test1
knitr::opts_chunk$set(echo = TRUE)
orgdat <- read.csv("organdata.csv", header = T)
head(orgdat)
nrow(orgdat)
## [1] 238
ncol(orgdat)
## [1] 21
summary(orgdat)
## country year donors pop
## Length:238 Length:238 Min. : 5.20 Min. : 3514
## Class :character Class :character 1st Qu.:13.00 1st Qu.: 6938
## Mode :character Mode :character Median :15.10 Median : 15531
## Mean :16.48 Mean : 39921
## 3rd Qu.:19.60 3rd Qu.: 57301
## Max. :33.90 Max. :288369
## NA's :34 NA's :17
## pop_dens gdp gdp_lag health
## Min. : 0.2204 Min. :12917 Min. :11434 Min. : 791
## 1st Qu.: 1.9377 1st Qu.:19546 1st Qu.:19034 1st Qu.:1581
## Median : 9.4908 Median :22756 Median :22158 Median :1956
## Mean :12.0011 Mean :22986 Mean :22575 Mean :2074
## 3rd Qu.:19.1106 3rd Qu.:26180 3rd Qu.:25887 3rd Qu.:2408
## Max. :38.8851 Max. :36554 Max. :36554 Max. :5665
## NA's :17 NA's :17
## health_lag pubhealth roads cerebvas
## Min. : 727 Min. :4.30 Min. : 58.21 Min. :300.0
## 1st Qu.:1542 1st Qu.:5.50 1st Qu.: 83.46 1st Qu.:500.0
## Median :1850 Median :6.00 Median :111.22 Median :604.0
## Mean :1973 Mean :6.19 Mean :113.04 Mean :610.8
## 3rd Qu.:2290 3rd Qu.:6.90 3rd Qu.:139.57 3rd Qu.:698.0
## Max. :5267 Max. :8.80 Max. :232.48 Max. :957.0
## NA's :21 NA's :17 NA's :17
## assault external txp_pop world
## Min. : 4.00 Min. :258.0 Min. :0.2204 Length:238
## 1st Qu.: 9.00 1st Qu.:367.0 1st Qu.:0.6268 Class :character
## Median : 11.00 Median :421.0 Median :0.7116 Mode :character
## Mean : 16.53 Mean :450.1 Mean :0.7214
## 3rd Qu.: 16.00 3rd Qu.:534.0 3rd Qu.:0.8313
## Max. :103.00 Max. :853.0 Max. :1.1191
## NA's :17 NA's :17 NA's :17
## opt consent_law consent_practice consistent
## Length:238 Length:238 Length:238 Length:238
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## ccode
## Length:238
## Class :character
## Mode :character
##
##
##
##
str(orgdat)
## 'data.frame': 238 obs. of 21 variables:
## $ country : chr "Australia" "Australia" "Australia" "Australia" ...
## $ year : chr NA "1/1/91" "1/1/92" "1/1/93" ...
## $ donors : num NA 12.1 12.3 12.5 10.2 ...
## $ pop : int 17065 17284 17495 17667 17855 18072 18311 18518 18711 18926 ...
## $ pop_dens : num 0.22 0.223 0.226 0.228 0.231 ...
## $ gdp : int 16774 17171 17914 18883 19849 21079 21923 22961 24148 25445 ...
## $ gdp_lag : int 16591 16774 17171 17914 18883 19849 21079 21923 22961 24148 ...
## $ health : num 1300 1379 1455 1540 1626 ...
## $ health_lag : num 1224 1300 1379 1455 1540 ...
## $ pubhealth : num 4.8 5.4 5.4 5.4 5.4 5.5 5.6 5.7 5.9 6.1 ...
## $ roads : num 137 122 113 111 108 ...
## $ cerebvas : int 682 647 630 611 631 592 576 525 516 493 ...
## $ assault : int 21 19 17 18 17 16 17 17 16 15 ...
## $ external : int 444 425 406 376 387 371 395 385 410 409 ...
## $ txp_pop : num 0.938 0.926 0.915 0.906 0.896 ...
## $ world : chr "Liberal" "Liberal" "Liberal" "Liberal" ...
## $ opt : chr "In" "In" "In" "In" ...
## $ consent_law : chr "Informed" "Informed" "Informed" "Informed" ...
## $ consent_practice: chr "Informed" "Informed" "Informed" "Informed" ...
## $ consistent : chr "Yes" "Yes" "Yes" "Yes" ...
## $ ccode : chr "Oz" "Oz" "Oz" "Oz" ...
factor(orgdat$world)
## [1] Liberal Liberal Liberal Liberal Liberal Liberal
## [7] Liberal Liberal Liberal Liberal Liberal Liberal
## [13] Liberal Liberal Corporatist Corporatist Corporatist Corporatist
## [19] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [25] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [31] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [37] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [43] Liberal Liberal Liberal Liberal Liberal Liberal
## [49] Liberal Liberal Liberal Liberal Liberal Liberal
## [55] Liberal Liberal SocDem SocDem SocDem SocDem
## [61] SocDem SocDem SocDem SocDem SocDem SocDem
## [67] SocDem SocDem SocDem SocDem SocDem SocDem
## [73] SocDem SocDem SocDem SocDem SocDem SocDem
## [79] SocDem SocDem SocDem SocDem SocDem SocDem
## [85] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [91] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [97] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [103] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [109] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [115] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [121] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [127] Liberal Liberal Liberal Liberal Liberal Liberal
## [133] Liberal Liberal Liberal Liberal Liberal Liberal
## [139] Liberal Liberal SocDem SocDem SocDem SocDem
## [145] SocDem SocDem SocDem SocDem SocDem SocDem
## [151] SocDem SocDem SocDem SocDem SocDem SocDem
## [157] SocDem SocDem SocDem SocDem SocDem SocDem
## [163] SocDem SocDem SocDem SocDem SocDem SocDem
## [169] <NA> <NA> <NA> <NA> <NA> <NA>
## [175] <NA> <NA> <NA> <NA> <NA> <NA>
## [181] <NA> <NA> SocDem SocDem SocDem SocDem
## [187] SocDem SocDem SocDem SocDem SocDem SocDem
## [193] SocDem SocDem SocDem SocDem Corporatist Corporatist
## [199] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [205] Corporatist Corporatist Corporatist Corporatist Corporatist Corporatist
## [211] Liberal Liberal Liberal Liberal Liberal Liberal
## [217] Liberal Liberal Liberal Liberal Liberal Liberal
## [223] Liberal Liberal Liberal Liberal Liberal Liberal
## [229] Liberal Liberal Liberal Liberal Liberal Liberal
## [235] Liberal Liberal Liberal Liberal
## Levels: Corporatist Liberal SocDem
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
library(gapminder)
## Warning: package 'gapminder' was built under R version 4.3.3
orgdat %>% select(1:6) %>% sample_n(size = 10)
p1 <- ggplot(data = orgdat, mapping = aes(x = year, y = donors))
p1 + geom_point()
## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_point()`).
p2 <- ggplot(data = orgdat, mapping = aes(x = year, y = donors))
p2 + geom_line(aes(group = country)) + facet_wrap(~country)
## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_line()`).
p3 <- ggplot(data = orgdat, mapping = aes(x = country, y = donors))
p3 + geom_boxplot()
## Warning: Removed 34 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
p4 <- ggplot(data = orgdat,
mapping = aes(x = reorder(country, donors, na.rm=TRUE), y = donors, color = world))
p4 + geom_point() + labs(x=NULL, y = "Donors") + coord_flip() + theme(legend.position = "top")
## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_point()`).
