#1 Import the GDP dataset and compute the difference in GDP between 2007 and 2017 for each country
setwd("~/NYU/classes/2. R/Assignments/Lesson 3")
library(readr)
gdp=read_csv("gdp.csv")
## Rows: 264 Columns: 60
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (2): Country Name, Country Code
## dbl (58): 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, ...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
dim(gdp)
## [1] 264 60
#create a column called 'diff' to compute the difference in GDP between 2007 and 2017
gdp$diff0717=gdp$`2007`-gdp$`2017`
gdp$diff0717
## [1] NA -1.097146e+10 -6.376008e+10 -2.338341e+09 1.004058e+09
## [6] -9.534800e+11 -1.246590e+11 -3.500590e+11 -2.330289e+09 NA
## [11] -2.209962e+08 -4.714570e+11 -2.790500e+10 -7.697448e+09 -2.121424e+09
## [16] -2.086000e+10 -3.304023e+09 -6.101837e+09 -1.701121e+11 -1.242004e+10
## [21] -1.357713e+10 -1.543760e+09 -2.392157e+09 -9.164975e+09 -5.474266e+08
## [26] NA -2.438846e+10 -6.584300e+11 -2.730960e+08 1.196052e+08
## [31] -1.315761e+09 -6.467477e+09 -2.518457e+08 -1.880600e+11 -1.848700e+11
## [36] -1.989740e+11 NA -1.034700e+11 -8.685520e+12 -2.004499e+10
## [41] -1.243333e+10 -2.050423e+10 -3.278647e+08 -1.017750e+11 -1.864674e+08
## [46] -2.398027e+08 -3.031350e+10 -8.660614e+09 NA NA
## [51] NA 2.425679e+09 -2.649900e+10 -2.374900e+11 -9.967555e+08
## [56] -1.411649e+08 -5.449000e+09 -3.176198e+10 -3.539400e+10 -1.000956e+13
## [61] -4.782240e+12 -1.178980e+13 -6.288900e+11 -2.615000e+11 -5.204922e+10
## [66] -1.048900e+11 2.841000e+11 NA 1.680200e+11 -3.684018e+09
## [71] -6.085388e+10 5.161000e+11 -3.848630e+11 3.500000e+09 -1.656152e+09
## [76] 7.471000e+10 NA -7.987943e+07 -2.183924e+09 4.519300e+11
## [81] -4.986412e+09 -2.257120e+10 NA -4.659796e+09 -2.157360e+08
## [86] -6.509433e+08 5.849649e+08 1.182100e+11 -3.601331e+08 NA
## [91] -4.150699e+10 NA -1.935297e+09 -7.773700e+12 -1.298520e+11
## [96] -1.070303e+10 -3.804610e+11 5.244042e+09 -2.522825e+09 7.160000e+08
## [101] -1.453720e+13 -1.567470e+13 -1.144593e+12 -4.804980e+11 -5.833230e+11
## [106] -6.672860e+11 NA -1.396380e+12 NA -6.381300e+10
## [111] -8.963200e+10 -1.088759e+11 -2.589343e+09 -1.721440e+11 2.682500e+11
## [116] -1.944040e+09 -2.295772e+10 -3.568800e+11 -5.455700e+10 -4.298000e+10
## [121] -3.762173e+09 -1.351897e+10 -6.539515e+07 -2.718460e+08 -4.080700e+11
## [126] -5.485000e+09 -1.427270e+12 -1.263012e+10 -2.726737e+10 -1.418973e+09
## [131] 1.653187e+10 -4.348124e+08 -2.009540e+12 -6.041550e+11 -2.728980e+11
## [136] NA -5.482350e+10 -3.429540e+12 -1.511460e+13 -8.185750e+08
## [141] -1.108013e+13 -7.430124e+09 -1.151633e+10 6.369446e+08 -3.202075e+10
## [146] NA -3.009771e+10 NA -3.727339e+09 -4.156880e+09
## [151] -2.728700e+09 -1.142430e+12 -9.722000e+10 -4.862275e+07 -1.483760e+13
## [156] -3.001349e+09 -7.142469e+09 -4.657242e+09 -4.913965e+10 -4.289200e+11
## [161] -1.093376e+09 -7.253047e+09 NA -2.967118e+09 -1.667949e+09
## [166] -5.188009e+09 -1.871085e+09 -1.209520e+11 -5.101500e+12 -4.503732e+09
## [171] NA -3.828369e+09 -2.093200e+11 -6.390884e+09 1.322000e+10
## [176] 2.251000e+09 -1.414640e+10 -9.345217e+07 -6.853700e+10 -6.592100e+12
## [181] -3.055735e+10 -1.367410e+11 -1.525660e+11 -4.054219e+10 -1.092180e+11
## [186] -1.642350e+11 -9.842460e+07 -1.154369e+10 -9.544600e+10 -6.658920e+11
## [191] NA NA 2.259800e+10 -1.593998e+10 -8.992300e+09
## [196] -3.610028e+09 -6.202700e+12 NA -8.789291e+10 -3.586900e+10
## [201] -2.778100e+11 -5.311901e+09 -1.803050e+12 -2.678620e+11 -7.158905e+10
## [206] -5.090141e+09 -1.439260e+11 -7.873794e+08 -1.615774e+09 -7.793689e+09
## [211] 8.295873e+08 NA -1.142092e+09 -7.169700e+11 NA
## [216] -7.174130e+11 -1.490120e+11 -2.462549e+08 -3.877733e+08 -9.464786e+09
## [221] -6.549673e+08 -5.022400e+10 -9.392003e+08 NA -4.524327e+08
## [226] NA NA -1.342592e+09 -9.990550e+12 -7.190900e+11
## [231] -2.289092e+09 -1.922780e+11 -3.426204e+09 -2.969126e+10 -1.938060e+12
## [236] -7.359504e+07 -4.196280e+11 -1.259144e+08 -1.803050e+12 -7.174130e+11
## [241] -4.624718e+08 -1.348606e+09 -1.753320e+11 -1.270094e+07 -3.058858e+10
## [246] -1.359825e+10 3.056500e+10 -1.139790e+13 -3.274640e+10 -4.913000e+12
## [251] -2.640629e+10 -1.377963e+08 NA NA NA
## [256] -1.464496e+11 -3.364515e+08 -2.285050e+13 -3.056559e+08 -2.295129e+09
## [261] NA -5.038500e+10 -1.175171e+10 -1.255387e+10
#2 Create a subset of countries that saw an increase of over one trillion dollars.
gdp_subset0717=subset(x=gdp, subset=abs(diff0717)>=1000000000000)
dim(gdp_subset0717)
## [1] 26 61
gdp_subset0717
## # A tibble: 26 x 61
## `Country Name` `Country Code` `1960` `1961` `1962` `1963` `1964` `1965`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 China CHN 5.97e10 5.01e10 4.72e10 5.07e10 5.97e10 7.04e10
## 2 East Asia & P~ EAP 8.03e10 7.04e10 6.46e10 6.99e10 8.10e10 9.46e10
## 3 Early-demogra~ EAR 1.52e11 1.53e11 1.59e11 1.68e11 1.92e11 2.10e11
## 4 East Asia & P~ EAS 1.53e11 1.54e11 1.57e11 1.75e11 2.02e11 2.25e11
## 5 High income HIC 1.08e12 1.15e12 1.24e12 1.32e12 1.45e12 1.58e12
## 6 IBRD only IBD 2.95e11 2.82e11 2.94e11 3.13e11 3.55e11 3.90e11
## 7 IDA & IBRD to~ IBT 3.32e11 3.22e11 3.37e11 3.62e11 4.03e11 4.44e11
## 8 IDA total IDA 3.85e10 4.06e10 4.38e10 4.93e10 4.82e10 5.44e10
## 9 India IND 3.65e10 3.87e10 4.16e10 4.78e10 5.57e10 5.88e10
## 10 Latin America~ LAC 5.22e10 5.48e10 6.32e10 7.00e10 7.22e10 7.73e10
## # ... with 16 more rows, and 53 more variables: 1966 <dbl>, 1967 <dbl>,
## # 1968 <dbl>, 1969 <dbl>, 1970 <dbl>, 1971 <dbl>, 1972 <dbl>, 1973 <dbl>,
## # 1974 <dbl>, 1975 <dbl>, 1976 <dbl>, 1977 <dbl>, 1978 <dbl>, 1979 <dbl>,
## # 1980 <dbl>, 1981 <dbl>, 1982 <dbl>, 1983 <dbl>, 1984 <dbl>, 1985 <dbl>,
## # 1986 <dbl>, 1987 <dbl>, 1988 <dbl>, 1989 <dbl>, 1990 <dbl>, 1991 <dbl>,
## # 1992 <dbl>, 1993 <dbl>, 1994 <dbl>, 1995 <dbl>, 1996 <dbl>, 1997 <dbl>,
## # 1998 <dbl>, 1999 <dbl>, 2000 <dbl>, 2001 <dbl>, 2002 <dbl>, 2003 <dbl>, ...