In carlos-r-git/scriptsearch: A package for searching for text within existing scripts

This script covers two exercises from the 17C practical

2. Testing, Data types and Reading in data. These are:

A. Importing data in the working directory and exploring

data with one group

B. Importing data not in the working directory and exploring #

data with more than one group

Note: keep your lines relatively short #

have spaces around all your operators

( <- , +, =, %>%, etc )

packages needed

it is good practice to load all the packages you are going to need

at the start.

library(tidyverse)

A. Importing data from the working directory

and exploring data with one group

The data in pigeon.txt are 40 measurements of interorbital width

(in mm) in a sample of domestic pigeons measured to the nearest

0.1mm

Read in the data

pigeon <- read.table("pigeon.txt")

examine the structure

str(pigeon)

it's a data.frame of 40 obs. of 1 variable: $interorbital: num

Change the variable name

names(pigeon)[1] <- "interorbital"

Exploring single group data

14th value in the column is extracted with

pigeon$interorbital[14]

or

pigeon[14,1]

Plot a histogram of the data.

hist(pigeon$interorbital, xlim = c(8, 14), main = NULL, xlab = "Width (mm)", ylab = "Number of pigeons", col = "grey")

mean, standard deviation and the number of cases

mean(pigeon$interorbital) sd(pigeon$interorbital) length(pigeon$interorbital)

A. Importing data not in the wd & exploring data in >1 group

Practice with 'paths' and 'relative paths'.

The data in data/pigeon2.txt are similar data of interorbital

distances in two columns rather than just one. They are from two

different populations, A and B.

Read the data

data2 <- read.table("data/pigeon2.txt", header = TRUE)

look at the structure

str(data2)

first value in each column as the variable name.

Mean for each population

mean(data2$A) mean(data2$B)

Put data in tidy format: measurements are all in one

column and a second column gives the group.

See Wickham, 2014

data3 <- gather(data = data2, key = population, value = distance) str(data3)

Note, an alternative way to do this is with the pipe

data3 <- data2 %>% gather(key = population, value = distance)

Examine the structure

str(data3)

reanme columns

names(data3) <- c("distance", "population")

Exploring this multi-group data

finding the mean by selection

mean(data3$distance[data3$population == "A"]) mean(data3$distance[data3$population == "B"])

Using group_by and summarise to find the means - easier for many groups

data3 %>% group_by(population) %>% summarise(mean(distance))

Using group_by and summarise to find the number of cases and variance of each group

data3 %>% group_by(population) %>% summarise(var(distance))

data3 %>% group_by(population) %>% summarise(length(distance))

Note we could do all three in one command

data3 %>% group_by(population) %>% summarise(mean(distance), var(distance), length(distance))

carlos-r-git/scriptsearch documentation built on Sept. 1, 2020, 6:38 p.m.