R for Data Science: Lunchbreak Lessons

مدرس Mark Niemann-Ross

انتشار 2018/01/10

به‌روز‌رسانی 2023/06/30

مدت زمان 19h 55m

سطح متوسط

فایل تمرینی دارد

آموزش R for Data Science: Lunch Break Lessons

برنامه نویسی در بیت های کوچک آموخته می شود. شما بر اساس مفاهیم اساسی بنا می کنید. شما دانشی را که از قبل دارید به زبان بعدی منتقل می کنید. Lunch Break Lessons R - یکی از محبوب‌ترین زبان‌های برنامه‌نویسی برای تجزیه و تحلیل داده‌ها و گزارش‌دهی - را در درس‌های کوتاهی که برنامه‌نویسان موجود قبلاً می‌دانند، آموزش می‌دهد.\r\n\r\n اصول زبان را مرور کنید، روش‌هایی را برای بهبود کد R موجود کشف کنید، ویژگی‌های جدید و جالب را کشف کنید، و با ابزارها و کتابخانه‌های توسعه مفیدی آشنا شوید که زمان برنامه‌نویسی با R را بسیار مفیدتر می‌کنند.\r\n\r\n

Data Analysis R (Programming Language)

Introduction

Welcome ()

Exercise files ()

1. R for Data Science Lessons (Jan-Mar 2018)

R built-in data sets ()

Vector math ()

Subsetting ()

R data types: Basic types ()

R data types: Vector ()

R data types: List ()

R data types: Factor ()

R data types: Matrix ()

R data types: Array ()

R data types: Data frame ()

Data frames: Order and merge ()

Data frames: Read and update ()

2. R for Data Science Lessons (Apr-Jun 2018)

Data frames: rbind ()

Dataframes: cbind ()

apply and lapply ()

mapply ()

plot ()

Brackets and double-brackets ()

mean, rowMeans, and colMeans ()

RSQLite ()

sqldf ()

Aggregate ()

Random numbers ()

Pipeline ()

Working with clipboards ()

3. R for Data Science Lessons (Jul-Sep 2018)

Style guides ()

cut ()

split ()

askYesNo ()

cdplot ()

Fun ()

boxplot ()

Histogram ()

Plot to file ()

coplot ()

cowsay ()

table ()

Look inside ()

4. R for Data Science Lessons (Oct-Dec 2018)

barplot ()

Pie chart ()

unlist ()

Joins: Inner and full ()

Joins: Left and right ()

Sets: Union, intersect, and difference ()

Sets: Equal and in ()

colors ()

ifelse ()

spineplot ()

browser ()

debugonce ()

Default mirror ()

5. R for Data Science Lessons (Jan-Mar 2019)

Dealing with NA ()

Using with() ()

Simple string matching ()

grep ()

dotchart ()

fourfoldplot ()

matplot ()

dimnames ()

mosaicplot ()

stemplot ()

stripchart ()

sunflower ()

Switch ()

6. R for Data Science Lessons (Apr-Jun 2019)

Switch on factors ()

Any/all ()

sub, gsub, regex, and backreferences ()

agrep and fuzzy matching ()

combn finds combinations ()

edit, fix, and dataentry ()

zeallot ()

menu ()

person ()

txtProgressBar ()

zip and tar ()

bitwise ()

by is like tapply ()

Update your R ()

7. R for Data Science Lessons (Jul-Sep 2019)

Be careful with transpose ()

Passwords ()

heatmap ()

combine ()

stopifnot ()

weighted.mean ()

chartr ()

file.choose ()

duplicated and unique ()

load and save ()

floor, round, ceiling, and trunc ()

expand.grid ()

Professional groups ()

8. R for Data Science Lessons (Oct-Dec 2019)

Simplify with c ()

Logical operators ()

char.expand ()

complete.cases ()

swirl ()

tryCatch ()

Double colons ()

for loop ()

The 100th episode ()

while loop ()

repeat loop ()

Create your own swirl lesson ()

Logic and flow control ()

9. R for Data Science Lessons (Jan-Mar 2020)

matrix, row, and column ()

cumsum, cumprod, cummax, an dcummin ()

issymetric ()

file.access ()

file.info ()

dput and dget ()

Sort a data frame by multiple columns ()

diag ()

crossprod ()

upper.tri and lower.tri ()

strsplit() splits strings at matched characters ()

Use setnames() to change the name of an object ()

Change the structure of a vector with stack() ()

10. R for Data Science Lessons (Apr-Jun 2020)

Use .Rmd for documentation ()

Use droplevels() to simplify factors ()

Use rep() to create long repetitive vectors ()

Use format() to improve readability ()

Use pmax() and pmin() to discover the scope of paired vectors ()

Use print() for more than you do now ()

Use range() and extendrange() to analyze and manipulate groups of numbers ()

Evaluate the importance of a number with rank() ()

Use saveRDS() and readRDS() to serialize objects ()

Use regular expressions with regexpr() and gregexpr() ()

message ()

regexpr ()

diff ()

11. R for Data Science Lessons (Jul-Sep 2020)

exists ()

formulas ()

RPres ()

lattice: Introduction ()

lattice: xyplot ()

lattice: cloud and wireframe ()

lattice: contourplot ()

lattice: barchart ()

lattice: splom charts ()

lattice: panels ()

lattice: stripplot ()

whichmin and whichmax ()

par: font, size, color ()

12. R for Data Science Lessons (Oct-Dec 2020)

identical ()

par: margins ()

par: pch and points ()

legend ()

Matrix math: Overview of functions ()

Matrix math review ()

matrix: solve systems ()

matrix: solve inverse ()

matrix: backsolve and forwardsolve ()

Matrix: Determinant ()

Arrays and outer ()

Matrix: Crossproduct ()

Matrix SVD and QR decomposition ()

13. R for Data Science Lessons (Jan-Mar 2021)

Matrix: Eigenvalues and eigenvectors ()

Locator ()

on.exit ()

missing ()

nargs ()

tidyverse ()

gutenbergr ()

Create and clean a natural language corpus ()

Remove stopwords from an NLP corpus ()

NLP and term-document matrix ()

14. R for Data Science Lessons (Apr-Jun 2021)

Analyze term-document matrix ()

NLP packages: Tidytext ()

NLP packages: Quanteda ()

NLP packages: Sentiment analysis ()

Word clouds ()

Hidden features of installr ()

Use the Matrix package ()

Create a sparse matrix ()

Sparse matrices, triangles, and more ()

Bootstrap analysis with R ()

checkUsage ()

15. R for Data Science Lessons (Jul-Sep 2021)

Use R on the Raspberry Pi ()

list2df() ()

Introduction to clustering ()

Clustering with kmeans ()

Clustering with pam and clara ()

Understanding silhouette graphs ()

Clustering with fanny ()

Clustering with hclust ()

Clustering with agnes ()

Clustering with diana ()

cutree and identify with hclust ()

Clustering with mona ()

Clustering: dist vs. daisy ()

16. R for Data Science Lessons (Oct-Dec 2021)

Parameterized R markdown ()

Run R on a schedule ()

The new forward pipe operator ()

Backslash lambda functions ()

Dist() in depth ()

Scale() ()

toJSON ()

fromJSON ()

Validate JSON ()

Plotmath and expression ()

Run R in batch mode ()

Explore music ()

BEEP ()

17. R for Data Science Lessons (Jan-Mar 2022)

install.packages ()

old.packages, new.packages, and update.packages ()

library and require ()

Excel in R: SUM ()

Excel in R: IF ()

Excel in R: LOOKUP ()

Excel in R: LEFT and RIGHT ()

Excel in R: MATCH ()

Excel in R: CHOOSE ()

Excel in R: DATE ()

Excel in R: DAYS ()

Excel in R: FIND and FINDB ()

18. R for Data Science Lessons (Apr-Jun 2022)

Excel in R: LEN ()

Excel in R: COUNTA ()

Excel in R: NETWORKDAYS ()

Excel in R: IFERROR ()

Excel in R: INDEX ()

Excel in R: COUNT ()

Excel in R: AVERAGE ()

Excel in R: SUMIF and AVERAGEIF ()

Excel in R: COUNTIF ()

Excel in R: CONCATENATE ()

Excel in R: MAX and MIN ()

Excel in R: PROPER ()

Excel in R: AND ()

19. R for Data Science Lessons (Jul-Sep 2022)

Citation ()

Vectorize ()

Powerpoint from R ()

Infix operator ()

Kronecker ()

Flowcharting ()

Glue ()

Crayon ()

COVID-19 ()

Apexcharter ()

Factorial ()

Download files ()

Choose ()

20. R for Data Science Lessons (Oct-Dec 2022)

Beta and gamma ()

as.Date() ()

as.POSIXlt() ()

as.POSIXct() ()

Lubridate ()

ISOdate() ()

system.timezone() and OlsonNames() ()

format() ()

difftime() ()

seq.Date() ()

weekdays(), months(), quarters(), Julian() ()

21. R for Data Science Lessons (Jan-Mar 2023)

Introduction to Plumber ()

Plumber request and response objects ()

getwd setwd ()

Use Visual Studio Code with R ()

Tibbles ()

Overview of dplyr ()

dplyr: mutate ()

dplyr: select ()

dplyr: filter ()

dplyr: slice and friends ()

dplyr: summarise ()

dplyr: arrange ()

dplyr: group_by ()

22. R for Data Science Lessons (Apr-Jun 2023)

dbplyr translates R to SQL ()

dplyr: pull ()

dplyr: joins ()

R7 OOP: Introduction ()

R7 OOP: Properties ()

R7 OOPS: Property getters and setters ()

R7 OOPS: Validators ()

R7 OOP: Class Inheritance ()

R7 OOP: Generics and Methods ()

Python with RStudio ()

Animating plots ()

Animating ggplot ()

Introduction to Quarto ()

Ex_Files_R_Data_Lunch_Break.zip (1.0 MB)

Ex_Files_R_for_Data_Sci_2021_Q3.zip (813 KB)

Ex_Files_R_for_Data_Sci_2021_Q4.zip (9 KB)

Ex_Files_R_for_Data_Sci_2022_Q1.zip (10 KB)

Ex_Files_R_for_Data_Sci_2022_Q2.zip (11 KB)

Ex_Files_R_for_Data_Sci_2022_Q3.zip (39 KB)

Ex_Files_R_for_Data_Sci_2022_Q4.zip (9 KB)

Ex_Files_R_for_Data_Sci_2023_Q1.zip (8 KB)

Ex_Files_R_for_Data_Sci_2023_Q2.zip (445 KB)

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