HW7_ADEGOKE
Abimbola
2022-11-29
Download the gapminder data set into
R Markdown session.
Perform quick EDA and pick up two variables you want to explore in more depth (for example, life expectancy and gdp) and a subset of data set (for instance, only certain continents, or countries, etc).
Prepare the data set that includes only variables of your interest in a suitable format for analysis (use
dlyrpackage andtidyrwhen necessary).
#install.packages("gapminder")
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(gapminder)
head(gapminder)## # A tibble: 6 × 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.View(gapminder)summarize the gapminder dataset
summary(gapminder)## 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
## find the dimension of the gapminder dataset
dim(gapminder)## [1] 1704 6create histogram of values for year
select two variables from dataset
gapminder %>%
select(country, lifeExp, gdpPercap)## # A tibble: 1,704 × 3
## country lifeExp gdpPercap
## <fct> <dbl> <dbl>
## 1 Afghanistan 28.8 779.
## 2 Afghanistan 30.3 821.
## 3 Afghanistan 32.0 853.
## 4 Afghanistan 34.0 836.
## 5 Afghanistan 36.1 740.
## 6 Afghanistan 38.4 786.
## 7 Afghanistan 39.9 978.
## 8 Afghanistan 40.8 852.
## 9 Afghanistan 41.7 649.
## 10 Afghanistan 41.8 635.
## # … with 1,694 more rowschoosing Albania from dataset
Albania <- filter(gapminder, country == "Albania")
Albania## # A tibble: 12 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Albania Europe 1952 55.2 1282697 1601.
## 2 Albania Europe 1957 59.3 1476505 1942.
## 3 Albania Europe 1962 64.8 1728137 2313.
## 4 Albania Europe 1967 66.2 1984060 2760.
## 5 Albania Europe 1972 67.7 2263554 3313.
## 6 Albania Europe 1977 68.9 2509048 3533.
## 7 Albania Europe 1982 70.4 2780097 3631.
## 8 Albania Europe 1987 72 3075321 3739.
## 9 Albania Europe 1992 71.6 3326498 2497.
## 10 Albania Europe 1997 73.0 3428038 3193.
## 11 Albania Europe 2002 75.7 3508512 4604.
## 12 Albania Europe 2007 76.4 3600523 5937.select(Albania, lifeExp, gdpPercap)## # A tibble: 12 × 2
## lifeExp gdpPercap
## <dbl> <dbl>
## 1 55.2 1601.
## 2 59.3 1942.
## 3 64.8 2313.
## 4 66.2 2760.
## 5 67.7 3313.
## 6 68.9 3533.
## 7 70.4 3631.
## 8 72 3739.
## 9 71.6 2497.
## 10 73.0 3193.
## 11 75.7 4604.
## 12 76.4 5937.mychosendata <- select(Albania, lifeExp, gdpPercap)
mychosendata## # A tibble: 12 × 2
## lifeExp gdpPercap
## <dbl> <dbl>
## 1 55.2 1601.
## 2 59.3 1942.
## 3 64.8 2313.
## 4 66.2 2760.
## 5 67.7 3313.
## 6 68.9 3533.
## 7 70.4 3631.
## 8 72 3739.
## 9 71.6 2497.
## 10 73.0 3193.
## 11 75.7 4604.
## 12 76.4 5937.performing EDA for selecting variables
## lifeExp gdpPercap
## Min. :55.23 Min. :1601
## 1st Qu.:65.87 1st Qu.:2451
## Median :69.67 Median :3253
## Mean :68.43 Mean :3255
## 3rd Qu.:72.24 3rd Qu.:3658
## Max. :76.42 Max. :5937## [1] 6.322911## [1] 1192.352
create scatterplot of lifeExp vs. gdpPercap
## `geom_smooth()` using formula = 'y ~ x'
- Explore two variables and how they are associated with each other (correlation analysis). Include the description of assumptions for correlation analysis, concluding with the type of analysis you choose. Create necessary visualisations to support your analysis (a histogram, a scatter plot, etc) and include your interpretation of the graphs.
Association between variables pop and year
##
## Call:
## lm(formula = pop ~ year, data = gapminder)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43318856 -27548179 -18558743 -9628265 1275164661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -972185807 294031308 -3.306 0.000965 ***
## year 506081 148532 3.407 0.000672 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105800000 on 1702 degrees of freedom
## Multiple R-squared: 0.006775, Adjusted R-squared: 0.006191
## F-statistic: 11.61 on 1 and 1702 DF, p-value: 0.0006716
Our conclusion is that since the p-value gotten is less than 0.05, then
there is significant relationship between year and pop
## # A tibble: 12 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Albania Europe 1952 55.2 1282697 1601.
## 2 Albania Europe 1957 59.3 1476505 1942.
## 3 Albania Europe 1962 64.8 1728137 2313.
## 4 Albania Europe 1967 66.2 1984060 2760.
## 5 Albania Europe 1972 67.7 2263554 3313.
## 6 Albania Europe 1977 68.9 2509048 3533.
## 7 Albania Europe 1982 70.4 2780097 3631.
## 8 Albania Europe 1987 72 3075321 3739.
## 9 Albania Europe 1992 71.6 3326498 2497.
## 10 Albania Europe 1997 73.0 3428038 3193.
## 11 Albania Europe 2002 75.7 3508512 4604.
## 12 Albania Europe 2007 76.4 3600523 5937.## [1] 0.2396469 -1.2672823 -0.6405017 1.0407503 1.8646130 2.4822764Create the other variable
## [1] -1.1011159 -3.2285025 -2.5252555 -0.1810685 0.9898952 2.0770148create a dataframe with the two variables and calculate Pearson’s correlation:
numbers <- data.frame(x,y)
head(numbers)## x y
## 1 0.2396469 -1.1011159
## 2 -1.2672823 -3.2285025
## 3 -0.6405017 -2.5252555
## 4 1.0407503 -0.1810685
## 5 1.8646130 0.9898952
## 6 2.4822764 2.0770148corr.1 <- round(cor(x, y), 2)
corr.1 ## [1] 0.78## # A tibble: 1,704 × 13
## country year pop lifeE…¹ lifeE…² lifeE…³ lifeE…⁴ lifeE…⁵ gdpPe…⁶ gdpPe…⁷
## <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Afghani… 1952 8.43e6 28.8 NA NA NA NA 779. NA
## 2 Afghani… 1957 9.24e6 30.3 NA NA NA NA 821. NA
## 3 Afghani… 1962 1.03e7 32.0 NA NA NA NA 853. NA
## 4 Afghani… 1967 1.15e7 34.0 NA NA NA NA 836. NA
## 5 Afghani… 1972 1.31e7 36.1 NA NA NA NA 740. NA
## 6 Afghani… 1977 1.49e7 38.4 NA NA NA NA 786. NA
## 7 Afghani… 1982 1.29e7 39.9 NA NA NA NA 978. NA
## 8 Afghani… 1987 1.39e7 40.8 NA NA NA NA 852. NA
## 9 Afghani… 1992 1.63e7 41.7 NA NA NA NA 649. NA
## 10 Afghani… 1997 2.22e7 41.8 NA NA NA NA 635. NA
## # … with 1,694 more rows, 3 more variables: gdpPercap_Africa <dbl>,
## # gdpPercap_Americas <dbl>, gdpPercap_Oceania <dbl>, and abbreviated variable
## # names ¹lifeExp_Asia, ²lifeExp_Europe, ³lifeExp_Africa, ⁴lifeExp_Americas,
## # ⁵lifeExp_Oceania, ⁶gdpPercap_Asia, ⁷gdpPercap_EuropeTo find the data for Albania
Albania <- filter(gapminder, country == "Albania")
head(Albania)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Albania Europe 1952 55.2 1282697 1601.
## 2 Albania Europe 1957 59.3 1476505 1942.
## 3 Albania Europe 1962 64.8 1728137 2313.
## 4 Albania Europe 1967 66.2 1984060 2760.
## 5 Albania Europe 1972 67.7 2263554 3313.
## 6 Albania Europe 1977 68.9 2509048 3533.Life expectancy in Albania greater than 50
X <- filter(Albania, (lifeExp > 50))
print(X)## # A tibble: 12 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Albania Europe 1952 55.2 1282697 1601.
## 2 Albania Europe 1957 59.3 1476505 1942.
## 3 Albania Europe 1962 64.8 1728137 2313.
## 4 Albania Europe 1967 66.2 1984060 2760.
## 5 Albania Europe 1972 67.7 2263554 3313.
## 6 Albania Europe 1977 68.9 2509048 3533.
## 7 Albania Europe 1982 70.4 2780097 3631.
## 8 Albania Europe 1987 72 3075321 3739.
## 9 Albania Europe 1992 71.6 3326498 2497.
## 10 Albania Europe 1997 73.0 3428038 3193.
## 11 Albania Europe 2002 75.7 3508512 4604.
## 12 Albania Europe 2007 76.4 3600523 5937.create correlation matrix of (rounded to 2 decimal places)
round(cor(gapminder[c('lifeExp', 'gdpPercap')]), 2)## lifeExp gdpPercap
## lifeExp 1.00 0.58
## gdpPercap 0.58 1.00count total missing values in each column
sapply(gapminder, function(x) sum(is.na(x)))## country continent year lifeExp pop gdpPercap
## 0 0 0 0 0 0Create a report in R Markdown with the following
sections:
- Introduction (brief description of the data set and variables)
- Questions for EDA
- Description of data cleaning and transformation
- Description of correlation analysis (steps for visualisation, checking
assumption for correlation analysis, performing correlation
analysis)
- Data interpretation and conclusions
The gapminder data contains data on life expectancy and GDP per capital by country and year.
#install.packages("knitr")
#install.packages("psych")
#describe(gapminder)
str(gapminder)## tibble [1,704 × 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 ...library(dplyr)
unique(gapminder$country)## [1] Afghanistan Albania Algeria
## [4] Angola Argentina Australia
## [7] Austria Bahrain Bangladesh
## [10] Belgium Benin Bolivia
## [13] Bosnia and Herzegovina Botswana Brazil
## [16] Bulgaria Burkina Faso Burundi
## [19] Cambodia Cameroon Canada
## [22] Central African Republic Chad Chile
## [25] China Colombia Comoros
## [28] Congo, Dem. Rep. Congo, Rep. Costa Rica
## [31] Cote d'Ivoire Croatia Cuba
## [34] Czech Republic Denmark Djibouti
## [37] Dominican Republic Ecuador Egypt
## [40] El Salvador Equatorial Guinea Eritrea
## [43] Ethiopia Finland France
## [46] Gabon Gambia Germany
## [49] Ghana Greece Guatemala
## [52] Guinea Guinea-Bissau Haiti
## [55] Honduras Hong Kong, China Hungary
## [58] Iceland India Indonesia
## [61] Iran Iraq Ireland
## [64] Israel Italy Jamaica
## [67] Japan Jordan Kenya
## [70] Korea, Dem. Rep. Korea, Rep. Kuwait
## [73] Lebanon Lesotho Liberia
## [76] Libya Madagascar Malawi
## [79] Malaysia Mali Mauritania
## [82] Mauritius Mexico Mongolia
## [85] Montenegro Morocco Mozambique
## [88] Myanmar Namibia Nepal
## [91] Netherlands New Zealand Nicaragua
## [94] Niger Nigeria Norway
## [97] Oman Pakistan Panama
## [100] Paraguay Peru Philippines
## [103] Poland Portugal Puerto Rico
## [106] Reunion Romania Rwanda
## [109] Sao Tome and Principe Saudi Arabia Senegal
## [112] Serbia Sierra Leone Singapore
## [115] Slovak Republic Slovenia Somalia
## [118] South Africa Spain Sri Lanka
## [121] Sudan Swaziland Sweden
## [124] Switzerland Syria Taiwan
## [127] Tanzania Thailand Togo
## [130] Trinidad and Tobago Tunisia Turkey
## [133] Uganda United Kingdom United States
## [136] Uruguay Venezuela Vietnam
## [139] West Bank and Gaza Yemen, Rep. Zambia
## [142] Zimbabwe
## 142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwecorrelation of the variables year and pop
cor(my_year, my_pop)## [1] 0.08230808Exploratory Data Analysis
GDP_2007 <- gapminder_unfiltered %>% filter(year==2007) %>% select(continent, country, gdpPercap)
ggplot(GDP_2007, aes(x=gdpPercap)) + geom_histogram(fill="cyan", bins=40) +
ylab("GDP per Capita") +
ggtitle("Distribution of GDP Per Capita for 2007 for all Countries") +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())