DDPAppPitch

Rahul Garg
July 13, 2020

CLEANED DATA

  • Cleaned data looks like following - 
head(gdp)

        CHN        IND        USA ts
X1970 19.30  5.1572297 -0.2540796  1
X1971  7.06  1.6429304  3.2933624  2
X1972  3.81 -0.5533013  5.2588954  3
X1973  7.76  3.2955211  5.6457195  4
X1974  2.31  1.1853363 -0.5405465  5
X1975  8.72  9.1499120 -0.2054640  6

Notably, ts represents the number of year from 1970 to 2019 (starting from 1). The row name contains the real year.

Embedded R Code - I - The Plot

This code is for internal processing of GDP data.

plot1 <- plot_ly() 
plot1 <- plot1 %>% 
        add_trace(x = ~gdp[,4], y = ~gdp[,1], type = "scatter", mode = "Lines", name = "China") %>%
        add_trace(x = ~gdp[,4], y = ~gdp[,2], type = "scatter", mode = "Lines", name = "India") %>% 
        add_trace(x = ~gdp[,4], y = ~gdp[,3],  type = "scatter", mode = "Lines", name = "USA")

THE PLOT OUTPUT

The previous code leads to following plot output - plot of chunk unnamed-chunk-4

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Comparing the GDP growth rates of countries

Select the Countries

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Embedded R Code - II - The Mean

  • “chn”, “ind”, “usa” represent choice by user
meanx <- as.data.frame(cbind(c("China", "India", "USA"),c(0,0,0)))
                meanx[,2] <- as.numeric(as.character(meanx[,2]))
                if (chn) {meanx[1,2] <- mean(gdp[,1])}
                if (ind) {meanx[2,2] <- mean(gdp[,2])}
                if (usa) {meanx[3,2] <- mean(gdp[,3])}
                colnames(meanx) <- c("country", "Average GDP growth rate")
                meanx

  country Average GDP growth rate
1   China                9.033272
2   India                5.482645
3     USA                2.746236

CONCLUSION

  • The GDP App provides a handy tool to compare the growth rates of countries over the time