README.md

In LinaQiu/packageForGapminder: Make it easier to see whether there is any linear relationship between pop/lifeExp/gdpPercap and year for countries in GAPMINDER dataset

About packageForGapminder

packageForGapminder is a package which makes model pop/lifeExp/gdpPercap VS year for countries in gapminder dataset easier, as well as makes it possible to use only one line code to help you get a intuitive view on how well pop/lifeExp/gdpPercap and year in gapminder dataset fits a linear model. All functions here are based on my homework4 for STAT545.

To install the package:

##Make sure you load devtools package first
library(devtools)
##Install packageForGapminder
devtools::install_github("LinaQiu/packageForGapminder")

Load packages here:

library(packageForGapminder) 

## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## 
## Loading required package: gapminder
## Loading required package: ggplot2

library(knitr)
options(knitr.table.format="markdown")        #Set the format for tables as "markdown"

In this package, we have three functions in total. See below.

• gapminder_lmfit(): Fit a linear model to pop/lifeExp/gdpPercap VS year for a country in gapminder dataset.

• gapminder_lm_table(): Create a table for fitted linear model between variables pop/lifeExp/gdpPercap and year in gapminder dataset.

• gapminder_lm_plot():Draw a plot to show how well pop/lifeExp/gdpPercap and year in gapminder dataset fits a linear model.

Besides, by default, all these three funtions will fit the linear model to data pop VS year for a gapminder country.

Using gapminder_lmfit():

Fit linear model to gapminder country data.

gapminder_lmfit("Canada")                   ##fit a linear model to pop VS year for "Canada"

## [[1]]
## intercept 
##  15412220 
## 
## [[2]]
##    slope 
## 329118.1 
## 
## [[3]]
## fitError(sumOFres^2) 
##         983734892937 
## 
## [[4]]
##  residuals          2          3          4          5          6 
## -626635.53  -47656.04  282448.45  470775.94  289918.43  156227.92 
##          7          8          9         10         11         12 
##  -83862.59 -381653.10  -53441.61   83308.88   34143.37 -123574.14

gapminder_lmfit("Canada","year","lifeExp")  ##fit a linear model to lifeExp VS year for "Canada"

## [[1]]
## intercept 
##  68.88385 
## 
## [[2]]
##     slope 
## 0.2188692 
## 
## [[3]]
## fitError(sumOFres^2) 
##            0.6212471 
## 
## [[4]]
##   residuals           2           3           4           5           6 
## -0.13384615 -0.01819231  0.22746154 -0.03688462 -0.38123077 -0.14557692 
##           7           8           9          10          11          12 
##  0.31007692  0.31573077  0.31138462 -0.12296154 -0.05730769 -0.26865385

gapminder_lmfit("China","year","gdpPercap") ##fit a linear model to gdpPercap VS year for "China"

## [[1]]
## intercept 
## -303.7785 
## 
## [[2]]
##    slope 
## 65.16677 
## 
## [[3]]
## fitError(sumOFres^2) 
##              5482861 
## 
## [[4]]
##  residuals          2          3          4          5          6 
##  704.22712  553.93165  139.78482  -61.01736 -322.65682 -584.15330 
##          7          8          9         10         11         12 
## -688.80324 -598.15446 -647.10817 -339.49205  164.72085 1678.72096

Using gapminder_lm_table():

Get the table which compares the actual pop/lifeExp/gdpPercap values with the predicted values that we compute from the linear model.

table1 <- gapminder_lm_table("Canada")
kable(table1)

| x| predicted_y| actual_y| D_value| |-----:|-------------:|----------:|-----------:| | 1952| 15412220| 14785584| -626635.53| | 1957| 17057810| 17010154| -47656.04| | 1962| 18703401| 18985849| 282448.45| | 1967| 20348991| 20819767| 470775.94| | 1972| 21994582| 22284500| 289918.43| | 1977| 23640172| 23796400| 156227.92| | 1982| 25285763| 25201900| -83862.59| | 1987| 26931353| 26549700| -381653.10| | 1992| 28576944| 28523502| -53441.61| | 1997| 30222534| 30305843| 83308.88| | 2002| 31868125| 31902268| 34143.37| | 2007| 33513715| 33390141| -123574.14|

table2 <- gapminder_lm_table("Canada","year","lifeExp")
kable(table2)

| x| predicted_y| actual_y| D_value| |-----:|-------------:|----------:|-----------:| | 1952| 68.88385| 68.750| -0.1338462| | 1957| 69.97819| 69.960| -0.0181923| | 1962| 71.07254| 71.300| 0.2274615| | 1967| 72.16688| 72.130| -0.0368846| | 1972| 73.26123| 72.880| -0.3812308| | 1977| 74.35558| 74.210| -0.1455769| | 1982| 75.44992| 75.760| 0.3100769| | 1987| 76.54427| 76.860| 0.3157308| | 1992| 77.63862| 77.950| 0.3113846| | 1997| 78.73296| 78.610| -0.1229615| | 2002| 79.82731| 79.770| -0.0573077| | 2007| 80.92165| 80.653| -0.2686538|

table3 <- gapminder_lm_table("China","year","gdpPercap")
kable(table3)

| x| predicted_y| actual_y| D_value| |-----:|-------------:|----------:|-----------:| | 1952| -303.77851| 400.4486| 704.22712| | 1957| 22.05535| 575.9870| 553.93165| | 1962| 347.88920| 487.6740| 139.78482| | 1967| 673.72306| 612.7057| -61.01736| | 1972| 999.55691| 676.9001| -322.65682| | 1977| 1325.39077| 741.2375| -584.15330| | 1982| 1651.22462| 962.4214| -688.80324| | 1987| 1977.05848| 1378.9040| -598.15446| | 1992| 2302.89233| 1655.7842| -647.10817| | 1997| 2628.72619| 2289.2341| -339.49205| | 2002| 2954.56004| 3119.2809| 164.72085| | 2007| 3280.39390| 4959.1149| 1678.72096|

Using gapminder_lm_plot():

Draw a plot to give you an intuitive view on how well a gapminder country data(pop/lifeExp/gdpPercap VS year) fits a linear model.

gapminder_lm_plot("Canada")

gapminder_lm_plot("Canada","year","lifeExp")

gapminder_lm_plot("China","year","gdpPercap")

packageForGapminder really makes it easier to check the linear relationships between pop/lifeExp/gdpPercap and year for a gapminder country. Enjoy it!

LinaQiu/packageForGapminder documentation built on May 8, 2019, 11:20 p.m.