R 101

1) First Command

print("Hello World")
## [1] "Hello World"

2) Install and Load Packages and Data

Now we need some data. Packages are collections of functions and/or data. There are published packages that you can use from the community such as these two packages, or you can make your own package for your own private use.

install.packages("babynames")  
install.packages("titanic")

Now that we have installed the packages, we need to load them.

library("babynames")
library("titanic")

Each installation of R comes with quite a bit of data! Now we want to load the “quake” data – there are lots of other options.

data("quakes")
data() #this will list all of the datasets available

3) Assigning Objects

Objects can be many different things ranging from a simple number to a giant matrix, but they refer to things that you can manipulate in R.

myString <- "Hello World" #notice how we need "" around words, aka strings
myString #take a look at myString
## [1] "Hello World"
A <- 14 #now we do not need "" around numbers
A #take a look at A
## [1] 14
A = 5 #can also use the equal sign to assign objects
A #notice how A has changed
## [1] 5

4) Assigning Objects with Multiple Elements

Now lets assign a more complex object

height <- c(5.5, 4.5, 5, 5.6, 5.8, 5.2, 6, 6.2, 5.9, 5.8, 6, 5.9) #this is called a vector
colors_to_use <- c("red", "blue")# a vector of strings

5) Classes

There are a variety of different object classes. We can use the function class() to tell us what class an object belongs to.

class(height) #this is a numeric vector
## [1] "numeric"
class(colors_to_use) #this is a character vector
## [1] "character"
heightdf<-data.frame(height, gender =c("F", "F", "F", "F", "F", "F", "M", "M", "M", "M", "M", "M"))
heightdf #take a look at the dataframe
##    height gender
## 1     5.5      F
## 2     4.5      F
## 3     5.0      F
## 4     5.6      F
## 5     5.8      F
## 6     5.2      F
## 7     6.0      M
## 8     6.2      M
## 9     5.9      M
## 10    5.8      M
## 11    6.0      M
## 12    5.9      M
class(heightdf) #check the class
## [1] "data.frame"
heightdf$height # we can refer to indivdual columns based on the column name
##  [1] 5.5 4.5 5.0 5.6 5.8 5.2 6.0 6.2 5.9 5.8 6.0 5.9
class(heightdf$gender)#here we see a factor(categorical variable - stored in R as with integer levels
## [1] "factor"
logical_variable<-height == heightdf$height #this shows that all the elements in the height column of the heightdf dataframe are equivalent to those of the height vector
class(logical_variable)
## [1] "logical"
matrix_variable <- matrix(height, nrow = 2, ncol = 3)#now we will make a matrix
matrix_variable #take a look at the matrix
##      [,1] [,2] [,3]
## [1,]  5.5  5.0  5.8
## [2,]  4.5  5.6  5.2
class(matrix_variable)
## [1] "matrix"

6) Subsetting Data

Now that we can assign or instantiate objects, let’s try to look at or manipulate specific parts of more complex objects.

Lets create an object of male heights by grabbing rows from heightdf.

maleIndex<-which(heightdf$gender == "M") #lets try subsetting just the male data out of the heightdf - first we need to determine which rows of the dataframe are male
maleIndex # this is now just a list of rows
## [1]  7  8  9 10 11 12
heightmale<-heightdf[maleIndex,] #now we will use the brackets to grab these rows - we use the comma to indicate that we want rows not columns
heightmale # now this is just the males
##    height gender
## 7     6.0      M
## 8     6.2      M
## 9     5.9      M
## 10    5.8      M
## 11    6.0      M
## 12    5.9      M

Here is another way using a package called dpylr:

install.packages("dplyr")

Here we are creating an object of height data for males over 6 feet.

library(dplyr) #load a useful package for subsetting data
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
#heightmale_over6feet <- dplyr::subset(heightdf, gender =="M" & height >=6)#need to use column names to describe what we want to pull out of our data
heightmale_over6feet <- subset(heightdf, gender =="M" & height >=6)#need to use column names to describe what we want to pull out of our data

heightmale_over6feet#now we just have the males 6 feet or over
##    height gender
## 7     6.0      M
## 8     6.2      M
## 11    6.0      M

Now let’s create an object by grabbing part of an object based on its columns.

gender1<-heightdf[2]#notice how here we use the brackets but no comma
gender1
##    gender
## 1       F
## 2       F
## 3       F
## 4       F
## 5       F
## 6       F
## 7       M
## 8       M
## 9       M
## 10      M
## 11      M
## 12      M
gender2<-heightdf$gender#this does the same thing #notice this way we loose the data architecture - no longer a dataframe
gender2
##  [1] F F F F F F M M M M M M
## Levels: F M
gender2<-data.frame(gender =heightdf$gender)# this however stays as a dataframe
gender2
##    gender
## 1       F
## 2       F
## 3       F
## 4       F
## 5       F
## 6       F
## 7       M
## 8       M
## 9       M
## 10      M
## 11      M
## 12      M
genderindex<- which(colnames(heightdf) == "gender") #now wwe will use which() to select columns named gender
genderindex
## [1] 2
gender3 <-heightdf[genderindex]#now we will use the brackets to grab just this column
gender3
##    gender
## 1       F
## 2       F
## 3       F
## 4       F
## 5       F
## 6       F
## 7       M
## 8       M
## 9       M
## 10      M
## 11      M
## 12      M
identical(gender1, gender2)#lets see if they are identical - this is a helpful function - can only compare two variables at a time
## [1] TRUE
gender1==gender2 # are they the same? should say true if they are
##       gender
##  [1,]   TRUE
##  [2,]   TRUE
##  [3,]   TRUE
##  [4,]   TRUE
##  [5,]   TRUE
##  [6,]   TRUE
##  [7,]   TRUE
##  [8,]   TRUE
##  [9,]   TRUE
## [10,]   TRUE
## [11,]   TRUE
## [12,]   TRUE
gender2==gender3 # are they the same? should say true if they are
##       gender
##  [1,]   TRUE
##  [2,]   TRUE
##  [3,]   TRUE
##  [4,]   TRUE
##  [5,]   TRUE
##  [6,]   TRUE
##  [7,]   TRUE
##  [8,]   TRUE
##  [9,]   TRUE
## [10,]   TRUE
## [11,]   TRUE
## [12,]   TRUE

Now let’s try to look at/grab specific values.

height2<-c(6, 5.5, 6, 6, 6, 6, 4.3) #6 and 5.5 are in our orignal height vector but not 4.3
which(height %in% height2) # what of our orignial data is also found in height2
## [1]  1  7 11
heightdf[which(height %in% height2),] # here we skipped making another variable for the index
##    height gender
## 1     5.5      F
## 7     6.0      M
## 11    6.0      M
#we can also use a function clalled grep
wanted_heights_index<-grep(5.9, heightdf$height)
heightdf[wanted_heights_index,] #now we just have the samples who are 5.9
##    height gender
## 9     5.9      M
## 12    5.9      M
#say we want to know the value of an element at a particular location
heightdf$height[2] #second value in the height column
## [1] 4.5
heightdf$height[1:3]# first three valeus in the height column
## [1] 5.5 4.5 5.0

This allows you to grab random data points.

sample(heightdf$height, 2)#takes a random sample from a vector of the specified number of elements
## [1] 5.5 5.9
sample.int(1000999, 2)#takes a random sample from 1 to the first whole number specified. Thue number of random values to output is given by the second number.
## [1] 161093 430020

7) Plotting Data

Now let’s try plotting some data and perform some statistical tests.

boxplot(heightdf$height~heightdf$gender)#simple boxplot

#lets make a fancy boxplot
boxplot(heightdf$height~heightdf$gender, col = c("red", "blue"), ylab = "Height", xlab = "Gender", main = "Relationship of gender and height", cex.lab =2, cex.main = 2, cex.axis = 1.3, par(mar=c(5, 5, 5, 5)))

hist(heightdf$height)#histogram

heightdf$age <-c(20, 30, 15, 20, 40, 14, 35, 40, 17, 16, 25, 16)##adding another variable to a dataframe
plot(heightdf$height)#one variable

plot(y=heightdf$height, x=heightdf$age)#scatterplot of 2 variables
height_age<-lm(heightdf$height~heightdf$age)#perform a regression on the data - evaluate height and age relationship
summary(height_age)#shows the stats results from the regression
## 
## Call:
## lm(formula = heightdf$height ~ heightdf$age)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1999 -0.1154  0.1348  0.3634  0.3943 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   5.28384    0.39367  13.422 1.01e-07 ***
## heightdf$age  0.01387    0.01527   0.908    0.385    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4973 on 10 degrees of freedom
## Multiple R-squared:  0.07616,    Adjusted R-squared:  -0.01622 
## F-statistic: 0.8244 on 1 and 10 DF,  p-value: 0.3853
abline(height_age)#add regression line to plot

cor.test(y=heightdf$height, x=heightdf$age)#shows the same p value when performing a correlation test
## 
##  Pearson's product-moment correlation
## 
## data:  heightdf$age and heightdf$height
## t = 0.90797, df = 10, p-value = 0.3853
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3539944  0.7336745
## sample estimates:
##       cor 
## 0.2759734

8) More Statistical Tests

t.test(heightdf$height~heightdf$gender)#try a t test between male height and female height - this is significant!
## 
##  Welch Two Sample t-test
## 
## data:  heightdf$height by heightdf$gender
## t = -3.4903, df = 5.8325, p-value = 0.01359
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.194177 -0.205823
## sample estimates:
## mean in group F mean in group M 
##        5.266667        5.966667
#if p<0.05 it is generally considered significant
fit <-aov(heightdf$height~heightdf$gender + heightdf$age)#now lets perform an anova or multiple regression
summary(fit)# here are the results
##                 Df Sum Sq Mean Sq F value  Pr(>F)   
## heightdf$gender  1 1.4700  1.4700  12.167 0.00685 **
## heightdf$age     1 0.1193  0.1193   0.987 0.34638   
## Residuals        9 1.0874  0.1208                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(fit)# same results
## Analysis of Variance Table
## 
## Response: heightdf$height
##                 Df  Sum Sq Mean Sq F value   Pr(>F)   
## heightdf$gender  1 1.47000 1.47000 12.1668 0.006851 **
## heightdf$age     1 0.11928 0.11928  0.9872 0.346383   
## Residuals        9 1.08739 0.12082                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit <-lm(heightdf$height~heightdf$gender + heightdf$age)# performing as multiple regression
summary(fit) #gives the same result as above - this is an anova but the results are presented differently
## 
## Call:
## lm(formula = heightdf$height ~ heightdf$gender + heightdf$age)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.83944 -0.07318  0.02918  0.12062  0.36706 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       5.01995    0.28600  17.552 2.86e-08 ***
## heightdf$genderM  0.68225    0.20148   3.386  0.00805 ** 
## heightdf$age      0.01065    0.01072   0.994  0.34638    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3476 on 9 degrees of freedom
## Multiple R-squared:  0.5938, Adjusted R-squared:  0.5035 
## F-statistic: 6.577 on 2 and 9 DF,  p-value: 0.01736
anova(fit)#also gives the same result
## Analysis of Variance Table
## 
## Response: heightdf$height
##                 Df  Sum Sq Mean Sq F value   Pr(>F)   
## heightdf$gender  1 1.47000 1.47000 12.1668 0.006851 **
## heightdf$age     1 0.11928 0.11928  0.9872 0.346383   
## Residuals        9 1.08739 0.12082                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Let’s do a more classic anova – using a categorical variable with more than two categories.

heightdf$country <-c("British", "French", "British", "Dutch", "Dutch", "French", "Dutch", "Dutch", "British", "French", "British", "French")
fit <-aov(heightdf$height~heightdf$gender + heightdf$age + heightdf$country)
summary(fit)
##                  Df Sum Sq Mean Sq F value  Pr(>F)   
## heightdf$gender   1 1.4700  1.4700  19.157 0.00325 **
## heightdf$age      1 0.1193  0.1193   1.554 0.25258   
## heightdf$country  2 0.5503  0.2751   3.586 0.08471 . 
## Residuals         7 0.5371  0.0767                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit <-aov(heightdf$height~ heightdf$country)
summary(fit)
##                  Df Sum Sq Mean Sq F value Pr(>F)
## heightdf$country  2 0.6067  0.3033   1.319  0.315
## Residuals         9 2.0700  0.2300
anova(fit)# we see the results of country but not each country
## Analysis of Variance Table
## 
## Response: heightdf$height
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## heightdf$country  2 0.60667 0.30333  1.3188 0.3146
## Residuals         9 2.07000 0.23000
TukeyHSD(fit)# this is how we get these results - none are significant
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = heightdf$height ~ heightdf$country)
## 
## $`heightdf$country`
##                 diff        lwr       upr     p adj
## Dutch-British   0.30 -0.6468152 1.2468152 0.6627841
## French-British -0.25 -1.1968152 0.6968152 0.7484769
## French-Dutch   -0.55 -1.4968152 0.3968152 0.2860337
fit <-lm(heightdf$height~heightdf$gender + heightdf$age + heightdf$country)
summary(fit) #gives the same result as above - this is an anova just different output
## 
## Call:
## lm(formula = heightdf$height ~ heightdf$gender + heightdf$age + 
##     heightdf$country)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.36474 -0.13454  0.03405  0.15333  0.33097 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             5.46051    0.28225  19.346 2.46e-07 ***
## heightdf$genderM        0.71905    0.16131   4.458  0.00294 ** 
## heightdf$age           -0.01143    0.01263  -0.905  0.39547    
## heightdf$countryDutch   0.46574    0.26813   1.737  0.12596    
## heightdf$countryFrench -0.25286    0.19590  -1.291  0.23778    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.277 on 7 degrees of freedom
## Multiple R-squared:  0.7993, Adjusted R-squared:  0.6847 
## F-statistic: 6.971 on 4 and 7 DF,  p-value: 0.01375

Baby Name Data

This is a very fun package to check out. If you have ever wondered about the popularity of your name or someone that you know, you will find this very interesting. I also have some friends who have used it to help them name their child.

#recall that we installed and loaded this data earlier
head(babynames)#this is a special data type called a tibble - it is basically a fancy dataframe
## # A tibble: 6 x 5
##    year sex   name          n   prop
##   <dbl> <chr> <chr>     <int>  <dbl>
## 1 1880. F     Mary       7065 0.0724
## 2 1880. F     Anna       2604 0.0267
## 3 1880. F     Emma       2003 0.0205
## 4 1880. F     Elizabeth  1939 0.0199
## 5 1880. F     Minnie     1746 0.0179
## 6 1880. F     Margaret   1578 0.0162
tail(babynames)# we can see the data goes up to 2015
## # A tibble: 6 x 5
##    year sex   name       n       prop
##   <dbl> <chr> <chr>  <int>      <dbl>
## 1 2015. M     Zyah       5 0.00000247
## 2 2015. M     Zykell     5 0.00000247
## 3 2015. M     Zyking     5 0.00000247
## 4 2015. M     Zykir      5 0.00000247
## 5 2015. M     Zyrus      5 0.00000247
## 6 2015. M     Zyus       5 0.00000247
#how many unique baby names are there?
length(unique(babynames$name))# that's a lot of baby names!
## [1] 95025
#check to see if your name is included
grep("Bob", unique(babynames$name))#looks like bob is in there
##  [1]  1148  2502  4948  6510  6573  6999  9443 10761 13598 13794 18059
## [12] 18701 19278 19812 20116 20921 22002 23289 26242 27453 30231 34262
## [23] 35357 37057 37702 38171 41808 42382 43247 44135 44778 46568 50097
#Let's look at the values
babynames$name [grep("Bob", unique(babynames$name))] # this is a vector so we don't need to specify rows with a comma
##  [1] "Scott"     "Tessie"    "Sadye"     "Una"       "Philomena"
##  [6] "Belva"     "Rufus"     "Dovie"     "Janette"   "Mammie"   
## [11] "Melinda"   "Honor"     "Arch"      "Denis"     "Orrie"    
## [16] "Floyd"     "Al"        "Selina"    "Clora"     "Elvin"    
## [21] "Lafayette" "Lovie"     "Armilda"   "Nola"      "Icy"      
## [26] "Mahalia"   "Gordon"    "Seth"      "Claudia"   "Glada"    
## [31] "Floyd"     "Theodora"  "Vella"
# oops! this didn't work. why? because were aren't subsetting with an index derived from the data
unique(babynames$name) [grep("Bob", unique(babynames$name))] # here we go
##  [1] "Bob"        "Bobbie"     "Bobby"      "Bobie"      "Bobbye"    
##  [6] "Bobbe"      "Bobette"    "Bobetta"    "Boby"       "Bobbette"  
## [11] "Bobbi"      "Bobo"       "Bobra"      "Bobi"       "Bobbee"    
## [16] "Bobb"       "Bobbetta"   "Bobbyetta"  "Bobbijo"    "Bobbiejo"  
## [21] "Bobbyjo"    "Bobbiejean" "Bobbilynn"  "Boban"      "Bobijo"    
## [26] "Bobbyjoe"   "Bobak"      "Bobbilee"   "Bobbisue"   "Bobbiesue" 
## [31] "Boback"     "Bobbylee"   "Bobbielee"
#now we can see all the variations of Bob in the data
Bob<- subset(babynames,babynames$name == "Bob")
#Let's see how much the name has been used in the past
plot(Bob$n~Bob$year)#Bob was popular but it isn't anymore  

#what is the line of samples at the bottom?
plot(Bob$n~Bob$year, col= c("red", "blue")[as.factor(Bob$sex)]) 

#looks like most people named Bob were male, the line of samples at the bottom are females
#lets try another name
Lori<- subset(babynames,babynames$name == "Lori")
plot(Lori$n~Lori$year, col= c("red", "blue")[as.factor(Lori$sex)])

Lori_M<- subset(babynames,name == "Lori" & sex == "M")#lets look at when exactly some males were named Lori
head(Lori_M)
## # A tibble: 6 x 5
##    year sex   name      n       prop
##   <dbl> <chr> <chr> <int>      <dbl>
## 1 1954. M     Lori      5 0.00000242
## 2 1955. M     Lori      5 0.00000239
## 3 1956. M     Lori     14 0.00000653
## 4 1957. M     Lori     20 0.00000914
## 5 1958. M     Lori     25 0.0000116 
## 6 1959. M     Lori     29 0.0000134
# lets see how many samples are present for each year in the data
table(babynames$year)# so there are 2000 samples from 1880 and 1935 samples from 1881
## 
##  1880  1881  1882  1883  1884  1885  1886  1887  1888  1889  1890  1891 
##  2000  1935  2127  2084  2297  2294  2392  2373  2651  2590  2695  2660 
##  1892  1893  1894  1895  1896  1897  1898  1899  1900  1901  1902  1903 
##  2921  2831  2941  3049  3091  3028  3264  3042  3731  3153  3362  3389 
##  1904  1905  1906  1907  1908  1909  1910  1911  1912  1913  1914  1915 
##  3561  3655  3633  3948  4018  4227  4629  4867  6351  6967  7964  9358 
##  1916  1917  1918  1919  1920  1921  1922  1923  1924  1925  1926  1927 
##  9696  9915 10401 10368 10756 10856 10757 10641 10869 10641 10460 10405 
##  1928  1929  1930  1931  1932  1933  1934  1935  1936  1937  1938  1939 
## 10159  9816  9788  9293  9383  9011  9181  9035  8893  8945  9030  8919 
##  1940  1941  1942  1943  1944  1945  1946  1947  1948  1949  1950  1951 
##  8960  9087  9424  9405  9153  9026  9702 10370 10237 10264 10309 10460 
##  1952  1953  1954  1955  1956  1957  1958  1959  1960  1961  1962  1963 
## 10654 10831 10963 11114 11339 11564 11521 11771 11924 12178 12206 12278 
##  1964  1965  1966  1967  1968  1969  1970  1971  1972  1973  1974  1975 
## 12394 11953 12148 12400 12930 13746 14782 15291 15414 15676 16243 16934 
##  1976  1977  1978  1979  1980  1981  1982  1983  1984  1985  1986  1987 
## 17395 18171 18224 19032 19439 19470 19680 19398 19501 20076 20642 21399 
##  1988  1989  1990  1991  1992  1993  1994  1995  1996  1997  1998  1999 
## 22360 23769 24715 25104 25421 25959 25998 26080 26420 26966 27894 28546 
##  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011 
## 29764 30264 30559 31179 32040 32539 34073 34941 35051 34689 34050 33880 
##  2012  2013  2014  2015 
## 33697 33229 33176 32952
# Ok, so more samples were included in the more recent years
hist(babynames$year)

Let’s look at some other data…

Titanic Data

This package contains real data about the passengers that were aboard the Titanic.

head(Titanic) # we can see that this may be an unusal data type
## [1]  0  0 35  0  0  0
class(Titanic) # indeed this appears to be a table
## [1] "table"
dim(Titanic)
## [1] 4 2 2 2
dimnames(Titanic)
## $Class
## [1] "1st"  "2nd"  "3rd"  "Crew"
## 
## $Sex
## [1] "Male"   "Female"
## 
## $Age
## [1] "Child" "Adult"
## 
## $Survived
## [1] "No"  "Yes"
str(Titanic) # shows the structure of the data
##  table [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ...
##  - attr(*, "dimnames")=List of 4
##   ..$ Class   : chr [1:4] "1st" "2nd" "3rd" "Crew"
##   ..$ Sex     : chr [1:2] "Male" "Female"
##   ..$ Age     : chr [1:2] "Child" "Adult"
##   ..$ Survived: chr [1:2] "No" "Yes"
help("titanic_test")#this shows more information about the data
help("titanic_train")# I would assume survived is a 1
#lets see if more males or females survived
boxplot(titanic_train$Survived~titanic_train$Sex)

table(titanic_train$Survived,titanic_train$Sex)# it looks like males laregly did not survive
##    
##     female male
##   0     81  468
##   1    233  109
# this might be a better way to view the data - here width represents the number of samples - thus there are more males overall
mosaicplot(Sex ~ Survived, data=titanic_train) 

# It looks like even though there were many more males, the female passangers were much more likely to survive

How about some more data…

Earthquake Data

Here we will scrape data (or obtain data from a website) from the USGS website about earthquake rates in different US states. See our previous post from S. Semick on scraping data from research articles for more information on how to do this.

install.packages("htmltab")
install.packages("reshape2")
library(htmltab)
library(reshape2)
## Warning: package 'reshape2' was built under R version 3.4.3
url<-"https://earthquake.usgs.gov/earthquakes/browse/stats.php"
eq <- htmltab(doc = url, which = 5)
## No encoding supplied: defaulting to UTF-8.
rownames(eq)<-eq$States
eq<-eq[-1]
head(eq)
##            2010 2011 2012 2013 2014 2015
## Alabama       1    1    0    0    2    6
## Alaska     2245 1409 1166 1329 1296 1575
## Arizona       6    7    4    3   31   10
## Arkansas     15   44    4    4    1    0
## California  546  195  243  240  191  130
## Colorado      4   23    7    2   13    7
eq2 <- as.data.frame(sapply(eq, function(x) as.numeric(as.character(x))))
head(eq2)
##   2010 2011 2012 2013 2014 2015
## 1    1    1    0    0    2    6
## 2 2245 1409 1166 1329 1296 1575
## 3    6    7    4    3   31   10
## 4   15   44    4    4    1    0
## 5  546  195  243  240  191  130
## 6    4   23    7    2   13    7
convertoChar<-function(x) as.numeric(as.character(x)) # or you could create a function to use multiple times
factor_to_fix<-as.factor(c(1,2))
class(factor_to_fix)
## [1] "factor"
class(trying_function<-convertoChar(x=factor_to_fix))# now the class is numeric
## [1] "numeric"
class(trying_function2<-convertoChar(factor_to_fix))# now the class is numeric
## [1] "numeric"
rownames(eq2)<-rownames(eq)
head(eq2)
##            2010 2011 2012 2013 2014 2015
## Alabama       1    1    0    0    2    6
## Alaska     2245 1409 1166 1329 1296 1575
## Arizona       6    7    4    3   31   10
## Arkansas     15   44    4    4    1    0
## California  546  195  243  240  191  130
## Colorado      4   23    7    2   13    7
colSums(eq2)#look at the col sums
## 2010 2011 2012 2013 2014 2015 
## 3026 1955 1603 1899 2628 3225
colMeans(eq2)# look at the col means
##  2010  2011  2012  2013  2014  2015 
## 60.52 39.10 32.06 37.98 52.56 64.50
rowMeans(eq2)
##        Alabama         Alaska        Arizona       Arkansas     California 
##      1.6666667   1503.3333333     10.1666667     11.3333333    257.5000000 
##       Colorado    Connecticut       Delaware        Florida        Georgia 
##      9.3333333      0.1666667      0.0000000      0.0000000      0.0000000 
##         Hawaii          Idaho       Illinois        Indiana           Iowa 
##     33.3333333     15.8333333      0.8333333      0.6666667      0.0000000 
##         Kansas       Kentucky      Louisiana          Maine       Maryland 
##     17.3333333      0.3333333      0.1666667      0.5000000      0.1666667 
##  Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
##      0.0000000      0.3333333      0.1666667      0.5000000      2.1666667 
##        Montana       Nebraska         Nevada  New Hampshire     New Jersey 
##     14.8333333      1.0000000     85.5000000      0.1666667      0.0000000 
##     New Mexico       New York North Carolina   North Dakota           Ohio 
##      6.3333333      0.5000000      0.1666667      0.1666667      1.0000000 
##       Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
##    285.6666667      2.8333333      0.0000000      0.0000000      0.5000000 
##   South Dakota      Tennessee          Texas           Utah        Vermont 
##      1.0000000      1.3333333     13.8333333     11.5000000      0.0000000 
##       Virginia     Washington  West Virginia      Wisconsin        Wyoming 
##      2.1666667     10.0000000      0.3333333      0.0000000     84.6666667
rowSums(eq2)
##        Alabama         Alaska        Arizona       Arkansas     California 
##             10           9020             61             68           1545 
##       Colorado    Connecticut       Delaware        Florida        Georgia 
##             56              1              0              0              0 
##         Hawaii          Idaho       Illinois        Indiana           Iowa 
##            200             95              5              4              0 
##         Kansas       Kentucky      Louisiana          Maine       Maryland 
##            104              2              1              3              1 
##  Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
##              0              2              1              3             13 
##        Montana       Nebraska         Nevada  New Hampshire     New Jersey 
##             89              6            513              1              0 
##     New Mexico       New York North Carolina   North Dakota           Ohio 
##             38              3              1              1              6 
##       Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
##           1714             17              0              0              3 
##   South Dakota      Tennessee          Texas           Utah        Vermont 
##              6              8             83             69              0 
##       Virginia     Washington  West Virginia      Wisconsin        Wyoming 
##             13             60              2              0            508
max(eq2)# maximum value
## [1] 2245
boxplot(eq2)

boxplot(eq2, ylim = c(0,40))# change the limit of the plot

boxplot(t(eq2), ylim =c(0,2000))#flip the data using t()

eq3<-melt(eq2)#this puts the data in long form
## No id variables; using all as measure variables
names(eq3)<-c("year", "quakes")
head(eq3)
##   year quakes
## 1 2010      1
## 2 2010   2245
## 3 2010      6
## 4 2010     15
## 5 2010    546
## 6 2010      4
fit<-aov(eq3$quakes~eq3$year)
summary(fit)#no significant difference by year
##              Df   Sum Sq Mean Sq F value Pr(>F)
## eq3$year      5    44161    8832   0.169  0.974
## Residuals   294 15405834   52401

In addition, these are functions that the members of LIBD Rstats use often:

• head() / tail() – see the head and the tail – also check out the corner function of the jaffelab package created by LIBD Rstats founding member E. Burke
• colnames() / rownames() – see and rename columns or row names
• colMeans() / rowMeans() / colSums() / rowSums() – get means and sums of columns and rows
• dim() and length() – determine the dimensions/size of a data set – need to use length() when evaluating a vector
• ncol() / nrow() – number of columns and rows
• str() – displays the structure of an object – this is very useful with complex data structures
• unique()/duplicated() – find unique and duplicated values
• order()/sort()– order and sort your data
• gsub() – replace values
• table() – summarize your data in table format
• t.test() – perform a t test
• cor.test() – perform a correlation test
• lm() – make a linear model
• summary() – if you use the lm() output – this will give you the results
• set.seed() – allows for random permutations or random data to be the same every time your run your code

Free courses and tutorials

• https://www.coursera.org/courses?query=r
• https://www.pluralsight.com/courses/r-programming-fundamentals
• https://www.rstudio.com/online-learning/

Community support

• https://community.rstudio.com/
• https://support.bioconductor.org/
• https://resources.rstudio.com/webinars/help-me-help-you-creating-reproducible-examples-jenny-bryan
• link helps you make examples of code errors that you need help with

Tips for help

• https://www.r-project.org/help.html
• Google! – stackoverflow, biostars
• https://www.rstudio.com/resources/cheatsheets/

Also follow our blog for more helpful posts.

Acknowledgements

This blog post was made possible thanks to:

• BiocStyle (Oleś, Morgan, and Huber, 2017)
• blogdown (Xie, Hill, and Thomas, 2017)
• knitcitations (Boettiger, 2017)
• sessioninfo (Csárdi, core, Wickham, Chang, et al., 2018)

References

[1] C. Boettiger. knitcitations: Citations for ‘Knitr’ Markdown Files. R package version 1.0.8. 2017. URL: https://CRAN.R-project.org/package=knitcitations.

[2] G. Csárdi, R. core, H. Wickham, W. Chang, et al. sessioninfo: R Session Information. R package version 1.1.1. 2018. URL: https://CRAN.R-project.org/package=sessioninfo.

[3] A. Oleś, M. Morgan and W. Huber. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.6.1. 2017. URL: https://github.com/Bioconductor/BiocStyle.

[4] Y. Xie, A. P. Hill and A. Thomas. blogdown: Creating Websites with R Markdown. ISBN 978-0815363729. Boca Raton, Florida: Chapman and Hall/CRC, 2017. URL: https://github.com/rstudio/blogdown.

Reproducibility

## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.4.0 (2017-04-21)
##  os       macOS Sierra 10.12.6        
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/New_York            
##  date     2018-11-19                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
##  package       * version   date       lib source                           
##  assertthat      0.2.0     2017-04-11 [1] CRAN (R 3.4.0)                   
##  babynames     * 0.3.0     2017-04-14 [1] CRAN (R 3.4.0)                   
##  backports       1.1.2     2018-04-18 [1] Github (r-lib/backports@cee9348) 
##  bibtex          0.4.2     2017-06-30 [1] CRAN (R 3.4.1)                   
##  bindr           0.1       2016-11-13 [1] CRAN (R 3.4.0)                   
##  bindrcpp        0.2       2017-06-17 [1] CRAN (R 3.4.0)                   
##  BiocStyle     * 2.6.1     2017-11-30 [1] Bioconductor                     
##  blogdown        0.5.9     2018-03-08 [1] Github (rstudio/blogdown@dc1f41c)
##  bookdown        0.7       2018-02-18 [1] CRAN (R 3.4.3)                   
##  cli             1.0.0     2017-11-05 [1] CRAN (R 3.4.2)                   
##  crayon          1.3.4     2017-09-16 [1] CRAN (R 3.4.1)                   
##  curl            3.2       2018-03-28 [1] CRAN (R 3.4.4)                   
##  digest          0.6.15    2018-01-28 [1] CRAN (R 3.4.3)                   
##  dplyr         * 0.7.4     2017-09-28 [1] CRAN (R 3.4.2)                   
##  evaluate        0.11      2018-07-17 [1] CRAN (R 3.4.4)                   
##  glue            1.3.0     2018-07-17 [1] CRAN (R 3.4.4)                   
##  htmltab       * 0.7.1     2016-12-29 [1] CRAN (R 3.4.0)                   
##  htmltools       0.3.6     2017-04-28 [1] CRAN (R 3.4.0)                   
##  httr            1.3.1     2017-08-20 [1] CRAN (R 3.4.1)                   
##  jsonlite        1.5       2017-06-01 [1] CRAN (R 3.4.0)                   
##  knitcitations * 1.0.8     2017-07-04 [1] CRAN (R 3.4.1)                   
##  knitr           1.20      2018-02-20 [1] CRAN (R 3.4.3)                   
##  lubridate       1.7.4     2018-04-11 [1] CRAN (R 3.4.4)                   
##  magrittr        1.5       2014-11-22 [1] CRAN (R 3.4.0)                   
##  pillar          1.2.1     2018-02-27 [1] CRAN (R 3.4.3)                   
##  pkgconfig       2.0.1     2017-03-21 [1] CRAN (R 3.4.0)                   
##  plyr            1.8.4     2016-06-08 [1] CRAN (R 3.4.0)                   
##  R6              2.2.2     2017-06-17 [1] CRAN (R 3.4.0)                   
##  Rcpp            0.12.16   2018-03-13 [1] CRAN (R 3.4.4)                   
##  RefManageR      1.2.0     2018-04-25 [1] CRAN (R 3.4.4)                   
##  reshape2      * 1.4.3     2017-12-11 [1] CRAN (R 3.4.3)                   
##  rlang           0.2.0     2018-02-20 [1] CRAN (R 3.4.3)                   
##  rmarkdown       1.10      2018-06-11 [1] CRAN (R 3.4.4)                   
##  rprojroot       1.3-2     2018-01-03 [1] CRAN (R 3.4.3)                   
##  sessioninfo   * 1.1.1     2018-11-05 [1] CRAN (R 3.4.4)                   
##  stringi         1.2.4     2018-07-20 [1] CRAN (R 3.4.4)                   
##  stringr         1.3.1     2018-05-10 [1] CRAN (R 3.4.4)                   
##  tibble          1.4.2     2018-01-22 [1] CRAN (R 3.4.3)                   
##  titanic       * 0.1.0     2015-08-31 [1] CRAN (R 3.4.0)                   
##  utf8            1.1.3     2018-01-03 [1] CRAN (R 3.4.3)                   
##  withr           2.1.2     2018-03-15 [1] CRAN (R 3.4.4)                   
##  xfun            0.3       2018-07-06 [1] CRAN (R 3.4.4)                   
##  XML             3.98-1.10 2018-02-19 [1] CRAN (R 3.4.3)                   
##  xml2            1.2.0     2018-01-24 [1] CRAN (R 3.4.3)                   
##  yaml            2.2.0     2018-07-25 [1] CRAN (R 3.4.4)                   
## 
## [1] /Library/Frameworks/R.framework/Versions/3.4/Resources/library