Purpose

This file contains commonly used R functions for data analysis, data management, and visualization. All code examples will use iris, a pre-loaded R data set. Type ?iris in the console to learn more information about the dataset.

Data Management

1. factor()

Package: base R

#Explanation: Specifies hierarchy among a set of values or a column. The code below sets setosa to the 1st level, versicolor to the 2nd, and virinica to the 3rd level. Factor is  helpful when dealing with categorical since it assigns numerical levels. 

# Convert the Species column to a factor.
iris$Species <- factor(iris$Species)

# Get the levels of the Species factor
levels(iris$Species)

## [1] "setosa"     "versicolor" "virginica"

# Check the class of the Species column
class(iris$Species)

## [1] "factor"

2. for()

Package: base R

#Explanation: For loops performs a set of operations on each value in a sequence. 

#Loop over the columns of the iris dataset
for (col in names(iris)) {
    # Calculate the mean of the column
    col_mean <- mean(iris[[col]])
    # Print the column name and mean
    cat("Mean of", col, ":", col_mean, "\n")
  }

## Mean of Sepal.Length : 5.843333 
## Mean of Sepal.Width : 3.057333 
## Mean of Petal.Length : 3.758 
## Mean of Petal.Width : 1.199333 
## Mean of Species : NA

3. function()

Package: base R

#Explanation: Makes a new function that can execute a specific task through a combination of specified functions

# Define a function that calculates the mean of a numeric column in a data frame
mean_column <- function(df, column) {
  if (!is.numeric(df[[column]])) {
    stop("Column must be numeric")
  }
  return(mean(df[[column]]))
}

# Calculate the mean of the Sepal.Length column using the mean_column() function
sepal_length_mean <- mean_column(iris, "Sepal.Length")
sepal_length_mean

## [1] 5.843333

4. ifelse()

Package: base R

#Explanation: If a condition is met, then a action or function is executed. If the condition is not met, a different action or function is executed.

# Create a new column called "SpeciesCode" based on the "Species" column
iris$SpeciesCode <- ifelse(iris$Species == "setosa", 1,
                           ifelse(iris$Species == "versicolor", 2, 3))

# Print the first 10 rows of the iris dataset
head(iris, 10)

5. is.na()

Package: base R

#Explanation: Identifies if value is missing (NA) or not

#Count all missing values in iris
sum(is.na(iris))

## [1] 0

#Count all non-missing values in iris
sum(!is.na(iris)) # ! = not

## [1] 900

6. lapply()

Package: base R

#Explanation: Applies a function on each column or value of a dataset and returns a list

# Apply the `mean()` function to each numeric column of the iris dataset
means <- lapply(iris[,1:4], mean)

# Print the resulting list of means
print(means)

## $Sepal.Length
## [1] 5.843333
## 
## $Sepal.Width
## [1] 3.057333
## 
## $Petal.Length
## [1] 3.758
## 
## $Petal.Width
## [1] 1.199333

7. merge()

Package: base R

#Explanation: Combines two dataframes which have one column with identical values

#Normally you merge two different datasets, but we will merge iris with itself
merged_iris <- merge(iris, iris, #names of two datasets
                     by = c("Species"), #shared column between two datasets
                     suffixes = c("_1", "_2")) #adding suffixes to distinguish orginal values 

head(merged_iris) #see the first 6 rows

8. read.csv()

Package: base R

#Explanation: Loads .csv files into R 

#iris2 <- read.csv("name_of_file.csv")
#make sure your working directory is set to your file location.

9. rename()

Package: dplyr

#Explanation: Changes column names
library(dplyr) #part of tidyverse

iris_renamed <- iris %>% rename(SL = Sepal.Length, #Changed Sepal.Length to SL
                                SW = Sepal.Width,  #Changed Sepal.Width to SW
                                PL = Petal.Length, #Changed Petal.Length to PL
                                PW = Petal.Width,  #Changed Petal.Width to PW
                                Class = Species)   #Changed Species to Class
iris_renamed

10. sapply()

Package: base R

#Explanation: Applies a function on each column or value of a dataset and returns a vector or matrix

# Apply the mean function to each column of the dataset
column_means <- sapply(X = iris, FUN = mean, na.rm = TRUE)

# View the column means
column_means #Species is categorical, so NA is returned

## Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species  SpeciesCode 
##     5.843333     3.057333     3.758000     1.199333           NA     2.000000

11. writeLines

Package: base R

#Explanation: Writes one or more character strings to text. Useful in working with strings.  

# Write the first 5 rows of the dataset to a text file
writeLines(colnames(iris))

## Sepal.Length
## Sepal.Width
## Petal.Length
## Petal.Width
## Species
## SpeciesCode

12. read.xlsx ()

Package: readxl

#Explanation: Loads .xlsx files into R 
library(readxl)

# Read the Excel file
# example <- read.xlsx("name_of_your_file.xlsx", 
#                      sheetIndex = 1, #will read only the first sheet
#                      startRow = 2, #will start reading the data at the 2nd row
#                      endRow = 100,  #will stop reading the data the 100th row
#                      colIndex = 1:5) #will only read columns 1-5

13. write.xlsx

Package: readxl

#Explanation: Saves dataframe as an .xlsx file type
# write.xlsx(df, #name of object
#            file = "example.xlsx", #name that of file being created
#            sheetName = "Sheet1",  #name that of sheet being created
#            row.names = FALSE)     #not including row names

14. str_c()

Package: stringr

#Explanation: Combines multiple values (characters, special characters, and/or numbers) together

library(stringr)

# Concatenate the species and petal width columns of the iris data frame
iris$Species_PetalWidth <- str_c(iris$Species, " - ", iris$Petal.Width)

# View the first few rows of the modified iris data frame
head(iris)

15. str_match()

Package: stringr

#Explanation: Locates and stores all occurrences of specificed characters, special characters, or numbers within a cell or cells.
library(stringr)

# Extract the petal length from the iris data frame using str_match
iris$Petal.Length.Extracted <- str_match(iris$Petal.Length, #variable
                                         "\\d+\\.\\d+") #searching for a digit (d), followed by a period (.), and followed by another digit (d)

# View the first few rows of the modified iris data frame
head(iris)

16. str_replace ()

Package: stringr

#Explanation: Replaces characters, special characters, or numbers in a column (or cell) with specified characters, special characters, or numbers
library(stringr)

# Replace the species names in the iris data frame with shorter names
iris$Species <- str_replace(iris$Species, #variable of interest
                            "versicolor", #character to search for 
                            "vers")       #replacement text

# View the change name 
table(iris$Species)

## 
##    setosa      vers virginica 
##        50        50        50

17. str_view_all()

Package: stringr

#Explanation: Shows all occurances of specified characters, special characters, or numbers within a cell or cells
library(stringr)

# Use str_view_all to visualize the matches of the "setosa" pattern in the Species column of the iris data frame
str_view_all(iris$Species, "setosa")

##  [1] │ <setosa>
##  [2] │ <setosa>
##  [3] │ <setosa>
##  [4] │ <setosa>
##  [5] │ <setosa>
##  [6] │ <setosa>
##  [7] │ <setosa>
##  [8] │ <setosa>
##  [9] │ <setosa>
## [10] │ <setosa>
## [11] │ <setosa>
## [12] │ <setosa>
## [13] │ <setosa>
## [14] │ <setosa>
## [15] │ <setosa>
## [16] │ <setosa>
## [17] │ <setosa>
## [18] │ <setosa>
## [19] │ <setosa>
## [20] │ <setosa>
## ... and 130 more

18. arrange()

Package: dplyr

#Explanation: Sorts rows in a dataframe based on one or more columns
library(tidyverse) #dplyr is part of tidyverse

# Sort the iris data frame by Petal.Length in ascending order
iris_sorted <- arrange(iris, Petal.Length)

# View the first few rows of the sorted data frame
head(iris_sorted)

19. bind_cols

Package: dplyr

#Explanation: Adds columns of a dataframe to another dataframe
library(tidyverse) #dplyr is part of tidyverse

# Create a new data frame with two columns
new_data <- data.frame(Sepal.Width = runif(150, 2.5, 4),
                       Sepal.Length = runif(150, 4, 7))

# Combine the iris and new_data data frames by column
iris_combined <- bind_cols(iris, new_data)

# View the first few rows of the combined data frame
head(iris_combined)

20. bind_rows

Package: dplyr

#Explanation: Adds rows of a dataframe to another dataframe
library(tidyverse) #dplyr is part of tidyverse

# Create a new data frame with two rows
new_data <- data.frame(Sepal.Width = c(3.5, 2.8),
                       Sepal.Length = c(6.2, 5.1),
                       Petal.Length = c(4.3, 1.5),
                       Petal.Width = c(1.3, 0.3),
                       Species = c("versicolor", "setosa"))

# Combine the iris and new_data data frames by row
iris_combined <- bind_rows(iris, new_data)

# View the last few rows of the combined data frame
tail(iris_combined)

21. filter()

Package: dplyr

#Explanation: Searches for values that fit one or more user-defined conditions
library(tidyverse) #dplyr is part of tidyverse

# Extract all rows where Species is "setosa"
setosa <- filter(iris, #df
                 Species == #variable
                   "setosa") #condition to search for

# Extract all rows where Sepal.Length is greater than 6.0
long_sepal <- filter(iris, Sepal.Length > 6.0)

# Extract all rows where Petal.Length is between 4.5 and 5.0
long_petal <- filter(iris, Petal.Length >= 4.5, Petal.Length <= 5.0)

22. mutate()

Package: dplyr

#Explanation: Creates a new column
library(tidyverse) #dplyr is part of tidyverse

# Add a new column that is the product of Sepal.Length and Sepal.Width
iris <- mutate(iris, #dataframe
               sepal_area = #name of new column
                 Sepal.Length * Sepal.Width) #formula to create values for new column

# Add a new column that is the ratio of Petal.Length to Petal.Width
iris <- mutate(iris, petal_ratio = Petal.Length / Petal.Width) 

# Add a new column that is the average of Sepal.Length and Petal.Length
iris <- mutate(iris,avg_length = (Sepal.Length + Petal.Length) / 2)

23. select()

Package: dplyr

#Explanation: Selects column(s) within a dataframe. Helpful when making a new dataframe with less columns.
library(tidyverse) #dplyr is part of tidyverse

# Select the columns Sepal.Length, Sepal.Width, and Species
iris_select <- select(iris, #dataframe 
                      Sepal.Length, Sepal.Width, Species) #variables 

# Select all columns except Sepal.Length and Sepal.Width
iris_select <- iris %>% select(-Sepal.Length, -Sepal.Width) #with pipes

# Select columns Petal.Length and Petal.Width, and all columns starting with Sepal
iris_select <- select(iris, Petal.Length, Petal.Width, starts_with("Sepal"))

Descriptive Analysis

24. lm()

Package: base R

#Explanation: Regresses one or more columns onto another column.

# Fit a linear regression model of Petal.Length on Sepal.Length and Sepal.Width
model <- lm(Petal.Length ~ #dependent variables 
              Sepal.Length + Sepal.Width, #independent variables
            data = iris) #data

# Print the summary of the model
summary(model)

## 
## Call:
## lm(formula = Petal.Length ~ Sepal.Length + Sepal.Width, data = iris)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.25582 -0.46922 -0.05741  0.45530  1.75599 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -2.52476    0.56344  -4.481 1.48e-05 ***
## Sepal.Length  1.77559    0.06441  27.569  < 2e-16 ***
## Sepal.Width  -1.33862    0.12236 -10.940  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6465 on 147 degrees of freedom
## Multiple R-squared:  0.8677, Adjusted R-squared:  0.8659 
## F-statistic:   482 on 2 and 147 DF,  p-value: < 2.2e-16

25. prop.table()

Package: base R

#Explanation: Converts a frequency or contignecy table into proportions 

# Compute the proportions of each species in the iris data set
prop.table(table(iris$Species))

## 
##    setosa      vers virginica 
## 0.3333333 0.3333333 0.3333333

# Compute the proportions of each species by combining the Species and Petal.Width variables
prop.table(table(iris$Species, iris$Petal.Width), margin = 1)

##            
##              0.1  0.2  0.3  0.4  0.5  0.6    1  1.1  1.2  1.3  1.4  1.5  1.6
##   setosa    0.10 0.58 0.14 0.14 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00
##   vers      0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.06 0.10 0.26 0.14 0.20 0.06
##   virginica 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.02
##            
##              1.7  1.8  1.9    2  2.1  2.2  2.3  2.4  2.5
##   setosa    0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
##   vers      0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00
##   virginica 0.02 0.22 0.10 0.12 0.12 0.06 0.16 0.06 0.06

26. Summary()

Package: base R

#Explanation: Lists the quarantines, median, mean, and range of one or more columns

# Obtain a summary of the dataset 
summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##    Species           SpeciesCode Species_PetalWidth Petal.Length.Extracted.V1
##  Length:150         Min.   :1    Length:150         Length:150               
##  Class :character   1st Qu.:1    Class :character   Class :character         
##  Mode  :character   Median :2    Mode  :character   Mode  :character         
##                     Mean   :2                                                
##                     3rd Qu.:3                                                
##                     Max.   :3                                                
##    sepal_area     petal_ratio       avg_length   
##  Min.   :10.00   Min.   : 2.125   Min.   :2.700  
##  1st Qu.:15.66   1st Qu.: 2.802   1st Qu.:3.362  
##  Median :17.66   Median : 3.300   Median :5.050  
##  Mean   :17.82   Mean   : 4.311   Mean   :4.801  
##  3rd Qu.:20.32   3rd Qu.: 4.667   3rd Qu.:5.800  
##  Max.   :30.02   Max.   :15.000   Max.   :7.300

27. table()

Package: base R

#Explanation: Creates a table that shows the amount of times a value is repeated in one or more columns

#Create table showing the count of 3 Species
table(iris$Species)

## 
##    setosa      vers virginica 
##        50        50        50

28. xtabs()

Package: base R

#Explanation: Creates a contingency table which shows the count for two or more variables

# Create a contingency table of Species and Sepal.Width
xtabs(~ Species + Sepal.Width, #two variables
      data = iris) #dataset

##            Sepal.Width
## Species      2 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9  3 3.1 3.2 3.3 3.4 3.5 3.6 3.7
##   setosa     0   0   1   0   0   0   0   0   1  6   4   5   2   9   6   3   3
##   vers       1   2   3   3   4   3   5   6   7  8   3   3   1   1   0   0   0
##   virginica  0   1   0   0   4   2   4   8   2 12   4   5   3   2   0   1   0
##            Sepal.Width
## Species     3.8 3.9  4 4.1 4.2 4.4
##   setosa      4   2  1   1   1   1
##   vers        0   0  0   0   0   0
##   virginica   2   0  0   0   0   0

29. aggregate()

Package: base R

#Explanation: Groups data by one or more variables and then applies a function (i.e. mean)  

# Summarize Sepal.Length by Species
aggregate(Sepal.Length ~ Species, #variables
          data = iris, #dataset
          FUN = mean) #function

30. tab1()

Package: epiDisplay

#Explanation: Creates a table that shows the frequnecy, percent, and cumulative percent for categorical data
library(epiDisplay)

tab1(iris$Species)

## iris$Species : 
##           Frequency Percent Cum. percent
## setosa           50    33.3         33.3
## vers             50    33.3         66.7
## virginica        50    33.3        100.0
##   Total         150   100.0        100.0

31. tbl_summary()

Package: gtsummary

#Explanation: Creates presentation style table that shows the subgroup count, percentages, and total count. 
library(gtsummary)

#Preloaded dataset 'mtcars' is used since function is better illustrated with this data. 

tbl_summary(mtcars, include = c("mpg", "cyl", "disp"))

Characteristic	N = 32¹
mpg	19.2 (15.4, 22.8)
cyl
4	11 (34%)
6	7 (22%)
8	14 (44%)
disp	196 (121, 326)
¹ Median (IQR); n (%)

32. count ()

Package: dplyr

#Explanation: Counts the number of rows in a dataframe or that number of rows that meet specified criteria
library(dplyr) #dplyr is part of tidyverse

#Count total observations in dataset
iris %>%
  count()

#Count observations by subgroup Species
iris %>%
  group_by(Species) %>%
  count()

33. group_by () & summarize ()

Package: dplyr

#Explanation: Group_by subgroups a variable. Summarize can apply a variety of statistical functions for the specified groups (mean, count, sum, etc.).
library(dplyr) #dplyr is part of tidyverse

iris %>%
  group_by(Species) %>%
  summarize(mean_sepal_length = mean(Sepal.Length),
            mean_sepal_width = mean(Sepal.Width),
            mean_petal_length = mean(Petal.Length),
            mean_petal_width = mean(Petal.Width))

Visualization

34. png()

Package: base R

#Explanation: Saves plots file type .png

#create simple bar plot of Petal.Length
barplot(iris$Petal.Length)

png("iris_boxplot.png") 
dev.off #Close PNG

## function (which = dev.cur()) 
## {
##     if (which == 1) 
##         stop("cannot shut down device 1 (the null device)")
##     .External(C_devoff, as.integer(which))
##     dev.cur()
## }
## <bytecode: 0x00000203ef189170>
## <environment: namespace:grDevices>

35. geom_bar

Package:ggplot2

#Explanation: Creates a bar chart for two columns
library(ggplot2) #ggplot2 is part of tidyverse


ggplot(data = iris, #data
       aes( #aes stands for aesthetics
         x = Species, #variable for x axis
         fill = Species)) +  #will color bins with subgroups of Species 
  geom_bar() #creates a bar chart

36. geom_point()

Package: ggplot2

#Explanation: Creates a scatter plot for two columns
library(ggplot2) #ggplot2 is part of tidyverse

ggplot(data = iris, #data 
       aes(x = Sepal.Length, y = Sepal.Width, #variables for x and y axes
           color = Species)) + #will color bins with subgroups of Species 
  geom_point() #creates scatter plot

37. scale()

Package: ggplot2

#Explanation: Adjusts scale of axes for scatterplots, bar charts, histrograms and other ggplot visuals 
library(ggplot2) #ggplot2 is part of tidyverse

# Create plot
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  
# Customize the x-axis scale
scale_x_continuous(name = "Length (cm)", breaks = seq(4, 8, 0.5), limits = c(4, 8)) +
  
# Customize the y-axis scale
scale_y_continuous(name = "Width (cm)", breaks = seq(2, 5, 0.5), limits = c(2, 5))

R Function Summary

Last Updated: May 14, 2023

Purpose

Data Management

1. factor()

2. for()

3. function()

4. ifelse()

5. is.na()

6. lapply()

7. merge()

8. read.csv()

9. rename()

10. sapply()

11. writeLines

12. read.xlsx ()

13. write.xlsx

14. str_c()

15. str_match()

16. str_replace ()

17. str_view_all()

18. arrange()

19. bind_cols

20. bind_rows

21. filter()

22. mutate()

23. select()

Descriptive Analysis

24. lm()

25. prop.table()

26. Summary()

27. table()

28. xtabs()

29. aggregate()

30. tab1()

31. tbl_summary()

32. count ()

33. group_by () & summarize ()

Visualization

34. png()

35. geom_bar

36. geom_point()

37. scale()