Publications by Daniel
Data visualization
Loading data set head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 5.1 3.5 1.4 0.2 setosa 4.9 3.0 1.4 0.2 setosa 4.7 3.2 1.3 0.2 setosa 4.6 3.1 1.5 0.2 setosa 5.0 3.6 1.4 0.2 setosa 5.4 3.9 1.7 0.4 setosa Scatter plot Create a empty canvas then create aesthetic mapping tell the function which dataset and variables t...
1162 sym R (4365 sym/32 pcs) 23 img 1 tbl
Deep neural networks- regression
Deep neural networks for regression Loading packages and data sets library(readr) library(keras) library(plotly) data("Boston", package = "MASS") data.set <- Boston dim(data.set) ## [1] 506 14 Convert dataframe to matrix without dimnames library(DT) # Cast dataframe as a matrix data.set <- as.matrix(data.set) # Remove column names dimn...
492 sym R (6569 sym/30 pcs) 3 img
Simple machine learning
Machine learning workflow Loading packages and datasets # load the Pima Indians dataset from the mlbench dataset library(mlbench) data(PimaIndiansDiabetes) # rename dataset to have shorter name because lazy diabetes <- PimaIndiansDiabetes look at the data set # install.packages(c('caret', 'skimr', 'RANN', 'randomForest', 'fastAdaboost', '...
1764 sym R (24889 sym/76 pcs) 7 img 3 tbl
Deep neural network
Load data # load the Pima Indians dataset from the mlbench dataset library(mlbench) data(PimaIndiansDiabetes) # rename dataset to have shorter name because lazy diabetes <- PimaIndiansDiabetes data.set <- diabetes # datatable(data.set[sample(nrow(data.set), # replace = FALSE, # size ...
675 sym R (6849 sym/45 pcs) 1 img
Data wrangling
How to do data wrangling We will use tidyverse package to work with data. Load data and package head (iris) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 ...
940 sym R (85314 sym/56 pcs)
R basics
The essentials of R Manipulation of vector vec <- c(3,5,2,1,5,"O",NA) length(unique(vec)) ## [1] 6 num_vec <- as.numeric(vec) log(num_vec) ## [1] 1.0986123 1.6094379 0.6931472 0.0000000 1.6094379 NA NA sum(c(num_vec, NA), na.rm=T) ## [1] 16 sort(num_vec, decreasing = T) ## [1] 5 5 3 2 1 is.na(num_vec) ## [1] FALSE FALSE FALSE FAL...
723 sym R (14643 sym/71 pcs) 3 img
Data summarization
How to do aggregation/ summarization Summarization after grouping library(tidyverse) iris %>% group_by(Species) %>% summarize(Support = mean(Sepal.Length)) %>% # average arrange(-Support) # sort ## # A tibble: 3 × 2 ## Species Support ## <fct> <dbl> ## 1 virginica 6.59 ## 2 versicolor ...
219 sym R (15463 sym/17 pcs)
Convolutional neural network
Import library library(keras) Importing the data mnist <- dataset_mnist() ## Loaded Tensorflow version 2.8.0 mnist is list; it contains trainx, trainy, testx, testy class(mnist) ## [1] "list" the dim of “mnist\(train\)x” is 60000 28 28 # head(mnist) preparing the data randomly sampling 1000 cases for training and 100 for testing set...
876 sym R (3372 sym/31 pcs) 1 img
Machine learning- KNN
KNN Classifier # Loading package # library(e1071) library(caTools) library(class) Splitting data # load the Pima Indians dataset from the mlbench dataset library(mlbench) data(PimaIndiansDiabetes) # rename dataset to have shorter name because lazy diabetes <- PimaIndiansDiabetes # Splitting data into train and test data set.seed(100) ...
416 sym R (11718 sym/37 pcs) 5 img
Data visualization 2
Data visualization course Summarization library(tidyverse) ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ── ## ✔ ggplot2 3.3.5 ✔ purrr 0.3.4 ## ✔ tibble 3.1.6 ✔ dplyr 1.0.8 ## ✔ tidyr 1.2.0 ✔ s...
647 sym R (9888 sym/45 pcs) 15 img 2 tbl