Data

We got the data from UNICEF website “State of the World children 2015 Tanzania Statistical table”

library(knitr)
data<-read.csv("C:/Users/User/Documents/R/Project Life.csv",head=T)
data
##    Age.Group X2013..Female X2013..Male X2012..Female X2012..Male
## 1          1      100000.0    100000.0      100000.0    100000.0
## 2          5       96640.0     96070.0       96520.0     95950.0
## 3         10       95186.6     94455.7       94973.6     94232.3
## 4         15       93821.6     92833.8       93514.2     92514.2
## 5         20       92808.2     91731.6       92424.2     91336.3
## 6         25       91718.0     90266.4       91227.4     89762.9
## 7         30       90301.7     88432.8       89728.9     87884.6
## 8         35       88247.9     86102.4       87257.0     85325.2
## 9         40       85827.7     83232.5       84268.2     82042.1
## 10        45       82838.0     79502.8       80414.6     77640.7
## 11        50       79889.9     75637.7       76936.6     73227.7
## 12        55       77121.9     71881.5       73857.2     69094.8
## 13        60       73957.8     67799.4       70604.7     64911.0
## 14        65       70146.9     62972.0       66832.0     60056.8
## 15        70       64741.0     56597.5       61565.6     53792.7
## 16        75       56863.9     48182.1       53954.0     45651.8
## 17        80       45786.3     37363.9       43306.5     35260.8
## 18        85       31914.1     24691.3       30057.1     23184.6
## 19        90       16005.2     11628.8       15227.1     10889.9
## 20        95        5709.2      3569.5        5227.3      3276.2
## 21       100        1200.9       575.4        1032.4       511.0
## 22       105         126.9        38.6          97.7        32.3
##    X2000..Female X2000..Male X1990..Female X1990..Male
## 1       100000.0    100000.0      100000.0    100000.0
## 2        92380.0     91560.1       90429.9     89409.9
## 3        87419.5     86319.9       84085.1     82727.0
## 4        85049.8     83833.1       81393.1     79873.9
## 5        83754.7     82531.5       80013.9     78480.6
## 6        82345.9     81025.1       78436.0     76828.8
## 7        79844.2     78600.9       76262.9     74319.6
## 8        73877.8     74389.9       73530.5     71379.1
## 9        68171.8     69255.6       70764.0     68278.3
## 10       62929.9     63389.7       67887.4     64954.9
## 11       59028.7     58462.6       64908.8     61480.1
## 12       55304.5     54128.7       61671.1     57801.8
## 13       51600.2     50054.5       58074.7     53450.2
## 14       47951.7     45028.8       53767.3     48042.3
## 15       43245.6     39064.2       47948.1     41585.1
## 16       36727.1     32125.0       40053.2     33618.6
## 17       28153.4     23716.1       29976.4     24076.1
## 18       18351.4     14614.2       18891.4     14188.3
## 19        8572.7      6234.9        8434.5      5676.3
## 20        2655.6      1654.2        2480.9      1386.7
## 21         474.5       224.8         421.9       172.1
## 22          42.1        13.1          36.5         9.6

To import the data to RMarkdown

data1<-read.csv("C:/Users/User/Documents/R/Project Life.csv",head=T, stringsAsFactors = FALSE)
data1
##    Age.Group X2013..Female X2013..Male X2012..Female X2012..Male
## 1          1      100000.0    100000.0      100000.0    100000.0
## 2          5       96640.0     96070.0       96520.0     95950.0
## 3         10       95186.6     94455.7       94973.6     94232.3
## 4         15       93821.6     92833.8       93514.2     92514.2
## 5         20       92808.2     91731.6       92424.2     91336.3
## 6         25       91718.0     90266.4       91227.4     89762.9
## 7         30       90301.7     88432.8       89728.9     87884.6
## 8         35       88247.9     86102.4       87257.0     85325.2
## 9         40       85827.7     83232.5       84268.2     82042.1
## 10        45       82838.0     79502.8       80414.6     77640.7
## 11        50       79889.9     75637.7       76936.6     73227.7
## 12        55       77121.9     71881.5       73857.2     69094.8
## 13        60       73957.8     67799.4       70604.7     64911.0
## 14        65       70146.9     62972.0       66832.0     60056.8
## 15        70       64741.0     56597.5       61565.6     53792.7
## 16        75       56863.9     48182.1       53954.0     45651.8
## 17        80       45786.3     37363.9       43306.5     35260.8
## 18        85       31914.1     24691.3       30057.1     23184.6
## 19        90       16005.2     11628.8       15227.1     10889.9
## 20        95        5709.2      3569.5        5227.3      3276.2
## 21       100        1200.9       575.4        1032.4       511.0
## 22       105         126.9        38.6          97.7        32.3
##    X2000..Female X2000..Male X1990..Female X1990..Male
## 1       100000.0    100000.0      100000.0    100000.0
## 2        92380.0     91560.1       90429.9     89409.9
## 3        87419.5     86319.9       84085.1     82727.0
## 4        85049.8     83833.1       81393.1     79873.9
## 5        83754.7     82531.5       80013.9     78480.6
## 6        82345.9     81025.1       78436.0     76828.8
## 7        79844.2     78600.9       76262.9     74319.6
## 8        73877.8     74389.9       73530.5     71379.1
## 9        68171.8     69255.6       70764.0     68278.3
## 10       62929.9     63389.7       67887.4     64954.9
## 11       59028.7     58462.6       64908.8     61480.1
## 12       55304.5     54128.7       61671.1     57801.8
## 13       51600.2     50054.5       58074.7     53450.2
## 14       47951.7     45028.8       53767.3     48042.3
## 15       43245.6     39064.2       47948.1     41585.1
## 16       36727.1     32125.0       40053.2     33618.6
## 17       28153.4     23716.1       29976.4     24076.1
## 18       18351.4     14614.2       18891.4     14188.3
## 19        8572.7      6234.9        8434.5      5676.3
## 20        2655.6      1654.2        2480.9      1386.7
## 21         474.5       224.8         421.9       172.1
## 22          42.1        13.1          36.5         9.6

Structure of the data

str(data1)
## 'data.frame':    22 obs. of  9 variables:
##  $ Age.Group    : int  1 5 10 15 20 25 30 35 40 45 ...
##  $ X2013..Female: num  100000 96640 95187 93822 92808 ...
##  $ X2013..Male  : num  100000 96070 94456 92834 91732 ...
##  $ X2012..Female: num  100000 96520 94974 93514 92424 ...
##  $ X2012..Male  : num  100000 95950 94232 92514 91336 ...
##  $ X2000..Female: num  100000 92380 87420 85050 83755 ...
##  $ X2000..Male  : num  100000 91560 86320 83833 82532 ...
##  $ X1990..Female: num  100000 90430 84085 81393 80014 ...
##  $ X1990..Male  : num  100000 89410 82727 79874 78481 ...
sapply(data1,mode)
##     Age.Group X2013..Female   X2013..Male X2012..Female   X2012..Male 
##     "numeric"     "numeric"     "numeric"     "numeric"     "numeric" 
## X2000..Female   X2000..Male X1990..Female   X1990..Male 
##     "numeric"     "numeric"     "numeric"     "numeric"

To extract the data

Year<-(Year= data1$Age.Group)

Extract 2013 Male and Female Survivals

Femal<-data1$X2013..Female
Mal<-data1$X2013..Male
Tanzania<-data.frame(Year,Femal,Mal)
Tanzania
##    Year    Femal      Mal
## 1     1 100000.0 100000.0
## 2     5  96640.0  96070.0
## 3    10  95186.6  94455.7
## 4    15  93821.6  92833.8
## 5    20  92808.2  91731.6
## 6    25  91718.0  90266.4
## 7    30  90301.7  88432.8
## 8    35  88247.9  86102.4
## 9    40  85827.7  83232.5
## 10   45  82838.0  79502.8
## 11   50  79889.9  75637.7
## 12   55  77121.9  71881.5
## 13   60  73957.8  67799.4
## 14   65  70146.9  62972.0
## 15   70  64741.0  56597.5
## 16   75  56863.9  48182.1
## 17   80  45786.3  37363.9
## 18   85  31914.1  24691.3
## 19   90  16005.2  11628.8
## 20   95   5709.2   3569.5
## 21  100   1200.9    575.4
## 22  105    126.9     38.6

To convert Female Survivals into percentage

Femal
##  [1] 100000.0  96640.0  95186.6  93821.6  92808.2  91718.0  90301.7
##  [8]  88247.9  85827.7  82838.0  79889.9  77121.9  73957.8  70146.9
## [15]  64741.0  56863.9  45786.3  31914.1  16005.2   5709.2   1200.9
## [22]    126.9
str(Femal)
##  num [1:22] 100000 96640 95187 93822 92808 ...
Female<-(Femal*100)/100000
Female<-round(Female,digits = 0)
Female
##  [1] 100  97  95  94  93  92  90  88  86  83  80  77  74  70  65  57  46
## [18]  32  16   6   1   0

To convert Male Survivals into percentage

Male<-(Mal*100)/100000
Male<-round(Male,digits = 0)
Male
##  [1] 100  96  94  93  92  90  88  86  83  80  76  72  68  63  57  48  37
## [18]  25  12   4   1   0

Data frame for percentage of survivals Male and Female

Tanzania1<-data.frame(Year,Female,Male)
Tanzania1
##    Year Female Male
## 1     1    100  100
## 2     5     97   96
## 3    10     95   94
## 4    15     94   93
## 5    20     93   92
## 6    25     92   90
## 7    30     90   88
## 8    35     88   86
## 9    40     86   83
## 10   45     83   80
## 11   50     80   76
## 12   55     77   72
## 13   60     74   68
## 14   65     70   63
## 15   70     65   57
## 16   75     57   48
## 17   80     46   37
## 18   85     32   25
## 19   90     16   12
## 20   95      6    4
## 21  100      1    1
## 22  105      0    0

Plot

plot(Female~Year,Tanzania1,ann=T,type= "l",lwd=2, col="red")
title(main = "Female Survival Function")
xf<-c(Year,1)
yf<-c(Female,0)
polygon(xf,yf, density=15,angle = 135)

plot(Male~Year,Tanzania1,ann=T,type="l",lwd=2, col="blue")
abline(v=c(0,105),lty=2)
title(main = "Male Survival Function")
xm<-c(Year,1)
ym<-c(Male,0)
polygon(xm,ym, density=15,angle = 135)

plot(Female~Year,Tanzania1,ann=F,type= "l",lwd=2,col="red")
lines(Male~Year,Tanzania1,ann=F,type="l",lwd=2,col="blue")
title(main = "Comparizon between Tanzania Life Expectacy \n of Male and Female",xlab = "Years", ylab = "survival rate in %")
text(x=c(60,80),y=c(60,80), labels = c("Male","Female"))

plot(Female~Year,Tanzania1,ann=F,type= "l",lwd=2,col="red")
lines(Male~Year,Tanzania1,ann=F,type="l",lwd=2,col="blue")
title(main = "Comparizon between Tanzania Life Expectacy \n of Male and Female",xlab = "Years", ylab = "survival rate in %")
text(x=c(60,80),y=c(60,80), labels = c("Male","Female"))
x<-c(xm,rev(xf))
y<-c(ym,rev(yf))
Tanzania2<-data.frame(x,y)
polygon(Tanzania2,angle=45,col="gray")

Grammer of graphics plot(ggplot)

library(ggplot2)
(g1<-ggplot() + geom_line(data=Tanzania1, aes(x=Year, y=Female, color="red")))

(g2<-g1 + geom_line(data=Tanzania1,aes(x=Year, y=Male,color="blue"))) 

  (g3<- g2 + scale_color_discrete(name = ("Indicator Group"), breaks = c("red","blue"),labels = c("Female", "Male")))

g4<-g3 +
  theme(legend.position = c(0.5,0.5))
(g5<- g4 + theme_bw())

(P1<-g5 + geom_polygon(data=Tanzania2,aes(x = x, y = y), alpha = 0.7, fill = "grey"))

(P2<-P1 + 
     xlab("Years") + ylab("SUrvival rate %") + 
  ggtitle("Comparizon between Tanzania Life Expectacy \n of Male and Female"))

(P3<-P2 +
     annotate("text", x=c(62,75), y= c(62,62), label=c("Male", "Female")))