Downloading packages
install.packages("tidyverse", repos = "https://cloud.r-project.org")
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## The downloaded binary packages are in
## /var/folders/5_/389qrkvs1sd7nkp792bslx5r0000gn/T//Rtmp9bshll/downloaded_packages
library(tidyverse)
## āā Attaching core tidyverse packages āāāāāāāāāāāāāāāāāāāāāāāā tidyverse 2.0.0 āā
## ā dplyr 1.1.1 ā readr 2.1.4
## ā forcats 1.0.0 ā stringr 1.5.0
## ā ggplot2 3.4.1 ā tibble 3.2.1
## ā lubridate 1.9.2 ā tidyr 1.3.0
## ā purrr 1.0.1
## āā Conflicts āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā tidyverse_conflicts() āā
## ā dplyr::filter() masks stats::filter()
## ā dplyr::lag() masks stats::lag()
## ā¹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
install.packages("psych", repos = "https://cloud.r-project.org")
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library(psych)
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## Attaching package: 'psych'
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## The following objects are masked from 'package:ggplot2':
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## %+%, alpha
install.packages("ggplto2", repos = "https://cloud.r-project.org")
## Warning: package 'ggplto2' is not available for this version of R
##
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
library(ggplot2)
install.packages("ggthemes",repos = "https://cloud.r-project.org")
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## The downloaded binary packages are in
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library(ggthemes)
install.packages("gt",repos = "https://cloud.r-project.org")
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library("gt")
install.packages("stargazer",repos = "https://cloud.r-project.org")
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## /var/folders/5_/389qrkvs1sd7nkp792bslx5r0000gn/T//Rtmp9bshll/downloaded_packages
library("stargazer")
##
## Please cite as:
##
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
install.packages("knitr",repos = "https://cloud.r-project.org")
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library("knitr")
install.packages("kableExtra",repos = "https://cloud.r-project.org")
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library("kableExtra")
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## Attaching package: 'kableExtra'
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## group_rows
Download data
df <- read_csv("Child.csv", show_col_types = FALSE)
Filtering data for only one year 2012
df2 <- df[df$Year == "2012",]
Filtering data for descriptive statistic for only the numeric
variables.
df3 <- select_if(df2, is.numeric)
df4 <- df3[,-1]
Using the describe command on our data and creating a table using
the gt package
des4 <- describe(df4)
?gt
table1 <- des4 %>% gt()
table1 %>%
tab_header(
title = "Descriptive Statistics",
subtitle = "For the 2012 year") %>%
opt_align_table_header(align = "left") %>%
fmt_number(columns = everything()) %>%
tab_source_note("**Sources**: www.childmortality.org , http://www.wssinfo.org, International Labour Organization, ILOSTAT database. Data retrieved in November 2017, World Bank staff estimates based on sources and methods in World Bank's The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (2011), http://www.fao.org/publications/en/), World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2014 Revision., washdata.org, United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.,UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.")
| Descriptive Statistics |
| For the 2012 year |
| vars |
n |
mean |
sd |
median |
trimmed |
mad |
min |
max |
range |
skew |
kurtosis |
se |
| 1.00 |
239.00 |
36.48 |
34.34 |
21.60 |
31.66 |
24.61 |
2.40 |
149.80 |
147.40 |
1.12 |
0.38 |
2.22 |
| 2.00 |
258.00 |
9.36 |
15.13 |
1.45 |
5.92 |
2.15 |
0.00 |
76.75 |
76.75 |
2.08 |
4.25 |
0.94 |
| 3.00 |
233.00 |
9.87 |
11.05 |
5.44 |
7.90 |
7.36 |
0.03 |
46.50 |
46.46 |
1.34 |
1.07 |
0.72 |
| 4.00 |
217.00 |
10,438.08 |
14,411.30 |
4,508.33 |
7,321.53 |
5,356.99 |
190.13 |
78,278.82 |
78,088.69 |
2.05 |
3.96 |
978.30 |
| 5.00 |
218.00 |
11.32 |
9.94 |
8.22 |
9.69 |
8.48 |
2.50 |
49.60 |
47.10 |
1.37 |
1.55 |
0.67 |
| 6.00 |
260.00 |
42.29 |
23.29 |
43.08 |
42.31 |
28.61 |
0.00 |
91.20 |
91.20 |
ā0.02 |
ā1.00 |
1.44 |
| 7.00 |
108.00 |
80.09 |
24.23 |
91.74 |
84.33 |
10.53 |
5.88 |
100.00 |
94.12 |
ā1.37 |
0.82 |
2.33 |
| 8.00 |
153.00 |
4.48 |
1.47 |
4.43 |
4.44 |
1.41 |
1.50 |
8.35 |
6.85 |
0.23 |
ā0.32 |
0.12 |
| 9.00 |
234.00 |
6.36 |
2.73 |
5.86 |
6.16 |
2.64 |
1.19 |
18.71 |
17.52 |
0.95 |
1.77 |
0.18 |
| 10.00 |
238.00 |
87.89 |
11.95 |
92.91 |
89.86 |
7.54 |
35.00 |
99.00 |
64.00 |
ā1.62 |
2.73 |
0.77 |
| 11.00 |
14.00 |
42.09 |
17.82 |
39.95 |
42.10 |
20.61 |
12.00 |
72.00 |
60.00 |
0.13 |
ā1.13 |
4.76 |
| 12.00 |
24.00 |
53.17 |
10.56 |
54.65 |
53.78 |
9.27 |
28.80 |
69.30 |
40.50 |
ā0.64 |
ā0.48 |
2.16 |
| 13.00 |
97.00 |
91.76 |
16.61 |
99.00 |
96.24 |
1.19 |
29.30 |
100.00 |
70.70 |
ā2.45 |
5.01 |
1.69 |
| **Sources**: www.childmortality.org , http://www.wssinfo.org, International Labour Organization, ILOSTAT database. Data retrieved in November 2017, World Bank staff estimates based on sources and methods in World Bank's The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (2011), http://www.fao.org/publications/en/), World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2014 Revision., washdata.org, United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.,UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys. |
Usiing Knitr package and KableExtra to create descriptive
table.
table2 <- knitr::kable(des4,
"html",
caption = "World Countries Data",
digits = 2) %>%
footnote(general = "Sources www.childmortality.org , www.wssinfo.org, International Labour Organization, ILOSTAT database. Data retrieved in November 2017, World Bank staff estimates based on sources and methods in World Bank's The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (2011), http://www.fao.org/publications/en/), World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2014 Revision., washdata.org, United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.,UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.") %>%
kable_styling(font_size = 8)
table2
World Countries Data
|
|
vars
|
n
|
mean
|
sd
|
median
|
trimmed
|
mad
|
min
|
max
|
range
|
skew
|
kurtosis
|
se
|
|
Mortality rate, under-5 (per 1,000 live births)
|
1
|
239
|
36.48
|
34.34
|
21.60
|
31.66
|
24.61
|
2.40
|
149.80
|
147.40
|
1.12
|
0.38
|
2.22
|
|
People practicing open defecation (% of population)
|
2
|
258
|
9.36
|
15.13
|
1.45
|
5.92
|
2.15
|
0.00
|
76.75
|
76.75
|
2.08
|
4.25
|
0.94
|
|
Contributing family workers, total (% of total employment) (modeled ILO
estimate)
|
3
|
233
|
9.87
|
11.05
|
5.44
|
7.90
|
7.36
|
0.03
|
46.50
|
46.46
|
1.34
|
1.07
|
0.72
|
|
Adjusted net national income per capita (current US$)
|
4
|
217
|
10438.08
|
14411.30
|
4508.33
|
7321.53
|
5356.99
|
190.13
|
78278.82
|
78088.69
|
2.05
|
3.96
|
978.30
|
|
Prevalence of undernourishment (% of population)
|
5
|
218
|
11.32
|
9.94
|
8.22
|
9.69
|
8.48
|
2.50
|
49.60
|
47.10
|
1.37
|
1.55
|
0.67
|
|
Rural population (% of total population)
|
6
|
260
|
42.29
|
23.29
|
43.08
|
42.31
|
28.61
|
0.00
|
91.20
|
91.20
|
-0.02
|
-1.00
|
1.44
|
|
People using safely managed drinking water services (% of population)
|
7
|
108
|
80.09
|
24.23
|
91.74
|
84.33
|
10.53
|
5.88
|
100.00
|
94.12
|
-1.37
|
0.82
|
2.33
|
|
Government expenditure on education, total (% of GDP)
|
8
|
153
|
4.48
|
1.47
|
4.43
|
4.44
|
1.41
|
1.50
|
8.35
|
6.85
|
0.23
|
-0.32
|
0.12
|
|
Current health expenditure (% of GDP)
|
9
|
234
|
6.36
|
2.73
|
5.86
|
6.16
|
2.64
|
1.19
|
18.71
|
17.52
|
0.95
|
1.77
|
0.18
|
|
Immunization, measles (% of children ages 12-23 months)
|
10
|
238
|
87.89
|
11.95
|
92.91
|
89.86
|
7.54
|
35.00
|
99.00
|
64.00
|
-1.62
|
2.73
|
0.77
|
|
Use of insecticide-treated bed nets (% of under-5 population)
|
11
|
14
|
42.09
|
17.82
|
39.95
|
42.10
|
20.61
|
12.00
|
72.00
|
60.00
|
0.13
|
-1.13
|
4.76
|
|
Diarrhea treatment (% of children under 5 receiving oral rehydration and
continued feeding)
|
12
|
24
|
53.17
|
10.56
|
54.65
|
53.78
|
9.27
|
28.80
|
69.30
|
40.50
|
-0.64
|
-0.48
|
2.16
|
|
Births attended by skilled health staff (% of total)
|
13
|
97
|
91.76
|
16.61
|
99.00
|
96.24
|
1.19
|
29.30
|
100.00
|
70.70
|
-2.45
|
5.01
|
1.69
|
|
Note:
|
|
Sources www.childmortality.org , www.wssinfo.org,
International Labour Organization, ILOSTAT database. Data retrieved in
November 2017, World Bank staff estimates based on sources and methods
in World Bankās The Changing Wealth of Nations: Measuring Sustainable
Development in the New Millennium (2011), http://www.fao.org/publications/en/), World Bank staff
estimates based on the United Nations Population Divisionās World
Urbanization Prospects: 2014 Revision., washdata.org, United Nations
Educational, Scientific, and Cultural Organization (UNESCO) Institute
for Statistics.,UNICEF, State of the Worldās Children, Childinfo, and
Demographic and Health Surveys.
|
Creating the Histogram for 1 variable
h1 <- hist(df2$`Mortality rate, under-5 (per 1,000 live births)`,
breaks = 80,
ylim = c(0,25),
xlim = c(0,175),
main = "Mortality rate under-5 (n =264)",
sub = "Source: www.childmortality.org.",
xlab = "Mortality rate, under-5 (per 1,000 live births)",
ylab = "frenquency",
col = "pink")
Creating second Histogram for 1 variable
h2 <- hist(df2$`Prevalence of undernourishment (% of population)`,
breaks = 80,
ylim = c(0,60),
xlim = c(0,50),
main = "Prevalence of undernourishment (n = 264 ",
sub = " Source:(http://www.fao.org/publications/en/).",
xlab = "Prevalence of undernourishment (%of population)",
ylab = "frenquency",
col = "pink")
Creating a scatterplot between 2 of our variable
plot(x = df2$`Rural population (% of total population)`, y = df2$`People using safely managed drinking water services (% of population)`,
main = "Rural popolutaion vs \nSafely driking water services (n = 264",
sub = " Sources: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2014 Revision. n/washdata.org",
xlab = "Rural polulation (%)",
ylab = "Poeple safely \ndrinking water services (%)",
font.main = 1,
cex.sub = 0.5,
cex.main = 1,
cex.lab = 0.6,
xlim = c(0,100),
ylim = c(0,100)
)
F) Creating the OLS regression
install.packages("robustbase",repos = "https://cloud.r-project.org")
##
## The downloaded binary packages are in
## /var/folders/5_/389qrkvs1sd7nkp792bslx5r0000gn/T//Rtmp9bshll/downloaded_packages
library(robustbase)
Creating the regression
set.seed(77)
lmro <- lmrob( df2$`Immunization, measles (% of children ages 12-23 months)` ~ df2$`Current health expenditure (% of GDP)`)
result1 <- summary(lmro)
result1
##
## Call:
## lmrob(formula = df2$`Immunization, measles (% of children ages 12-23 months)` ~
## df2$`Current health expenditure (% of GDP)`)
## \--> method = "MM"
## Residuals:
## Min 1Q Median 3Q Max
## -53.03374 -7.20673 -0.09119 4.81568 10.71892
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 87.1545 3.4444 25.304
## df2$`Current health expenditure (% of GDP)` 0.6086 0.3754 1.621
## Pr(>|t|)
## (Intercept) <2e-16 ***
## df2$`Current health expenditure (% of GDP)` 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Robust residual standard error: 6.532
## (35 observations deleted due to missingness)
## Multiple R-squared: 0.04171, Adjusted R-squared: 0.03758
## Convergence in 19 IRWLS iterations
##
## Robustness weights:
## 6 observations c(1,33,53,124,153,183)
## are outliers with |weight| = 0 ( < 0.00043);
## 9 weights are ~= 1. The remaining 219 ones are summarized as
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01122 0.83680 0.93120 0.83460 0.97510 0.99880
## Algorithmic parameters:
## tuning.chi bb tuning.psi refine.tol
## 1.548e+00 5.000e-01 4.685e+00 1.000e-07
## rel.tol scale.tol solve.tol eps.outlier
## 1.000e-07 1.000e-10 1.000e-07 4.274e-04
## eps.x warn.limit.reject warn.limit.meanrw
## 3.403e-11 5.000e-01 5.000e-01
## nResample max.it best.r.s k.fast.s k.max
## 500 50 2 1 200
## maxit.scale trace.lev mts compute.rd fast.s.large.n
## 200 0 1000 0 2000
## psi subsampling cov
## "bisquare" "nonsingular" ".vcov.avar1"
## compute.outlier.stats
## "SM"
## seed : int(0)
Using the Stargazer package to create a table for the
regression
stargazer(lmro,
title = "Measles Immunization based on Goverment healt spending",
dep.var.caption = "DV: Measles Immunization (% of children ages 12-23 months)",
covariate.labels = c("Goverment health speding"),
notes.label = "Source: (http://www.who.int/immunization/monitoring_surveillance/en/). ; (UNESCO) Institute for Statistics || Significance level:",
add.lines = TRUE,
keep.stat = c("n","rsq"),
digits = 2,
type = "html",
out = "Economretics.htm")
##
## <table style="text-align:center"><caption><strong>Measles Immunization based on Goverment healt spending</strong></caption>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>DV: Measles Immunization (% of children ages 12-23 months)</td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>`Immunization, measles (% of children ages 12-23 months)`</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Goverment health speding</td><td>0.61</td></tr>
## <tr><td style="text-align:left"></td><td>(0.38)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>87.15<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(3.44)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">TRUE</td><td></td></tr>
## <tr><td style="text-align:left">Observations</td><td>234</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.04</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Source: (http://www.who.int/immunization/monitoring; (UNESCO) Institute for Statistics || Significance level:</td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Testing theā¦ at significance level of
significant <- result1$coefficients < 0.07
significant2 <- result1$coefficients > 0.07