Waleed HW7
Introduction
Download the gapminder data set into R Markdown session. To start with the EDA first I download the data set gap minder. The gap minder data contains data on life expectancy and GDP per capita by country and year. I will import the data set and perform EDA.
## Warning: package 'dplyr' was built under R version 4.1.3## Warning: package 'gapminder' was built under R version 4.1.3## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.Performing EDA
The data type of the Varaibles are as following
## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...There is no NA/Null values in the data set.
## [1] 0What is the mean of the life expectancy, population and GDP Per Cap
## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
## Discription of data cleaning and transformation
## # A tibble: 6 x 5
## country year lifeExp pop gdpPercap
## <fct> <int> <dbl> <int> <dbl>
## 1 Albania 1952 55.2 1282697 1601.
## 2 Albania 1957 59.3 1476505 1942.
## 3 Albania 1962 64.8 1728137 2313.
## 4 Albania 1967 66.2 1984060 2760.
## 5 Albania 1972 67.7 2263554 3313.
## 6 Albania 1977 68.9 2509048 3533.Corelation between two variables
Now I will do analysis on two variables and find the corelation.
## `geom_smooth()` using formula 'y ~ x'
From the plot we can see that they have weak correlation.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
As we can see they both dont follow any normal distribution.
Now i see if there is any relation lets check more!
## [1] 0.1097244As we can see the corelation cofficent of both variables is 0.10. Which means the corelation is weak positive.
Hypothesis testing
State the null hypothesis and the alternative hypothesis.
Null hypothesis: There is no relation Alternative: There is relation
Report on collected data and sample size.
The data collected was from Europe, the dataset had 360 obs
Perform Pearson correlation test between two variables.
##
## Pearson's product-moment correlation
##
## data: newdf$pop and newdf$gdpPercap
## t = 2.0887, df = 358, p-value = 0.03744
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.006435561 0.210696641
## sample estimates:
## cor
## 0.1097244Decide whether to reject or fail to reject your null hypothesis, report selected significance level.
As we can see that the p value is 0.03744 which is less the 0.05 hence we can reject the null hypothesis.
Interpret and report the results.
We can reject the null hypothesis therefore population and GDP have relation.