kandi background
Explore Kits

pacer | My original graph database DSL machine

 by   pangloss Ruby Version: Current License: Non-SPDX

 by   pangloss Ruby Version: Current License: Non-SPDX

Download this library from

kandi X-RAY | pacer Summary

pacer is a Ruby library typically used in User Interface, Neo4j, Eclipse applications. pacer has no vulnerabilities and it has low support. However pacer has 1 bugs and it has a Non-SPDX License. You can download it from GitHub.
Pacer is a JRuby library that enables very expressive graph traversals. It currently supports all of the major graph databases including OrientDB, Neo4j and Dex thanks to the Tinkerpop graphdb stack. Plus there's a very convenient in-memory graph called TinkerGraph which is part of Blueprints.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • pacer has a low active ecosystem.
  • It has 177 star(s) with 30 fork(s). There are 16 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 42 have been closed. On average issues are closed in 920 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of pacer is current.
pacer Support
Best in #Ruby
Average in #Ruby
pacer Support
Best in #Ruby
Average in #Ruby

quality kandi Quality

  • pacer has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 247 code smells.
pacer Quality
Best in #Ruby
Average in #Ruby
pacer Quality
Best in #Ruby
Average in #Ruby

securitySecurity

  • pacer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • pacer code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
pacer Security
Best in #Ruby
Average in #Ruby
pacer Security
Best in #Ruby
Average in #Ruby

license License

  • pacer has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
pacer License
Best in #Ruby
Average in #Ruby
pacer License
Best in #Ruby
Average in #Ruby

buildReuse

  • pacer releases are not available. You will need to build from source code and install.
  • Installation instructions, examples and code snippets are available.
  • pacer saves you 57504 person hours of effort in developing the same functionality from scratch.
  • It has 65889 lines of code, 1006 functions and 197 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
pacer Reuse
Best in #Ruby
Average in #Ruby
pacer Reuse
Best in #Ruby
Average in #Ruby
Top functions reviewed by kandi - BETA

kandi has reviewed pacer and discovered the below as its top functions. This is intended to give you an instant insight into pacer implemented functionality, and help decide if they suit your requirements.

  • Processes new queue
  • Build a new element for the given index .
  • Initialize a new Job
  • Add a new edge to the graph
  • Return a list of all elements in the collection .
  • Determine if the element is defined
  • Starts a transaction .
  • Return the next Pair of the next Pair
  • Returns true if the given path is available
  • Accessor for a route .

pacer Key Features

My original graph database DSL machine

Documentation

copy iconCopydownload iconDownload
  gem install yard
  yard server

Installation

copy iconCopydownload iconDownload
gem install pacer

Example traversals

copy iconCopydownload iconDownload
    friends = person.out_e(:friend).in_v(:type => 'person')
    friends.out_e(:friend).in_v(:type => 'person').except(friends).except(person).most_frequent(0...10)

How do I create two new variables for how many days between the most recent game played by each team?

copy iconCopydownload iconDownload
library(tidyverse)
library(lubridate)

games %>%
  mutate(Game = row_number()) %>%
  pivot_longer(cols = starts_with("team"), 
               values_to = "Team", 
               names_to = "Number", 
               names_pattern = "team(\\d+)") %>%
  group_by(Team) %>%
  arrange(date) %>%
  mutate(Days_Between = c(0, diff(date))) %>%
  pivot_wider(id_cols = c(Game, date), 
              names_from = Number, 
              values_from = c(Team, Days_Between))
   Game date       Team_1         Team_2            Days_Between_1 Days_Between_2
  <int> <date>     <chr>          <chr>                      <dbl>          <dbl>
1     4 2021-05-05 Boston Celtics Orlando Magic                  0              0
2     5 2021-05-05 Phoenix Suns   Atlanta Hawks                  0              0
3     3 2021-05-06 Atlanta Hawks  Indiana Pacers                 1              0
4     1 2021-05-07 Boston Celtics Chicago Bulls                  2              0
5     2 2021-05-07 Orlando Magic  Charlotte Hornets              2              0
-----------------------
library(tidyverse)
library(lubridate)

games %>%
  mutate(Game = row_number()) %>%
  pivot_longer(cols = starts_with("team"), 
               values_to = "Team", 
               names_to = "Number", 
               names_pattern = "team(\\d+)") %>%
  group_by(Team) %>%
  arrange(date) %>%
  mutate(Days_Between = c(0, diff(date))) %>%
  pivot_wider(id_cols = c(Game, date), 
              names_from = Number, 
              values_from = c(Team, Days_Between))
   Game date       Team_1         Team_2            Days_Between_1 Days_Between_2
  <int> <date>     <chr>          <chr>                      <dbl>          <dbl>
1     4 2021-05-05 Boston Celtics Orlando Magic                  0              0
2     5 2021-05-05 Phoenix Suns   Atlanta Hawks                  0              0
3     3 2021-05-06 Atlanta Hawks  Indiana Pacers                 1              0
4     1 2021-05-07 Boston Celtics Chicago Bulls                  2              0
5     2 2021-05-07 Orlando Magic  Charlotte Hornets              2              0

Adding p-values to a polr model (for modelsummary)

copy iconCopydownload iconDownload
library(MASS)
library(AER)
library(modelsummary)

tidy_custom.polr <- function(x, ...) {
  s <- coeftest(x)
  out <- data.frame(
    term = row.names(s),
    p.value = s[, "Pr(>|z|)"])
  out
}

mod = list(
  "LM" = lm(gear ~ hp + mpg, data = mtcars),
  "POLR" = polr(as.ordered(gear) ~ hp + mpg, data = mtcars))

modelsummary(mod, stars = TRUE)

Is there a way to assign a value of 1 in all rows if any of two value within a group appears in another column?

copy iconCopydownload iconDownload
library(dplyr)
df1 %>%
     group_by(Name) %>%
     mutate(value = +(any(c('Knicks', 'Nets') %in% Teams))) %>%
     ungroup
# A tibble: 14 x 3
   Name           Teams    value
   <chr>          <chr>    <int>
 1 Baron Davis    Hornets      1
 2 Baron Davis    Warriors     1
 3 Baron Davis    Knicks       1
 4 Jason Kidd     Suns         1
 5 Jason Kidd     Nets         1
 6 Jason Kidd     Mavs         1
 7 Jason Kidd     Knicks       1
 8 Kevin Durant   Thunder      1
 9 Kevin Durant   Nets         1
10 Kevin Durant   Warriors     1
11 Michael Jordan Bulls        0
12 Reggie Miller  Pacers       0
13 Allan Houston  Knicks       1
14 Allan Houston  Pistons      1
df1 <- structure(list(Name = c("Baron Davis", "Baron Davis", "Baron Davis", 
"Jason Kidd", "Jason Kidd", "Jason Kidd", "Jason Kidd", "Kevin Durant", 
"Kevin Durant", "Kevin Durant", "Michael Jordan", "Reggie Miller", 
"Allan Houston", "Allan Houston"), Teams = c("Hornets", "Warriors", 
"Knicks", "Suns", "Nets", "Mavs", "Knicks", "Thunder", "Nets", 
"Warriors", "Bulls", "Pacers", "Knicks", "Pistons")),
class = "data.frame", row.names = c(NA, 
-14L))
-----------------------
library(dplyr)
df1 %>%
     group_by(Name) %>%
     mutate(value = +(any(c('Knicks', 'Nets') %in% Teams))) %>%
     ungroup
# A tibble: 14 x 3
   Name           Teams    value
   <chr>          <chr>    <int>
 1 Baron Davis    Hornets      1
 2 Baron Davis    Warriors     1
 3 Baron Davis    Knicks       1
 4 Jason Kidd     Suns         1
 5 Jason Kidd     Nets         1
 6 Jason Kidd     Mavs         1
 7 Jason Kidd     Knicks       1
 8 Kevin Durant   Thunder      1
 9 Kevin Durant   Nets         1
10 Kevin Durant   Warriors     1
11 Michael Jordan Bulls        0
12 Reggie Miller  Pacers       0
13 Allan Houston  Knicks       1
14 Allan Houston  Pistons      1
df1 <- structure(list(Name = c("Baron Davis", "Baron Davis", "Baron Davis", 
"Jason Kidd", "Jason Kidd", "Jason Kidd", "Jason Kidd", "Kevin Durant", 
"Kevin Durant", "Kevin Durant", "Michael Jordan", "Reggie Miller", 
"Allan Houston", "Allan Houston"), Teams = c("Hornets", "Warriors", 
"Knicks", "Suns", "Nets", "Mavs", "Knicks", "Thunder", "Nets", 
"Warriors", "Bulls", "Pacers", "Knicks", "Pistons")),
class = "data.frame", row.names = c(NA, 
-14L))
-----------------------
library(dplyr)
df1 %>%
     group_by(Name) %>%
     mutate(value = +(any(c('Knicks', 'Nets') %in% Teams))) %>%
     ungroup
# A tibble: 14 x 3
   Name           Teams    value
   <chr>          <chr>    <int>
 1 Baron Davis    Hornets      1
 2 Baron Davis    Warriors     1
 3 Baron Davis    Knicks       1
 4 Jason Kidd     Suns         1
 5 Jason Kidd     Nets         1
 6 Jason Kidd     Mavs         1
 7 Jason Kidd     Knicks       1
 8 Kevin Durant   Thunder      1
 9 Kevin Durant   Nets         1
10 Kevin Durant   Warriors     1
11 Michael Jordan Bulls        0
12 Reggie Miller  Pacers       0
13 Allan Houston  Knicks       1
14 Allan Houston  Pistons      1
df1 <- structure(list(Name = c("Baron Davis", "Baron Davis", "Baron Davis", 
"Jason Kidd", "Jason Kidd", "Jason Kidd", "Jason Kidd", "Kevin Durant", 
"Kevin Durant", "Kevin Durant", "Michael Jordan", "Reggie Miller", 
"Allan Houston", "Allan Houston"), Teams = c("Hornets", "Warriors", 
"Knicks", "Suns", "Nets", "Mavs", "Knicks", "Thunder", "Nets", 
"Warriors", "Bulls", "Pacers", "Knicks", "Pistons")),
class = "data.frame", row.names = c(NA, 
-14L))

Is there a simple way of extract the N first words from a local macro which is comma or space+comma separated in Stata?

copy iconCopydownload iconDownload
clear 
set seed 2021
set obs 5000
gen id = round(_n/5)
gen x1 = rnormal()

sum * 
levelsof id if x1>2, local(levels) sep(", ")
sum * if x1>2 // if threshold is small enough, there will be too many values for inlist()
sum * if inlist(id, `levels')

//This will do the same thing
gen over_threshold = x1>2 
egen id_over_thresh = max(over_threshold), by(id)

sum * if id_over_thresh

Firebase Cloud Functions: Scraping function runs on US server although different region is set

copy iconCopydownload iconDownload
All the IP addresses Google provides to GCP user are registered with ARIN
under the Google HQ in Mountain View, California (in other words SWIP to be
Mountain View, CA). So, all geolocations lookup of the IP addresses will 
resolve to the United States even though the actual server is located 
somewhere else. In addition to that, in GCP, it's not uncommon to remap a 
block of IPs from one location to another, especially given the elasticity of 
IP addresses for the GCP and they way they are recycled or reused.

How to apply the transform method to two variables in pandas?

copy iconCopydownload iconDownload
def np_dot(grp, columns):
    columns = [grp[column].transform(lambda x: x) for column in columns]
    return np.dot(*columns)


(
    df.assign(
        brand=df.make.str.split().str[0],
        price_mean=lambda df: df.groupby("brand").price.transform("mean"),
        size=np.where(df.length > 200, 1, 0),
    )
    .query('make.str.contains("Olds")', engine="python")
    .assign(result=lambda df: df.groupby("brand").pipe(np_dot, ["price", "size"]))
)

Unable to apply a SAS macro to a column

copy iconCopydownload iconDownload
data cars2;
  * calculate;
  if mpg < 20 then varnew = weight + mpg;
  else
  if mpg >= 20 then varnew = weight - mpg;
run;
  if PARAMETER_2 < 20 then RESULT_VAR = PARAMETER_1 + PARAMETER_2;
  else
  if PARAMETER_2 >= 20 then RESULT_VAR = PARAMETER_1 - PARAMETER_2;
  ifn (PARAMETER_2 < 20, PARAMETER_1 + PARAMETER_2, PARAMETER_1 - PARAMETER_2)
%macro calculate(result_var, parameter_1, parameter_2);
  /* generate DATA Step source code, using the passed parameters */

  if &PARAMETER_2 < 20 then &RESULT_VAR = &PARAMETER_1 + &PARAMETER_2;
  else
  if &PARAMETER_2 >= 20 then &RESULT_VAR = &PARAMETER_1 - &PARAMETER_2;
%mend;

data cars2;
  %calculate(varnew,weight,mpg);
run;
%macro calculate(parameter_1, parameter_2);
  /* generate source code that is valid as right hand side of assignment */

  ifn (&PARAMETER_2 < 20, &PARAMETER_1 + &PARAMETER_2, &PARAMETER_1 - &PARAMETER_2)
%mend;

data cars2;
  varnew = %calculate(weight,mpg);
run;
-----------------------
data cars2;
  * calculate;
  if mpg < 20 then varnew = weight + mpg;
  else
  if mpg >= 20 then varnew = weight - mpg;
run;
  if PARAMETER_2 < 20 then RESULT_VAR = PARAMETER_1 + PARAMETER_2;
  else
  if PARAMETER_2 >= 20 then RESULT_VAR = PARAMETER_1 - PARAMETER_2;
  ifn (PARAMETER_2 < 20, PARAMETER_1 + PARAMETER_2, PARAMETER_1 - PARAMETER_2)
%macro calculate(result_var, parameter_1, parameter_2);
  /* generate DATA Step source code, using the passed parameters */

  if &PARAMETER_2 < 20 then &RESULT_VAR = &PARAMETER_1 + &PARAMETER_2;
  else
  if &PARAMETER_2 >= 20 then &RESULT_VAR = &PARAMETER_1 - &PARAMETER_2;
%mend;

data cars2;
  %calculate(varnew,weight,mpg);
run;
%macro calculate(parameter_1, parameter_2);
  /* generate source code that is valid as right hand side of assignment */

  ifn (&PARAMETER_2 < 20, &PARAMETER_1 + &PARAMETER_2, &PARAMETER_1 - &PARAMETER_2)
%mend;

data cars2;
  varnew = %calculate(weight,mpg);
run;
-----------------------
data cars2;
  * calculate;
  if mpg < 20 then varnew = weight + mpg;
  else
  if mpg >= 20 then varnew = weight - mpg;
run;
  if PARAMETER_2 < 20 then RESULT_VAR = PARAMETER_1 + PARAMETER_2;
  else
  if PARAMETER_2 >= 20 then RESULT_VAR = PARAMETER_1 - PARAMETER_2;
  ifn (PARAMETER_2 < 20, PARAMETER_1 + PARAMETER_2, PARAMETER_1 - PARAMETER_2)
%macro calculate(result_var, parameter_1, parameter_2);
  /* generate DATA Step source code, using the passed parameters */

  if &PARAMETER_2 < 20 then &RESULT_VAR = &PARAMETER_1 + &PARAMETER_2;
  else
  if &PARAMETER_2 >= 20 then &RESULT_VAR = &PARAMETER_1 - &PARAMETER_2;
%mend;

data cars2;
  %calculate(varnew,weight,mpg);
run;
%macro calculate(parameter_1, parameter_2);
  /* generate source code that is valid as right hand side of assignment */

  ifn (&PARAMETER_2 < 20, &PARAMETER_1 + &PARAMETER_2, &PARAMETER_1 - &PARAMETER_2)
%mend;

data cars2;
  varnew = %calculate(weight,mpg);
run;
-----------------------
data cars2;
  * calculate;
  if mpg < 20 then varnew = weight + mpg;
  else
  if mpg >= 20 then varnew = weight - mpg;
run;
  if PARAMETER_2 < 20 then RESULT_VAR = PARAMETER_1 + PARAMETER_2;
  else
  if PARAMETER_2 >= 20 then RESULT_VAR = PARAMETER_1 - PARAMETER_2;
  ifn (PARAMETER_2 < 20, PARAMETER_1 + PARAMETER_2, PARAMETER_1 - PARAMETER_2)
%macro calculate(result_var, parameter_1, parameter_2);
  /* generate DATA Step source code, using the passed parameters */

  if &PARAMETER_2 < 20 then &RESULT_VAR = &PARAMETER_1 + &PARAMETER_2;
  else
  if &PARAMETER_2 >= 20 then &RESULT_VAR = &PARAMETER_1 - &PARAMETER_2;
%mend;

data cars2;
  %calculate(varnew,weight,mpg);
run;
%macro calculate(parameter_1, parameter_2);
  /* generate source code that is valid as right hand side of assignment */

  ifn (&PARAMETER_2 < 20, &PARAMETER_1 + &PARAMETER_2, &PARAMETER_1 - &PARAMETER_2)
%mend;

data cars2;
  varnew = %calculate(weight,mpg);
run;
-----------------------
data cars2;
  * calculate;
  if mpg < 20 then varnew = weight + mpg;
  else
  if mpg >= 20 then varnew = weight - mpg;
run;
  if PARAMETER_2 < 20 then RESULT_VAR = PARAMETER_1 + PARAMETER_2;
  else
  if PARAMETER_2 >= 20 then RESULT_VAR = PARAMETER_1 - PARAMETER_2;
  ifn (PARAMETER_2 < 20, PARAMETER_1 + PARAMETER_2, PARAMETER_1 - PARAMETER_2)
%macro calculate(result_var, parameter_1, parameter_2);
  /* generate DATA Step source code, using the passed parameters */

  if &PARAMETER_2 < 20 then &RESULT_VAR = &PARAMETER_1 + &PARAMETER_2;
  else
  if &PARAMETER_2 >= 20 then &RESULT_VAR = &PARAMETER_1 - &PARAMETER_2;
%mend;

data cars2;
  %calculate(varnew,weight,mpg);
run;
%macro calculate(parameter_1, parameter_2);
  /* generate source code that is valid as right hand side of assignment */

  ifn (&PARAMETER_2 < 20, &PARAMETER_1 + &PARAMETER_2, &PARAMETER_1 - &PARAMETER_2)
%mend;

data cars2;
  varnew = %calculate(weight,mpg);
run;

Switch-case statement destroys rendered components

copy iconCopydownload iconDownload
const relations = {
  [Constants.TEAM_76ERS]: Logos.PHI,
  // etc...
};
export function PickTeam({team, size}) {
  const Found = relations[team];
  return Found ? <Found size={size} /> : <div/>
}
export default function TeamScoreBox(props) {
  return (
    <div className="mainScoreBarTeam">
      <OtherFunctions.PickTeam team={props.teamAbbreviation} size={100} /> 
    </div>
  );
}
export const Home = () => {
  return (
    <div>
      <Logos.BOS/><Logos.PHX/><Logos.BOS/><Logos.PHI/><Logos.PHI/>
      <OtherFunctions.PickTeam team={"PHI"} size={100} /> 
    </div>
  );
}
-----------------------
const relations = {
  [Constants.TEAM_76ERS]: Logos.PHI,
  // etc...
};
export function PickTeam({team, size}) {
  const Found = relations[team];
  return Found ? <Found size={size} /> : <div/>
}
export default function TeamScoreBox(props) {
  return (
    <div className="mainScoreBarTeam">
      <OtherFunctions.PickTeam team={props.teamAbbreviation} size={100} /> 
    </div>
  );
}
export const Home = () => {
  return (
    <div>
      <Logos.BOS/><Logos.PHX/><Logos.BOS/><Logos.PHI/><Logos.PHI/>
      <OtherFunctions.PickTeam team={"PHI"} size={100} /> 
    </div>
  );
}
-----------------------
const relations = {
  [Constants.TEAM_76ERS]: Logos.PHI,
  // etc...
};
export function PickTeam({team, size}) {
  const Found = relations[team];
  return Found ? <Found size={size} /> : <div/>
}
export default function TeamScoreBox(props) {
  return (
    <div className="mainScoreBarTeam">
      <OtherFunctions.PickTeam team={props.teamAbbreviation} size={100} /> 
    </div>
  );
}
export const Home = () => {
  return (
    <div>
      <Logos.BOS/><Logos.PHX/><Logos.BOS/><Logos.PHI/><Logos.PHI/>
      <OtherFunctions.PickTeam team={"PHI"} size={100} /> 
    </div>
  );
}
-----------------------
    useEffect(() => {
        setLoading(true);
        axios({
            method: 'GET',
            url: 'https://localhost:44324/apidata/todaygames',
            headers: {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
                'Access-Control-Allow-Methods': 'GET,PUT,POST,DELETE,PATCH,OPTIONS',
            },
        }).then((res) => {
            setMatches(res.data);
            setLoading(false);
        }).catch((error) => {
            console.log(error);
        });
    }, []);
    // Invert condition
    return !loading
         ? (
            <Row className="mainScoreBar">
                {matches.map((key) => {
                    return (
                        <Col key={key.homeTeam + key.visitorTeam} sm={4} style={{ display: "inline-block" }}>
                            <Score gameData={key} />
                        </Col>
                    );
                })}
            </Row>
         )
         : <Loading/>
-----------------------
    useEffect(() => {
        setLoading(true);
        axios({
            method: 'GET',
            url: 'https://localhost:44324/apidata/todaygames',
            headers: {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
                'Access-Control-Allow-Methods': 'GET,PUT,POST,DELETE,PATCH,OPTIONS',
            },
        }).then((res) => {
            setMatches(res.data);
            setLoading(false);
        }).catch((error) => {
            console.log(error);
        });
    }, []);
    // Invert condition
    return !loading
         ? (
            <Row className="mainScoreBar">
                {matches.map((key) => {
                    return (
                        <Col key={key.homeTeam + key.visitorTeam} sm={4} style={{ display: "inline-block" }}>
                            <Score gameData={key} />
                        </Col>
                    );
                })}
            </Row>
         )
         : <Loading/>
-----------------------
export default React.memo(function PickTeam({team, size}) {
    const Found = Constants.TEAMS_LOGOS[team];
    return Found ? <Found size={size} /> : <div />;
})
      <defs>
        <path id="A" d="M0 0h93.554v104H0z" />
        <path id="B" d="M0 0h93.554v104H0z" />
        <path
          id="C"
          d="M93.554 48.9c0-12.494-..."
        />
      </defs>
      <g transform="translate(25 20)" fill="none" fillRule="evenodd">
        <path
          d="M99.87 52.054c0-13.33-..."
          fill="#fefefe"
        />
        <g transform="translate(3 3)">
          <mask id="D" fill="#fff">
            <use xlinkHref="#A" />
          </mask>
          <use xlinkHref="#C" fill="#fff" mask="url(#D)" />
          <mask id="E" fill="#fff">
            <use xlinkHref="#B" />
          </mask>
          <use xlinkHref="#C" fill="#061922" mask="url(#E)" />
        </g>
-----------------------
export default React.memo(function PickTeam({team, size}) {
    const Found = Constants.TEAMS_LOGOS[team];
    return Found ? <Found size={size} /> : <div />;
})
      <defs>
        <path id="A" d="M0 0h93.554v104H0z" />
        <path id="B" d="M0 0h93.554v104H0z" />
        <path
          id="C"
          d="M93.554 48.9c0-12.494-..."
        />
      </defs>
      <g transform="translate(25 20)" fill="none" fillRule="evenodd">
        <path
          d="M99.87 52.054c0-13.33-..."
          fill="#fefefe"
        />
        <g transform="translate(3 3)">
          <mask id="D" fill="#fff">
            <use xlinkHref="#A" />
          </mask>
          <use xlinkHref="#C" fill="#fff" mask="url(#D)" />
          <mask id="E" fill="#fff">
            <use xlinkHref="#B" />
          </mask>
          <use xlinkHref="#C" fill="#061922" mask="url(#E)" />
        </g>

If cell has 2 words, extract only 1st word and if cell has 3 words, extract 2 first words - PANDAS/REGEX

copy iconCopydownload iconDownload
team_words = team_name.split()
team_city = team_words[:-1]
city = ' '.join(team_city)
city = ' '.join(team_name.split()[:-1])
-----------------------
team_words = team_name.split()
team_city = team_words[:-1]
city = ' '.join(team_city)
city = ' '.join(team_name.split()[:-1])
-----------------------
^(\S+(?:\s+\S+(?=\s+\S+))?)

full_join adding extra rows and NA in r

copy iconCopydownload iconDownload
df1_new <- df1 %>% as_tibble() %>%
  mutate(Team = sub(" +", "", as.character(Team)))
df2_new <- df2 %>%
  mutate(Team = as.character(Team))

df1_new %>% full_join(df2_new, by = c("Team", "HomevsAway"))

# A tibble: 58 x 5
   Team      Injury.Count HomevsAway   t_1   t_3
   <chr>            <int> <fct>      <dbl> <dbl>
 1 Bucks                3 0           32.2 126. 
 2 Bucks                3 1           38.0 112. 
 3 Bull                 1 0           NA    NA  
 4 Bulls                1 1            0     0  
 5 Cavaliers            1 0           24.3  91.6
 6 Cavaliers            2 1           57.3 167. 
 7 Celtics              0 0            0     0  
 8 Celtics              2 1           18.0 104. 
 9 Clippers             1 0            0     0  
10 Clippers             1 1           19.0  71.1

Community Discussions

Trending Discussions on pacer
  • How do I create two new variables for how many days between the most recent game played by each team?
  • Adding p-values to a polr model (for modelsummary)
  • Is there a way to assign a value of 1 in all rows if any of two value within a group appears in another column?
  • Is there a simple way of extract the N first words from a local macro which is comma or space+comma separated in Stata?
  • Firebase Cloud Functions: Scraping function runs on US server although different region is set
  • How to apply the transform method to two variables in pandas?
  • Unable to apply a SAS macro to a column
  • Switch-case statement destroys rendered components
  • If cell has 2 words, extract only 1st word and if cell has 3 words, extract 2 first words - PANDAS/REGEX
  • How to prevent rerender when using react functional components
Trending Discussions on pacer

QUESTION

How do I create two new variables for how many days between the most recent game played by each team?

Asked 2021-May-09 at 18:28

I would like to create two new variables (one for team1, another for team2). Each variable should tell me how many days between the most recent game played by each team.

library(tidyverse)
library(lubridate)

date <- c(mdy("May 7, 2021", "May 7, 2021", "May 6, 2021", "May 5, 2021", "May 5, 2021"))
team1 <- c("Boston Celtics", "Orlando Magic", "Atlanta Hawks", "Boston Celtics", "Phoenix Suns")
team2 <- c("Chicago Bulls", "Charlotte Hornets", "Indiana Pacers", "Orlando Magic", "Atlanta Hawks")
games <- data.frame(date, team1, team2)

ANSWER

Answered 2021-May-09 at 18:28

Let me know if this provides the output you are interested in.

In this answer, you can first assign each row of data to a unique Game number. Then, put data into long form, and calculate days between games for each team. Finally, if desired, you can put data into wide format again.

library(tidyverse)
library(lubridate)

games %>%
  mutate(Game = row_number()) %>%
  pivot_longer(cols = starts_with("team"), 
               values_to = "Team", 
               names_to = "Number", 
               names_pattern = "team(\\d+)") %>%
  group_by(Team) %>%
  arrange(date) %>%
  mutate(Days_Between = c(0, diff(date))) %>%
  pivot_wider(id_cols = c(Game, date), 
              names_from = Number, 
              values_from = c(Team, Days_Between))

Output

   Game date       Team_1         Team_2            Days_Between_1 Days_Between_2
  <int> <date>     <chr>          <chr>                      <dbl>          <dbl>
1     4 2021-05-05 Boston Celtics Orlando Magic                  0              0
2     5 2021-05-05 Phoenix Suns   Atlanta Hawks                  0              0
3     3 2021-05-06 Atlanta Hawks  Indiana Pacers                 1              0
4     1 2021-05-07 Boston Celtics Chicago Bulls                  2              0
5     2 2021-05-07 Orlando Magic  Charlotte Hornets              2              0

Source https://stackoverflow.com/questions/67460965

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pacer

JRuby 1.7.x Recommended: Use RVM to install and manage all Ruby (and JRuby) versions on your machine.
RubyGems

Support

Check out the Pacer docs for detailed explanations of many of Pacer's features. Feel free to contribute to it, by submitting a pull-request to the gh-pages branch of this repo, or by opening issues.

DOWNLOAD this Library from

Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
over 430 million Knowledge Items
Find more libraries
Reuse Solution Kits and Libraries Curated by Popular Use Cases

Save this library and start creating your kit

Explore Related Topics

Share this Page

share link
Reuse Pre-built Kits with pacer
Compare Ruby Libraries with Highest Support
Compare Ruby Libraries with Highest Reuse
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
over 430 million Knowledge Items
Find more libraries
Reuse Solution Kits and Libraries Curated by Popular Use Cases

Save this library and start creating your kit

  • © 2022 Open Weaver Inc.