Supporting Statistical Analysis for Research

3.2 Relationship between two continuous variables

1. Import the Mroz.csv data set.

   library(tidyverse)

   mroz_path <- file.path("..", "datasets", "Mroz.csv")
   mroz <- read_csv(mroz_path, guess_max = 100000, col_types = cols())

   Warning: Missing column names filled in: 'X1' [1]

   glimpse(mroz)

   Observations: 753
   Variables: 9
   $ X1   <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17...
   $ lfp  <chr> "yes", "yes", "yes", "yes", "yes", "yes", "yes", "yes", "...
   $ k5   <dbl> 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...
   $ k618 <dbl> 0, 2, 3, 3, 2, 0, 2, 0, 2, 2, 1, 1, 2, 2, 1, 3, 2, 5, 0, ...
   $ age  <dbl> 32, 30, 35, 34, 31, 54, 37, 54, 48, 39, 33, 42, 30, 43, 4...
   $ wc   <chr> "no", "no", "no", "no", "yes", "no", "yes", "no", "no", "...
   $ hc   <chr> "no", "no", "no", "no", "no", "no", "no", "no", "no", "no...
   $ lwg  <dbl> 1.2101647, 0.3285041, 1.5141279, 0.0921151, 1.5242802, 1....
   $ inc  <dbl> 10.910001, 19.500000, 12.039999, 6.800000, 20.100000, 9.8...

2. Plot inc against lwg.

   ggplot(mroz, aes(x = lwg, y = inc)) + 
     geom_point() +
     theme_bw()

   

3. Plot age against lwg. Add a loess line to the plot.

   ggplot(mroz, aes(x = age, y = lwg)) + 
     geom_point() +
     geom_smooth(color = "blue") +
     theme_bw()

   `geom_smooth()` using method = 'loess' and formula 'y ~ x'