A New Week!

We aren’t computer scientists and that’s okay!

We make lots of mistakes. Mistakes are funny. You can laugh with us.

Let’s go, Simba, Pumbaa, and Timon!

1 Column names

1.1 rename()

The rename(new_name = old_name) function in dplyr package is designed to change the name of a dataframe

library(tidyverse)

colnames(airquality)

airquality %>% 
  rename(Solar_R = Solar.R,
         Temp_F = Temp)

1.2 clean_names()

The clean_names() function in “janitor” package modifies column names consisit of number, letters, and "_".

library(janitor)

colnames(airquality)

airquality %>% 
  clean_names()

Exercise 1

import data from the “ozone_samples_demo.csv” file and clean the column names

2 Control structures

Control structures in R allow you to control the flow of execution of a series of R expressions.

The if and else combination is probably the most commonly used control structure in R. It is a great tool to test a condition and take actions depending on whether the condition is TRUE or FALSE.

2.1 if-else

Use if and else statements to test one condition.

# basic if-else structure

if (test) {  # test a condition in the parentheses
  #do something in the braces when the test is true   
} else {          
  #do something else when the test is false
}


# try a simple example
x <- runif(1, 0, 10) # create a random number between 0-10

if (x > 5) {
  y <- 10
} else {
  y <- 0
}

You can just use if statement.

if (test) {
  # do something
} 

x <- runif(1, 0, 10) # create a random number between 0-10

y <- 2

if (x > 5) {
  y <- 10
}

You can test a series of conditions

if (test1) {  
  #do something    
} else if (test2){          
  #do something different      
} else {
  # do something different
}

score <- 85

if (score >= 90) {
  grade <- "A"
}else if (score >= 80) {
  grade <- "B"
}else if (score >= 70){
  grade <- "C"
}else {
  grade <- "F"
}

2.2 ifelse()

The ifelse() funtion is a shorthand form of the if-else statement. It can take a logical vector for testing.

ifelse(test, do something when test is TRUE, do something when test is FALSE)

#Example

x <- c(5,8,2,9)

# test if the modulus (remainder from division) of x is zero
ifelse(x %% 2 == 0, "even", "odd") 

#what happen if we use if - else statement
if (x %% 2 == 0) {
  print("even")
} else {
  print("odd")
}

Exercise 2

x <- runif(10, -20, 20)

with the given vector x, write R code to tell if each element is negative or positive.

Treat zero as positive.

ifelse() and mutate() make a powerful combination in creating new columns.

# if we want to compare ozone between weekday and weekend

library(lubridate)

airquality %>% 
  mutate(year = 1973, # create a year column
         date = make_date(year, Month, Day), # create date
         wday = wday(date, label = T), # create day of a week
         week = ifelse(wday %in% c("Sun", "Sat"), "weekend", "weekday")) %>%  # seperate weekend and weekday
  ggplot(aes(x = week, y = Ozone)) +
  geom_boxplot() +
  facet_grid(. ~ factor(Month))

Exercise 3

Calculate and compare mean ozone concentration between weekdays and weekends using the data in exercise 1

2.3 case_when()

The case_when() function allows you to work on multiple tests. It also takes the logical vector for testing.

case_when(test1 ~ do something when test1 is true, test2 ~ do something different when test2 is true)

score <- runif(10, 60, 100)

grade <- case_when(score >= 90 ~ "A",
                   score >= 80 ~ "B",
                   score >= 70 ~ "C",
                   score >= 60 ~ "D")

# if we want to compare ozone among seasonas
airquality %>% 
  mutate(season = case_when(Month %in% c(3, 4, 5) ~ "spring",
                            Month %in% c(6, 7, 8) ~ "summer",
                            Month %in% c(9, 10, 11) ~ "fall")) %>% 
  ggplot(aes(x = season, y=Ozone))+
  geom_boxplot()
  
#factor(season, levels = c("spring","summer","fall"))

Exercise 4

Calculate and compare mean ozone concentration among seasons using the data in exercise 1

3 Recap

column names
control structures: if - else, ifelse(), case_when()