library(tidyverse)
library(kableExtra)
library(knitr)
library(stringr)
library(readr)
library(plyr)
library(dplyr)
library(data.table)
library(readxl)
require(xlsx)
library(rJava)
library(xlsx)

Tidy dataset 1: Movies database

link to source

Load in Data from xlsx file

Dataframe requires tidying
- Join individual movies into one movies column
- Separate timestamp by day and time
  - Could perform some interesting time series analysis on the effects of time of day of review
- Omit NA Values

movies <- read.xlsx("Movie_Ratings.xlsx",1)
movies%<>% 
    gather(Movies,n,3:12) %>% 
    select(Timestamp,Name,Movies,n,Number.of.Movies.Seen) %>%   
    separate('Timestamp',c("day","time")," ") %>% 
    na.omit() %>% 
    arrange(Name)
View(movies)
    
movies %>% 
    dplyr::group_by(Movies) %>% 
    dplyr::summarise(round(mean(n),2))

## # A tibble: 10 x 2
##    Movies                                      `round(mean(n), 2)`
##    <chr>                                                     <dbl>
##  1 Alien..Covenant                                            0.38
##  2 Blade.Runner.2049                                          1.54
##  3 Ghost.in.the.Shell                                         0.69
##  4 Guardians.of.the.Galaxy.2                                  2.73
##  5 Spider.Man..Homecoming                                     1.58
##  6 Star.Wars..The.Last.Jedi                                   2.92
##  7 Thor..Ragnarok                                             2.08
##  8 Valerian.and.the.City.of.a.Thousand.Planets                0.54
##  9 War.for.the.Planet.of.the.Apes                             0.96
## 10 Wonder.Woman                                               3.12

data Visualization

Display average rating by user +Display average rating by movie

movies %>%  
    dplyr::group_by(Name) %>% 
    dplyr::summarise(Average_Rating_by_user=round(mean(n),2)) %>% 
    arrange(desc(Average_Rating_by_user)) %>% 
    ggplot(., aes(x=Name , Average_Rating_by_user)) + 
    geom_bar(aes(fill = Name), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=90,hjust=1))+
    coord_flip()

movies %>%  
    dplyr::group_by(Movies) %>% 
    dplyr::summarise(Average_movie_rating=round(mean(n),2)) %>% 
    arrange(desc(Average_movie_rating)) %>% 
    ggplot(., aes(x=Movies , Average_movie_rating)) + 
    geom_bar(aes(fill = Movies), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

World Bank info for Infant Mortality, Tuberculosis, and Aids

Binish recommended this dataset website
link to source
Randomly chose 4 countires with the letter U
Read in files individually/with functional

#Manually Load in CSV
Uganda<- read.csv("https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/uganda.csv",na.strings=c("","NA"))
Ukraine <- read.csv("https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/Ukraine.csv",na.strings=c("","NA"))
United_kingdom <- read.csv("https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/United_Kingdom.csv",na.strings=c("","NA"))
United_states <- read.csv("https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/United_states.csv",na.strings=c("","NA"))


# Functional to load csv
files <- c("https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/uganda.csv", "https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/Ukraine.csv", "https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/United_Kingdom.csv", "https://raw.githubusercontent.com/justinherman42/Justin-Data-607/master/tidy_data_week4/United_states.csv")
temp = files
myfiles <-lapply(temp,read.csv)

Join on shared row index values(survey questions)

This will decrease our sample to only those questions that are answered in every dataset

merged_df<- inner_join(United_states,United_kingdom) %>% 
    inner_join(.,Ukraine) %>% 
    inner_join(.,Uganda)

# With Functional data
merged_df_2<- inner_join(myfiles[[1]],myfiles[[2]]) %>% 
    inner_join(myfiles[[3]]) %>%   
    inner_join(myfiles[[4]])
#kable(merged_df_2[1:15,])

Tidy Dataset

getting_tidy <- merged_df[-(1),] %>% 
    gather(United_states,Values,2:57) %>% 
    mutate(Year=rep(unlist(lapply(2015:2002,function(x) rep(x,23))),4)) %>% 
    spread(X, Values)

## Warning: attributes are not identical across measure variables;
## they will be dropped

Get rid of special characters `]` `[` `-` and add spacing for next pipe of tidying

Use pipe to Tidy Mean[min-max] columns into mean,min,max columns

getting_tidy <-  as_data_frame(lapply(getting_tidy,function(x){ str_replace_all(x,"\\]|\\["," ")}))

## Warning: `as_data_frame()` is deprecated, use `as_tibble()` (but mind the new semantics).
## This warning is displayed once per session.

getting_tidy <-  as_data_frame(lapply(getting_tidy,function(x){ str_replace_all(x,"-","  ")}))



final_df <- getting_tidy %>% 
    separate(.,`Incidence of tuberculosis (per 100 000 population per year)`,into=c('Mean_Incidence_Tuberculosis_100,000','Min_Tuberculosis',"Max_Tuberculosis")) %>% 
    separate(.,`Antiretroviral therapy coverage among people with HIV infection eligible for ART according to 2010 guidelines (%)`,into=c('Mean_Antiretroviral_coverage','Min_Antiretroviral_coverage','Max_Antiretroviral_coverage')) %>% 
    separate(.,`Infant mortality rate (probability of dying between birth and age 1 per 1000 live births)`,into=c('Mean_Infant mortality_Rate/1000','Min_Infant_Mortality rate',"Max_Infant_Mortality_Rate"),sep= "  ") %>% 
    separate(.,`Under-five mortality rate (probability of dying by age 5 per 1000 live births)`,into=c('Mean_Under-five_Mortality_Rate/1000','Min_Under-five_Mortality_Rate',"Max_Under-five_Mortality_Rate"), sep="  ") %>% 
    separate(.,`Tuberculosis treatment coverage`,into=c('Mean_Tuberculosis_Coverage','Min_Tuberculosis_Coverage',"Max_Tuberculosis_Coverage"))

## Warning: Expected 3 pieces. Additional pieces discarded in 56 rows [1, 2,
## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

## Warning: Expected 3 pieces. Additional pieces discarded in 2 rows [8, 22].

## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 2 rows [36,
## 50].

## Warning: Expected 3 pieces. Additional pieces discarded in 55 rows [1, 2,
## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, ...].

## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 1 rows [18].

More piping

First column needs to be renamed to country column
Eliminate the .integers after the values in country column
Trim df
Convert columns to numeric for analysis

final_df %<>% 
    plyr::rename(.,c('United_states'= 'Country')) %>% 
    mutate(Country=rep(c("Uganda","Ukraine","United_Kingdom","United_States"),each=14)) %>% 
    select(colnames(.)[c(1,2,10,11,12,13,14,15,16,17,21,22,23,30,31,32,33,34,35)]) %>% 
    arrange(.,`Country`)  

final_df[] <- lapply(final_df,function(x){ str_trim(x)}) 
final_df[3:19] <- lapply(final_df[3:19],function(x) as.numeric(as.character(x)))

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

final_df <- as_data_frame(final_df)
kable(final_df)

Country	Year	Gross national income per capita (PPP int. $)	Hospital beds (per 10 000 population)	Mean_Incidence_Tuberculosis_100,000	Min_Tuberculosis	Max_Tuberculosis	Mean_Infant mortality_Rate/1000	Min_Infant_Mortality rate	Max_Infant_Mortality_Rate	Number of neonatal deaths (thousands)	Number of under-five deaths (thousands)	Poliomyelitis - number of reported cases	Mean_Tuberculosis_Coverage	Min_Tuberculosis_Coverage	Max_Tuberculosis_Coverage	Mean_Under-five_Mortality_Rate/1000	Min_Under-five_Mortality_Rate	Max_Under-five_Mortality_Rate
Uganda	2015	NA	NA	202	120	304	39.2	35.4	43.3	38	93	NA	52	34	87	55.9	49.3	63.3
Uganda	2014	NA	NA	202	127	294	41.0	37.8	44.5	38	97	0	56	39	89	60.1	54.4	66.4
Uganda	2005	890	NA	233	156	325	74.4	70.3	78.7	38	155	NA	62	44	92	120.3	112.8	128.3
Uganda	2004	840	NA	240	169	323	80.5	76.3	85.0	39	164	NA	66	49	94	131.3	123.5	139.6
Uganda	2003	800	NA	248	172	336	86.7	82.0	91.4	39	173	NA	65	48	94	142.5	133.9	151.3
Uganda	2002	760	NA	256	171	358	92.5	87.5	97.5	40	181	NA	62	44	92	153.1	143.8	162.6
Uganda	2013	NA	NA	203	134	288	43.9	40.8	47.2	38	103	0	60	42	91	65.0	59.4	71.1
Uganda	2012	1320	NA	205	135	289	46.5	43.2	50.0	38	109	NA	60	43	91	70.2	64.1	76.8
Uganda	2011	1310	NA	207	143	283	49.5	46.1	53.3	38	114	NA	64	47	92	75.5	69.1	82.3
Uganda	2010	1240	5	210	139	296	52.7	49.2	56.4	38	119	NA	60	43	91	81.0	74.6	87.7
Uganda	2009	1200	4	213	140	302	55.7	52.2	59.3	38	124	NA	60	42	91	86.4	80.1	93.0
Uganda	2008	1150	NA	217	145	304	58.9	55.4	62.5	38	130	NA	61	44	92	92.6	86.2	99.4
Uganda	2007	1070	NA	222	146	313	63.3	59.6	67.3	38	137	NA	60	43	92	100.5	93.6	107.7
Uganda	2006	990	NA	227	150	319	68.6	64.7	72.7	38	145	NA	61	43	92	109.8	102.7	117.3
Ukraine	2015	NA	NA	91	59	130	8.1	7.8	8.4	3	5	NA	74	52	110	9.4	9.1	9.8
Ukraine	2014	NA	NA	94	61	135	8.4	8.1	8.7	3	5	0	75	52	120	9.8	9.5	10.1
Ukraine	2005	6410	87	127	82	181	12.5	12.0	12.9	4	6	NA	67	47	100	14.5	13.9	15.0
Ukraine	2004	6000	87	127	82	182	13.0	12.3	13.6	4	6	NA	NA	NA	NA	15.1	14.3	15.8
Ukraine	2003	5170	88	126	81	180	13.7	12.8	14.5	4	6	NA	130	91	200	15.9	14.8	16.8
Ukraine	2002	4590	89	123	80	176	14.3	13.2	15.4	4	7	NA	68	48	110	16.7	15.4	18.0
Ukraine	2013	NA	88	96	62	138	8.8	8.5	9.0	3	5	0	84	59	130	10.2	9.9	10.5
Ukraine	2012	8670	89	101	65	144	9.2	8.9	9.4	3	5	NA	90	63	140	10.7	10.3	11.0
Ukraine	2011	8170	90	105	68	150	9.6	9.3	9.9	3	5	NA	71	50	110	11.2	10.8	11.5
Ukraine	2010	7590	94	110	71	157	10.1	9.8	10.4	3	6	NA	67	47	100	11.7	11.4	12.1
Ukraine	2009	7130	94	115	74	164	10.6	10.3	10.9	4	6	NA	68	48	110	12.3	12.0	12.7
Ukraine	2008	8370	87	119	77	170	11.1	10.8	11.4	4	6	NA	69	48	110	12.9	12.5	13.2
Ukraine	2007	7930	87	123	79	175	11.6	11.3	11.9	4	6	NA	66	46	100	13.4	13.1	13.8
Ukraine	2006	7110	87	125	81	179	12.0	11.7	12.3	4	6	NA	71	49	110	14.0	13.6	14.3
United_Kingdom	2015	NA	NA	10	9	1	3.7	3.6	3.9	2	4	NA	89	81	98	4.4	4.3	4.6
United_Kingdom	2014	NA	NA	11	10	13	3.8	3.7	3.9	2	4	0	89	81	98	4.5	4.4	4.6
United_Kingdom	2005	33820	37	15	14	17	5.1	5.0	5.2	3	4	NA	89	81	98	6.0	5.9	6.2
United_Kingdom	2004	32430	39	13	12	15	5.3	5.2	5.4	2	4	NA	89	81	98	6.2	6.0	6.3
United_Kingdom	2003	30450	40	13	12	15	5.4	5.3	5.4	2	4	NA	89	81	98	6.3	6.1	6.4
United_Kingdom	2002	29390	40	13	12	14	5.4	5.3	5.5	2	4	NA	89	81	98	6.3	6.2	6.5
United_Kingdom	2013	NA	28	13	12	14	3.9	3.8	4.0	2	4	0	89	81	98	4.6	4.5	4.7
United_Kingdom	2012	34640	28	14	13	16	4.1	4.0	4.2	2	4	NA	89	81	98	4.8	4.7	4.9
United_Kingdom	2011	35270	29	15	13	16	4.2	4.2	4.3	2	4	NA	89	81	98	5.0	4.9	5.1
United_Kingdom	2010	34510	30	14	13	15	4.4	4.3	4.5	2	4	NA	89	81	98	5.2	5.1	5.3
United_Kingdom	2009	35260	33	15	13	16	4.6	4.5	4.7	2	4	NA	89	81	98	5.4	5.3	5.5
United_Kingdom	2008	37110	34	15	13	16	4.8	4.7	4.9	2	4	NA	89	81	98	5.6	5.5	5.7
United_Kingdom	2007	36480	34	15	13	16	4.9	4.8	5.0	3	4	NA	89	81	98	5.8	5.6	5.9
United_Kingdom	2006	35620	36	15	14	17	5.0	4.9	5.1	3	4	NA	89	81	98	5.9	5.8	6.0
United_States	2015	NA	NA	3	3	2	5.7	5.4	5.9	15	26	NA	87	75	100	6.6	6.4	6.9
United_States	2014	NA	NA	3	2	2	5.8	5.7	6.0	16	27	0	87	75	100	6.8	6.6	7.0
United_States	2005	44740	NA	5	5	4	6.8	6.7	6.9	19	33	NA	87	75	100	8.0	7.8	8.1
United_States	2004	42260	NA	5	7	4	6.9	6.8	7.0	19	33	NA	87	75	100	8.1	7.9	8.2
United_States	2003	39960	NA	5	9	5	6.9	6.7	7.0	19	33	NA	87	75	100	8.1	8.0	8.3
United_States	2002	38590	NA	6	5	2	6.9	6.8	7.0	19	33	NA	87	75	100	8.2	8.1	8.4
United_States	2013	NA	29	3	3	2	5.9	5.8	6.0	16	27	0	87	75	100	6.9	6.8	7.1
United_States	2012	52620	29	3	7	3	6.0	5.9	6.1	16	28	NA	87	75	100	7.0	6.9	7.2
United_States	2011	50860	29	3	9	3	6.1	6.0	6.3	16	29	NA	87	75	100	7.2	7.1	7.3
United_States	2010	48880	30	4	2	3	6.2	6.1	6.3	17	30	NA	87	75	100	7.3	7.2	7.5
United_States	2009	47240	31	4	3	3	6.4	6.3	6.5	17	31	NA	87	75	100	7.5	7.4	7.6
United_States	2008	48650	NA	4	9	4	6.5	6.4	6.6	17	31	NA	87	75	100	7.6	7.5	7.8
United_States	2007	48420	NA	5	1	4	6.6	6.5	6.7	18	32	NA	87	75	100	7.8	7.6	7.9
United_States	2006	47390	NA	5	3	4	6.7	6.6	6.8	18	33	NA	87	75	100	7.9	7.7	8.0

Visual exploratory analysis of Infant Mortality

final_df %>%
    mutate(GNP=as.numeric(`Gross national income per capita (PPP int. $)`)) %>% 
    dplyr::group_by(Country) %>% 
    ggplot(., aes(x=Year ,y=GNP))+ 
    geom_bar(aes(fill = Country), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

## Warning: Removed 12 rows containing missing values (geom_bar).

final_df %>%
    dplyr::group_by(Country) %>% 
    ggplot(., aes(x=Year ,y=`Mean_Infant mortality_Rate/1000`))+ 
    geom_bar(aes(fill = Country), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

final_df_display <- final_df %>% 
    arrange(Country,Year) %>% 
    select(.,Country,`Mean_Infant mortality_Rate/1000`)

Uganda seems to have reduced it’s infant mortality rate by over 50%

Numerical display of Infant Mortality

## Numerical display     
kable(final_df_display)

Country	Mean_Infant mortality_Rate/1000
Uganda	92.5
Uganda	86.7
Uganda	80.5
Uganda	74.4
Uganda	68.6
Uganda	63.3
Uganda	58.9
Uganda	55.7
Uganda	52.7
Uganda	49.5
Uganda	46.5
Uganda	43.9
Uganda	41.0
Uganda	39.2
Ukraine	14.3
Ukraine	13.7
Ukraine	13.0
Ukraine	12.5
Ukraine	12.0
Ukraine	11.6
Ukraine	11.1
Ukraine	10.6
Ukraine	10.1
Ukraine	9.6
Ukraine	9.2
Ukraine	8.8
Ukraine	8.4
Ukraine	8.1
United_Kingdom	5.4
United_Kingdom	5.4
United_Kingdom	5.3
United_Kingdom	5.1
United_Kingdom	5.0
United_Kingdom	4.9
United_Kingdom	4.8
United_Kingdom	4.6
United_Kingdom	4.4
United_Kingdom	4.2
United_Kingdom	4.1
United_Kingdom	3.9
United_Kingdom	3.8
United_Kingdom	3.7
United_States	6.9
United_States	6.9
United_States	6.9
United_States	6.8
United_States	6.7
United_States	6.6
United_States	6.5
United_States	6.4
United_States	6.2
United_States	6.1
United_States	6.0
United_States	5.9
United_States	5.8
United_States	5.7

Display of infant Mortality with the 3 samples closer in scale

final_df %>%
    dplyr::filter(., Country %in% c("United_Kingdom", "Ukraine","United_States")) %>% 
    dplyr::group_by(Country) %>% 
    ggplot(., aes(x=Year ,y=`Mean_Infant mortality_Rate/1000`))+ 
    geom_bar(aes(fill = Country), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

Ukraine has seen a reduction of around 40%
US and UK have also seen a reduction, however UK seems to have a more substantial reduction(30%) over the US reduction of (17%)

Look at Tuberculosis

final_df %>%
    dplyr::filter(., Country %in% c("Uganda", "Ukraine","United_Kingdom","United_States")) %>% 
    dplyr::group_by(Country) %>% 
    ggplot(., aes(x=Year ,y=`Mean_Incidence_Tuberculosis_100,000`))+ 
    geom_bar(aes(fill = Country), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

Uganda seems to have seen some moderate improvement over the past 13 years in tuberculosis, but the rate is relatively high still
Ukraine seems to have has a similar improvement

Look at Tuberculosis in Heavily industrialized nations( UK, US)

final_df %>%
    dplyr::filter(., Country %in% c("United_Kingdom","United_States")) %>% 
    dplyr::group_by(Country) %>% 
    ggplot(., aes(x=Year ,y=`Mean_Incidence_Tuberculosis_100,000`))+ 
    geom_bar(aes(fill = Country), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

England appears to have made some moderate improvement in the past several years
Tuberculosis in the United states is almost non-existant and it appears it is decreasing

Third Dataset- Gapminder

Burcu recommended the Gapminder data website
link to source
mix of WHO datasets and

War or food?

Are nations dealing with war more food insecure?
How do state expenditures differ on war versus food?

Load in third dataset

file 1 iis a file with death rates out of 100k from war link to source
file 2 is a measurement of daily calories consumption link to source +file 3 is GDP War expenditure % link to source
file 4 is GDP Agricultural Expenditure link to source

delete.na <- function(DF, n=0) {
  DF[rowSums(is.na(DF)) <= n,]
}


War<- read.xlsx("War.xlsx", header=TRUE,1,)
food_security <- read.xlsx("indicator food_consumption.xlsx", header=TRUE,1,)
Agricultural_expend<- read.xlsx("Agricultural_expenditure.xlsx", header=TRUE,1,)
military_expend <- read.xlsx("military_expenditure.xlsx", header=TRUE,1,)

dim(food_security)

## [1] 259  48

dim(military_expend)

## [1] 270  25

dim(Agricultural_expend)

## [1] 270  52

dim(War)

## [1] 192   4

DF Preparation

Eliminate rows with substantial NA’s
Convert columns to numeric
Rename columns to acoid duplication of Column name sfor eventual Join
Filter df’s for common shared year range of 1988-2007

military_expend <- delete.na(military_expend, 20)
Agricultural_expend <- delete.na(Agricultural_expend, 15)
food_security <- delete.na(food_security, 20)
War <- as_data_frame(lapply(War, function(x) if(is.numeric(x)) round(x,2) else x))
military_expend <- as_data_frame(lapply(military_expend, function(x) if(is.numeric(x)) round(x,2) else x))
War <- War[2:4]
colnames(War) <- c("Country", 'war_2002','war_2004')
colnames(military_expend) <-  c("Country",paste(1988:2011,'Military_expend'))
colnames(food_security) <- c("Country",paste (1961:2007,"food_security"))
colnames(Agricultural_expend) <- c("Country",paste(1961:2011,"Agricultural_expend"))
Agricultural_expend <- Agricultural_expend[c(1,29:48)]
food_security <- food_security[c(1,29:48)]
military_expend <- military_expend[c(1:21)]


merged_df_3<- inner_join(War,military_expend) %>%
    inner_join(.,food_security) %>% 
    inner_join(.,Agricultural_expend)

## Joining, by = "Country"

## Warning: Column `Country` joining factors with different levels, coercing
## to character vector

## Joining, by = "Country"

## Warning: Column `Country` joining character vector and factor, coercing
## into character vector

## Joining, by = "Country"

## Warning: Column `Country` joining character vector and factor, coercing
## into character vector

dim(merged_df_3)

## [1] 83 63

Exploratory Analysis of Death from wars data

explore war death df

merged_df_3$war_2002-merged_df_3$war_2004

##  [1]  14.90   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00
## [11]   0.00 154.58   0.00   0.01  -0.20   0.00   0.00   0.34  52.60   0.00
## [21]   8.06  88.66  -0.02   0.01   0.00   0.00   0.00   0.00   0.00   0.00
## [31]   0.00   0.00   0.00   0.00   0.02   2.71   0.12   0.00  -0.02   0.00
## [41]   0.00   4.02   1.41   0.00   0.00   0.00   0.00   0.00   0.00   0.00
## [51]   0.00   0.00   0.18 -19.12  -0.02   0.00   0.01   0.00   0.32   0.00
## [61]   0.03   1.55   0.00   0.88  -0.75   9.93   0.00  -0.44  -0.07 -44.86
## [71]   0.00   0.00   0.00   0.00   0.00   0.16   7.46   0.00  -0.07  -0.33
## [81]   0.00   0.00  33.29

merged_df_3[1:3] %>% 
    mutate(.,average_deaths=(war_2002+war_2004)/2) %>% 
    arrange(.,average_deaths) %>%  
    kable(.)

Country	war_2002	war_2004	average_deaths
Argentina	0.00	0.00	0.000
Australia	0.00	0.00	0.000
Austria	0.00	0.00	0.000
Belgium	0.00	0.00	0.000
Benin	0.00	0.00	0.000
Bolivia	0.00	0.00	0.000
Botswana	0.00	0.00	0.000
Brazil	0.00	0.00	0.000
Brunei	0.00	0.00	0.000
Burkina Faso	0.00	0.00	0.000
Cameroon	0.00	0.00	0.000
Chile	0.00	0.00	0.000
China	0.00	0.00	0.000
Cuba	0.00	0.00	0.000
Fiji	0.00	0.00	0.000
Finland	0.00	0.00	0.000
France	0.00	0.00	0.000
Gabon	0.00	0.00	0.000
Gambia	0.00	0.00	0.000
Germany	0.00	0.00	0.000
Ghana	0.00	0.00	0.000
Guyana	0.00	0.00	0.000
Honduras	0.00	0.00	0.000
Ireland	0.00	0.00	0.000
Japan	0.00	0.00	0.000
Lesotho	0.00	0.00	0.000
Madagascar	0.00	0.00	0.000
Malaysia	0.00	0.00	0.000
Mali	0.00	0.00	0.000
Malta	0.00	0.00	0.000
Mauritius	0.00	0.00	0.000
Mexico	0.00	0.00	0.000
New Zealand	0.00	0.00	0.000
Norway	0.00	0.00	0.000
Paraguay	0.00	0.00	0.000
Portugal	0.00	0.00	0.000
Swaziland	0.00	0.00	0.000
Sweden	0.00	0.00	0.000
Togo	0.00	0.00	0.000
Tunisia	0.00	0.00	0.000
United Arab Emirates	0.00	0.00	0.000
Venezuela	0.00	0.00	0.000
Canada	0.01	0.00	0.005
Egypt	0.01	0.00	0.005
Denmark	0.00	0.02	0.010
Hungary	0.01	0.01	0.010
Italy	0.00	0.02	0.010
Netherlands	0.00	0.02	0.010
South Africa	0.01	0.01	0.010
Peru	0.03	0.00	0.015
United Kingdom	0.00	0.07	0.035
Zambia	0.04	0.04	0.040
Niger	0.06	0.05	0.055
Iran	0.12	0.00	0.060
Jordan	0.07	0.07	0.070
Malawi	0.07	0.07	0.070
Turkey	0.16	0.00	0.080
Mauritania	0.09	0.09	0.090
United States	0.01	0.34	0.175
Spain	0.00	0.44	0.220
Saudi Arabia	0.03	0.78	0.405
India	0.53	0.51	0.520
Kuwait	1.41	0.00	0.705
Thailand	0.99	0.99	0.990
Chad	2.03	2.23	2.130
Indonesia	3.98	1.27	2.625
Philippines	4.10	2.55	3.325
Kenya	5.47	1.45	3.460
Pakistan	4.30	3.98	4.140
Sri Lanka	4.88	4.95	4.915
Sierra Leone	9.93	0.00	4.965
Rwanda	9.04	8.16	8.600
Myanmar	9.52	9.34	9.430
Algeria	18.31	3.41	10.860
Nepal	6.25	25.37	15.810
Zimbabwe	33.80	0.51	17.155
Colombia	20.33	19.99	20.160
Congo, Rep.	52.60	0.00	26.300
Cote d’Ivoire	32.25	24.19	28.220
Uganda	43.92	36.46	40.190
Sudan	57.91	102.77	80.340
Congo, Dem. Rep.	130.20	41.54	85.870
Burundi	193.64	39.06	116.350

Create bins of death from wars to categorize Nations

Add average deaths from war column
Categorize deaths from war by levels
- no deaths, low_deaths, moderate_deaths, high_deaths

food_war <- merged_df_3 %>% 
    gather(Wars,Values,4:63) %>% 
    separate(.,Wars,into= c("Year", "Type"),sep=" ") %>% 
    spread(Type,Values) %>% 
    mutate(.,average_deaths=(war_2002+war_2004)/2) %>% 
    select(-c(war_2002,war_2004)) %>% 
    mutate(Deaths_from_war=cut(average_deaths, breaks=c(-1,0,1,10,Inf), labels= c("no_deaths", "low_deaths", "moderate_deaths", "high_deaths")))
   
    

kable(food_war[1:50,])

Country	Year	Agricultural_expend	food_security	Military_expend	average_deaths	Deaths_from_war
Algeria	1988	12.167656	2795.00	1.75	10.86	high_deaths
Algeria	1989	13.039015	2862.84	1.54	10.86	high_deaths
Algeria	1990	11.358267	2856.21	1.46	10.86	high_deaths
Algeria	1991	10.167058	2835.47	1.23	10.86	high_deaths
Algeria	1992	12.126846	2972.77	2.19	10.86	high_deaths
Algeria	1993	12.097073	2971.61	2.56	10.86	high_deaths
Algeria	1994	10.058437	2865.20	3.14	10.86	high_deaths
Algeria	1995	10.497818	2891.30	2.95	10.86	high_deaths
Algeria	1996	11.766699	2893.72	3.09	10.86	high_deaths
Algeria	1997	9.482320	2844.07	3.63	10.86	high_deaths
Algeria	1998	12.533281	2904.07	3.96	10.86	high_deaths
Algeria	1999	12.201079	2957.91	3.77	10.86	high_deaths
Algeria	2000	8.879884	2928.84	3.44	10.86	high_deaths
Algeria	2001	10.407560	3003.63	3.80	10.86	high_deaths
Algeria	2002	10.003598	3034.33	3.67	10.86	high_deaths
Algeria	2003	10.491237	3073.26	3.25	10.86	high_deaths
Algeria	2004	10.188493	3090.13	3.30	10.86	high_deaths
Algeria	2005	8.221657	3059.24	2.85	10.86	high_deaths
Algeria	2006	7.988789	3101.20	2.64	10.86	high_deaths
Algeria	2007	8.025346	3153.38	2.90	10.86	high_deaths
Argentina	1988	8.977868	3028.90	1.98	0.00	no_deaths
Argentina	1989	9.616065	2980.35	1.78	0.00	no_deaths
Argentina	1990	8.123676	2924.80	1.37	0.00	no_deaths
Argentina	1991	6.716492	3024.96	1.42	0.00	no_deaths
Argentina	1992	5.990787	3087.95	1.34	0.00	no_deaths
Argentina	1993	5.492025	3123.27	1.34	0.00	no_deaths
Argentina	1994	5.440196	3162.52	1.46	0.00	no_deaths
Argentina	1995	5.698112	3168.17	1.47	0.00	no_deaths
Argentina	1996	5.997435	3162.69	1.24	0.00	no_deaths
Argentina	1997	5.599945	3135.69	1.14	0.00	no_deaths
Argentina	1998	5.617612	3166.62	1.14	0.00	no_deaths
Argentina	1999	4.735356	3266.75	1.22	0.00	no_deaths
Argentina	2000	4.971221	3271.70	1.15	0.00	no_deaths
Argentina	2001	4.814450	3183.34	1.18	0.00	no_deaths
Argentina	2002	10.683021	2965.90	1.09	0.00	no_deaths
Argentina	2003	10.986832	3059.74	1.06	0.00	no_deaths
Argentina	2004	10.412916	3029.91	0.96	0.00	no_deaths
Argentina	2005	9.401029	3091.79	0.93	0.00	no_deaths
Argentina	2006	8.392957	2969.12	0.86	0.00	no_deaths
Argentina	2007	9.394875	2940.98	0.88	0.00	no_deaths
Australia	1988	5.208421	3121.54	2.28	0.00	no_deaths
Australia	1989	5.364690	3141.58	2.15	0.00	no_deaths
Australia	1990	4.923955	3177.79	2.10	0.00	no_deaths
Australia	1991	3.631461	3109.40	2.15	0.00	no_deaths
Australia	1992	3.520368	3116.99	2.19	0.00	no_deaths
Australia	1993	3.710071	3044.14	2.21	0.00	no_deaths
Australia	1994	3.799483	3038.66	2.17	0.00	no_deaths
Australia	1995	3.416733	3081.55	2.07	0.00	no_deaths
Australia	1996	3.819477	3056.62	1.97	0.00	no_deaths
Australia	1997	3.666767	3091.38	1.91	0.00	no_deaths

Summary Explorations

# 
food_war <-food_war %>%
    dplyr::group_by(Deaths_from_war,Year)
agricultural_spending_by_year <- food_war %>%
    dplyr::summarize(agri_exp_mean=mean(Agricultural_expend, na.rm = TRUE))
#food_war %>%
   # dplyr::summarize(mean(Military_expend, na.rm = TRUE))
food_security_by_year <- food_war %>%
    dplyr::summarize(daily_calories=mean(food_security, na.rm = TRUE))

food_war <-food_war %>% 
     dplyr::group_by(Deaths_from_war)
# food_war %>% 
#     dplyr::summarize(mean(Agricultural_expend, na.rm = TRUE))
# food_war %>% 
#     dplyr::summarize(mean(Military_expend, na.rm = TRUE))
# food_war %>% 
#     dplyr::summarize(mean(food_security, na.rm = TRUE))
# 
# food_war

Observations

My categories aren’t of even sizes so my clustering could effect my observations and lead to large impact from outliers
I don’t have an overall GDP per country, this could make a big difference in relative % GDP expenditures

+Tentative Observations + Areas with the most death from wars seem to spend more of their GDP on agricultural expenditures + Areas with the most deaths don’t spend much on military expenditures, and low death areas actually spend more than high death areas + High and moderate death areas are significantly more food insecure. Calorie consumption is about 30% lower in these areas

Data Visualization

food_security_by_year %>% 
    ggplot(.,aes(x=Year,y=daily_calories))+
    geom_bar(aes(fill = Deaths_from_war), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

Caloric consumption seems rather Flat in all the categories over the last three decades

agricultural_spending_by_year %>% 
    ggplot(.,aes(x=Year,y=agri_exp_mean))+
    geom_bar(aes(fill = Deaths_from_war), position = "dodge", stat = "identity")+
    theme(axis.text.x=element_text(angle=45,hjust=1))

As a percent of GDP agricultural expenditure has decreased across all categories over last three decades
Area’s with higher casualties of war seem to have made large cuts to expenditures

food_war %>% 
    ggplot(.,aes(x=Agricultural_expend,y=food_security,shape=Deaths_from_war,color=Deaths_from_war))+
    geom_point()

## Warning: Removed 15 rows containing missing values (geom_point).

food_war %>% 
    filter(.,Military_expend <20) %>% 
    ggplot(.,aes(x=Military_expend,y=food_security,shape=Deaths_from_war,color=Deaths_from_war))+
    geom_point()

The Scatter plots are very Interesting

A handful of outliers were removed for better visualization

Graph 1 Agricultural exp vs Food Security

Two Clear Clusters develop in the first graph
- Areas with high death rates seem to be the least food secure and silumtaneously spend the most on agricultural subsidy
- Areas with low death rates spends the least on food subsidy, and are the most food secure

Graph 2 Military exp vs Food Security

Areas with low death rates seem to spend the most on the Military

general visualization and data manipulation

Justin Herman

January 11, 2019

Tidy dataset 1: Movies database

Load in Data from xlsx file

data Visualization

World Bank info for Infant Mortality, Tuberculosis, and Aids

Join on shared row index values(survey questions)

Tidy Dataset

Get rid of special characters `]` `[` `-` and add spacing for next pipe of tidying

More piping

Visual exploratory analysis of Infant Mortality

Numerical display of Infant Mortality

Display of infant Mortality with the 3 samples closer in scale

Look at Tuberculosis

Look at Tuberculosis in Heavily industrialized nations( UK, US)

Third Dataset- Gapminder

War or food?

Load in third dataset

DF Preparation

Exploratory Analysis of Death from wars data

Create bins of death from wars to categorize Nations

Summary Explorations

Observations

Data Visualization

The Scatter plots are very Interesting

Graph 1 Agricultural exp vs Food Security

Graph 2 Military exp vs Food Security

general visualization and data manipulation

Justin Herman

January 11, 2019

Tidy dataset 1: Movies database

Load in Data from xlsx file

data Visualization

World Bank info for Infant Mortality, Tuberculosis, and Aids

Join on shared row index values(survey questions)

Tidy Dataset

Get rid of special characters ] [ - and add spacing for next pipe of tidying

More piping

Visual exploratory analysis of Infant Mortality

Numerical display of Infant Mortality

Display of infant Mortality with the 3 samples closer in scale

Look at Tuberculosis

Look at Tuberculosis in Heavily industrialized nations( UK, US)

Third Dataset- Gapminder

War or food?

Load in third dataset

DF Preparation

Exploratory Analysis of Death from wars data

Create bins of death from wars to categorize Nations

Summary Explorations

Observations

Data Visualization

The Scatter plots are very Interesting

Graph 1 Agricultural exp vs Food Security

Graph 2 Military exp vs Food Security

Get rid of special characters `]` `[` `-` and add spacing for next pipe of tidying