library(ggplot2)
ThirtyDaySurv <-read.csv("01-1-child-heart-survival-x.csv", header=TRUE) # reads data into ThirtyDaySurv data frame
-nhosp=length(ThirtyDaySurv$Hospital)
-p <- ggplot(ThirtyDaySurv, aes(x=reorder(Hospital,nhosp:1), y= ThirtyDaySurvival, fill=Hospital)) # constructs initial plot object, , starting with top row
+attach(ThirtyDaySurv)
+nhosp=length(Hospital)First in R base graphics
+par(mar=c(5,15,4,2))
+barplot(ThirtyDaySurvival,names.arg=Hospital,horiz=T,xlim=c(86,100), xpd=F,las=1, xlab="% surviving 30 days")
Now in ggplot2
+p <- ggplot(ThirtyDaySurv, aes(x=reorder(Hospital,nhosp:1), y= ThirtyDaySurvival, fill=Hospital)) # constructs initial plot object, , starting with top row
p <- p + geom_bar(stat = "identity") # assigns bar chart-type
p <- p + coord_flip(ylim = c(86,100)) # flips to horizontal bars and limits y-axis
p <- p + scale_y_continuous(breaks=seq(86, 100, 2)) # assigns breaks every 2 percent
@@ -390,7 +396,17 @@ Figure 1.1: Survival rates following child heart surgery in thirteen hospita
p # draws the plot
Figure 1.1 Bar-chart of 30-day survival rates for thirteen hospitals. The choice of the start of the horizontal axis, here 86%, can have a crucial effect on the impression given by the graphic. If the axis starts at 0%, all the hospitals will look indistinguishable, whereas if we started at 95% the differences would look misleadingly dramatic.
-For other ways of displaying and explaining this data, and more recent results, see childrensheartsurgery.info.
+For other ways of displaying and explaining this data, and more recent results, see childrensheartsurgery.info, which uses a dot-plot. This may be more appropriate.
+p <- ggplot(ThirtyDaySurv, aes(x=reorder(Hospital,nhosp:1), y= ThirtyDaySurvival, fill=Hospital)) # constructs initial plot object, , starting with top row
+p <- p + geom_dotplot(binaxis="y",stackdir="center",dotsize=2) # assigns dots chart-type
+p <- p + coord_flip(ylim = c(86,100)) # flips to horizontal bars and limits y-axis
+p <- p + scale_y_continuous(breaks=seq(86, 100, 2)) # assigns breaks every 2 percent
+p <- p + theme(legend.position="none") # removes the legend
+p <- p + labs(x="", y="% surviving 30 days") # Adds y-axis label
+p # draws the plot## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
Figure 1.1 Bar-chart of 30-day survival rates for thirteen hospitals represented as a dot-plot, in which the non-zero axis is less important as, unlike a bar, it is not directly connected to the data-point.
diff --git a/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.Rmd b/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.Rmd index fc5ffd5..5005fd9 100644 --- a/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.Rmd +++ b/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.Rmd @@ -16,6 +16,18 @@ library(ggplot2) df <- read.csv("01-1-child-heart-survival-x.csv", header=TRUE) # reads csv into dataframe, df df$Percentage = 100*df$Operations/sum(df$Operations) df$Pos= rank(df$Percentage) +``` +First in R base graphics + +```{r} +par(mar=c(5,15,4,2)) +barplot(df$Percentage,names.arg=df$Hospital,horiz=T, xpd=F,las=1, xlab="Percentage of all operations in 2012-15 \nthat are carried out in each hospital") + +``` + +Now in ggplot2 + +```{r} bp <- ggplot(df, aes(x=reorder(Hospital,-Pos), y=Percentage, fill=Hospital)) #sets initial plot object from the dataframe for Hospitals, reordered by Percentage (descending) as the y-values, colour-filled by Hospital bp <- bp + geom_bar(stat = "identity") + labs(x="Hospital") # makes the plot a bar-chart bp <- bp + coord_flip() # makes it an horizontal bar chart diff --git a/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.html b/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.html index 4cb010f..80c1812 100644 --- a/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.html +++ b/01-1-2-3-child-heart-survival-times/01-3-child-heart-proportions-x.html @@ -380,8 +380,13 @@First in R base graphics
+par(mar=c(5,15,4,2))
+barplot(df$Percentage,names.arg=df$Hospital,horiz=T, xpd=F,las=1, xlab="Percentage of all operations in 2012-15 \nthat are carried out in each hospital")
Now in ggplot2
+bp <- ggplot(df, aes(x=reorder(Hospital,-Pos), y=Percentage, fill=Hospital)) #sets initial plot object from the dataframe for Hospitals, reordered by Percentage (descending) as the y-values, colour-filled by Hospital
bp <- bp + geom_bar(stat = "identity") + labs(x="Hospital") # makes the plot a bar-chart
bp <- bp + coord_flip() # makes it an horizontal bar chart
bp <- bp + scale_y_continuous(breaks=seq(2,16,2)) # breaks every two-count
diff --git a/02-4-reported-partners/02-4-sexual-partners-x.Rmd b/02-4-reported-partners/02-4-sexual-partners-x.Rmd
index 2bf9f95..3af8a30 100644
--- a/02-4-reported-partners/02-4-sexual-partners-x.Rmd
+++ b/02-4-reported-partners/02-4-sexual-partners-x.Rmd
@@ -49,4 +49,4 @@ p # draw the plot
```
-Figure 2.4 Data provided by Natsal-3 based on interviews between 2010 and 2012. The series have been truncated at 50 for reasons of space - the totals go up to 500 for both men and women. Note the clear use of round numbers for ten or more partners, and the tendency for men to report more partners than women.
\ No newline at end of file
+_Figure 2.4 Data provided by Natsal-3 based on interviews between 2010 and 2012. The series have been truncated at 50 for reasons of space - the totals go up to 500 for both men and women. Note the clear use of round numbers for ten or more partners, and the tendency for men to report more partners than women._
\ No newline at end of file
diff --git a/02-4-reported-partners/02-4-sexual-partners-x.html b/02-4-reported-partners/02-4-sexual-partners-x.html
index 97f32b3..61e0690 100644
--- a/02-4-reported-partners/02-4-sexual-partners-x.html
+++ b/02-4-reported-partners/02-4-sexual-partners-x.html
@@ -419,7 +419,7 @@ Figure 2.4 (page 55) Bar chart
p # draw the plot## Warning: Removed 174 rows containing missing values (geom_bar).
Figure 2.4 Data provided by Natsal-3 based on interviews between 2010 and 2012. The series have been truncated at 50 for reasons of space - the totals go up to 500 for both men and women. Note the clear use of round numbers for ten or more partners, and the tendency for men to report more partners than women.
+Figure 2.4 Data provided by Natsal-3 based on interviews between 2010 and 2012. The series have been truncated at 50 for reasons of space - the totals go up to 500 for both men and women. Note the clear use of round numbers for ten or more partners, and the tendency for men to report more partners than women.
diff --git a/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.Rmd b/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.Rmd index 1da7a8f..e6813b7 100755 --- a/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.Rmd +++ b/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.Rmd @@ -11,19 +11,32 @@ Data from 2012-2014 were shown in Table 1.1 (page 23) and are contained in [02-5 ### Figure 2.5 (page 57) Scatterplots -```{r} +Quickly in qplot +```{r} library(ggplot2) # (a) Survival in under-1s, 1991-1995 child.1991 <- read.csv("02-5-child-heart-surgery-1991-x.csv") # read data into dataframe +attach(child.1991) +qplot(Operations,100*Survivors/Operations,xlim = c(0,700),ylim=c(70,100),ylab = "% 30-day survival", label = Hospital, geom=c("point", "text"),hjust=1, vjust=-1,size=2, main = "(a) Survival in under-1s, 1991-1995") + theme(legend.position="none") +# (a) Survival in under-1s, 1991-1995 +#(b) Survival for all children, 2012-2015 +child.2012 <- read.csv("02-5-child-heart-surgery-2012-x.csv") # read data into dataframe all +attach(child.2012) +qplot(Operations,100*Survivors/Operations,xlim = c(0,2000),ylim=c(95,100),ylab = "% 30-day survival", label = Hospital, geom=c("point", "text"),hjust=1, vjust=-1,size=2, main = "(b) Survival for all children, 2012-2015") + theme(legend.position="none") + + +``` + +```{r} p <- ggplot(child.1991, aes(x=Operations, y=100*Survivors/Operations, col=Hospital)) #defines plot axis data fields and colour legend data field p <- p + geom_point(aes(size=1.5)) # defines scatter-type plot p <- p + expand_limits(x = c(0,700),y=c(70,100)) p <- p + scale_size_continuous(name = "Size", guide = FALSE) # turns off otherwise added size legend p <- p + labs(x="Number of operations", y = "% 30-day survival", title="(a) Survival in under-1s, 1991-1995") # Adds title, subtitle, and caption -p <- p + theme(plot.caption=element_text(hjust = 0.5)) # centre justifies the caption p + #(b) Survival for all children, 2012-2015 child.2012 <- read.csv("02-5-child-heart-surgery-2012-x.csv") # read data into dataframe all q <- ggplot(child.2012, aes(x=Operations, y=100*Survivors/Operations,col=Hospital)) #defines plot axis data fields and colour legend data field @@ -35,8 +48,8 @@ q ``` -Figure 2.5 -Scatter-plots of survival rates against number of operations in child heart surgery. For (a) 1991-1995, the Pearson correlation is 0.59 and the rank correlation is 0.85, for (b) 2012-2015, the Pearson correlation is 0.17 and the rank correlation is -0:03. +_Figure 2.5 +Scatter-plots of survival rates against number of operations in child heart surgery. For (a) 1991-1995, the Pearson correlation is 0.59 and the rank correlation is 0.85, for (b) 2012-2015, the Pearson correlation is 0.17 and the rank correlation is -0:03._ ### Correlations in (a) 1991-1995 data diff --git a/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.html b/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.html index 7684e4e..214c53c 100644 --- a/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.html +++ b/02-5-survival-vs-numbers/02-5-child-heart-surgery-x.html @@ -377,16 +377,28 @@Data from 2012-2014 were shown in Table 1.1 (page 23) and are contained in 02-5-child-heart-surgery-2012-x.csv. The data were originally presented in the NCHDA 2012-15 report, but are best seen on childrensheartsurgery.info.
Quickly in qplot
library(ggplot2)
# (a) Survival in under-1s, 1991-1995
child.1991 <- read.csv("02-5-child-heart-surgery-1991-x.csv") # read data into dataframe
-
-p <- ggplot(child.1991, aes(x=Operations, y=100*Survivors/Operations, col=Hospital)) #defines plot axis data fields and colour legend data field
+attach(child.1991)
+qplot(Operations,100*Survivors/Operations,xlim = c(0,700),ylim=c(70,100),ylab = "% 30-day survival", label = Hospital, geom=c("point", "text"),hjust=1, vjust=-1,size=2, main = "(a) Survival in under-1s, 1991-1995") + theme(legend.position="none")
# (a) Survival in under-1s, 1991-1995
+#(b) Survival for all children, 2012-2015
+child.2012 <- read.csv("02-5-child-heart-surgery-2012-x.csv") # read data into dataframe all
+attach(child.2012)## The following objects are masked from child.1991:
+##
+## Deaths, Hospital, Operations, PercentageDying, Survivors,
+## ThirtyDaySurvivalqplot(Operations,100*Survivors/Operations,xlim = c(0,2000),ylim=c(95,100),ylab = "% 30-day survival", label = Hospital, geom=c("point", "text"),hjust=1, vjust=-1,size=2, main = "(b) Survival for all children, 2012-2015") + theme(legend.position="none")
p <- ggplot(child.1991, aes(x=Operations, y=100*Survivors/Operations, col=Hospital)) #defines plot axis data fields and colour legend data field
p <- p + geom_point(aes(size=1.5)) # defines scatter-type plot
p <- p + expand_limits(x = c(0,700),y=c(70,100))
p <- p + scale_size_continuous(name = "Size", guide = FALSE) # turns off otherwise added size legend
p <- p + labs(x="Number of operations", y = "% 30-day survival", title="(a) Survival in under-1s, 1991-1995") # Adds title, subtitle, and caption
-p <- p + theme(plot.caption=element_text(hjust = 0.5)) # centre justifies the caption
p
#(b) Survival for all children, 2012-2015
@@ -398,12 +410,20 @@ Figure 2.5 (page 57) Scatterplots
q <- q + labs(x="Number of operations", y = "% 30-day survival", title="(b) Survival for all children, 2012-2015") # Adds title, subtitle
q
Figure 2.5 Scatter-plots of survival rates against number of operations in child heart surgery. For (a) 1991-1995, the Pearson correlation is 0.59 and the rank correlation is 0.85, for (b) 2012-2015, the Pearson correlation is 0.17 and the rank correlation is -0:03.
+Figure 2.5 Scatter-plots of survival rates against number of operations in child heart surgery. For (a) 1991-1995, the Pearson correlation is 0.59 and the rank correlation is 0.85, for (b) 2012-2015, the Pearson correlation is 0.17 and the rank correlation is -0:03.
attach(child.1991)
-cor.test(Operations, 100*Survivors/Operations,method="pearson")attach(child.1991)## The following objects are masked from child.2012:
+##
+## Deaths, Hospital, Operations, PercentageDying, Survivors,
+## ThirtyDaySurvival## The following objects are masked from child.1991 (pos = 4):
+##
+## Deaths, Hospital, Operations, PercentageDying, Survivors,
+## ThirtyDaySurvivalcor.test(Operations, 100*Survivors/Operations,method="pearson")##
## Pearson's product-moment correlation
##
@@ -454,7 +474,15 @@ Correlations in (a) 1991-1995 data, without Bristol
Correlations in (b) 2012-2014 data
attach(child.2012)
-## The following objects are masked from child.1991:
+## The following objects are masked from child.1991 (pos = 3):
+##
+## Deaths, Hospital, Operations, PercentageDying, Survivors,
+## ThirtyDaySurvival
+## The following objects are masked from child.2012 (pos = 4):
+##
+## Deaths, Hospital, Operations, PercentageDying, Survivors,
+## ThirtyDaySurvival
+## The following objects are masked from child.1991 (pos = 5):
##
## Deaths, Hospital, Operations, PercentageDying, Survivors,
## ThirtyDaySurvival
diff --git a/02-6-zero-correlations/02-6-zero-correlations-x.Rmd b/02-6-zero-correlations/02-6-zero-correlations-x.Rmd
index 2cec963..c9a4310 100644
--- a/02-6-zero-correlations/02-6-zero-correlations-x.Rmd
+++ b/02-6-zero-correlations/02-6-zero-correlations-x.Rmd
@@ -26,4 +26,4 @@ p
```
-Figure 2.6 Two sets of (fictitious) data-points for which the Pearson correlation coefficients are both 0. This clearly does not mean there is no relationship between the two variables being plotted.
+_Figure 2.6 Two sets of (fictitious) data-points for which the Pearson correlation coefficients are both 0. This clearly does not mean there is no relationship between the two variables being plotted._
diff --git a/02-6-zero-correlations/02-6-zero-correlations-x.html b/02-6-zero-correlations/02-6-zero-correlations-x.html
index badfae1..626ab4f 100644
--- a/02-6-zero-correlations/02-6-zero-correlations-x.html
+++ b/02-6-zero-correlations/02-6-zero-correlations-x.html
@@ -393,7 +393,7 @@ Art of Statistics: Figure 2.6 (page 59) Pearson Cor
p
-Figure 2.6 Two sets of (fictitious) data-points for which the Pearson correlation coefficients are both 0. This clearly does not mean there is no relationship between the two variables being plotted.
+Figure 2.6 Two sets of (fictitious) data-points for which the Pearson correlation coefficients are both 0. This clearly does not mean there is no relationship between the two variables being plotted.
diff --git a/02-7-population-trends/02-7-pop-past-future million in 51.csv b/02-7-population-trends/02-7-pop-past-future million in 51.csv
new file mode 100644
index 0000000..7d58858
--- /dev/null
+++ b/02-7-population-trends/02-7-pop-past-future million in 51.csv
@@ -0,0 +1,67 @@
+Year,China,India,United States of America,Russian Federation,Japan,Indonesia,Germany,Brazil,United Kingdom,Northern Africa,Italy,France,Bangladesh,Nigeria,Pakistan,Ukraine,Mexico,Spain,Viet Nam,Poland,Turkey,Thailand,Egypt,Republic of Korea,Philippines,Ethiopia,Iran (Islamic Republic of),Argentina,Myanmar,Caribbean,Romania,Southern Africa,Canada,South Africa,Oceania,Colombia,Democratic Republic of the Congo,Australia/New Zealand,Dem. People's Republic of Korea,Netherlands,Hungary,Morocco,Algeria,Czechia,Belgium,Nepal,Portugal,Australia,Sri Lanka,Peru,"China, Taiwan Province of China",United Republic of Tanzania,Afghanistan,Greece,Belarus,Bulgaria,Sweden,Austria,Serbia,Kazakhstan,Uzbekistan,Chile,Malaysia,Mozambique,Kenya,Cuba,Iraq,Sudan,Venezuela (Bolivarian Republic of),Uganda,Ghana,Mali,Switzerland,Angola,Cambodia,Yemen,Cameroon,Burkina Faso,Denmark,Madagascar,Finland,Croatia,Tunisia,Ecuador,Georgia,Slovakia,Syrian Arab Republic,Norway,Haiti,Guatemala,Saudi Arabia,Guinea,Bolivia (Plurinational State of),Malawi,Azerbaijan,Ireland,Zimbabwe,Bosnia and Herzegovina,Cote d'Ivoire,Niger,South Sudan,Lithuania,Senegal,Chad,Dominican Republic,Republic of Moldova,Zambia,Burundi,Somalia,Uruguay,Benin,Rwanda,El Salvador,Puerto Rico,Melanesia,"China, Hong Kong SAR",Sierra Leone,New Zealand,Latvia,Kyrgyzstan,Lao People's Democratic Republic,Papua New Guinea,Honduras,Tajikistan,Paraguay,Slovenia,Jamaica,Togo,Armenia,Lebanon,Israel,Central African Republic,Nicaragua,TFYR Macedonia,Albania,Turkmenistan,Eritrea,Libya,Estonia,Singapore
+1950,554419.28,376325.2,158804.4,102798.66,82802.08,69543.32,69966.24,53974.73,50616.01,49048.94,46598.6,41879.61,37894.68,37859.74,37542.38,37297.65,28012.56,28069.74,24809.91,24824.02,21408.4,20710.36,20713.33,19211.39,18580.49,18128.03,17119.27,17150.34,17151.51,17075.67,16236.3,15533.18,13733.4,13628.43,12647.78,12340.9,12183.66,10085.34,10549.47,10042.04,9337.72,8985.99,8872.25,8902.62,8637.52,8483.33,8416.97,8177.34,7971.1,7727.73,7623.36,7649.76,7752.12,7668.79,7745,7251,7009.91,6936.44,6732.26,6702.99,6264.06,6188.28,6109.92,6152.24,6076.76,5919.99,5719.19,5733.79,5481.98,5158.19,4980.88,4708.43,4668.09,4548.02,4432.71,4402.32,4307.02,4284.46,4268.27,4083.55,4008.29,3850.3,3605.31,3470.16,3527.01,3436.57,3413.33,3265.27,3221.28,3114.96,3121.34,3093.65,3089.65,2953.87,2927.92,2913.09,2746.85,2661.3,2630.13,2559.7,2582.93,2567.4,2486.64,2502.32,2364.65,2341,2310.44,2308.92,2264.08,2238.51,2255.23,2186.19,2199.9,2218,2165.34,1974,2041.2,1908,1926.57,1740,1682.92,1674.04,1546.72,1531.5,1473.25,1473.09,1402.9,1395.46,1353.51,1334.62,1257.97,1326.65,1294.99,1254.45,1263.17,1210.99,1142.15,1124.52,1101,1022.1
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\ No newline at end of file
diff --git a/02-7-population-trends/02-7-pop-past-future-2.7-necessities.csv b/02-7-population-trends/02-7-pop-past-future-2.7-necessities.csv
new file mode 100644
index 0000000..c6ad2e8
--- /dev/null
+++ b/02-7-population-trends/02-7-pop-past-future-2.7-necessities.csv
@@ -0,0 +1,67 @@
+Year,Europe,World,Africa,Asia,Oceania,NorthernAmerica,LatinAmericaandCarribean,,,,,,,
+1950,549,2536,229,1404,13,173,169,,,,,,,
+1951,554,2584,233,1435,13,175,173,,,,,,,
+1952,560,2631,238,1463,13,178,178,,,,,,,
+1953,566,2677,243,1491,14,181,183,,,,,,,
+1954,571,2724,248,1518,14,184,188,,,,,,,
+1955,577,2772,254,1546,14,188,193,,,,,,,
+1956,583,2821,260,1575,14,191,198,,,,,,,
+1957,589,2872,266,1604,15,195,204,,,,,,,
+1958,594,2924,272,1635,15,198,209,,,,,,,
+1959,600,2978,278,1667,15,201,215,,,,,,,
+1960,606,3033,285,1700,16,205,221,,,,,,,
+1961,612,3090,292,1735,16,208,227,,,,,,,
+1962,618,3149,299,1771,16,211,234,,,,,,,
+1963,624,3210,307,1808,17,214,240,,,,,,,
+1964,630,3274,314,1848,17,217,247,,,,,,,
+1965,635,3340,322,1891,18,220,253,,,,,,,
+1966,640,3408,331,1937,18,222,260,,,,,,,
+1967,645,3479,339,1985,18,224,267,,,,,,,
+1968,649,3552,348,2035,19,227,274,,,,,,,
+1969,653,3626,357,2086,19,229,281,,,,,,,
+1970,657,3701,366,2138,20,231,288,,,,,,,
+1971,662,3776,376,2189,20,233,295,,,,,,,
+1972,666,3852,386,2241,20,236,303,,,,,,,
+1973,670,3928,396,2293,21,238,310,,,,,,,
+1974,674,4003,407,2344,21,240,318,,,,,,,
+1975,678,4079,418,2394,22,242,325,,,,,,,
+1976,681,4154,429,2444,22,245,333,,,,,,,
+1977,685,4229,441,2493,22,247,341,,,,,,,
+1978,688,4304,454,2542,22,250,348,,,,,,,
+1979,691,4381,467,2592,23,252,356,,,,,,,
+1980,694,4458,480,2642,23,254,364,,,,,,,
+1981,697,4538,494,2694,23,257,372,,,,,,,
+1982,700,4619,508,2747,24,259,380,,,,,,,
+1983,703,4702,522,2802,24,262,389,,,,,,,
+1984,705,4786,537,2858,25,264,397,,,,,,,
+1985,708,4874,553,2916,25,267,405,,,,,,,
+1986,711,4964,569,2976,25,269,413,,,,,,,
+1987,714,5056,585,3038,26,272,421,,,,,,,
+1988,717,5149,601,3100,26,275,430,,,,,,,
+1989,719,5241,618,3162,27,278,438,,,,,,,
+1990,722,5331,635,3221,27,280,446,,,,,,,
+1991,724,5419,652,3279,28,283,454,,,,,,,
+1992,725,5504,669,3334,28,286,462,,,,,,,
+1993,727,5588,687,3387,28,289,471,,,,,,,
+1994,728,5670,705,3439,29,292,479,,,,,,,
+1995,728,5751,723,3489,29,295,487,,,,,,,
+1996,728,5832,741,3539,30,299,495,,,,,,,
+1997,728,5911,760,3588,30,302,503,,,,,,,
+1998,728,5989,779,3636,30,306,511,,,,,,,
+1999,727,6067,798,3683,31,309,518,,,,,,,
+2000,727,6145,818,3730,31,313,526,,,,,,,
+2001,727,6223,838,3777,32,316,533,,,,,,,
+2002,728,6302,859,3824,32,319,540,,,,,,,
+2003,728,6381,880,3871,33,322,547,,,,,,,
+2004,729,6461,902,3918,33,325,555,,,,,,,
+2005,730,6542,925,3964,34,328,562,,,,,,,
+2006,732,6624,948,4011,34,331,569,,,,,,,
+2007,733,6706,972,4057,35,334,576,,,,,,,
+2008,734,6790,997,4103,35,337,583,,,,,,,
+2009,736,6874,1023,4149,36,340,590,,,,,,,
+2010,737,6958,1049,4194,37,343,598,,,,,,,
+2011,738,7043,1077,4240,37,346,605,,,,,,,
+2012,739,7128,1105,4286,38,348,612,,,,,,,
+2013,740,7213,1134,4331,38,351,619,,,,,,,
+2014,740,7298,1164,4376,39,353,626,,,,,,,
+2015,741,7383,1194,4420,40,356,632,,,,,,,
diff --git a/02-7-population-trends/02-7-population-history-x.Rmd b/02-7-population-trends/02-7-population-history-x.Rmd
new file mode 100644
index 0000000..8b05377
--- /dev/null
+++ b/02-7-population-trends/02-7-population-history-x.Rmd
@@ -0,0 +1,59 @@
+---
+ title: "Art of Statistics: Figure 2.7 (pages 62) World Population Trends"
+ #output: md_document
+ output: html_document
+---
+
+Data is from
+[United Nations Population Division: World Population Prospects 2017](https://population.un.org/wpp/Download/Standard/Population/), specifically [Total Population - Both Sexes](https://population.un.org/wpp/DVD/Files/1_Indicators%20(Standard)/EXCEL_FILES/1_Population/WPP2017_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx)
+
+```{r}
+library(ggplot2)
+
+popTrends<-read.csv("02-7-pop-past-future-2.7-necessities.csv",header=TRUE) # reads data into popTrends data frame
+
+p <- ggplot(popTrends, aes(x=Year, y)) # constructs initial plot object, p
+p <- p + geom_line(aes(y = Europe, col = "Europe"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = World, col = "World"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = Africa, col = "Africa"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = Oceania, col = "Oceania"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = Asia, col = "Asia"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = LatinAmericaandCarribean, col = "S America"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = NorthernAmerica, col = "N America"), size=1.5) # adds a y-series
+p <- p + labs(subtitle="(a) On a standard scale", y="Millions", x="Year") # Adds title, subtitle
+
+# Some of the colours rather pale to see in palette Accent, Set1 seems better
+p <- p + scale_colour_brewer(palette = "Set1")
+#p <- p + scale_colour_brewer(palette = "Accent")
+
+p <- p + scale_size_continuous(name = "Size", guide = FALSE) # turns off unwanted size legend
+p <- p + theme(legend.position="bottom", legend.box = "horizontal") # positions the colour legend
+p
+```
+
+_Figure 2.7(a) . Total population for the world and continents between 1950 and 2015, both sexes combined._
+
+```{r}
+# now for the logarithmic version
+millIn51 <- read.csv("02-7-pop-past-future million in 51.csv",header=TRUE) # reads data into millIn51 data frame
+
+logbreaks <- c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)
+q <- p + labs(subtitle="(b) On a logarithmic scale", y="Millions", x="Year") # reword title, subtitle, and caption
+
+# try trick from https://www.biostars.org/p/234142/ using aes_string?
+
+plotIt <- function(q, index){ #recursive function to draw lines for countries that had at least a million population in 1951
+ if(index==1){return(q)}else{ #end recursion when index reaches 1
+ q <- q + geom_line(aes(y=millIn51[,index]/1000), col = "grey76", size=0.5) # or add the y-series
+ plotIt(q, index-1) # and resursively call next index
+ }
+}
+q <- plotIt(q, 141) # call recursive function for the 140 countries in the CSV (recursion ends when index reaches 1, the x=Year field)
+q <- q + geom_line(aes(y = Oceania, col = "Oceania"), size=1.5) # for visibility put Oceania back on top of the sea of grey
+q <- q + scale_y_log10(breaks=logbreaks) # change to logarithmic y-scale
+q <- q + theme(legend.position="bottom", legend.box = "horizontal")
+q
+
+```
+
+_Figure 2.7(b) . Total population for the world and continents between 1950 and 2015, both sexes combined, on a logarithmic scale, with the trend-lines for individual countries that had a population of at least one million in 1951._
\ No newline at end of file
diff --git a/02-7-population-trends/02-7-population-history-x.html b/02-7-population-trends/02-7-population-history-x.html
new file mode 100644
index 0000000..a7f0288
--- /dev/null
+++ b/02-7-population-trends/02-7-population-history-x.html
@@ -0,0 +1,451 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+Art of Statistics: Figure 2.7 (pages 62) World Population Trends
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Art of Statistics: Figure 2.7 (pages 62) World Population Trends
+
+
+
+
+Data is from United Nations Population Division: World Population Prospects 2017, specifically Total Population - Both Sexes
+library(ggplot2)
+
+popTrends<-read.csv("02-7-pop-past-future-2.7-necessities.csv",header=TRUE) # reads data into popTrends data frame
+
+p <- ggplot(popTrends, aes(x=Year, y)) # constructs initial plot object, p
+p <- p + geom_line(aes(y = Europe, col = "Europe"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = World, col = "World"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = Africa, col = "Africa"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = Oceania, col = "Oceania"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = Asia, col = "Asia"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = LatinAmericaandCarribean, col = "S America"), size=1.5) # adds a y-series
+p <- p + geom_line(aes(y = NorthernAmerica, col = "N America"), size=1.5) # adds a y-series
+p <- p + labs(subtitle="(a) On a standard scale", y="Millions", x="Year") # Adds title, subtitle
+
+# Some of the colours rather pale to see in palette Accent, Set1 seems better
+p <- p + scale_colour_brewer(palette = "Set1")
+#p <- p + scale_colour_brewer(palette = "Accent")
+
+p <- p + scale_size_continuous(name = "Size", guide = FALSE) # turns off unwanted size legend
+p <- p + theme(legend.position="bottom", legend.box = "horizontal") # positions the colour legend
+p
+
+Figure 2.7(a) . Total population for the world and continents between 1950 and 2015, both sexes combined.
+# now for the logarithmic version
+millIn51 <- read.csv("02-7-pop-past-future million in 51.csv",header=TRUE) # reads data into millIn51 data frame
+
+logbreaks <- c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)
+q <- p + labs(subtitle="(b) On a logarithmic scale", y="Millions", x="Year") # reword title, subtitle, and caption
+
+# try trick from https://www.biostars.org/p/234142/ using aes_string?
+
+plotIt <- function(q, index){ #recursive function to draw lines for countries that had at least a million population in 1951
+ if(index==1){return(q)}else{ #end recursion when index reaches 1
+ q <- q + geom_line(aes(y=millIn51[,index]/1000), col = "grey76", size=0.5) # or add the y-series
+ plotIt(q, index-1) # and resursively call next index
+ }
+}
+q <- plotIt(q, 141) # call recursive function for the 140 countries in the CSV (recursion ends when index reaches 1, the x=Year field)
+q <- q + geom_line(aes(y = Oceania, col = "Oceania"), size=1.5) # for visibility put Oceania back on top of the sea of grey
+q <- q + scale_y_log10(breaks=logbreaks) # change to logarithmic y-scale
+q <- q + theme(legend.position="bottom", legend.box = "horizontal")
+q
+
+Figure 2.7(b) . Total population for the world and continents between 1950 and 2015, both sexes combined, on a logarithmic scale, with the trend-lines for individual countries that had a population of at least one million in 1951.
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/08-1-chevalier-de-mere/08-1-mere-simulation-x.Rmd b/08-1-chevalier-de-mere/08-1-mere-simulation-x.Rmd
index 6a4d350..3cb0b52 100644
--- a/08-1-chevalier-de-mere/08-1-mere-simulation-x.Rmd
+++ b/08-1-chevalier-de-mere/08-1-mere-simulation-x.Rmd
@@ -1,7 +1,8 @@
---
- title: "Art of Statistics: Figure 8-1 (page 206) Simulation of two dice games played by the Chevaler de Mere"
+ title: "Art of Statistics: Figure 8-1 (page 206) Simulation of two dice games played by the Chevalier de Mere"
#output: md_document
output: html_document
+ #output: pdf_document
---
The data is simulated within the code, and so will be different every time - this code will not produce the plots in the book, as I forgot to save the random-number seed.
@@ -15,7 +16,34 @@ sixes2<-rbinom(nsims,24,1/36) # simulate the number of six-pairs in 24 throws of
game1=cumsum(sixes1>0)/(1:nsims) # proportion of 'successes'
game2=cumsum(sixes2>0)/(1:nsims)
df=cbind.data.frame(n,game1,game2)
+```
+First in R base graphics
+```{r}
+par(mfrow=c(2,1),mar=c(2,4,1,2))
+nsim1=100
+plot(1:nsim1, game1[1:nsim1], type="n",xlab="number of games",ylab="Proportion of wins",
+ ylim=c(0,1.0),axes=F, main="A simulation of the Chevalier playing his two games")
+axis(1) #,at=(seq(0,100,by=5)))
+axis(2, las=1 )
+lines(1:nsim1,game1[1:nsim1],lty=1, lwd=5,col="blue")
+lines(1:nsim1,game2[1:nsim1],lty=1, lwd=5,col="red")
+lines(c(1,nsim1),c(0.5,0.5),lty=3,lwd=1)
+legend(20,1,bty="n",col=c("blue","red"),legend=c("Game 1: one die","Game 2: two dice"),lwd=5, cex=0.8)
+
+plot(nsim1:nsims, type="n",xlab="number of games",ylab="Proportion of wins",
+ ylim=c(0.4,0.6),axes=F, main="")
+axis(1) #,at=(seq(0,100,by=5)))
+axis(2, las=1 )
+lines(nsim1:nsims,game1[nsim1:nsims],lty=1, lwd=5,col="blue")
+lines(nsim1:nsims,game2[nsim1:nsims],lty=1, lwd=5,col="red")
+lines(c(1,nsims),c(0.5,0.5),lty=3,lwd=1)
+```
+
+_Figure 8.1. A computer simulation of 10,000 repeats of two games. In Game 1, you win if you throw a six in at most 4 throws of a fair die; in Game 2, you win if you throw a double-six in at most 24 throws of two fair dice. Over the first 100 games of each (upper chart) it may sometimes look like there is a higher chance of winning Game 2, but after thousands of plays (lower chart) it becomes apparent that Game 1 provides a marginally better bet._
+
+Now in ggplot
+```{r}
library(ggplot2)
P <- list() # prepare list for the plots
for(i in 1:2) # 2 runs with parameter i
@@ -28,7 +56,7 @@ for(i in 1:2) # 2 runs with parameter i
if (i==1) {p <- p + geom_text(aes(x = 80, y = 1, label = "Game 1: one die", color = "Game 1", hjust=0))
p <- p + geom_text(aes(x = 80, y = 0.9, label = "Game 2: two dice", color = "Game 2", hjust=0))}
p <- p +labs(x="Number of games", y="Proportion of wins") # Adds title, subtitle, and caption
- p <- p + scale_colour_brewer(palette = "Accent") # sets the colour palette
+ p <- p + scale_colour_brewer(palette = "Set1") # sets the colour palette
p <- p + theme(legend.position="none")#, legend.box = "horizontal") # removes the legend
p <- p + theme(plot.caption=element_text(hjust = 0.5)) # centre justifies any caption
P <- c(P, list(p)) # add plot to the plot-list
@@ -42,4 +70,3 @@ for(i in 1:2) # 2 runs with parameter i
```
-Figure 8.1. A computer simulation of 10,000 repeats of two games. In Game 1, you win if you throw a six in at most 4 throws of a fair die; in Game 2, you win if you throw a double- six in at most 24 throws of two fair dice. Over the first 100 games of each (upper chart) it looks like there is a higher chance of winning Game 2, but after thousands of plays (lower chart) it becomes apparent that Game 1 provides a marginally better bet.
\ No newline at end of file
diff --git a/08-1-chevalier-de-mere/08-1-mere-simulation-x.html b/08-1-chevalier-de-mere/08-1-mere-simulation-x.html
index 5e03e12..08c193d 100644
--- a/08-1-chevalier-de-mere/08-1-mere-simulation-x.html
+++ b/08-1-chevalier-de-mere/08-1-mere-simulation-x.html
@@ -11,7 +11,7 @@
-Art of Statistics: Figure 8-1 (page 206) Simulation of two dice games played by the Chevaler de Mere
+Art of Statistics: Figure 8-1 (page 206) Simulation of two dice games played by the Chevalier de Mere