Agenda

• Loading a .csv file
• (Re)coding factor variables
• Lab 05: on your own

Reading in a .csv file

• So far, the data we've been using has been included in an R package
• To access this data we just run data("data set")
• What if we want to read in other data, for example from a .csv file?
• enter: read_csv()
• read_csv() is a function from the readr package, which is included when you load the tidyverse
• it works like this:

df <- read_csv("the-path-to-your-file.csv")

Where df can be whatever you'd like to call your new dataset

Recoding factor variables

• Sometimes variables come in as numeric, but we want them to be factors

Recoding factor variables

• This dataset contains data on a sample of 1450 birth records that statistician John Holcomb selected from the North Carolina State Center for Health and Environmental Statistics.

glimpse(NCbirths)

## Observations: 1,450
## Variables: 15
## $ ID            <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,…
## $ Plural        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Sex           <int> 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1,…
## $ MomAge        <int> 32, 32, 27, 27, 25, 28, 25, 15, 21, 27, 26, 20, 19…
## $ Weeks         <int> 40, 37, 39, 39, 39, 43, 39, 42, 39, 40, 41, 41, 40…
## $ Marital       <int> 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1,…
## $ RaceMom       <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 5, 1,…
## $ HispMom       <fct> N, N, N, N, N, N, N, N, N, N, N, N, N, P, N, M, N,…
## $ Gained        <int> 38, 34, 12, 15, 32, 32, 75, 25, 28, 37, 45, 52, 26…
## $ Smoke         <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,…
## $ BirthWeightOz <int> 111, 116, 138, 136, 121, 117, 143, 113, 120, 124, …
## $ BirthWeightGm <dbl> 3146.85, 3288.60, 3912.30, 3855.60, 3430.35, 3316.…
## $ Low           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ Premie        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ MomRace       <fct> white, white, white, white, white, white, white, w…

Recoding factor variables

lm(BirthWeightOz ~ RaceMom, data = NCbirths)

## 
## Call:
## lm(formula = BirthWeightOz ~ RaceMom, data = NCbirths)
## 
## Coefficients:
## (Intercept)      RaceMom  
##   116.27732     -0.01624

Recoding factor variables

NCbirths <- NCbirths %>%
  mutate(
    RaceMom = recode_factor(
      RaceMom,
      `1` = "white",
      `2` = "black",
      `3` = "American Indian",
      `4` = "Chinese",
      `5` = "Japanese",
      `6` = "Hawaiian",
      `7` = "Filipino",
      `8` = "Other Asian or Pacific Islander"
    )
  )

Recoding factor variables

lm(BirthWeightOz ~ RaceMom, data = NCbirths)

## 
## Call:
## lm(formula = BirthWeightOz ~ RaceMom, data = NCbirths)
## 
## Coefficients:
##                            (Intercept)  
##                               117.8720  
##                           RaceMomblack  
##                                -7.3087  
##                 RaceMomAmerican Indian  
##                                -2.5538  
##                         RaceMomChinese  
##                                 8.1280  
##                        RaceMomJapanese  
##                                 0.6463  
##                        RaceMomFilipino  
##                               -20.8720  
## RaceMomOther Asian or Pacific Islander  
##                                 1.1280

• What is the referent category?
• What if I wanted to change that?

Recoding factor variables

new_levels <- c("American Indian", "white", "black",  "Chinese", "Japanese", "Filipino", "Other Asian or Pacific Islander")
NCbirths <- NCbirths %>%
  mutate(
    RaceMom = fct_relevel(RaceMom, new_levels)
  )

Recoding factor variables

• Sometimes variables come in as numeric, but we want them to be factors

Open Lab 05: multiple regression in RStudio