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MTrungDang

Trung Dang

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Scatter plots
Ridgeline Plots in R
Simple for loop
Cycle plot
Measures of effect and of association using R
Calculating measures of occurrence using R
Map using ggplot2 and socviz packages
tidyverse - Data collection, Data processing, and Data cleaning
Creating line graphs with ggplot2
Customize background color of ggtitle
Indexing
SwD-Choosing an efffective visual
Recreation of Cole Nussbaumer Knaflic’s Storytelling with Data
Storytelling with Data (SWD) community chanllenge: optimize for audience
storytelling with data - SWD community
Undertake targeted practice by completing the latest exercise or browse the exercise bank and tackle one relevant to a current project or your data communication development goals.
Small multiples
Recreation of Cole Nussbaumer Knaflic's Storytelling with Data
Explanatory visuals, part 3: Many ways to direct attention
Explanatory visuals, part 2: Focusing attention
Explanatory visuals, part 1: Choosing an effective visual
Choosing an effective visual
One way ANOVA
Data exploration in R and Python: Movies data
Making comparisons using graphical data analysis
Depicting groups of numerical data- Boxplot, Violin plot, and Sinaplot plot
Depicting groups of numerical data using boxplot
TWO-SAMPLE T-TEST
ONE-TAILED, ONE-SAMPLE T-TEST
Crimes in Toronto : Data wrangling and Exploratory data analysis
Visualizing Times Series
Correlograms
Wilcoxon Rank Sum test
Notes: Dates and times with lubridate package
Working with date time using lubridate package
Summary table with across() in dplyr
relocate() function in tidyverse
Income Census- Exploratory data analysis and classification
Revisiting the Gapminder dataset using dplyr and ggplot2
Visualizing amounts: Dot plots and their derivatives
Revisiting the Gapminder dataset using dplyr and ggplot2
Document
Reshaping Data
Visualization Proportion-Stacked bars
Apache Hive and R
UIC heart desease data
Converting multiple character columns to factor
Income Census- Exploratory data analysis and classification
Descriptive analysis - Univariate and bivariate analysis of BRFSS data
Ensuring the quality of data
Tidytrick-Replacing string patterns (Regrouping categorical variables)
Tidytrick-Finding rows with a specific pattern
Tidytrick-Converting multiple character columns to factor
ggplot2-Adding a reference line
Inference from small sample: One sample t-test
Dealing with missing data- Par1 : Mean imputation
RFM score as input for K-means clustering
Data visualization
Kickstarter project-Part 2: Creating consistent data, Performing Exploratoy data analysis, and building the Decision tree
Kickstarter project-Part 1: Using SQL to create data table and Preliminary EDA
Using tidyverse to clean and preliminarily to explore data
Exploratory Analysis of the San Francisco Bay Area Bike Share Program
Crime in Toronto, Part 1: Data cleaning, initial analysis, and univariate analysis
In this post, the package tidyverse is used to wrangle the Toronto crime dataset (167525 records and 29 variables). * Finding 47 rows with missing values. Removing all of rows containing missing values as these rows are not useful for future analysis. * 11973 duplicates have been found and removed. * The number of columns reduced from 29 to 19. Columns were transformed to the appropriate data types. * Initial analysis and univariate analysis is performed.