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weste
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started births scripts. basketball counts and births don't work quite yet.
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‎data_scripts/births/baseball_births.R

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library(tidyverse)
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# Grabs data from Lahman database, which is available in R.
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baseball_births <- Lahman::People %>%
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select(nameGiven, playerID, birthMonth, birthDay, birthCountry, birthDate, debut, finalGame) %>%
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rename_all("str_to_lower") %>%
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rename_all(.funs = funs(str_remove_all(.,"birth"))) %>%
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mutate(from = year(ymd(debut)), to = year(ymd(finalgame)), yrs = to - from) %>%
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select(player = namegiven, birthday = date, yrs, from, to, everything(), -playerid, -debut, -finalgame) %>%
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filter(!is.na(birthday))
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# Data details
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dpr_document(baseball_births, extension = ".md.R", export_folder = usethis::proj_get(),
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object_name = "baseball_births",
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title = "The birth dates of MLB players",
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description = "Data obtained from Lahman http://www.seanlahman.com/baseball-archive/statistics/",
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source = "https://github.com/cdalzell/Lahman",
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var_details = baseball_description)

‎data_scripts/births/basketball_births.R

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pacman::p_load(tidyverse, glue, rvest)
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# Make month_days tibble for wrangling later
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month_days <- bind_rows(
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tibble(month = 1, days = 1:31),
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tibble(month = 2, days = 1:29),
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tibble(month = 3, days = 1:31),
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tibble(month = 4, days = 1:30),
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tibble(month = 5, days = 1:31),
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tibble(month = 6, days = 1:30),
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tibble(month = 7, days = 1:31),
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tibble(month = 8, days = 1:31),
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tibble(month = 9, days = 1:30),
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tibble(month = 10, days = 1:31),
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tibble(month = 11, days = 1:30),
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tibble(month = 12, days = 1:31),
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)
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basketball_date_rip <- function(m,d) {
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urlpath <- glue("https://www.basketball-reference.com/friv/birthdays.fcgi?month={month}&day={day}",
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month = m, day = d)
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out <- read_html(urlpath) %>%
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html_nodes("table") %>%
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html_table() %>%
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.[[1]]
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col_names <- str_remove_all(out[1, 2:4], "\\(s\\)") %>% str_to_lower()
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out <- out %>%
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.[-1, 2:4]
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colnames(out) <- col_names
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print(str_c(m,"/", d))
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out %>%
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mutate(birthday = ymd(str_c(born,"-", m,"-", d))) %>%
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select(player, birthday, everything(),-born)
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}
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basketball_list <- map2(month_days$month, month_days$days, ~basketball_date_rip(.x, .y))
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basketball_births <- basketball_list %>%
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bind_rows() %>%
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as_tibble() %>%
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mutate(yrs = as.integer(yrs))
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# write_csv(basketball_births, "basketball.csv")
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# Data details
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dpr_document(basketball_births, extension = ".md.R", export_folder = usethis::proj_get(),
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object_name = "basketball_births",
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title = "The birth dates of NBA/ABA players",
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description = "Data obtained from https://www.basketball-reference.com",
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source = "https://www.basketball-reference.com/friv/birthdays.fcgi?month=1&day=1",
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var_details = basketball_description)

‎data_scripts/births/counts_baseball.R

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library(tidyverse)
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# Grabs data from Lahman database, which is available in R (from baseball_births)
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baseball_births <- Lahman::People %>%
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select(nameGiven, playerID, birthMonth, birthDay, birthCountry, birthDate, debut, finalGame) %>%
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rename_all("str_to_lower") %>%
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rename_all(.funs = funs(str_remove_all(.,"birth"))) %>%
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mutate(from = year(ymd(debut)), to = year(ymd(finalgame)), yrs = to - from) %>%
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select(player = namegiven, birthday = date, yrs, from, to, everything(), -playerid, -debut, -finalgame) %>%
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filter(!is.na(birthday))
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# Wrangling baseball_births into counts_baseball
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counts_baseball <- baseball_births %>%
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filter(country == "USA", year(birthday) > 1925, year(birthday) < 2015) %>%
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count(month, day, name = "births") %>%
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mutate(day_of_year = 1:n()) %>%
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left_join(tibble(month = 1:12, month_name = month.name)) %>%
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select(month_number = month, month_name, day_of_month = day, day_of_year, births)
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# Data details
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dpr_document(counts_baseball, extension = ".md.R", export_folder = usethis::proj_get(),
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object_name = "counts_baseball",
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title = "The count of births of MLB players for US born players from 1925 to 2015",
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description = "Data obtained from Lahman http://www.seanlahman.com/baseball-archive/statistics/",
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source = "https://github.com/cdalzell/Lahman",
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var_details = counts_description)

‎data_scripts/births/counts_basketball.R

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pacman::p_load(tidyverse, glue, rvest)
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# Make basketball_births first
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# Make month_days tibble for wrangling later
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month_days <- bind_rows(
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tibble(month = 1, days = 1:31),
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tibble(month = 2, days = 1:29),
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tibble(month = 3, days = 1:31),
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tibble(month = 4, days = 1:30),
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tibble(month = 5, days = 1:31),
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tibble(month = 6, days = 1:30),
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tibble(month = 7, days = 1:31),
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tibble(month = 8, days = 1:31),
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tibble(month = 9, days = 1:30),
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tibble(month = 10, days = 1:31),
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tibble(month = 11, days = 1:30),
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tibble(month = 12, days = 1:31),
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)
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basketball_date_rip <- function(m,d) {
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urlpath <- glue("https://www.basketball-reference.com/friv/birthdays.fcgi?month={month}&day={day}",
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month = m, day = d)
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out <- read_html(urlpath) %>%
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html_nodes("table") %>%
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html_table() %>%
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.[[1]]
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col_names <- str_remove_all(out[1, 2:4], "\\(s\\)") %>% str_to_lower()
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out <- out %>%
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.[-1, 2:4]
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colnames(out) <- col_names
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print(str_c(m,"/", d))
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out %>%
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mutate(birthday = ymd(str_c(born,"-", m,"-", d))) %>%
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select(player, birthday, everything(),-born)
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}
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basketball_list <- map2(month_days$month, month_days$days, ~basketball_date_rip(.x, .y))
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basketball_births <- basketball_list %>%
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bind_rows() %>%
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as_tibble() %>%
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mutate(yrs = as.integer(yrs))
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# Convert basketball_births into counts_basketball
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counts_basketball <- basketball_births %>%
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mutate(month = month(birthday), day = mday(birthday)) %>%
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count(month, day, name = "births") %>%
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mutate(day_of_year = 1:n()) %>%
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left_join(tibble(month = 1:12, month_name = month.name)) %>%
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select(month_number = month, month_name, day_of_month = day, day_of_year, births)
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# Data details
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dpr_document(counts_basketball, extension = ".md.R", export_folder = usethis::proj_get(),
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object_name = "counts_basketball",
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title = "The count of births of NBA/ABA players",
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description = "Data obtained from https://www.basketball-reference.com",
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source = "https://www.basketball-reference.com/friv/birthdays.fcgi?month=1&day=1",
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var_details = counts_description)

‎data_scripts/births/counts_us.R

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library(tidyverse)
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# US population data
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dat_94_03 <- read_csv("https://github.com/fivethirtyeight/data/raw/master/births/US_births_1994-2003_CDC_NCHS.csv")
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dat_00_14 <- read_csv("https://github.com/fivethirtyeight/data/raw/master/births/US_births_2000-2014_SSA.csv")
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counts_us <- dat_94_03 %>%
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filter(year < 2000) %>%
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bind_rows(dat_00_14) %>%
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group_by(month, date_of_month) %>%
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summarise(births = sum(births)) %>%
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ungroup() %>%
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mutate(day_of_year = 1:n()) %>%
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left_join(tibble(month = 1:12, month_name = month.name)) %>%
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select(month_number = month, month_name, day_of_month = date_of_month, day_of_year, births)
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# Data details
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dpr_document(counts_us, extension = ".md.R", export_folder = usethis::proj_get(),
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object_name = "counts_us", title = "The count of births in the United States from 1994-2014",
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description = "Data obtained from the CDC and Census parsed by FiveThirtyEight ",
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source = "https://github.com/fivethirtyeight/data/tree/master/births",
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var_details = counts_description)

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