Could someone help me in assigning a manual colour to each group? I want to use these two colours for my data #F96167 for beer, and #FCE77D for Whisky.
Also, in the last graph, I would like different plot symbols (e.g. ∆ for Beer, O for Whisky) for each group.
library(rgeos)
#> Loading required package: sp
#> rgeos version: 0.5-9, (SVN revision 684)
#> GEOS runtime version: 3.9.1-CAPI-1.14.2
#> Please note that rgeos will be retired by the end of 2023,
#> plan transition to sf functions using GEOS at your earliest convenience.
#> GEOS using OverlayNG
#> Linking to sp version: 1.5-0
#> Polygon checking: TRUE
library(sp)
library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.6-2
library(tidyverse)
library(Momocs)
#>
#> Attaching package: 'Momocs'
#> The following objects are masked from 'package:dplyr':
#>
#> arrange, combine, filter, mutate, rename, sample_frac, sample_n,
#> select, slice
#> The following object is masked from 'package:tidyr':
#>
#> chop
#> The following object is masked from 'package:stats':
#>
#> filter
library(caret)
#>
#> Attaching package: 'caret'
#> The following object is masked from 'package:purrr':
#>
#> lift
#> The following object is masked from 'package:vegan':
#>
#> tolerance
library(doParallel)
#> Loading required package: foreach
#>
#> Attaching package: 'foreach'
#> The following objects are masked from 'package:purrr':
#>
#> accumulate, when
#> Loading required package: iterators
#> Loading required package: parallel
library(xlsx)
library(foreach)
library(broom)
library(MASS)
#>
#> Attaching package: 'MASS'
#> The following object is masked from 'package:Momocs':
#>
#> select
#> The following object is masked from 'package:dplyr':
#>
#> select
library(scales)
#>
#> Attaching package: 'scales'
#> The following object is masked from 'package:Momocs':
#>
#> rescale
#> The following object is masked from 'package:purrr':
#>
#> discard
#> The following object is masked from 'package:readr':
#>
#> col_factor
library(RColorBrewer)
library(ggspatial)
data(bot)
bot
#> Out (outlines)
#> - 40 outlines, 162 +/- 21 coords (in $coo)
#> - 2 classifiers (in $fac):
#> # A tibble: 40 × 2
#> type fake
#> <fct> <fct>
#> 1 whisky a
#> 2 whisky a
#> 3 whisky a
#> 4 whisky a
#> 5 whisky a
#> 6 whisky a
#> # … with 34 more rows
#> - also: $ldk
panel(bot, fac="type", names=TRUE)
bot.f <- efourier(bot, nb.h=10)
#> 'norm=TRUE' is used and this may be troublesome. See ?efourier
bot.f
#> An OutCoe object [ elliptical Fourier analysis ]
#> --------------------
#> - $coe: 40 outlines described, 10 harmonics
#> # A tibble: 40 × 2
#> type fake
#> <fct> <fct>
#> 1 whisky a
#> 2 whisky a
#> 3 whisky a
#> 4 whisky a
#> 5 whisky a
#> 6 whisky a
#> # … with 34 more rows
# mean shape
ms_ <- MSHAPES(bot.f, fac="type")
ms_ <- ms_$shp
datams_<-rbind(data.frame(ms_$beer, Group="Beer"),
data.frame(ms_$whisky, Group="Whisky"))
ggplot(datams_)+theme_bw()+geom_path( aes(x,y, color=Group, linetype=Group), size=0.5)+theme_void()+theme(legend.position = c(0.5,0.6), axis.title = element_blank(), axis.ticks = element_blank(), axis.text = element_blank())+coord_equal()+
theme(legend.key.size = unit(5, units = "mm"))
####Also, in this code, I would like different plot symbols (e.g. ∆ for Beer, O for Whisky) for each group.
pca.fourier = bot.f %>% PCA %>% plot_PCA(~type)
layer_ellipses( conf = 0.9, lwd = 1, alpha = 0)%>%
layer_axes(lwd = 1)%>%
layer_axesvar(cex=1.5)%>%
layer_ellipsesaxes (conf = 0.5,lwd=1.5)%>%
layer_grid( col = "#999999", lty = 3, grid = 3)%>%
layer_stars(alpha = 0.8)%>%
layer_points( cex=1.3) %>%
#layer_eigen( nb_max =5, cex = 1 )%>%
layer_legend( cex = 1)%>%
layer_title( title = "Study", cex =1)
#> Error in is.factor(x$f): argument "x" is missing, with no default
Created on 2022-07-12 by the reprex package (v2.0.1)
I have a factor that's words (instances of words that difference participants said). I want to collapse it so that there are the categories "that" (every instance of the word "that") and notThat (all other words combined into one category). Naturally there are a lot of other words, and I don't want to go through and type them all. I've tried using != in various places, but it won't work. Maybe I just have the syntax wrong?
Anyway, is there a way to do this? That is, collapse all words that aren't "that" into one group?
How about this:
library(forcats)
x <- c("that", "something", "else")
fct_collapse(x, that = c("that"), other_level="notThat")
#> [1] that notThat notThat
#> Levels: that notThat
Created on 2022-02-15 by the reprex package (v2.0.1)
Edit to show in a data frame
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(forcats)
dat <- data.frame(
gender = factor(c(1,0,1,1,1,0), labels=c("male", "female")),
age = round(runif(6, 18,85)),
word = c("that", "something", "altogether", "different", "entirely", "that"))
dat %>%
mutate(word_collapse = fct_collapse(word, that="that", other_level="notThat"))
#> gender age word word_collapse
#> 1 female 74 that that
#> 2 male 72 something notThat
#> 3 female 57 altogether notThat
#> 4 female 44 different notThat
#> 5 female 79 entirely notThat
#> 6 male 81 that that
Created on 2022-02-15 by the reprex package (v2.0.1)
I am doing some work with bipartite projections and onemode networks in igraph. Is there a way to label the edges of one onemode network (network of artists below) using the vertices of the other (network of hometowns below)?
I want to do something like the below, but am worried it will not scale as the number of artist nodes expand and relationships between hometowns begin to appear (e.g., artists who split time between two towns).
Thanks!
library(tibble)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(igraph)
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:dplyr':
#>
#> as_data_frame, groups, union
#> The following object is masked from 'package:tibble':
#>
#> as_data_frame
#> The following objects are masked from 'package:stats':
#>
#> decompose, spectrum
#> The following object is masked from 'package:base':
#>
#> union
df <- tibble(user = c("Mac Miller", "Wiz Khalifa", "Arizona Zervas", "Lil Wayne", "Birdman", "Logic"), hometown = c("Pittsburgh", "Pittsburgh", "DC", "New Orleans", "New Orleans", "DC"))
g <- graph_from_data_frame(df) %>%
igraph::set_vertex_attr(., name = "type", value = V(.)$name %in% df$hometown) %>%
bipartite_projection()
igraph::get.edgelist(g$proj1)
#> [,1] [,2]
#> [1,] "Mac Miller" "Wiz Khalifa"
#> [2,] "Arizona Zervas" "Logic"
#> [3,] "Lil Wayne" "Birdman"
g1 <- igraph::set.edge.attribute(graph = g$proj1, name = "hometown", value = get.vertex.attribute(g$proj2)$name)
get.edgelist(g1)
#> [,1] [,2]
#> [1,] "Mac Miller" "Wiz Khalifa"
#> [2,] "Arizona Zervas" "Logic"
#> [3,] "Lil Wayne" "Birdman"
get.edge.attribute(g1)
#> $weight
#> [1] 1 1 1
#>
#> $hometown
#> [1] "Pittsburgh" "DC" "New Orleans"
Created on 2020-08-19 by the reprex package (v0.3.0)
I have an issue with creating a time period in R.
The data that I have on hand is enter image description here
Now I want to do the following.
Identify the hour intervals between start and end time and create a list
Identify the hour intervals b/n start and the break time
Finally, remove the break intervals to find the total time
and then create an output.
Could you please assist me?
I am not entirely sure what exactly you need, but from what i understand here is something to get you going:
library(tidyverse)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:base':
#>
#> date
t <- tibble(
place = "xyz",
st = "9:00",
et = "13:00"
)
t <- t %>%
mutate(stdur = period_to_seconds(hm(st))) %>%
mutate(etdur = period_to_seconds(hm(et))) %>%
mutate(interval = dseconds(etdur - stdur)) %>%
mutate(interval_hours = seconds_to_period(interval))
glimpse(t)
#> Observations: 1
#> Variables: 7
#> $ place <chr> "xyz"
#> $ st <chr> "9:00"
#> $ et <chr> "13:00"
#> $ stdur <dbl> 32400
#> $ etdur <dbl> 46800
#> $ interval <Duration> 14400s (~4 hours)
#> $ interval_hours <Period> 4H 0M 0S
Created on 2020-02-10 by the reprex package (v0.3.0)
I have sample data frame as below:
quoteiD <- c("q1","q2","q3","q4", "q5")
quote <- c("Unthinking respect for authority is the greatest enemy of truth.",
"In the middle of difficulty lies opportunity.",
"Intelligence is the ability to adapt to change.",
"Science is not only a disciple of reason but, also, one of romance and passion.",
"If I have seen further it is by standing on the shoulders of Giants.")
library(dplyr)
quotes <- tibble(quoteiD = quoteiD, quote= quote)
quotes
I have created some tidy text as below
library(tidytext)
data(stop_words)
tidy_words <- quotes %>%
unnest_tokens(word, quote) %>%
anti_join(stop_words) %>%
count( word, sort = TRUE)
tidy_words
Further, I have searched the synonyms using qdap package as below
library(qdap)
syns <- synonyms(tidy_words$word)
The qdap out put is a list , and I am looking to pick the first 5 synonym for each word in the tidy data frame and create a column called synonyms as below:
word n synonyms
ability 1 adeptness, aptitude, capability, capacity, competence
adapt 1 acclimatize, accommodate, adjust, alter, apply,
authority 1 ascendancy, charge, command, control, direction
What is an elegant way of merging the list of 5 words from qdap synonym function and separate by commas?
One way this can be done using a tidyverse solution is
library(plyr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:plyr':
#>
#> arrange, count, desc, failwith, id, mutate, rename, summarise,
#> summarize
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidytext)
library(qdap)
#> Loading required package: qdapDictionaries
#> Loading required package: qdapRegex
#>
#> Attaching package: 'qdapRegex'
#> The following object is masked from 'package:dplyr':
#>
#> explain
#> Loading required package: qdapTools
#>
#> Attaching package: 'qdapTools'
#> The following object is masked from 'package:dplyr':
#>
#> id
#> The following object is masked from 'package:plyr':
#>
#> id
#> Loading required package: RColorBrewer
#>
#> Attaching package: 'qdap'
#> The following object is masked from 'package:dplyr':
#>
#> %>%
#> The following object is masked from 'package:base':
#>
#> Filter
library(tibble)
library(tidyr)
#>
#> Attaching package: 'tidyr'
#> The following object is masked from 'package:qdap':
#>
#> %>%
quotes <- tibble(quoteiD = paste0("q", 1:5),
quote= c(".\n\nthe ebodac consortium consists of partners: janssen (efpia), london school of hygiene and tropical medicine (lshtm),",
"world vision) mobile health software development and deployment in resource limited settings grameen\n\nas such, the ebodac consortium is well placed to tackle.",
"Intelligence is the ability to adapt to change.",
"Science is a of reason of romance and passion.",
"If I have seen further it is by standing on ."))
quotes
#> # A tibble: 5 x 2
#> quoteiD quote
#> <chr> <chr>
#> 1 q1 ".\n\nthe ebodac consortium consists of partners: janssen (efpia~
#> 2 q2 "world vision) mobile health software development and deployment~
#> 3 q3 Intelligence is the ability to adapt to change.
#> 4 q4 Science is a of reason of romance and passion.
#> 5 q5 If I have seen further it is by standing on .
data(stop_words)
tidy_words <- quotes %>%
unnest_tokens(word, quote) %>%
anti_join(stop_words) %>%
count( word, sort = TRUE)
#> Joining, by = "word"
tidy_words
#> # A tibble: 33 x 2
#> word n
#> <chr> <int>
#> 1 consortium 2
#> 2 ebodac 2
#> 3 ability 1
#> 4 adapt 1
#> 5 change 1
#> 6 consists 1
#> 7 deployment 1
#> 8 development 1
#> 9 efpia 1
#> 10 grameen 1
#> # ... with 23 more rows
syns <- synonyms(tidy_words$word)
#> no match for the following:
#> consortium, ebodac, consists, deployment, efpia, grameen, janssen, london, lshtm, partners, settings, software, tropical
#> ========================
syns %>%
plyr::ldply(data.frame) %>% # Change the list to a dataframe (See https://stackoverflow.com/questions/4227223/r-list-to-data-frame)
rename("Word_DefNumber" = 1, "Syn" = 2) %>% # Rename the columns with a name that is more intuitive
separate(Word_DefNumber, c("Word", "DefNumber"), sep = "\\.") %>% # Find the word part of the word and definition number
group_by(Word) %>% # Group by words, so that when we select rows it is done for each word
slice(1:5) %>% # Keep the first 5 rows for each word
summarise(synonyms = paste(Syn, collapse = ", ")) %>% # Combine the synonyms together comma separated using paste
ungroup() # So there are not unintended effects of having the data grouped when using the data later
#> # A tibble: 20 x 2
#> Word synonyms
#> <chr> <chr>
#> 1 ability adeptness, aptitude, capability, capacity, competence
#> 2 adapt acclimatize, accommodate, adjust, alter, apply
#> 3 change alter, convert, diversify, fluctuate, metamorphose
#> 4 development advance, advancement, evolution, expansion, growth
#> 5 health fitness, good condition, haleness, healthiness, robustness
#> 6 hygiene cleanliness, hygienics, sanitary measures, sanitation
#> 7 intelligence acumen, alertness, aptitude, brain power, brains
#> 8 limited bounded, checked, circumscribed, confined, constrained
#> 9 medicine cure, drug, medicament, medication, nostrum
#> 10 mobile ambulatory, itinerant, locomotive, migrant, motile
#> 11 passion animation, ardour, eagerness, emotion, excitement
#> 12 reason apprehension, brains, comprehension, intellect, judgment
#> 13 resource ability, capability, cleverness, ingenuity, initiative
#> 14 romance affair, affaire (du coeur), affair of the heart, amour, at~
#> 15 school academy, alma mater, college, department, discipline
#> 16 science body of knowledge, branch of knowledge, discipline, art, s~
#> 17 standing condition, credit, eminence, estimation, footing
#> 18 tackle accoutrements, apparatus, equipment, gear, implements
#> 19 vision eyes, eyesight, perception, seeing, sight
#> 20 world earth, earthly sphere, globe, everybody, everyone
Created on 2019-04-05 by the reprex package (v0.2.1)
Please note that plyr should be loaded before dplyr