 1.4 -0.3 4.6
8 speciesD 6.2 5.8 6.6 1.1 5.4 11.1 -1.1 0.0 7.9 0.4
9 speciesG 3.5 2.8 1.4 11.6 -2.8 11.0 3.5 2.8 3.1 4.8
10 speciesK 0.9 4.9 5.4 2.7 -0.7 5.1 18.3 4.9 2.5 -0.7
tree15 = data_values[match(trees$`15`$tip.label, names(data_values))]
tree15 = llply(tree15, function(x) sapply(x, as.numeric))
tree15_df = ldply(tree15, .fun=identity)
.id 1 2 3 4 5 6 7 8 9 10
1 speciesE -0.5 3.4 2.0 5.3 3.7 8.2 3.5 -2.0 3.1 10.2
2 speciesL 6.8 4.3 7.1 5.5 4.9 2.5 0.3 -3.8 4.1 6.4
3 speciesA 2.5 2.5 9.6 10.6 2.2 7.1 4.1 4.4 6.0 6.7
4 speciesI -3.5 7.2 6.8 2.8 7.5 8.9 13.4 13.1 1.8 5.5
5 speciesC 4.3 2.2 10.0 7.4 4.4 8.3 -0.7 3.6 9.2 6.3
6 speciesH 6.3 6.1 2.2 4.6 7.4 7.3 2.9 0.6 3.0 5.2
7 speciesB 8.3 1.7 -0.1 4.5 9.4 -0.2 7.5 1.4 -0.3 4.6
... this operation goes until tree23
This has already been answered at this link (subset by at least two out of multiple conditions), however, I have an additional query to this. Following is my dataframe (df)
a b c d1 d2 e z
3.2 0.6 5.8 143.7 95.0 2.9 2
3.3 1.3 5.3 137.3 73.3 1.0 1
2.8 1.3 5.6 135.3 79.3 1.8 2
2.9 1.4 5.3 137.7 82.0 1.9 2
4.7 1.8 5.5 143.0 86.5 1.5 1
3.2 1.4 5.8 125.3 79.0 1.5 2
2.6 1.8 5.8 137.3 79.0 1.0 1
3.4 1.4 5.1 132.0 72.3 1.0 1
3.5 1.8 5.0 130.7 75.7 2.0 2
2.1 1.2 4.6 108.3 70.7 1.5 2
3.8 1.7 5.1 133.5 79.8 1.8 2
3.3 1.3 5.1 121.7 79.7 1.5 2
5.2 1.5 5.2 144.7 88.3 1.5 2
4.8 1.2 5.3 127.7 78.0 1.8 2
2.8 0.6 5.4 116.7 61.7 2.0 2
3.7 1.4 4.7 101.0 63.3 1.6 2
2.9 1.4 5.0 121.3 76.3 1.5 2
2.2 1.5 5.3 144.3 83.7 1.6 2
4.4 0.8 5.1 140.0 84.7 1.4 2
5.0 2.4 5.5 124.3 83.0 1.6 2
1.9 0.9 5.4 143.0 79.7 1.1 1
4.5 1.7 5.8 143.7 91.7 0.9 1
3.3 0.7 5.1 127.3 69.3 2.2 2
3.4 1.3 5.6 161.0 87.7 1.7 2
4.5 1.8 6.1 139.7 75.3 1.2 1
3.9 0.8 5.2 99.3 61.0 1.2 2
2.6 2.4 4.8 127.0 79.3 1.8 2
3.4 0.9 5.3 130.0 79.0 1.0 1
2.7 0.4 4.8 135.0 83.7 1.0 2
2.9 1.9 4.7 132.7 90.3 1.5 2
3.9 1.1 6.5 126.3 68.0 1.3 2
3.1 0.9 5.9 152.0 98.3 1.3 1
4.6 1.7 6.0 144.0 96.3 1.5 1
4.1 4.8 5.1 132.7 70.3 0.8 1
5.9 1.2 5.6 130.3 79.0 1.4 2
3.9 2.9 5.3 128.0 76.3 0.7 1
3.2 1.3 5.9 151.7 88.7 1.4 2
3.7 4.0 6.4 133.0 82.7 1.2 2
3.1 1.4 6.6 124.7 76.0 1.0 1
2.9 0.6 5.4 121.0 74.0 2.1 2
3.4 4.1 5.1 137.3 69.0 0.8 1
3.4 2.7 4.9 136.3 78.3 1.4 1
4.0 0.9 4.8 123.0 71.0 2.1 2
2.5 0.8 4.5 175.3 107.8 1.7 1
5.0 2.2 5.2 151.7 78.7 1.3 1
3.9 6.4 5.6 128.7 85.3 0.6 1
3.4 1.5 5.7 131.0 81.0 1.5 1
3.7 0.9 5.3 104.7 67.0 0.9 2
2.3 1.8 5.8 126.3 78.7 1.0 1
5.0 1.3 5.5 134.7 85.7 1.2 1
3.2 1.9 6.1 130.7 77.7 0.9 2
3.8 1.8 5.8 123.0 75.0 1.4 1
3.6 2.1 5.0 135.3 87.0 1.3 1
3.7 3.5 6.0 145.8 80.3 1.4 1
3.2 0.6 4.7 114.0 71.0 1.9 2
3.9 1.5 5.3 129.7 87.0 1.2 1
4.3 1.4 4.9 105.0 67.7 1.2 2
4.2 2.7 6.3 122.0 76.7 1.2 2
4.8 2.9 5.6 131.0 76.3 1.1 1
2.5 2.2 5.4 115.3 70.7 1.3 1
2.5 1.4 5.1 148.3 93.3 2.4 2
3.7 0.8 4.7 117.3 77.7 1.2 2
4.0 2.7 6.2 127.3 79.3 1.1 2
2.6 1.2 5.6 155.3 109.7 1.5 1
3.3 2.1 5.1 118.7 72.3 1.4 2
4.2 0.8 5.4 126.0 73.7 2.0 2
4.0 1.6 5.3 153.0 86.7 1.4 2
3.8 1.2 6.7 154.3 84.0 1.6 2
3.2 1.8 5.4 168.7 87.7 1.2 1
3.2 1.3 5.2 135.0 74.3 1.2 1
3.5 1.2 5.9 138.3 75.3 1.4 1
3.6 1.4 5.1 126.7 81.0 1.1 2
3.3 1.7 6.4 152.3 87.7 1.5 1
2.6 0.7 5.6 134.3 74.7 2.2 2
4.1 1.8 5.8 154.8 83.0 1.7 2
2.5 1.0 4.6 147.7 93.0 1.2 1
4.0 1.7 5.9 132.3 80.7 1.3 1
3.2 1.5 6.1 144.3 85.0 1.3 1
2.8 1.6 4.7 115.3 81.0 1.4 2
3.4 1.0 6.0 130.8 80.3 1.2 1
2.9 1.3 5.5 132.7 82.3 1.5 2
4.0 1.9 5.9 114.0 67.7 1.7 2
4.1 1.3 5.3 129.7 77.0 1.4 2
1.9 1.1 6.1 124.3 58.0 1.5 2
3.0 1.2 5.0 129.3 81.7 1.6 2
4.1 0.9 5.0 129.7 80.3 1.5 1
3.2 2.8 5.5 127.3 72.8 1.0 1
3.2 1.0 4.6 135.7 80.0 2.8 2
3.0 1.7 5.7 154.3 88.3 1.4 2
3.2 3.1 6.2 129.3 76.7 1.2 1
I want to subset this in such a way that at least 2 of the following 5 conditions are met:
a >= 4.11
b >= 2.26
c >= 5.6
d1 <= 140 and/or d2 <= 90 (considering both these variables d1 and/or d2 as one condition)
e <= 1.03 mmol/L (when z == 1) and e <= 1.29 mmol/L (when z == 2)
I understand how to add the first 3 in the following code, but can anyone help me with how can I add the last 2 conditions as well?
df_new <- df[rowSums(cbind(df$a >= 4.11, df$b >= 2.26, df$c >= 5.6)) > 1,]
Thanks in advance.
By combining filter with filter_if from the dplyr package you can filter (subset) your data based on your conditions
library(dplyr)
as_data_frame(df) -> df
# commas represent AND statements
df %>%
filter(
a >= 4.11,
b >= 2.26,
c >= 5.6,
d1 <=140,
d2 <= 90
) %>%
filter_if(
z == 1 & e <= 1.29, e <=1.03 # conditional filering
)->df_new
I was expecting points but got this when I did
plot(data$v3,data$v2)
my data
V2 V3
2 -2.0 2.7
3 0.5 3.9
4 1.3 4.5
5 5.7 6.0
6 10.4 8.7
7 3.4 2.7
8 7.6 3.2
9 4.1 5.6
10 5.0 9.2
11 8.5 11.7
12 12.3 6.8
13 16.1 13.0
14 13.2 11.9
15 8.8 8.6
16 7.9 6.1
17 1.1 4.9
18 3.0 1.0
19 4.5 7.2
20 2.7 2.7
21 7.6 7.6
I tried searching but from my understanding the function is supposed to give points, not bars. How do I fix this?