Publications by Claire Morrison
interactions 2
let’s say we are interested in whether age and/ or amount of miles ran impacts race time. d <- read.csv("/Users/clairemorrison/Desktop/gradstats2022/run.csv", header = T) head(d) ## time age miles ## 1 24.91 29 10 ## 2 21.82 25 20 ## 3 21.54 27 40 ## 4 23.03 25 50 ## 5 25.35 37 20 ## 6 22.84 31 40 if we run a simple �...
2097 sym 2 img
fortnite
fn <- read_sheet('https://docs.google.com/spreadsheets/d/1wFb31JqI0QDXmijN82p_oJt3Km28_WsI0Fa8VfFjkp0/edit#gid=0') head(fn) ## # A tibble: 6 × 14 ## place elim accuracy damage_to_p distance_m mats_used damage_ta…¹ hits player ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> ## 1 27 0 0 ...
2456 sym 21 img
ABCD zygosity
zygosity first need to deal with families that have more than 1 or 2 sibs/twins # read in raw data files acspsw03 <- fread("/Users/clairemorrison/Desktop/ctc_sleep_psych/acspsw03.txt", header=T, data.table=F) # just keep baseline for dems/ids # it looks like zygo is only in baseline ids1 <- acspsw03[ which(acspsw03$eventname=='baseline_year_1_a...
825 sym R (39969 sym/101 pcs)