R analysis visual practical data (flight \u education \u restaurant \u tenant \u change \u life \u safety)

R Data Science

graphics

Statistics

Descriptive statistics | Frequency and contingency table | Correlation and covariance | t- test | Nonparametric statistics

adopt sapply() Make descriptive statistics

> mystats <- function(x, na.omit=FALSE){
    
 if (na.omit)
 x <- x[!is.na(x)]
 m <- mean(x)
 n <- length(x)
 s <- sd(x)
 skew <- sum((x-m)^3/s^3)/n
 kurt <- sum((x-m)^4/s^4)/n - 3
 return(c(n=n, mean=m, stdev=s,
 skew=skew, kurtosis=kurt))
 }
> myvars <- c("mpg", "hp", "wt")
> sapply(mtcars[myvars], mystats)
 mpg hp wt
n 32.000 32.000 32.0000
mean 20.091 146.688 3.2172
stdev 6.027 68.563 0.9785
skew 0.611 0.726 0.4231
kurtosis -0.373 -0.136 -0.0227

Use by() Descriptive statistics of groups

> dstats <- function(x)sapply(x, mystats)
> myvars <- c("mpg", "hp", "wt")
> by(mtcars[myvars], mtcars$am, dstats)
mtcars$am: 0
 mpg hp wt
n 19.000 19.0000 19.000
mean 17.147 160.2632 3.769
stdev 3.834 53.9082 0.777
skew 0.014 -0.0142 0.976
kurtosis -0.803 -1.2097 0.142
----------------------------------------
mtcars$am: 1
 mpg hp wt
n 13.0000 13.000 13.000
mean 24.3923 126.846 2.411
stdev 6.1665 84.062 0.617
skew 0.0526 1.360 0.210
kurtosis -1.4554 0.563 -1.174

Return to

Fitting and interpreting linear models | Assessment model assumptions | Choose in the competitive model

Variable analysis

Use R Model the basic experimental design | Fit and explain ANOVA Type model | Assessment model assumptions

Efficacy analysis

Determine sample size requirements | Calculate the effect size | Evaluate statistical power

Intermediate graphics

Visualize bivariate and multivariable relationships | Use scatter and line charts | understand corrgram | Use mosaic and correlation diagrams

Resampling statistics and bootstrapping

Understand the logic of replacement testing | Apply displacement test to linear model | Use bootstrapping to obtain confidence intervals

Generalized linear model

Develop a generalized linear model | Forecast classification results | Modeling count data

Principal component and factor analysis

Principal component analysis | Exploratory factor analysis | Understand other latent variable models

The time series

Create time series | Decompose the time series into components | Develop predictive models | Forecast future data

R Analyze and visualize practical data

application 1： Explore flight delays

application 2： Explore the level of education

application 3： The location of the specialty restaurant on the map

application 4： The tenant's heat map shows

application 5： Analysis of urban change

application 6： Life expectancy analysis

application 7： Public safety analysis

For details, please refer to - Yatu inter