MarkdownReports_in_Action.r.log

Snowflakes

	Modified: 02/08/2018 | 12:44 | by: MarkdownReports_in_Action.r

I will show an (imaginary) example workflow on complitely made up data.

This example is for MarkdownReports v3.1

Other major version (v2, v4-dev) might not run !

Hey Snowflake collector, welcome back from Reykjavik! How big are the snowflakes over there?

Take a look at the raw numbers:

SnowflakeSizes_Reykjavik

09-Jan	10-Jan	11-Jan	12-Jan	15-Jan	16-Jan	17-Jan	18-Jan	19-Jan	20-Jan	21-Jan	22-Jan	23-Jan
1.21	1.31	1.14	1.3	1.11	1.09	1.22	1.19	1.31	1.16	1.19	1.23	1.29

The code:

md.tableWriter.VEC.w.names(SnowflakeSizes_Reykjavik)

Let's visualize them:

The code:

wbarplot(SnowflakeSizes_Reykjavik)

NOTE: use the mdlink = FALSE argument if you don not want to save this specific plot. See wiki for more

At first we would like to throw away every measurement where the measurement bias (reported by your snowflake collecting machine) is above 10%:

76.9 % or 10 of 13 entries in Measurement_Bias fall below a threshold value of: 10

The code:

wbarplot(Measurement_Bias, ylab = "Measurement Bias (%)", hline = thresholdX, filtercol = -1)
barplot_label(Measurement_Bias, TopOffset = 2)

The code:

wpie(Nr_of_measurements, both_pc_and_value = F)

Let's see how it compares with snow flakes from other cities?

The code:

wstripchart(SnowflakeSizes, tilted_text = T)

The code: wvioplot_list(SnowflakeSizes, tilted_text = T, yoffset = -.2)

Let's say, we also measured the temperature of the flakes. We can color flakes that had temperature below -10:

The code:

SnowflakeTemperature = list( c(-13.3, -13.1, -11.4, -15, -15, -6.28, -9.02),
							 c(-9.02, -5.98, -10.5, 0.48, 4.56, -16.4),
       c(-8.76, -12.6, -9.02, -13.2, -13.5, -10.9, -12.2, -11.6, -10.7, -9.27) )

       colz = lapply(SnowflakeTemperature, function(x) (x< -10)+1)
       SnowflakeSizes_colored_by_temp = SnowflakeSizes
       wstripchart_list(SnowflakeSizes_colored_by_temp, tilted_text = T, bg = colz)

And let's see how the correlation looks like for snowflakes in each city:

The code:

	"Temperature" = unlist(SnowflakeTemperature),
       "Size" = unlist(SnowflakeSizes)
)

Mean_Snowflake_Size_and_Temp = cbind(
"Temperature" = unlist(lapply(SnowflakeTemperature, mean)),
"Size" = unlist(lapply(SnowflakeSizes, mean))
)

sem <- function(x, na.rm=T) sd(unlist(x), na.rm = na.rm)/sqrt(length(na.omit.strip(as.numeric(x))))  # Calculates the standard error of the mean (SEM) for a numeric vector (it excludes NA-s by default)
Snowflakes_SEM = cbind(
"Temperature" = unlist(lapply(SnowflakeTemperature, sem)),
"Size" = unlist(lapply(SnowflakeSizes, sem))
)

llprint("### And lets see how the correlation looks like for snowflakes in each city:")
wplot(Mean_Snowflake_Size_and_Temp, errorbar = T, upper = Snowflakes_SEM[,"Size"], left = Snowflakes_SEM[,"Temperature"], col =3:5, cex=2)

legend_=3:5
names(legend_) = rownames(Mean_Snowflake_Size_and_Temp)
wlegend( fill_= legend_, poz = 3,bty="n")

# linear regression and correlation coefficient
wLinRegression(Mean_Snowflake_Size_and_Temp, lty=3 )