Looking for Understanding & Researching the City Tutorial on Friday Afternoon slides? Download it from here (updated by 27/03/2020) understanding-and-researching-the-city-tutorial-3Download Note for R users:1. Original slides are produced in RStudio as HTML format. I personalised the "reveal.js" template to generate the slides (reference: https://bookdown.org/yihui/rmarkdown/revealjs.html).2. The personalised output format code: title: "Understanding and Researching the City … Continue reading Teaching slides weekly update
Author: Juntao Lyu
How to publish on WordPress from RStudio
I have been looking for an easy way to publish my data analysis results from RStudio directly to WordPress. In this way I can save time editing my post. I tried the package "RWordPress". It is ablt to transfer my R file as a post draft into my WordPress account. The code and the results … Continue reading How to publish on WordPress from RStudio
ASPC Presentation Slides “Social Health Insurance Inequalities in China” Download (& R markdown scripts below)
Slides download: https://jtlyuhome.files.wordpress.com/2019/09/aspc-presentation-slides-3.pdf R coding:
Data analysis of the ASPC Paper “Social Health Insurance Inequalities in China” – RStudio Scripts
#RStudio setting: # Import the data library(readxl)## readxl_example()## ??readxlhealthcare<-read_excel("Healthcare2014.xlsx", "Sheet1")head(healthcare)str(healthcare)sum(is.na(healthcare))save(healthcare,file = "healthcare.Rda") # Select variables library(dplyr)load("healthcare.Rda")healthcare01<- dplyr::select (healthcare, provcode, Ab01,Ac01, Ad01,Ae01,Ag01,Ah001,Ai01,Aj01,Ah101,Ah201,Ah301,Ah401,Ah501,Ah601,Ah701,Ah801, B01_1,B02_1,B07_1,B07_1,B07_2,B07_3,B07_4,B07_5,B07_6,B07_7,B07_8,B07_9, D01,D06,F01,F02,F021,F022,F023,F024,F025,F031,F041,F042,F043,F04C,F03,F06)useBytes = TRUEstr(healthcare01)save(healthcare01,file = "healthcare01.Rda")load("healthcare01.Rda")library(dplyr)h01<-filter(healthcare01,Ab01>0&Ac01>0&Ae01>0&Ag01>0&Ai01>0&Ah001>=0&Ah101>=0&Ah201>=0&Ah301>=0&Ah401>=0&Ah501>=0&Ah601>=0&Ah701>=0&Ah801>=0&F01>0&D01>=0&F01>0&F03>0)h01<-filter(h01,B01_1>0|B01_1< -7)h01<-filter(h01,F041< -7 |F041>= 0)h01<-filter(h01,F042< -7 |F042>= 0)sum(is.na(h01))str(h01) # Rename variables ## provinceh01$provcode[h01$provcode== 13]<- "Hebei"h01$provcode[h01$provcode== 23]<- "Heilongjiang"h01$provcode[h01$provcode== 31]<- "Shanghai"h01$provcode[h01$provcode== 33]<- "Zhejiang"h01$provcode[h01$provcode== 34]<- "Anhui"h01$provcode[h01$provcode== 44]<- "Guangdong"h01$provcode[h01$provcode== 51]<- "Sichuan"h01$provcode[h01$provcode== 61]<- "Shannxi"h01$province<-h01$provcode## … Continue reading Data analysis of the ASPC Paper “Social Health Insurance Inequalities in China” – RStudio Scripts
Comparative Data Visualisation in R: an Example of Building a Data Frame from Scratch
When we do data analysis, we often deal with existed data-sets from an official database or survey. That is because R is a powerful tool allowing us to import a wide range of data formats, including excel, SPSS, Stata and other formats. In most occasions, we do not need to create a data frame ourselves. … Continue reading Comparative Data Visualisation in R: an Example of Building a Data Frame from Scratch
Dummy variables in R – an example for logistic regression modeling
Doing social research in a quantitative way means we have to fix our data with our expected theories. This is a very different approach from qualitative research, as the grounded theory is not very likely to be purely constructed by numbers. Thus, we sometimes need to fix our data in order to meet our theoretical … Continue reading Dummy variables in R – an example for logistic regression modeling
