Note
Resources
Import the csv files to create following datasets
Inner join
- complete cases or omit NA
- exclude 3 variables with many missing values from the join
  - complete cases or omit NA
Left join
- exclude 3 variables with many missing values from the join
  - complete cases or omit NA
Right join
- exclude 3 variables with many missing values from the join
  - complete cases or omit NA
Full join
- exclude 3 variables with many missing values from the join
  - complete cases or omit NA

Note

-In base R, character variables are often presented as factor

Resources

-Download the following csv files from the gapminder web site (https://www.gapminder.org).

“cholesterol_fat_in_blood_women_mmolperl.csv”
“body_mass_index_infant.mortality_women_kgperm2.csv”
“blood_pressure_sbp_women_mmhg.csv”
“children_per_woman_total_fertility.csv”
“contraceptive_use_percent_of_women_ages_15_49.csv”
“breast_cancer_number_of_female_deaths.csv”
“infant_mortality_rate_per_1000_births.csv”
“maternal_deaths_total_number.csv”
“maternal_mortality_ratio_per_100000_live_births.csv”

Import the csv files to create following datasets

cholesterol_fat_in_blood_women_mmolperl -> fm.tc -> fm.tc.long

fm.tc <- read.csv("gpmlib/cholesterol_fat_in_blood_women_mmolperl.csv")

fm.tc[c(1, 100, 189), c(1, 10,11, 20, 21, 30 )]

	country	X1988	X1989	X1998	X1999	X2008
1	Afghanistan	4.58	4.57	4.37	4.36	4.24
100	Madagascar	4.52	4.50	4.40	4.39	4.30
189	Zimbabwe	4.59	4.57	4.43	4.43	4.33

dim(fm.tc)

## [1] 189  30

fm.tc.long <-reshape(fm.tc, idvar = "country", varying=2:30, sep = "", direction = "long") 

rownames(fm.tc.long) <- c()

names(fm.tc.long) <- c("country", "year", "tc")

head(fm.tc.long)

country	year	tc
Afghanistan	1980	4.64
Albania	1980	5.04
Algeria	1980	4.98
Andorra	1980	6.13
Angola	1980	4.79
Antigua and Barbuda	1980	5.01

dim(fm.tc.long)

## [1] 5481    3

body_mass_index_bmi_women_kgperm2 -> fm.bmi -> fm.bmi.long

fm.bmi <-read.csv("gpmlib/body_mass_index_bmi_women_kgperm2.csv")

fm.bmi[c(1, 100, 189), c(1, 10,11, 20,21, 30)]

	country	X1988	X1989	X1998	X1999	X2008
1	Afghanistan	20.7	20.7	20.6	20.6	21.1
100	Madagascar	20.4	20.5	20.6	20.6	20.7
189	Zimbabwe	25.0	24.9	24.7	24.7	24.6

dim(fm.bmi)

## [1] 189  30

fm.bmi.long <-reshape(fm.bmi, idvar = "country", varying=2:30, sep = "", direction = "long") 

rownames(fm.bmi.long) <- c()

names(fm.bmi.long) <- c("country", "year", "bmi")

head(fm.bmi.long)

country	year	bmi
Afghanistan	1980	20.4
Albania	1980	25.2
Algeria	1980	23.7
Andorra	1980	25.7
Angola	1980	20.1
Antigua and Barbuda	1980	24.2

dim(fm.bmi.long)

## [1] 5481    3

blood_pressure_sbp_women_mmhg -> fm.sbp -> fm.sbp.long

fm.sbp <- read.csv("gpmlib/blood_pressure_sbp_women_mmhg.csv")

fm.sbp[c(1, 100, 189), c(1, 10,11, 20,21, 30)]

	country	X1988	X1989	X1998	X1999	X2008
1	Afghanistan	124	124	124	124	125
100	Madagascar	128	128	130	130	133
189	Zimbabwe	130	130	131	131	132

dim(fm.sbp)

## [1] 189  30

fm.sbp.long <-reshape(fm.sbp, idvar = "country", varying=2:30, sep = "", direction = "long") 

rownames(fm.sbp.long) <- c()

names(fm.sbp.long) <- c("country", "year", "sbp")

head(fm.sbp.long)

country	year	sbp
Afghanistan	1980	122
Albania	1980	132
Algeria	1980	131
Andorra	1980	137
Angola	1980	130
Antigua and Barbuda	1980	126

dim(fm.sbp.long)

## [1] 5481    3

children_per_woman_total_fertility -> fm.fertility -> fm.fertility.long

fm.fertility <- read.csv("gpmlib/children_per_woman_total_fertility.csv")

fm.fertility[c(1, 100, 184), c(1, 100,101, 200,201, 220)]

	country	X1898	X1899	X1998	X1999	X2018
1	Afghanistan	7.00	7.00	7.62	7.57	4.33
100	Malaysia	5.45	5.45	3.04	2.91	2.00
184	Zimbabwe	6.75	6.75	4.16	4.10	3.61

dim(fm.fertility)

## [1] 184 220

fm.fertility.long <-reshape(fm.fertility, idvar = "country", varying=2:220, sep = "", direction = "long") 

rownames(fm.fertility.long) <- c()

names(fm.fertility.long) <- c("country", "year", "total_fertility")

head(fm.fertility.long)

country	year	total_fertility
Afghanistan	1800	7.00
Albania	1800	4.60
Algeria	1800	6.99
Angola	1800	6.93
Antigua and Barbuda	1800	5.00
Argentina	1800	6.80

dim(fm.fertility.long)

## [1] 40296     3

contraceptive_use_percent_of_women_ages_15_49.csv -> fm.contracept.use -> fm.contracept.use.long

fm.contracept.use <- read.csv("gpmlib/contraceptive_use_percent_of_women_ages_15_49.csv")

fm.contracept.use[c(1, 100, 185), c(1, 52:58)]

	country	X2011	X2012	X2013	X2014	X2015	X2016	X2017
1	Afghanistan	21.2	NA	NA	NA	22.5	NA	NA
100	Malta	NA	NA	NA	NA	NA	NA	NA
185	Zimbabwe	58.5	NA	NA	66.9	66.8	NA	NA

dim(fm.contracept.use)

## [1] 185  58

fm.contracept.use.long <-reshape(fm.contracept.use, idvar = "country", varying=2:58, sep = "", direction = "long") 

rownames(fm.contracept.use.long) <- c()

names(fm.contracept.use.long) <- c("country", "year", "contraceptive_use_percent")

tail(fm.contracept.use.long)

	country	year	contraceptive_use_percent
10540	Vanuatu	2017	NA
10541	Venezuela	2017	NA
10542	Vietnam	2017	NA
10543	Yemen	2017	NA
10544	Zambia	2017	NA
10545	Zimbabwe	2017	NA

dim(fm.contracept.use.long)

## [1] 10545     3

breast_cancer_number_of_female_deaths -> fm.brstc.death -> fm.brstc.death.long

fm.brstc.death <- read.csv("gpmlib/breast_cancer_number_of_female_deaths.csv")

fm.brstc.death[c(1, 100, 187), c(1, 24:28)]

	country	X2012	X2013	X2014	X2015	X2016
1	Afghanistan	2340	2420	2510	2600	2690
100	Madagascar	789	823	856	892	922
187	Zimbabwe	764	770	819	841	870

dim(fm.brstc.death)

## [1] 187  28

fm.brstc.death.long <-reshape(fm.brstc.death, idvar = "country", varying=2:28, sep = "", direction = "long") 

rownames(fm.brstc.death.long) <- c()

names(fm.brstc.death.long) <- c("country", "year", "breast_cancer_deaths")

head(fm.brstc.death.long)

country	year	breast_cancer_deaths
Afghanistan	1990	761.00
Albania	1990	100.00
Algeria	1990	701.00
Andorra	1990	9.80
Angola	1990	420.00
Antigua and Barbuda	1990	5.71

dim(fm.brstc.death.long)

## [1] 5049    3

infant_mortality_rate_per_1000_births.csv -> infant.mortality -> infant.mortality.long

infant.mortality <- read.csv("gpmlib/infant_mortality_rate_per_1000_births.csv")

infant.mortality[c(1, 100, 194), c(1, 100, 101, 200, 201, 217)]

	country	X1898	X1899	X1998	X1999	X2015
1	Afghanistan	NA	NA	98.9	97.2	66.3
100	Macedonia, FYR	NA	NA	16.1	15.0	4.8
194	Zimbabwe	NA	NA	63.3	63.5	46.6

dim(infant.mortality)

## [1] 194 217

infant.mortality.long <-reshape(infant.mortality, idvar = "country", varying=2:217, sep = "", direction = "long") 

names(infant.mortality.long) <- c("country", "year", "infant_mortality_rate")

rownames(infant.mortality.long) <- NULL

tail(infant.mortality.long)

	country	year	infant_mortality_rate
41899	Vanuatu	2015	23.1
41900	Venezuela	2015	12.9
41901	Vietnam	2015	17.3
41902	Yemen	2015	33.8
41903	Zambia	2015	43.3
41904	Zimbabwe	2015	46.6

dim(infant.mortality.long)

## [1] 41904     3

maternal_deaths_total_number.csv -> maternal.death -> maternal.death.long

maternal.death <- read.csv("gpmlib/maternal_deaths_total_number.csv")

maternal.death[c(1, 100, 183), c(1, 100,101, 200,201, 215)]

	country	X1898	X1899	X1998	X1999	X2013
1	Afghanistan	NA	NA	NA	NA	4200
100	Maldives	NA	NA	NA	NA	2
183	Zimbabwe	NA	NA	NA	NA	2100

dim(maternal.death)

## [1] 183 215

maternal.death.long <-reshape(maternal.death, idvar = "country", varying=2:215, sep = "", direction = "long") 

rownames(maternal.death.long) <- c()

names(maternal.death.long) <- c("country", "year", "maternal_deaths")

tail(maternal.death.long)

	country	year	maternal_deaths
39157	Vanuatu	2013	6
39158	Venezuela	2013	650
39159	Vietnam	2013	690
39160	Yemen	2013	2100
39161	Zambia	2013	1800
39162	Zimbabwe	2013	2100

dim(maternal.death.long)

## [1] 39162     3

maternal_mortality_ratio_per_100000_live_births.csv -> maternal.mortality.ratio -> maternal.mortality.ratio.long

maternal.mortality.ratio <- read.csv("gpmlib/maternal_mortality_ratio_per_100000_live_births.csv")

maternal.mortality.ratio[c(1, 100, 187), c(1, 100, 101, 200, 201, 215)]

	country	X1898	X1899	X1998	X1999	X2013
1	Afghanistan	NA	NA	NA	NA	400
100	Madagascar	NA	NA	NA	NA	440
187	Zimbabwe	NA	NA	NA	NA	470

dim(maternal.mortality.ratio)

## [1] 187 215

maternal.mortality.ratio.long <-reshape(maternal.mortality.ratio, idvar = "country", varying=2:215, sep = "", direction = "long") 
rownames(maternal.mortality.ratio.long) <- c()

names(maternal.mortality.ratio.long) <- c("country", "year", "maternal_mortality_ratio")

tail(maternal.mortality.ratio.long)

	country	year	maternal_mortality_ratio
40013	Vanuatu	2013	86
40014	Venezuela	2013	110
40015	Vietnam	2013	49
40016	Yemen	2013	270
40017	Zambia	2013	280
40018	Zimbabwe	2013	470

dim(maternal.mortality.ratio.long)

## [1] 40018     3

Inner join

Output: Total rows: 3382 and Total columns: 11

gpm.i <- fm.tc.long %>% 
        merge(fm.bmi.long) %>%
        merge(fm.sbp.long) %>%
        merge(fm.tc.long) %>%
        merge(fm.fertility.long)%>%
        merge(fm.contracept.use.long)%>% 
        merge(fm.brstc.death.long)%>%
        merge(infant.mortality.long)%>%
        merge(maternal.death.long) %>%
        merge(maternal.mortality.ratio.long)

head(gpm.i, n=3)

country	year	tc	bmi	sbp	total_fertility	contraceptive_use_percent	breast_cancer_deaths	infant_mortality_rate	maternal_deaths	maternal_mortality_ratio
Afghanistan	1990	4.55	20.7	124	7.47	NA	761	123	7520	1200
Afghanistan	1991	4.53	20.7	124	7.48	NA	817	118	NA	NA
Afghanistan	1992	4.51	20.7	124	7.50	NA	894	114	NA	NA

dim(gpm.i)

## [1] 3382   11

complete cases or omit NA

Output: Total rows: 177 and Total columns: 11

cs.i <- complete.cases(gpm.i)

head(gpm.i[cs.i,], n=3)

	country	year	tc	bmi	sbp	total_fertility	contraceptive_use_percent	breast_cancer_deaths	infant_mortality_rate	maternal_deaths	maternal_mortality_ratio
11	Afghanistan	2000	4.33	20.6	124	7.49	5.3	1470	95.4	10700	1100
16	Afghanistan	2005	4.26	20.8	125	6.83	13.6	1880	84.4	7900	730
30	Albania	2000	4.92	25.1	129	2.16	57.5	150	23.2	14	28

dim(gpm.i[cs.i,])

## [1] 177  11

head(na.omit(gpm.i), n=3)

	country	year	tc	bmi	sbp	total_fertility	contraceptive_use_percent	breast_cancer_deaths	infant_mortality_rate	maternal_deaths	maternal_mortality_ratio
11	Afghanistan	2000	4.33	20.6	124	7.49	5.3	1470	95.4	10700	1100
16	Afghanistan	2005	4.26	20.8	125	6.83	13.6	1880	84.4	7900	730
30	Albania	2000	4.92	25.1	129	2.16	57.5	150	23.2	14	28

dim(na.omit(gpm.i))

## [1] 177  11

exclude 3 variables with many missing values from the join

Output: Total rows: 3477 and Total columns: 8

gpm.i.1 <- fm.tc.long %>% 
        merge(fm.bmi.long) %>%
        merge(fm.sbp.long) %>%
        merge(fm.fertility.long) %>%
        merge(fm.brstc.death.long) %>%
        merge(infant.mortality.long)

head(gpm.i.1, n=3)

country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
Afghanistan	1990	4.55	20.7	124	7.47	761	123
Afghanistan	1991	4.53	20.7	124	7.48	817	118
Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(gpm.i.1)

## [1] 3477    8

complete cases or omit NA

Output: Total rows: 3477 and Total columns: 8

cs.i.1 <- complete.cases(gpm.i.1)

head(gpm.i.1[cs.i.1,], n=3)

country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
Afghanistan	1990	4.55	20.7	124	7.47	761	123
Afghanistan	1991	4.53	20.7	124	7.48	817	118
Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(gpm.i.1[cs.i.1,])

## [1] 3477    8

head(na.omit(gpm.i.1), n=3)

country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
Afghanistan	1990	4.55	20.7	124	7.47	761	123
Afghanistan	1991	4.53	20.7	124	7.48	817	118
Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(na.omit(gpm.i))

## [1] 177  11

Left join

Output: Total rows: 5481 and Total columns: 11

gpm.l <-fm.tc.long %>% merge(fm.bmi.long, all.x = TRUE) %>%
        merge(fm.sbp.long, all.x = TRUE) %>%
        merge(fm.tc.long, all.x = TRUE) %>%
        merge(fm.fertility.long, all.x = TRUE)%>%
        merge(fm.contracept.use.long, all.x = TRUE)%>% 
        merge(fm.brstc.death.long, all.x = TRUE)%>%
        merge(infant.mortality.long, all.x = TRUE)%>%
        merge(maternal.death.long, all.x = TRUE) %>%
        merge(maternal.mortality.ratio.long, all.x = TRUE)

head(gpm.l, n=3)

country	year	tc	bmi	sbp	total_fertility	contraceptive_use_percent	breast_cancer_deaths	infant_mortality_rate	maternal_deaths	maternal_mortality_ratio
Afghanistan	1980	4.64	20.4	122	7.45	NA	NA	166	10600	1640
Afghanistan	1981	4.64	20.5	122	7.45	NA	NA	162	NA	NA
Afghanistan	1982	4.63	20.5	123	7.45	NA	NA	158	NA	NA

dim(gpm.l)

## [1] 5481   11

exclude 3 variables with many missing values from the join

Output: Total rows: 5481 and Total columns: 8

gpm.l.1 <-fm.tc.long %>% merge(fm.bmi.long, all.x = TRUE) %>%
        merge(fm.sbp.long, all.x = TRUE) %>%
        merge(fm.tc.long, all.x = TRUE) %>%
        merge(fm.fertility.long, all.x = TRUE)%>%
        merge(fm.brstc.death.long, all.x = TRUE)%>%
        merge(infant.mortality.long, all.x = TRUE)

head(gpm.l.1, n=3)

country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
Afghanistan	1980	4.64	20.4	122	7.45	NA	166
Afghanistan	1981	4.64	20.5	122	7.45	NA	162
Afghanistan	1982	4.63	20.5	123	7.45	NA	158

dim(gpm.i.1)

## [1] 3477    8

complete cases or omit NA

Output: Total rows: 3477 and Total columns: 8

cs.l.1 <- complete.cases(gpm.l.1)

head(gpm.l.1[cs.l.1,], n=3)

	country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
11	Afghanistan	1990	4.55	20.7	124	7.47	761	123
12	Afghanistan	1991	4.53	20.7	124	7.48	817	118
13	Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(gpm.l.1[cs.l.1, ])

## [1] 3477    8

head(na.omit(gpm.l.1), n=3)

	country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
11	Afghanistan	1990	4.55	20.7	124	7.47	761	123
12	Afghanistan	1991	4.53	20.7	124	7.48	817	118
13	Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(na.omit(gpm.i))

## [1] 177  11

Right join

Output: Total rows: 41904 and Total columns: 11

gpm.r <- merge(fm.tc.long, fm.bmi.long, all.y = TRUE) %>%
        merge(fm.sbp.long, all.y = TRUE) %>%
        merge(fm.tc.long, all.y = TRUE) %>%
        merge(fm.fertility.long, all.y = TRUE)%>%
        merge(fm.contracept.use.long, all.y = TRUE)%>% 
        merge(fm.brstc.death.long, all.y = TRUE)%>%
        merge(infant.mortality.long, all.y = TRUE)%>%
        merge(maternal.death.long, all.y = TRUE) %>%
        merge(maternal.mortality.ratio.long, all.y = TRUE)

head(gpm.r, n=3)

country	year	tc	bmi	sbp	total_fertility	contraceptive_use_percent	breast_cancer_deaths	infant_mortality_rate	maternal_deaths	maternal_mortality_ratio
Afghanistan	1800	NA	NA	NA	NA	NA	NA	NA	NA	NA
Afghanistan	1801	NA	NA	NA	NA	NA	NA	NA	NA	NA
Afghanistan	1802	NA	NA	NA	NA	NA	NA	NA	NA	NA

dim(gpm.r)

## [1] 40018    11

exclude 3 variables with many missing values from the join

Output: Total rows: 41904 and Total columns: 8

gpm.r.1 <- merge(fm.tc.long, fm.bmi.long, all.y = TRUE) %>%
        merge(fm.sbp.long, all.y = TRUE) %>%
        merge(fm.tc.long, all.y = TRUE) %>%
        merge(fm.fertility.long, all.y = TRUE)%>%
        merge(fm.brstc.death.long, all.y = TRUE)%>%
        merge(infant.mortality.long, all.y = TRUE)

head(gpm.r.1, n=3)

country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
Afghanistan	1800	NA	NA	NA	NA	NA	NA
Afghanistan	1801	NA	NA	NA	NA	NA	NA
Afghanistan	1802	NA	NA	NA	NA	NA	NA

dim(gpm.r.1)

## [1] 41904     8

complete cases or omit NA

Output: Total rows: 3477 and Total columns: 8

cs.r.1 <- complete.cases(gpm.r.1)

head(gpm.r.1[cs.r.1,], n=3)

	country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
191	Afghanistan	1990	4.55	20.7	124	7.47	761	123
192	Afghanistan	1991	4.53	20.7	124	7.48	817	118
193	Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(gpm.r.1[cs.r.1,])

## [1] 3477    8

head(na.omit(gpm.r.1), n=3)

	country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
191	Afghanistan	1990	4.55	20.7	124	7.47	761	123
192	Afghanistan	1991	4.53	20.7	124	7.48	817	118
193	Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(na.omit(gpm.r.1))

## [1] 3477    8

Full join

Output: Total rows: 42469 and Total columns: 11

gpm.f <- merge(fm.tc.long, fm.bmi.long, all.x = TRUE, all.y = TRUE) %>%
        merge(fm.sbp.long, all.x = TRUE, all.y = TRUE) %>%
        merge(fm.tc.long, all.x = TRUE, all.y = TRUE) %>%
        merge(fm.fertility.long, all.x = TRUE, all.y = TRUE)%>%
        merge(fm.contracept.use.long, all.x = TRUE, all.y = TRUE)%>% 
        merge(fm.brstc.death.long, all.x = TRUE, all.y = TRUE)%>%
        merge(infant.mortality.long, all.x = TRUE, all.y = TRUE)%>%
        merge(maternal.death.long, all.x = TRUE, all.y = TRUE) %>%
        merge(maternal.mortality.ratio.long, all.x = TRUE, all.y = TRUE)

head(gpm.f, n=3)

country	year	tc	bmi	sbp	total_fertility	contraceptive_use_percent	breast_cancer_deaths	infant_mortality_rate	maternal_deaths	maternal_mortality_ratio
Afghanistan	1800	NA	NA	NA	7	NA	NA	NA	NA	NA
Afghanistan	1801	NA	NA	NA	7	NA	NA	NA	NA	NA
Afghanistan	1802	NA	NA	NA	7	NA	NA	NA	NA	NA

dim(gpm.f)

## [1] 42469    11

exclude 3 variables with many missing values from the join

Output: Total rows: 42459 and Total columns: 8

gpm.f.1 <- merge(fm.tc.long, fm.bmi.long, all.x = TRUE, all.y = TRUE) %>%
        merge(fm.sbp.long, all.x = TRUE, all.y = TRUE) %>%
        merge(fm.tc.long, all.x = TRUE, all.y = TRUE) %>%
        merge(fm.fertility.long, all.x = TRUE, all.y = TRUE)%>%
        merge(fm.brstc.death.long, all.x = TRUE, all.y = TRUE)%>%
        merge(infant.mortality.long, all.x = TRUE, all.y = TRUE)

head(gpm.f.1, n=3)

country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
Afghanistan	1800	NA	NA	NA	7	NA	NA
Afghanistan	1801	NA	NA	NA	7	NA	NA
Afghanistan	1802	NA	NA	NA	7	NA	NA

dim(gpm.f.1)

## [1] 42459     8

complete cases or omit NA

Output: Total rows: 3477 and Total columns: 8

cs.f.1 <- complete.cases(gpm.f.1)

head(gpm.f.1[cs.f.1,], n=3)

	country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
191	Afghanistan	1990	4.55	20.7	124	7.47	761	123
192	Afghanistan	1991	4.53	20.7	124	7.48	817	118
193	Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(gpm.f.1[cs.f.1,])

## [1] 3477    8

head(na.omit(gpm.f.1), n=3)

	country	year	tc	bmi	sbp	total_fertility	breast_cancer_deaths	infant_mortality_rate
191	Afghanistan	1990	4.55	20.7	124	7.47	761	123
192	Afghanistan	1991	4.53	20.7	124	7.48	817	118
193	Afghanistan	1992	4.51	20.7	124	7.50	894	114

dim(na.omit(gpm.f.1))

## [1] 3477    8

Create long forms of tables in base R

Bhagirathi Dash

9/24/2019

Note

Resources

Import the csv files to create following datasets

cholesterol_fat_in_blood_women_mmolperl -> fm.tc -> fm.tc.long

body_mass_index_bmi_women_kgperm2 -> fm.bmi -> fm.bmi.long

blood_pressure_sbp_women_mmhg -> fm.sbp -> fm.sbp.long

children_per_woman_total_fertility -> fm.fertility -> fm.fertility.long

contraceptive_use_percent_of_women_ages_15_49.csv -> fm.contracept.use -> fm.contracept.use.long

breast_cancer_number_of_female_deaths -> fm.brstc.death -> fm.brstc.death.long

infant_mortality_rate_per_1000_births.csv -> infant.mortality -> infant.mortality.long

maternal_deaths_total_number.csv -> maternal.death -> maternal.death.long

maternal_mortality_ratio_per_100000_live_births.csv -> maternal.mortality.ratio -> maternal.mortality.ratio.long

Inner join

complete cases or omit NA

exclude 3 variables with many missing values from the join

complete cases or omit NA

Left join

exclude 3 variables with many missing values from the join

complete cases or omit NA

Right join

exclude 3 variables with many missing values from the join

complete cases or omit NA

Full join

exclude 3 variables with many missing values from the join

complete cases or omit NA