identify_missing: Find Missing Data

In guytuori/simScores: Similarity Scores for Baseball Players

Description

Identifies players with missing data. Records these players and saves files with these players' MLBID so that missing data may be filled in by the user at a later time.

Usage

identify_missing(dat, ignore)

Arguments

dat	data.frame. This contains both statistics and bio data so that missing data may be identified by the user. Obtained from combine.
ignore	data.frame. This contains MLBID that the user wants to ignore when identifying missing data. Must be a data.frame with a column named MLBID

Value

a data.frame containing bio info for any player with missing data. Most of the time, it should just be bio data that is missing. If however, a batting/pitching statistic is missing, the player with the missing data will be identified.

Examples

curr_wd <- getwd()
setwd("N:/Apps/simScoresApp/data")
stats <- read.csv("4-mles/bat-mles.csv", header = T, stringsAsFactors = F) %>% tbl_df() %>% mutate(MLBID = as.character(MLBID))
bio <- read.csv("manual-info/bio_bat.csv", header = T, stringsAsFactors = F) %>% tbl_df() %>% mutate(MLBID = as.character(MLBID))
pos <- read.csv("manual-info/positions.csv", header = T, stringsAsFactors = F) %>% tbl_df() %>% mutate(MLBID = as.character(MLBID))
dl <- read.csv("manual-info/injuries.csv", header = T, stringsAsFactors = F) %>% tbl_df() %>% mutate(MLBID = as.character(MLBID))
ignore <- read.csv("manual-info/players_to_ignore.csv", header = T, stringsAsFactors = F) %>% tbl_df() %>% mutate(MLBID = as.character(MLBID))
x <- combine(stats, bio, pos, dl)
m <- identify_missing(x, ignore)
setwd(curr_wd)

guytuori/simScores documentation built on May 17, 2019, 9:29 a.m.