stat545a-2013-hw03

Tong Wang

This is a practice for data aggregation.

Install plyr and load the package.

install.packages("plyr", dependencies = TRUE)

## Error: trying to use CRAN without setting a mirror

library(plyr)
library(lattice)

Import data

## data import from URL
gdURL <- "http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt"
gDat <- read.delim(file = gdURL)

Overview of Data

summary(gDat)

##         country          year           pop              continent  
##  Afghanistan:  12   Min.   :1952   Min.   :6.00e+04   Africa  :624  
##  Albania    :  12   1st Qu.:1966   1st Qu.:2.79e+06   Americas:300  
##  Algeria    :  12   Median :1980   Median :7.02e+06   Asia    :396  
##  Angola     :  12   Mean   :1980   Mean   :2.96e+07   Europe  :360  
##  Argentina  :  12   3rd Qu.:1993   3rd Qu.:1.96e+07   Oceania : 24  
##  Australia  :  12   Max.   :2007   Max.   :1.32e+09                 
##  (Other)    :1632                                                   
##     lifeExp       gdpPercap     
##  Min.   :23.6   Min.   :   241  
##  1st Qu.:48.2   1st Qu.:  1202  
##  Median :60.7   Median :  3532  
##  Mean   :59.5   Mean   :  7215  
##  3rd Qu.:70.8   3rd Qu.:  9325  
##  Max.   :82.6   Max.   :113523  
## 

Data aggregation

Get the max and minimum LifeExp for all continents in a “wide” format. The result is sorted based on the value of maximum Life Expectancy.

maxminLeByCont <- ddply(gDat, ~ continent, summarize, maxLifeExp = max(lifeExp), minLifeExp = min(lifeExp))
##(maxminLeByCont[order(maxminLeByCont$maxLifeExp),])
(maxminLeByCont <- arrange (maxminLeByCont, maxLifeExp))

##   continent maxLifeExp minLifeExp
## 1    Africa      76.44      23.60
## 2  Americas      80.65      37.58
## 3   Oceania      81.23      69.12
## 4    Europe      81.76      43.59
## 5      Asia      82.60      28.80

Compute a trimmed mean of life expectancy for different years.

##    year meanLifeExp
## 1  1952       49.03
## 2  1957       51.50
## 3  1962       53.62
## 4  1967       55.70
## 5  1972       57.68
## 6  1977       59.65
## 7  1982       61.59
## 8  1987       63.27
## 9  1992       64.34
## 10 1997       65.11
## 11 2002       65.77
## 12 2007       67.09

View life expectancy changing over time on each continents, in a Tall format.

(LeByYear <- ddply(gDat, .(year, continent), summarize, mlifeExp = mean(lifeExp)))

##    year continent mlifeExp
## 1  1952    Africa    39.14
## 2  1952  Americas    53.28
## 3  1952      Asia    46.31
## 4  1952    Europe    64.41
## 5  1952   Oceania    69.25
## 6  1957    Africa    41.27
## 7  1957  Americas    55.96
## 8  1957      Asia    49.32
## 9  1957    Europe    66.70
## 10 1957   Oceania    70.30
## 11 1962    Africa    43.32
## 12 1962  Americas    58.40
## 13 1962      Asia    51.56
## 14 1962    Europe    68.54
## 15 1962   Oceania    71.09
## 16 1967    Africa    45.33
## 17 1967  Americas    60.41
## 18 1967      Asia    54.66
## 19 1967    Europe    69.74
## 20 1967   Oceania    71.31
## 21 1972    Africa    47.45
## 22 1972  Americas    62.39
## 23 1972      Asia    57.32
## 24 1972    Europe    70.78
## 25 1972   Oceania    71.91
## 26 1977    Africa    49.58
## 27 1977  Americas    64.39
## 28 1977      Asia    59.61
## 29 1977    Europe    71.94
## 30 1977   Oceania    72.85
## 31 1982    Africa    51.59
## 32 1982  Americas    66.23
## 33 1982      Asia    62.62
## 34 1982    Europe    72.81
## 35 1982   Oceania    74.29
## 36 1987    Africa    53.34
## 37 1987  Americas    68.09
## 38 1987      Asia    64.85
## 39 1987    Europe    73.64
## 40 1987   Oceania    75.32
## 41 1992    Africa    53.63
## 42 1992  Americas    69.57
## 43 1992      Asia    66.54
## 44 1992    Europe    74.44
## 45 1992   Oceania    76.94
## 46 1997    Africa    53.60
## 47 1997  Americas    71.15
## 48 1997      Asia    68.02
## 49 1997    Europe    75.51
## 50 1997   Oceania    78.19
## 51 2002    Africa    53.33
## 52 2002  Americas    72.42
## 53 2002      Asia    69.23
## 54 2002    Europe    76.70
## 55 2002   Oceania    79.74
## 56 2007    Africa    54.81
## 57 2007  Americas    73.61
## 58 2007      Asia    70.73
## 59 2007    Europe    77.65
## 60 2007   Oceania    80.72

Count the number of countries with low life expectancy over time by continent. “Tall” format. (Low life expectancy is defined as below 48.20 (1st Qu).) First, select the pieces of data which is low life expectancy country.

jDat <- subset(gDat, lifeExp < 48.2)
(jLowLeCountries <- ddply(jDat, .(continent, year), summarize, nUniqCountries = length(unique(country))))

##    continent year nUniqCountries
## 1     Africa 1952             49
## 2     Africa 1957             47
## 3     Africa 1962             42
## 4     Africa 1967             37
## 5     Africa 1972             28
## 6     Africa 1977             24
## 7     Africa 1982             19
## 8     Africa 1987             16
## 9     Africa 1992             14
## 10    Africa 1997             16
## 11    Africa 2002             19
## 12    Africa 2007             14
## 13  Americas 1952              8
## 14  Americas 1957              6
## 15  Americas 1962              4
## 16  Americas 1967              2
## 17  Americas 1972              2
## 18      Asia 1952             21
## 19      Asia 1957             16
## 20      Asia 1962             15
## 21      Asia 1967              9
## 22      Asia 1972              5
## 23      Asia 1977              5
## 24      Asia 1982              1
## 25      Asia 1987              1
## 26      Asia 1992              1
## 27      Asia 1997              1
## 28      Asia 2002              1
## 29      Asia 2007              1
## 30    Europe 1952              1
## 31    Europe 1957              1

xyplot(nUniqCountries ~ year | continent, jLowLeCountries, type = c("p", "r"))

As shown in the figures, the number of low life expectancy countries are decreasing over all continents. Africa witnessed the most significant decreases during the past 50 years.

Considering continents have different number of countries, the proportion of low life expectancy is calculated.

## noCountryByContinent <- ddply(gDat, ~continent, summarize, nUniqCountries
## = length(unique(country))) jDat <- subset (gDat, lifeExp < 48.20) fn <-
## function (continent){ if (continent == 'Africa') {pUniqCountries =
## length(unique(country) / 52 } else if (continent == 'Americas')
## {pUniqCountries = length(unique(country) / 25 } else if (continent ==
## 'Asia') {pUniqCountries = length(unique(country) / 33 } else if (continent
## == 'Europe') {pUniqCountries = length(unique(country) / 30 } else if
## (continent == 'Oceania') pUniqCountries = length(unique(country) / 2
## return (pUniqCountries)}

## (pjLowLeCountries <- ddply(jDat, .(continent,year), transform, pCountries
## = fn(continent)))

Error: unexpected '}' ??

Relative abundance of countries with low life expectancy over time by continent, tall format.

install.packages("xtable", dependencies = TRUE)

## Error: trying to use CRAN without setting a mirror

library(xtable)

jTab <- ddply(jDat, .(year, continent, country), summarize, abundance = gdpPercap)
rpt <- jTab[sample(nrow(jTab)), ]
rpt <- arrange(rpt, year, continent, abundance)
rpt <- xtable(rpt)
print(rpt, type = "html", include.rownames = FALSE)

Linear regression of life expectancy vs. time among low life expectancy countries.

yearMin <- min(gDat$year)
jFun <- function(x) {
    estCoefs <- coef(lm(lifeExp ~ I(year - yearMin), x))
    names(estCoefs) <- c("intercept", "slope")
    return(estCoefs)
}
jCoefs <- ddply(gDat, ~country + continent, jFun)
(jCoefs <- arrange(jCoefs, continent, slope))

##                      country continent intercept    slope
## 1                   Zimbabwe    Africa     55.22 -0.09302
## 2                     Zambia    Africa     47.66 -0.06043
## 3                     Rwanda    Africa     42.74 -0.04583
## 4                   Botswana    Africa     52.93  0.06067
## 5           Congo, Dem. Rep.    Africa     41.96  0.09392
## 6                  Swaziland    Africa     46.39  0.09507
## 7                    Lesotho    Africa     47.38  0.09557
## 8                    Liberia    Africa     39.84  0.09599
## 9                     Uganda    Africa     44.28  0.12159
## 10             Cote d'Ivoire    Africa     44.85  0.13056
## 11                   Burundi    Africa     40.58  0.15413
## 12              South Africa    Africa     49.34  0.16916
## 13                  Tanzania    Africa     43.11  0.17469
## 14  Central African Republic    Africa     38.81  0.18391
## 15               Congo, Rep.    Africa     47.14  0.19510
## 16                     Kenya    Africa     47.00  0.20651
## 17                   Nigeria    Africa     37.86  0.20807
## 18                    Angola    Africa     32.13  0.20934
## 19              Sierra Leone    Africa     30.88  0.21403
## 20                Mozambique    Africa     34.21  0.22449
## 21                   Somalia    Africa     34.68  0.22957
## 22                   Namibia    Africa     47.13  0.23116
## 23                    Malawi    Africa     36.91  0.23423
## 24                  Cameroon    Africa     41.25  0.25015
## 25                      Chad    Africa     39.81  0.25324
## 26             Guinea-Bissau    Africa     31.74  0.27175
## 27                  Ethiopia    Africa     36.03  0.30719
## 28         Equatorial Guinea    Africa     34.43  0.31017
## 29                     Ghana    Africa     43.49  0.32174
## 30                     Benin    Africa     39.59  0.33423
## 31     Sao Tome and Principe    Africa     48.53  0.34068
## 32                     Niger    Africa     35.15  0.34211
## 33                 Mauritius    Africa     55.37  0.34845
## 34              Burkina Faso    Africa     34.68  0.36397
## 35                  Djibouti    Africa     36.28  0.36740
## 36                   Eritrea    Africa     35.70  0.37469
## 37                      Mali    Africa     33.05  0.37681
## 38                      Togo    Africa     40.98  0.38259
## 39                     Sudan    Africa     37.87  0.38277
## 40                Madagascar    Africa     36.67  0.40373
## 41                    Guinea    Africa     31.56  0.42483
## 42                Mauritania    Africa     40.03  0.44642
## 43                     Gabon    Africa     38.94  0.44673
## 44                   Comoros    Africa     40.00  0.45039
## 45                   Reunion    Africa     54.00  0.45988
## 46                   Senegal    Africa     36.75  0.50470
## 47                   Morocco    Africa     42.69  0.54247
## 48                     Egypt    Africa     40.97  0.55545
## 49                   Algeria    Africa     43.37  0.56928
## 50                    Gambia    Africa     28.40  0.58183
## 51                   Tunisia    Africa     44.56  0.58784
## 52                     Libya    Africa     42.10  0.62554
## 53                  Paraguay  Americas     62.48  0.15735
## 54       Trinidad and Tobago  Americas     62.05  0.17366
## 55                   Uruguay  Americas     65.74  0.18327
## 56             United States  Americas     68.41  0.18417
## 57               Puerto Rico  Americas     66.95  0.21057
## 58                    Canada  Americas     68.88  0.21887
## 59                   Jamaica  Americas     62.66  0.22139
## 60                 Argentina  Americas     62.69  0.23171
## 61                      Cuba  Americas     62.21  0.32115
## 62                 Venezuela  Americas     57.51  0.32972
## 63                    Panama  Americas     58.06  0.35421
## 64                  Colombia  Americas     53.43  0.38075
## 65                    Brazil  Americas     51.51  0.39009
## 66                     Haiti  Americas     39.25  0.39706
## 67                Costa Rica  Americas     59.10  0.40279
## 68                    Mexico  Americas     53.01  0.45103
## 69        Dominican Republic  Americas     48.60  0.47115
## 70                     Chile  Americas     54.32  0.47684
## 71               El Salvador  Americas     46.51  0.47714
## 72                   Bolivia  Americas     38.76  0.49993
## 73                   Ecuador  Americas     49.07  0.50005
## 74                      Peru  Americas     44.35  0.52770
## 75                 Guatemala  Americas     42.12  0.53127
## 76                  Honduras  Americas     42.99  0.54285
## 77                 Nicaragua  Americas     43.05  0.55652
## 78                      Iraq      Asia     50.11  0.23521
## 79                 Sri Lanka      Asia     59.79  0.24489
## 80                   Lebanon      Asia     58.69  0.26103
## 81                    Israel      Asia     66.30  0.26711
## 82               Afghanistan      Asia     29.91  0.27533
## 83          Korea, Dem. Rep.      Asia     54.91  0.31643
## 84                    Taiwan      Asia     61.34  0.32724
## 85                 Singapore      Asia     61.85  0.34089
## 86                  Thailand      Asia     52.66  0.34705
## 87                     Japan      Asia     65.12  0.35290
## 88          Hong Kong, China      Asia     63.43  0.36597
## 89                  Cambodia      Asia     37.02  0.39590
## 90                  Pakistan      Asia     43.72  0.40579
## 91                    Kuwait      Asia     57.46  0.41684
## 92               Philippines      Asia     49.40  0.42047
## 93                   Myanmar      Asia     41.41  0.43310
## 94                  Mongolia      Asia     43.83  0.43869
## 95                  Malaysia      Asia     51.51  0.46452
## 96                   Bahrain      Asia     52.75  0.46751
## 97                      Iran      Asia     44.98  0.49664
## 98                Bangladesh      Asia     36.14  0.49813
## 99                     India      Asia     39.27  0.50532
## 100                    Nepal      Asia     34.43  0.52926
## 101                    China      Asia     47.19  0.53071
## 102                    Syria      Asia     46.10  0.55436
## 103              Korea, Rep.      Asia     49.73  0.55540
## 104                   Jordan      Asia     44.06  0.57173
## 105       West Bank and Gaza      Asia     43.80  0.60110
## 106              Yemen, Rep.      Asia     30.13  0.60546
## 107                Indonesia      Asia     36.88  0.63464
## 108             Saudi Arabia      Asia     40.81  0.64962
## 109                  Vietnam      Asia     39.01  0.67162
## 110                     Oman      Asia     37.21  0.77218
## 111                  Denmark    Europe     71.03  0.12133
## 112                  Hungary    Europe     65.99  0.12365
## 113                   Norway    Europe     72.21  0.13194
## 114          Slovak Republic    Europe     67.01  0.13404
## 115              Netherlands    Europe     71.89  0.13669
## 116           Czech Republic    Europe     67.53  0.14482
## 117                 Bulgaria    Europe     65.74  0.14569
## 118                  Romania    Europe     63.96  0.15740
## 119                  Iceland    Europe     71.96  0.16538
## 120                   Sweden    Europe     71.61  0.16625
## 121           United Kingdom    Europe     68.81  0.18597
## 122                   Poland    Europe     64.78  0.19622
## 123                  Ireland    Europe     67.54  0.19912
## 124                 Slovenia    Europe     66.09  0.20052
## 125                  Belgium    Europe     67.89  0.20908
## 126                  Germany    Europe     67.57  0.21368
## 127              Switzerland    Europe     69.45  0.22223
## 128                  Croatia    Europe     63.86  0.22546
## 129                  Finland    Europe     66.45  0.23793
## 130                   France    Europe     67.79  0.23850
## 131                  Austria    Europe     66.45  0.24199
## 132                   Greece    Europe     67.07  0.24240
## 133                   Serbia    Europe     61.53  0.25515
## 134                    Italy    Europe     66.60  0.26971
## 135                    Spain    Europe     66.48  0.28093
## 136               Montenegro    Europe     62.24  0.29300
## 137                  Albania    Europe     59.23  0.33468
## 138                 Portugal    Europe     61.15  0.33720
## 139   Bosnia and Herzegovina    Europe     58.09  0.34976
## 140                   Turkey    Europe     46.02  0.49724
## 141              New Zealand   Oceania     68.69  0.19282
## 142                Australia   Oceania     68.40  0.22772

(hljCoefs <- ddply(jCoefs, ~continent, summarize, maxSlope = max(slope), minSlope = min(slope)))

##   continent maxSlope minSlope
## 1    Africa   0.6255 -0.09302
## 2  Americas   0.5565  0.15735
## 3      Asia   0.7722  0.23521
## 4    Europe   0.4972  0.12133
## 5   Oceania   0.2277  0.19282

Given the sample size of each continent, regression for Africa has higher power. Libya is shown to be the country with the biggest slope rate among Africa countries. Taking a deeper look at the data of Libya.

(libya <- subset(gDat, country == "Libya"))

##     country year     pop continent lifeExp gdpPercap
## 901   Libya 1952 1019729    Africa   42.72      2388
## 902   Libya 1957 1201578    Africa   45.29      3448
## 903   Libya 1962 1441863    Africa   47.81      6757
## 904   Libya 1967 1759224    Africa   50.23     18773
## 905   Libya 1972 2183877    Africa   52.77     21011
## 906   Libya 1977 2721783    Africa   57.44     21951
## 907   Libya 1982 3344074    Africa   62.16     17364
## 908   Libya 1987 3799845    Africa   66.23     11771
## 909   Libya 1992 4364501    Africa   68.75      9640
## 910   Libya 1997 4759670    Africa   71.56      9467
## 911   Libya 2002 5368585    Africa   72.74      9535
## 912   Libya 2007 6036914    Africa   73.95     12057