luisDVA

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####        Regex for data cleaning - your turn         ####
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# 1. Download CRAN package descriptions
# 2. Select Package name, author, description, and all variables that end in 'ports'
# 3. Filter rows for packages with names that:
#    - end in plot

luisDVA

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####   Understanding a regular expression - your turn   ####
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# Write regular expressions to match:
# Test with stringr, regexplain, or a web-based regex tester

# "cute, cuuute, cuuuuuute, and cuuuuuuuuute"

luisDVA

library(ggplot2)
library(dplyr)

# datos internos que vienen con ggplot2
animals <-
  msleep %>% slice_max(sleep_rem, n = 30) %>% # seleccionando top 30
  ggplot(aes(x = forcats::fct_reorder(name, sleep_total), y = sleep_total, label = name)) +
  geom_bar(stat = "identity") + xlab("") +
  geom_text(nudge_y = 2, hjust = 0) +
  theme(

luisDVA

# 2020 (2019) AKC dog bump chart
library(ggplot2) # CRAN v3.3.0
library(ggbump) # CRAN v0.1.0
library(dplyr) # [github::tidyverse/dplyr] v0.8.99.9003
library(tidyr) # CRAN v1.0.3
library(ggimage) # CRAN v0.2.8
library(scico) # CRAN v1.1.0
library(extrafont) # CRAN v0.17

# make dataset

luisDVA

library(purrr)
library(dplyr)
library(here)
library(magick)
library(fs)
library(stringr)
library(ggplot2)
library(ggimage)
library(tidyr)
library(hrbrthemes)

luisDVA

# shot richness
# access shot chart data from the API
library(nbastatR)

# team shot chart stats
mavs <- teams_shots(
  teams = "Dallas Mavericks",
  seasons = 2019
)
hawks <- teams_shots(

luisDVA

library(nbastatR) # if needed install_github("abresler/nbastatR") if needed

# id for Devin Booker franchise scoring record vs Boston
phoBos <- 21601076

# team shot chart for 2017 season
phx <- teams_shots(teams = "Phoenix Suns",
                   seasons = 2017)
library(dplyr)
library(stringr)

luisDVA

library(nbastatR) #install_github("abresler/nbastatR") if needed

# team shot chart for current season
hou <- teams_shots(teams = "Houston Rockets",
                  seasons = 2019)

library(dplyr) # because some functions conflict

last3games <- hou %>% select(idGame) %>% 
  unique() %>% top_n(3) %>% pull(idGame)

luisDVA

library(dplyr)
library(ggplot2)
library(gganimate)
library(tidyr)
library(stringr)
library(extrafont)

# set up data
dat <- data_frame(type=sort(rep(c("canine","feline","avian"),3)),
                  name=c("Sam","Austin","Squawk",

luisDVA

library(dplyr)
library(tidyr)


survey <-  data_frame(ID= factor(seq.int(1,6,1)), answers=c("a","b,c","d,e","c,d","b,c,e","e"))


survey %>%  unnest(answers=strsplit(answers,","))

# A tibble: 11 x 2