18  Base R vs tidy

Author

Sean Davis, Martin Morgan, Lori Kern

Published

August 27, 2025

Modified

August 29, 2025

R is flexible and often has multiple ways of accomplishing the same or similar tasks. Find the options, packages, styles, etc. that work best for you. In this chapter, we will recap some of the previous BRFSS study that utilized base R objects and graphing, and compare doing the same exact anaysis using tidyverse and ggplot2.

18.1 Loading the Dataset

First, let’s load the dataset into R. We will use the read.csv() function from the base R package to read the data and store it in a data frame called brfss. We will also use the read_csv() function from the readr package to load the tidyverse tibble data.frame. Make sure the CSV file is in your working directory, or provide the full path to the file.

path <- file.choose()    # look for BRFSS-subset.csv

# We will be using dplyr throughout so let's load now
library(dplyr)
# loading using base R
stopifnot(file.exists(path))
brfss_DF <- read.csv(path)
# loading using readr
library(readr)
brfss_tbl <- readr::read_csv(path)

Let’s examine our objects:

# Classic data frame
head(brfss_DF)
  Age   Weight    Sex Height Year
1  31 48.98798 Female 157.48 1990
2  57 81.64663 Female 157.48 1990
3  43 80.28585   Male 177.80 1990
4  72 70.30682   Male 170.18 1990
5  31 49.89516 Female 154.94 1990
6  58 54.43108 Female 154.94 1990
# Tidyverse tibble
head(brfss_tbl)
# A tibble: 6 × 5
    Age Weight Sex    Height  Year
  <dbl>  <dbl> <chr>   <dbl> <dbl>
1    31   49.0 Female   157.  1990
2    57   81.6 Female   157.  1990
3    43   80.3 Male     178.  1990
4    72   70.3 Male     170.  1990
5    31   49.9 Female   155.  1990
6    58   54.4 Female   155.  1990
# Classic data frame
class(brfss_DF)
[1] "data.frame"
# Tidyverse tibble
class(brfss_tbl)
[1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"
Note

Note: A tidyverse tibble object inherits a data.frame class. This means that most data.frame operations like dim(), colnames(), $, [, etc. will work on the tibble object as well.

18.2 Clean data

Both ‘Sex’ and ‘Year’ are really factor values (each can only take on specific levels, ‘Female’ and ‘Male’ for ‘Sex’, and ‘1990’ and ‘2010’ for ‘Year’).

# base R / data.frame
brfss_DF$Year <- factor(brfss_DF$Year)
brfss_DF$Sex <- factor(brfss_DF$Sex)
# dplyr / tibble 
brfss_tbl <-
brfss_tbl |>
    mutate(
        Sex = factor(Sex,
          levels = c("Female", "Male")),
        Year = factor(Year,
          levels = c("1990", "2010"))
    )

18.3 Data Exploration

Let’s execute some basic exploration. summary() works the same for both objects but lets looks at some summary tables and counts. They produce the same results but in different formats.

We’ll start with basic table of a single variable:

# base R / data.frame
table(brfss_DF$Year)
# dplyr / tibble
brfss_tbl |> count(Year)

 1990  2010 
10000 10000 
# A tibble: 2 × 2
  Year      n
  <fct> <int>
1 1990  10000
2 2010  10000
# base R / data.frame
table(brfss_DF$Sex)
# dplyr / tibble
brfss_tbl |> count(Sex)

Female   Male 
 12039   7961 
# A tibble: 2 × 2
  Sex        n
  <fct>  <int>
1 Female 12039
2 Male    7961

Now let’s look at contingency table

# base R / data.frame
table(brfss_DF$Sex, brfss_DF$Year)
        
         1990 2010
  Female 5718 6321
  Male   4282 3679
# dplyr / tibble
brfss_tbl |> count(Sex, Year)
# A tibble: 4 × 3
  Sex    Year      n
  <fct>  <fct> <int>
1 Female 1990   5718
2 Female 2010   6321
3 Male   1990   4282
4 Male   2010   3679

We can get the tidy table to look even more similar to the base R table with the help of the tidyr package’s function pivot_wider

# base R / data.frame
table(brfss_DF$Sex, brfss_DF$Year)
        
         1990 2010
  Female 5718 6321
  Male   4282 3679
# dplyr / tibble
library(tidyr)
brfss_tbl |> count(Sex, Year) |>
    tidyr::pivot_wider(names_from = "Year", values_from = "n")
# A tibble: 2 × 3
  Sex    `1990` `2010`
  <fct>   <int>  <int>
1 Female   5718   6321
2 Male     4282   3679

What about some summary statistics on the columns of data? summarize() will create the new data.frame automatically; base R you have to create your own.

# base R / data.frame
data.frame(
  avg_age = mean(brfss_DF$Age, na.rm = TRUE),
  ave_wt  = mean(brfss_DF$Weight, na.rm = TRUE),
  ave_ht  = mean(brfss_DF$Height, na.rm = TRUE)
)
# dplyr / tibble
brfss_tbl |>
    summarize(
        avg_age = mean(Age, na.rm = TRUE),
        ave_wt = mean(Weight, na.rm = TRUE),
        ave_ht = mean(Height, na.rm = TRUE)
    )
   avg_age   ave_wt   ave_ht
1 50.99164 75.42455 169.2131
# A tibble: 1 × 3
  avg_age ave_wt ave_ht
    <dbl>  <dbl>  <dbl>
1    51.0   75.4   169.

If we want to get more complex with groupings by Year and Sex, dlpyr uses group_by where base R would use aggregate.

# base R / data.frame
aggregate(
  cbind(Age, Weight, Height) ~ Sex + Year,
  data = brfss_DF,
  FUN = function(x) mean(x, na.rm = TRUE)
)
# dplyr / tibble
brfss_tbl |>
    group_by(Sex, Year) |>
    summarize(
        avg_age = mean(Age, na.rm = TRUE),
        ave_wt = mean(Weight, na.rm = TRUE),
        ave_ht = mean(Height, na.rm = TRUE)
    )
     Sex Year      Age   Weight   Height
1 Female 1990 46.09153 64.84333 163.2914
2   Male 1990 43.87574 81.19496 178.2242
3 Female 2010 57.07807 73.03178 163.2469
4   Male 2010 56.25465 88.91136 178.0139
# A tibble: 4 × 5
# Groups:   Sex [2]
  Sex    Year  avg_age ave_wt ave_ht
  <fct>  <fct>   <dbl>  <dbl>  <dbl>
1 Female 1990     46.2   64.8   163.
2 Female 2010     57.1   73.0   163.
3 Male   1990     43.9   81.2   178.
4 Male   2010     56.2   88.8   178.

18.4 Visualization

Before we start visualizing, lets create a few different subsets of data.

# base R / data.frame
brfss_female_DF <-
    brfss_DF[brfss_DF$Sex == "Female",]
brfss_male_DF <-
    brfss_DF[brfss_DF$Sex == "Male",]
brfss_2010_DF <-
    brfss_DF[brfss_DF$Year == "2010",]
# dplyr / tibble
brfss_male_tbl <-
    brfss_tbl |> filter(Sex == "Male")
brfss_female_tbl <-
    brfss_tbl |> filter(Sex == "Female")
brfss_2010_tbl <-
    brfss_tbl |> filter(Year == "2010")

We should also load the ggplot2 package so we can compare base R graphics vs ggplot2

library(ggplot2)

Let’s start with a boxplot that compares the Weights of Males vs Females for the 2010 dataset.

# base R
plot(Weight ~ Sex, brfss_2010_DF)
# ggplot2
ggplot(brfss_2010_tbl) +
    aes(x = Sex, y = Weight) +
    geom_boxplot()

Let’s look at some density and scatterplots.

# base R
den_male <- density(brfss_2010_DF$Weight[brfss_2010_DF$Sex == "Male"], na.rm = TRUE)
den_female <- density(brfss_2010_DF$Weight[brfss_2010_DF$Sex == "Female"], na.rm = TRUE)
plot(den_male, 
     col = "skyblue", lwd = 2,
     main = "Density of Weight by Sex",
     xlab = "Weight")

lines(den_female, 
      col = "lightsalmon", lwd = 2)

legend("topright",
    legend = c("Male", "Female"),
    col = c("skyblue", "lightsalmon"), lwd = 2)
# ggplot2
brfss_2010_tbl |>
    ggplot() +
    aes(x = Weight, color= Sex) +
    geom_density()

Presumably taller people are heavier than shorter people. Let’s examine this relationship.

# base R
plot(Weight ~ Height, brfss_2010_DF)
# ggplot2
brfss_2010_tbl |>
    ggplot() +
    aes(x = Height, y = Weight) +
    geom_point()

Let’s fit the linear regression

# base R
plot(Weight ~ Height, brfss_2010_DF)
fit <- lm(Weight ~ Height, brfss_2010_DF)
abline(fit, col="blue", lwd=2)
# ggplot2
brfss_2010_tbl |>
    ggplot() +
    aes(x = Height, y = Weight) +
    geom_point() +
    geom_smooth(method = "lm")

We saw that there could be a difference based on Sex. Let’s add color to the points

# base R
colors <- c("Female" = "lightsalmon", "Male" = "skyblue")
plot(Weight ~ Height, brfss_2010_DF,
  col = colors[Sex], pch = 16)
for (sex in levels(brfss_2010_DF$Sex)) {
  subset_data <- subset(brfss_2010_DF, Sex == sex)
  fit <- lm(Weight ~ Height, data = subset_data)
  abline(fit, col = colors[sex], lwd = 2)
}
legend("topleft", legend = levels(brfss_2010_DF$Sex), 
       col = colors, pch = 16, bty = "n")
# ggplot2
brfss_2010_tbl |>
    ggplot() +
    aes(x = Height, y = Weight, color = Sex) +
    geom_point() +
    geom_smooth(method = "lm")

Let’s dig into some visualizations of the 2010 Males and recreate the histograms of Weight but we did not create that subset.

# base R
brfss_2010_Male <- subset(brfss_DF,
    Year == 2010 & Sex == "Male")
hist(brfss_2010_Male$Weight)
# ggplot2
brfss_2010_tbl |> filter(Sex == "Male") |>
    ggplot() +
    aes(x = Weight) +
    geom_histogram(col = "white")

What if we took all the Males and looked to see if the relationship of Height and Weight changed between 1990 and 2010.

# base R
colors <- c("1990" = "lightsalmon","2010" = "skyblue")
plot(log10(Weight) ~ Height, brfss_male_DF,
  col = colors[Year], pch = 16, ylab = "log10(Weight)")
for (yr in levels(brfss_male_DF$Year)) {
  subset_data <- subset(brfss_male_DF, Year == yr)
  fit <- lm(log10(Weight) ~ Height, data = subset_data)
  abline(fit, col = colors[yr], lwd = 2)
}
legend("topleft", legend = levels(brfss_male_DF$Year), 
       col = colors, pch = 16, bty = "n")
# ggplot2
ggplot(brfss_male_tbl) +
    aes(x = Height, y = log10(Weight), color = Year) +
    geom_point() +
    geom_smooth(method = "lm") +
    labs(title = "BRFSS Male Subset")

18.5 Summary

There are many visualization packages in R. You can explore the many options and what each has to offer to design high quality, customized plots for reporting.