MartinMacharia

### Title: Back to basics: High quality plots using base R graphics
###	An interactive tutorial for the Davis R Users Group meeting on April 24, 2015
###
### Date created: 20150418
### Last updated: 20150423
###
### Author: Michael Koontz
### Email: [email protected]
###	Twitter: @michaeljkoontz
###

MartinMacharia

#Rgdal is what allows R to understand the structure of shapefiles by providing functions to read and 
#convert spatial data into easy to work with R dataframes. 
#Sp enables transformations and projections of the data and provides
#functions for working with the loaded spatial polygon objects.
#R to use .shp files.
# Some of these layers are in geographic coordinates;
#+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
#others in laea; +proj=laea +lat_0=5 +lon_0=20 +x_0=0 +y_0=0 +datum=WGS84
#+units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
#The two coordinate referencing systems have to be harmonised i.e all set as laea for example

MartinMacharia

require(agricolae) 
treatments=c("0N0P0K","30N0P0K","60N0POK","90N0P0K","120N0P0K","0N15P0K","30N15P0K","60N15P0K","90N15P0K","120N15P0K","90N7.5P0K","90N22.5P0K","90N15P10K","90N15P20K","90N15P30K","Diagnostic")=c("0N0P0K","30N0P0K","60N0POK","90N0P0K","120N0P0K","0N15P0K","30N15P0K","60N15P0K","90N15P0K","120N15P0K","90N7.5P0K","90N22.5P0K","90N15P10K","90N15P20K","90N15P30K","Diagnostic")
treatments
Fieldbook=design.rcbd(treatments,3,serie=2, 34589,"Super-Duper", first=TRUE)
Fieldbook

outdesign=design.rcbd(treatments,3,serie=2, 34589,"Super-Duper", first=TRUE)
outdesign

book=outdesign$book

MartinMacharia

x2=c(4,25,13,7,56,90,2,82)
x2=array(x2,c(2,4))
x2
tab=LTT.Kenya_Exe
tab
tab2=tab[1:464, 1:7]
tab2
names(tab2)
tab3=subset(tab,Site=="Bon" & "Nin"=="N0", select=c(Site,Var,Fert,Nin,Yield))
write.csv(tab, "LLT_BonN0")

MartinMacharia

Kenya <- read.csv("C:/Users/machariam/Desktop/Spatial_layers/Kenya.csv", na.strings=c("NA"))
names(Kenya)
#create a new dataset with less variable of interst
str(Kenya)
Kenya_1=Kenya[ , c("Country","District","Center","Site","Longitudes","Latitudes","Elevation","Year","Crop_1","Variety_1",
           "Crop_2","Crop_2_Variety","Crop_3" ,"Crop_3_Variety","PlantMon","PrevCrop","AppliMethod",
           "Manure","Inorganic", "Crop_Fallow","pH_water","PH_Other","Soil_type","Nrate","Prate","Krate",
            "Srate","Znrate","Curate","MgRate","Brate","Other_Nutrients","FYM","Compost","Grain_Yield",
           "Stover_yield", "Additional_comments")]

MartinMacharia

#1988

#Import data
OlNgarua=read.csv("C:/Users/machariam/Desktop/Legacy_data_Analysis/OlNgarua.csv")
OlNgarua
names(OlNgarua)
str(OlNgarua)
OlNgarua_1=OlNgarua[ ,c("District","Center","Longitudes","Latitudes","Year","Crop",
                        "Variety","PlantMon","AppliMethod","Nrate","Prate","Grain_Yield")]
summary(OlNgarua_1)

MartinMacharia

#Here is a document.
A single # represents a tittle, 2 represents a subtittle)

`Back quotes (``)are use to represent the inside text in a type writter format` 

*The star signs between a text  italicize the text* 

**Double stars make the text  bold**

* **Bulleting items, star space then item. This will not only bullet but also make the bulleted text bold**

MartinMacharia

# load the required libraries 
library(sp) 
library(rgdal)

# First load the data from the Washington department of ecology website

# Data source
#
# Data:      ftp://www.ecy.wa.gov/gis_a/inlandWaters/wria.zip

MartinMacharia

# Markus Gesmann
library(arm) # for 'display' function only
icecream <- data.frame(
  # http://www.statcrunch.com/5.0/viewreport.php?reportid=34965&groupid=1848
  temp=c(11.9, 14.2, 15.2, 16.4, 17.2, 18.1, 
         18.5, 19.4, 22.1, 22.6, 23.4, 25.1),
  units=c(185L, 215L, 332L, 325L, 408L, 421L, 
          406L, 412L, 522L, 445L, 544L, 614L)
)
basicPlot <- function(...){

MartinMacharia

This post provides an overview of performing diagnostic and performance evaluation on logistic regression models in R. After training a statistical model, it’s important to understand how well that model did in regards to it’s accuracy and predictive power. The following content will provide the background and theory to ensure that the right technique are being utilized for evaluating logistic regression models in R.

Logistic Regression Example
We will use the GermanCredit dataset in the caret package for this example. It contains 62 characteristics and 1000 observations, with a target variable (Class) that is allready defined. The response variable is coded 0 for bad consumer and 1 for good. It’s always recommended that one looks at the coding of the response variable to ensure that it’s a factor variable that’s coded accurately with a 0/1 scheme or two factor levels in the right order. The first step is to partition the data into training and testing sets.

library(caret)
data(GermanCredit)
Train <- create