Reading data

Our first exercise entails reading data from files and summarizing and combining the records. This is very common and important step; but somewhat tricky to learn. We will use the data in a file named “Operating-Health-Facilities-with-location-2014.csv” under “data” read the data in the file into a data.frame and answer all the questions by using R code only.

Replace the ___________ with the appropriate code

Feel free to use materials of Introduction to R in: http://rspatial.org/intr/.

  1. Read the file into a data.frame.

d <- read.csv(___________, stringsAsFactors=FALSE)

  1. how many rows and columns are there?
  1. what are the variable names?
  1. What is the number of facilities for each zone? See data exploration

table(_________)

  1. Which region has most facilities (and how many), and which has least (and how many)?

x <- table(_________)
names(which.min(_________))
names(x)[which.min(x)]
min(_________)

  1. What is the average elevation of all clinics? > x <- d$ALTITUDE[________==“Clinic”] > mean(x, na.rm=_________)

  2. Create a data.frame with four columns: ZONE, REGION, the number of facilities, and their average elevation (by REGION)

we want something like this

ZONE REGION elevation n
1 Central Dodoma 1059.4 369
2 Central Singida 1355.4 210
3 Eastern Dar es Salaam 54.4 599

We can use the aggregate function. If you struggle, divide and conquer. Do what you can do first. Split the task into parts, and then combine them again later.

One approach:
a1 <- aggregate(_________, d[, c(‘ZONE’, ‘REGION’)], FUN=_________, na.rm=_________)
a2 <- aggregate(d$ALTITUDE, d[, c(‘ZONE’, ‘REGION’)], FUN=_________)
a <- merge(_________, _________, by=_________)

7b. provide appropriate column names

colnames(a)____ <- c(‘elevation’, ‘n’)

7c. sort by ZONE, REGION. See Introduction to R. Chapter 11

a <- a[order(_________, _________), ]

7d. elevation should be rounded to the nearest 10 cm

a$elevation <- _________(_________ , _________)

7e. show the first five lines of the data.frame

a[_________, ]

7f. Save “a” in a file on your disk (comma separated by values format)

write._________(_________, _________, row.names=FALSE)

  1. Also read fdile “Tanzania-All-Waterpoints-July-2015.csv”

w <- read.csv(_________, stringsAsFactors=FALSE)

  1. For each region, compute the total number of water points, and the total number of people served (POPULATION.SERVED), and the most common quantity (“WATER_QUAN”) and type (“WATERPOINT”) (either use the “modal” function from the raster package; or use the table function

number of water points:
wa <- aggregate(w[, _________], w[, ‘REGION’, drop=FALSE], _________)
colnames(wa)[2] <- ‘nwp’
number of people served
wb <- aggregate(w[, _________, drop=FALSE], w[, ‘REGION’, drop=FALSE], _________, na.rm=TRUE)
Get modal function from the raster package

library(raster)
wc <- aggregate(w[, _________], w[, ‘REGION’, drop=FALSE], modal, na.rm=TRUE)
or make a function using which.max()
f <- function(x) names(which.max(table(x)))
wc <- aggregate(w[, _________], w[, ‘REGION’, drop=FALSE], f)
combine:

water <- merge(_________, _________, by=‘REGION’)
water <- merge(water, _________, by=‘REGION’)

  1. Make a table that shows counts of water quality types (“WATER_QUAL”) by region

qual <- table(_________, _________)

  1. Make a new data.frame that combines the REGION level totals/averages for the Health facilities and for the water points, and for the water quality to get something like:
REGION ZONE elevation n nwp POPULATION.SERVED WATER_QUAN WATERPOINT coloured fluoride fluoride abandoned good milky salty salty abandoned soft unknown
Arusha Northern 1542.9 334 4564 1776454 insufficient communal standpipe 18 161 1 0 5 20 1 4227 131
Dodoma Central 1059.4 369 3532 4239573 enough communal standpipe 38 0 0 0 21 594 22 2694 163
Geita Lake 1721.7 143 1944 384 enough hand pump 32 1 0 0 33 51 1 1561 265
Iringa Southern Highlands 1541.1 241 2292 108092 enough communal standpipe 3 0 0 0 0 27 4 2191 67
Kagera Lake 1426.5 298 4531 572969 enough communal standpipe 64 5 0 0 76 117 2 4066 201
Katavi Southern Highlands 1162.5 77 1069 217 enough hand pump 6 0 0 0 6 127 2 907 21

m <- merge(a, water, by=‘REGION’)
q <- as.data.frame.matrix(qual)
result <- merge(_________, _________, by.x=‘REGION’, by.y=0)
head(result)
or
m <- merge(a, water, by=‘REGION’)
qq <- as.data.frame(qual)
qq <- reshape(, direction=‘wide’, timevar=‘Var2’, idvar=‘Var1’)
result <- merge(_________, _________, by.x=‘REGION’, by.y=1)