16  Grouping columns with dplyr

author: Юрій Клебан

Before start load packages

library(dplyr) # for demos
#install.packages("gapminder")
library(gapminder)  # load package and dataset

16.1 group_by() + summarise()

group_by(.data, ..., add = FALSE) returns copy of table grouped by defined columns.

Let’s find average by lifeExp for each continent in 2002 (ouput is continent, lifeExpAvg2002, countriesCount, year = 2002):

gapminder |>
    filter(year == 2002) |> # year
    group_by(continent) |> # grouping condition, you ca
    summarise(
        lifeExpAvg2002 = mean(lifeExp),
        countriesCount = n() # n() count of rows in group  
        )
A tibble: 5 × 3
continent lifeExpAvg2002 countriesCount
<fct> <dbl> <int>
Africa 53.32523 52
Americas 72.42204 25
Asia 69.23388 33
Europe 76.70060 30
Oceania 79.74000 2

Let’s find total population for each continent in 2002 (ouput is continent, totalPop, year):

gapminder |>
    filter(year == 2002) |> # year
    group_by(continent, year) |> # grouping condition
    summarise(totalPop = sum(pop), .groups = "keep")
A grouped_df: 5 × 3
continent year totalPop
<fct> <int> <dbl>
Africa 2002 833723916
Americas 2002 849772762
Asia 2002 3601802203
Europe 2002 578223869
Oceania 2002 23454829

There are additional variations of summarise():

16.1.1 Task on Credits (rewrite it)

library(ISLR)

group_inc <- aggregate(Income ~ Age + Gender, data = Credit, mean)

m_data <- group_inc[group_inc$Gender == " Male", ]
nrow(m_data)

f_data <- group_inc[group_inc$Gender == "Female", ]
nrow(f_data)
with(m_data, plot(Age, Income, type = "l", col="red"))
with(f_data, lines(Age, Income, type = "l", col ="blue"))
63
62 

cd <- Credit %>%
select(Income, Age, Gender) %>%
group_by(Age, Gender) %>%
summarize(Income = mean(Income))

m_data <- cd %>% filter(Gender == " Male")
nrow(m_data)

f_data <- cd %>% filter(Gender == "Female")
nrow(f_data)

with(m_data, plot(Age, Income, type = "l", col="red"))
with(f_data, lines(Age, Income, type = "l", col ="blue"))
`summarise()` has grouped output by 'Age'. You can override using the `.groups`
argument.
63
62

16.2 Refences

  1. dplyr: A Grammar of Data Manipulation on https://cran.r-project.org/.
  2. Data Transformation with splyr::cheat sheet.
  3. DPLYR TUTORIAL : DATA MANIPULATION (50 EXAMPLES) by Deepanshu Bhalla.
  4. Dplyr Intro by Stat 545. 6.R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread). Introduction to Data Analysis
  5. Loan Default Prediction. Beginners data set for financial analytics Kaggle