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library(dslabs)
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library(highcharter)
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Attaching package: 'highcharter'
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library(RColorBrewer)
Here, the appropriate libraries are loaded .
help("gapminder")
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This function helps us understand the data set we’re considering.
data(gapminder)head(gapminder)
country year infant_mortality life_expectancy fertility
1 Albania 1960 115.40 62.87 6.19
2 Algeria 1960 148.20 47.50 7.65
3 Angola 1960 208.00 35.98 7.32
4 Antigua and Barbuda 1960 NA 62.97 4.43
5 Argentina 1960 59.87 65.39 3.11
6 Armenia 1960 NA 66.86 4.55
population gdp continent region
1 1636054 NA Europe Southern Europe
2 11124892 13828152297 Africa Northern Africa
3 5270844 NA Africa Middle Africa
4 54681 NA Americas Caribbean
5 20619075 108322326649 Americas South America
6 1867396 NA Asia Western Asia
We proceed by loading “gap-minder”; the data set we’ll be exploring .
summary(gapminder)
country year infant_mortality life_expectancy
Albania : 57 Min. :1960 Min. : 1.50 Min. :13.20
Algeria : 57 1st Qu.:1974 1st Qu.: 16.00 1st Qu.:57.50
Angola : 57 Median :1988 Median : 41.50 Median :67.54
Antigua and Barbuda: 57 Mean :1988 Mean : 55.31 Mean :64.81
Argentina : 57 3rd Qu.:2002 3rd Qu.: 85.10 3rd Qu.:73.00
Armenia : 57 Max. :2016 Max. :276.90 Max. :83.90
(Other) :10203 NA's :1453
fertility population gdp continent
Min. :0.840 Min. :3.124e+04 Min. :4.040e+07 Africa :2907
1st Qu.:2.200 1st Qu.:1.333e+06 1st Qu.:1.846e+09 Americas:2052
Median :3.750 Median :5.009e+06 Median :7.794e+09 Asia :2679
Mean :4.084 Mean :2.701e+07 Mean :1.480e+11 Europe :2223
3rd Qu.:6.000 3rd Qu.:1.523e+07 3rd Qu.:5.540e+10 Oceania : 684
Max. :9.220 Max. :1.376e+09 Max. :1.174e+13
NA's :187 NA's :185 NA's :2972
region
Western Asia :1026
Eastern Africa : 912
Western Africa : 912
Caribbean : 741
South America : 684
Southern Europe: 684
(Other) :5586
Summary () gives us some important general insights . For this assignment , we’ll pay attention to the null values. We’ll need to filter some of these values to make our final plot accurate.
Gap_clean is our cleaned data set. With is code we’ve taken gap-minder and filtered out null values of the following columns : fertility, infant_mortality, and gdp.
cols<-brewer.pal(5,"Set2")highchart() %>%hc_add_series( gap_clean, "scatter", hcaes( x = fertility, y = infant_mortality, group= continent) )%>%hc_colors(cols) %>%hc_title(text ="Fertility vs Infant Mortality") %>%hc_subtitle(text =" Grouped by Continents ") %>%hc_xAxis(title =list(text ="Fertility Rate")) %>%hc_yAxis(title =list(text ="Infant Mortality")) %>%hc_caption(text ="Source: DS Labs") %>%hc_add_theme(hc_theme_smpl())
This code creates an interactive scatter plot using the highcharter package. We first set five colors from the “Set2” palette using RColorBrewer then we plot the relationship between fertility rate (x-axis) and infant mortality (y-axis) from the gap_clean dataset, grouping points by continent so each group is shown in a different color.