Study Guide
Sean Cranston
11/30/2020
1.
Create a vector. Access certain elements of it. know as.numeric FUN.
V <- c(1:10)
#its already numeric but if class(vector) ~= "numeric" we would do
as.numeric(V)## [1] 1 2 3 4 5 6 7 8 9 10V[3:5]## [1] 3 4 52.
Create a data frame. access certain columns/rows. Know difference between mtcars[,“mpg”], mtcars[“mpg”], mtcars$mpg, mtcars$“mpg”.
DF <- data.frame(Id = c(1:3),
class = c("Math", "STAT", "CSCI"),
students = c(20,25,30))
DF## Id class students
## 1 1 Math 20
## 2 2 STAT 25
## 3 3 CSCI 30mtcars[,"mpg"] #returns values associated with mpg## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4mtcars["mpg"] #returns data.frame with id col. and mpg col.## mpg
## Mazda RX4 21.0
## Mazda RX4 Wag 21.0
## Datsun 710 22.8
## Hornet 4 Drive 21.4
## Hornet Sportabout 18.7
## Valiant 18.1
## Duster 360 14.3
## Merc 240D 24.4
## Merc 230 22.8
## Merc 280 19.2
## Merc 280C 17.8
## Merc 450SE 16.4
## Merc 450SL 17.3
## Merc 450SLC 15.2
## Cadillac Fleetwood 10.4
## Lincoln Continental 10.4
## Chrysler Imperial 14.7
## Fiat 128 32.4
## Honda Civic 30.4
## Toyota Corolla 33.9
## Toyota Corona 21.5
## Dodge Challenger 15.5
## AMC Javelin 15.2
## Camaro Z28 13.3
## Pontiac Firebird 19.2
## Fiat X1-9 27.3
## Porsche 914-2 26.0
## Lotus Europa 30.4
## Ford Pantera L 15.8
## Ferrari Dino 19.7
## Maserati Bora 15.0
## Volvo 142E 21.4mtcars$mpg #returns values associated with mpg## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4mtcars$"mpg" #returns values associated with mpg## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.43.
Create a list. Access certain elements of it.
L <- list(subj = "STAT", num = "360", something = "awesome")
L[[1]] # first object in list ## [1] "STAT"L$num #get all the num values## [1] "360"4.
Write a function to convert the first letter of each string in a given character vector to uppercase.
ex = names(mtcars)
f <- function(x){
UP = toupper(x) %>% substr(.,start = 1,stop = 1)
low = substr(x,start = 2, stop = 1000000L)
return(paste0(UP,low))
}
f(ex)## [1] "Mpg" "Cyl" "Disp" "Hp" "Drat" "Wt" "Qsec" "Vs" "Am" "Gear"
## [11] "Carb"5.
Plot, in one graph with the “highcharter” package, the trajectories of confirmed cases for these countries USA, Brazil, Russia, India, and China.
library(highcharter)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoox <- c(rnorm(10000), rnorm(1000, 4, 0.5))
hchart(x, name = "data") 6.
Use ggplot to create bar charts,lines,scatterplots (with fitted lines), boxplots, and side-by-side plots with legends. know how to use labs(), theme(), facet_grid(), facet_wrap()
##bar charts
a <- ggplot(mpg) + geom_bar(aes(y = class))+
labs(x = "COUNT",
y = "CLASS",
title = "title")+
theme(plot.title = element_text(hjust = 0.5))
#this is just to get some cont. data
x <- seq(0.01, .99, length.out = 100)
df <- data.frame(
x = rep(x, 2),
y = c(qlogis(x), 2 * qlogis(x)),
group = rep(c("a","b"),
each = 100)
)
b <- ggplot(df, aes(x=x, y=y, group=group))+
geom_line(aes(colour = group), linetype = 2)
## scatterplot with fitted line
c <- ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +
geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm",
col = "#C42126",
se = FALSE,
size = 1)
##boxplots
d <- ggplot(mpg, aes(class, hwy))+
geom_boxplot()
##facet_grid()
e <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
geom_point() +
facet_grid(vars(cyl), scales = "free")
## facet_wrap()
f <- ggplot(mpg, aes(displ, hwy)) + geom_point()+
facet_wrap(vars(class))7.
Make plots interactive with Plotly.
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggiris <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species)
ggplotly(ggiris)8.
shiny app involving reactive() Refer to Project 10 for an example.A reactive expression is simply a potential part of a shiny server that is capable of updating variables for other parts of the server if the input changes.
9.
Create simple dashboard with the flexdashboard package. Refer to Project5 for an example. Made in an rmarkdown file. great reference at https://rmarkdown.rstudio.com/flexdashboard/using.html#overview. Dashboard is very nice for presenting data and you can even run shiny on it.
10.
choropleth map
library(maps)
mapStates = map("state", fill = TRUE, plot = FALSE)
leaflet(data = mapStates) %>% addTiles() %>%
addPolygons(fillColor = topo.colors(10, alpha = NULL), stroke = FALSE)11.
Use dplry to rename, subset, group_by, arranging, mutate, summarize, count, and so forth
library(dplyr)
rename(iris, petal_length = Petal.Length) # how to rename## Sepal.Length Sepal.Width petal_length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicasubset(airquality, Day == 1, select = -Temp) # get only data with day == 1 and all columns except temp## Ozone Solar.R Wind Month Day
## 1 41 190 7.4 5 1
## 32 NA 286 8.6 6 1
## 62 135 269 4.1 7 1
## 93 39 83 6.9 8 1
## 124 96 167 6.9 9 1by_cyl <- mtcars %>% group_by(cyl)
summarise(by_cyl, disp = mean(disp),hp = mean(hp)) #Group_by changes how to dataframe acts with other dplyr verbs## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 3 x 3
## cyl disp hp
## <dbl> <dbl> <dbl>
## 1 4 105. 82.6
## 2 6 183. 122.
## 3 8 353. 209.arrange(mtcars, cyl, disp) #orders the columns from least to greatest## mpg cyl disp hp drat wt qsec vs am gear carb
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4starwars %>% select(name, mass) %>% mutate(mass2 = mass * 2, mass2_squared = mass2 * mass2)## # A tibble: 87 x 4
## name mass mass2 mass2_squared
## <chr> <dbl> <dbl> <dbl>
## 1 Luke Skywalker 77 154 23716
## 2 C-3PO 75 150 22500
## 3 R2-D2 32 64 4096
## 4 Darth Vader 136 272 73984
## 5 Leia Organa 49 98 9604
## 6 Owen Lars 120 240 57600
## 7 Beru Whitesun lars 75 150 22500
## 8 R5-D4 32 64 4096
## 9 Biggs Darklighter 84 168 28224
## 10 Obi-Wan Kenobi 77 154 23716
## # ... with 77 more rowsstarwars %>% count(species)## # A tibble: 38 x 2
## species n
## <chr> <int>
## 1 Aleena 1
## 2 Besalisk 1
## 3 Cerean 1
## 4 Chagrian 1
## 5 Clawdite 1
## 6 Droid 6
## 7 Dug 1
## 8 Ewok 1
## 9 Geonosian 1
## 10 Gungan 3
## # ... with 28 more rows12.
Using the melt() function from the reshape2 package.
library(reshape2)
melt(airquality, id = c("Month", "Day"))## Month Day variable value
## 1 5 1 Ozone 41.0
## 2 5 2 Ozone 36.0
## 3 5 3 Ozone 12.0
## 4 5 4 Ozone 18.0
## 5 5 5 Ozone NA
## 6 5 6 Ozone 28.0
## 7 5 7 Ozone 23.0
## 8 5 8 Ozone 19.0
## 9 5 9 Ozone 8.0
## 10 5 10 Ozone NA
## 11 5 11 Ozone 7.0
## 12 5 12 Ozone 16.0
## 13 5 13 Ozone 11.0
## 14 5 14 Ozone 14.0
## 15 5 15 Ozone 18.0
## 16 5 16 Ozone 14.0
## 17 5 17 Ozone 34.0
## 18 5 18 Ozone 6.0
## 19 5 19 Ozone 30.0
## 20 5 20 Ozone 11.0
## 21 5 21 Ozone 1.0
## 22 5 22 Ozone 11.0
## 23 5 23 Ozone 4.0
## 24 5 24 Ozone 32.0
## 25 5 25 Ozone NA
## 26 5 26 Ozone NA
## 27 5 27 Ozone NA
## 28 5 28 Ozone 23.0
## 29 5 29 Ozone 45.0
## 30 5 30 Ozone 115.0
## 31 5 31 Ozone 37.0
## 32 6 1 Ozone NA
## 33 6 2 Ozone NA
## 34 6 3 Ozone NA
## 35 6 4 Ozone NA
## 36 6 5 Ozone NA
## 37 6 6 Ozone NA
## 38 6 7 Ozone 29.0
## 39 6 8 Ozone NA
## 40 6 9 Ozone 71.0
## 41 6 10 Ozone 39.0
## 42 6 11 Ozone NA
## 43 6 12 Ozone NA
## 44 6 13 Ozone 23.0
## 45 6 14 Ozone NA
## 46 6 15 Ozone NA
## 47 6 16 Ozone 21.0
## 48 6 17 Ozone 37.0
## 49 6 18 Ozone 20.0
## 50 6 19 Ozone 12.0
## 51 6 20 Ozone 13.0
## 52 6 21 Ozone NA
## 53 6 22 Ozone NA
## 54 6 23 Ozone NA
## 55 6 24 Ozone NA
## 56 6 25 Ozone NA
## 57 6 26 Ozone NA
## 58 6 27 Ozone NA
## 59 6 28 Ozone NA
## 60 6 29 Ozone NA
## 61 6 30 Ozone NA
## 62 7 1 Ozone 135.0
## 63 7 2 Ozone 49.0
## 64 7 3 Ozone 32.0
## 65 7 4 Ozone NA
## 66 7 5 Ozone 64.0
## 67 7 6 Ozone 40.0
## 68 7 7 Ozone 77.0
## 69 7 8 Ozone 97.0
## 70 7 9 Ozone 97.0
## 71 7 10 Ozone 85.0
## 72 7 11 Ozone NA
## 73 7 12 Ozone 10.0
## 74 7 13 Ozone 27.0
## 75 7 14 Ozone NA
## 76 7 15 Ozone 7.0
## 77 7 16 Ozone 48.0
## 78 7 17 Ozone 35.0
## 79 7 18 Ozone 61.0
## 80 7 19 Ozone 79.0
## 81 7 20 Ozone 63.0
## 82 7 21 Ozone 16.0
## 83 7 22 Ozone NA
## 84 7 23 Ozone NA
## 85 7 24 Ozone 80.0
## 86 7 25 Ozone 108.0
## 87 7 26 Ozone 20.0
## 88 7 27 Ozone 52.0
## 89 7 28 Ozone 82.0
## 90 7 29 Ozone 50.0
## 91 7 30 Ozone 64.0
## 92 7 31 Ozone 59.0
## 93 8 1 Ozone 39.0
## 94 8 2 Ozone 9.0
## 95 8 3 Ozone 16.0
## 96 8 4 Ozone 78.0
## 97 8 5 Ozone 35.0
## 98 8 6 Ozone 66.0
## 99 8 7 Ozone 122.0
## 100 8 8 Ozone 89.0
## 101 8 9 Ozone 110.0
## 102 8 10 Ozone NA
## 103 8 11 Ozone NA
## 104 8 12 Ozone 44.0
## 105 8 13 Ozone 28.0
## 106 8 14 Ozone 65.0
## 107 8 15 Ozone NA
## 108 8 16 Ozone 22.0
## 109 8 17 Ozone 59.0
## 110 8 18 Ozone 23.0
## 111 8 19 Ozone 31.0
## 112 8 20 Ozone 44.0
## 113 8 21 Ozone 21.0
## 114 8 22 Ozone 9.0
## 115 8 23 Ozone NA
## 116 8 24 Ozone 45.0
## 117 8 25 Ozone 168.0
## 118 8 26 Ozone 73.0
## 119 8 27 Ozone NA
## 120 8 28 Ozone 76.0
## 121 8 29 Ozone 118.0
## 122 8 30 Ozone 84.0
## 123 8 31 Ozone 85.0
## 124 9 1 Ozone 96.0
## 125 9 2 Ozone 78.0
## 126 9 3 Ozone 73.0
## 127 9 4 Ozone 91.0
## 128 9 5 Ozone 47.0
## 129 9 6 Ozone 32.0
## 130 9 7 Ozone 20.0
## 131 9 8 Ozone 23.0
## 132 9 9 Ozone 21.0
## 133 9 10 Ozone 24.0
## 134 9 11 Ozone 44.0
## 135 9 12 Ozone 21.0
## 136 9 13 Ozone 28.0
## 137 9 14 Ozone 9.0
## 138 9 15 Ozone 13.0
## 139 9 16 Ozone 46.0
## 140 9 17 Ozone 18.0
## 141 9 18 Ozone 13.0
## 142 9 19 Ozone 24.0
## 143 9 20 Ozone 16.0
## 144 9 21 Ozone 13.0
## 145 9 22 Ozone 23.0
## 146 9 23 Ozone 36.0
## 147 9 24 Ozone 7.0
## 148 9 25 Ozone 14.0
## 149 9 26 Ozone 30.0
## 150 9 27 Ozone NA
## 151 9 28 Ozone 14.0
## 152 9 29 Ozone 18.0
## 153 9 30 Ozone 20.0
## 154 5 1 Solar.R 190.0
## 155 5 2 Solar.R 118.0
## 156 5 3 Solar.R 149.0
## 157 5 4 Solar.R 313.0
## 158 5 5 Solar.R NA
## 159 5 6 Solar.R NA
## 160 5 7 Solar.R 299.0
## 161 5 8 Solar.R 99.0
## 162 5 9 Solar.R 19.0
## 163 5 10 Solar.R 194.0
## 164 5 11 Solar.R NA
## 165 5 12 Solar.R 256.0
## 166 5 13 Solar.R 290.0
## 167 5 14 Solar.R 274.0
## 168 5 15 Solar.R 65.0
## 169 5 16 Solar.R 334.0
## 170 5 17 Solar.R 307.0
## 171 5 18 Solar.R 78.0
## 172 5 19 Solar.R 322.0
## 173 5 20 Solar.R 44.0
## 174 5 21 Solar.R 8.0
## 175 5 22 Solar.R 320.0
## 176 5 23 Solar.R 25.0
## 177 5 24 Solar.R 92.0
## 178 5 25 Solar.R 66.0
## 179 5 26 Solar.R 266.0
## 180 5 27 Solar.R NA
## 181 5 28 Solar.R 13.0
## 182 5 29 Solar.R 252.0
## 183 5 30 Solar.R 223.0
## 184 5 31 Solar.R 279.0
## 185 6 1 Solar.R 286.0
## 186 6 2 Solar.R 287.0
## 187 6 3 Solar.R 242.0
## 188 6 4 Solar.R 186.0
## 189 6 5 Solar.R 220.0
## 190 6 6 Solar.R 264.0
## 191 6 7 Solar.R 127.0
## 192 6 8 Solar.R 273.0
## 193 6 9 Solar.R 291.0
## 194 6 10 Solar.R 323.0
## 195 6 11 Solar.R 259.0
## 196 6 12 Solar.R 250.0
## 197 6 13 Solar.R 148.0
## 198 6 14 Solar.R 332.0
## 199 6 15 Solar.R 322.0
## 200 6 16 Solar.R 191.0
## 201 6 17 Solar.R 284.0
## 202 6 18 Solar.R 37.0
## 203 6 19 Solar.R 120.0
## 204 6 20 Solar.R 137.0
## 205 6 21 Solar.R 150.0
## 206 6 22 Solar.R 59.0
## 207 6 23 Solar.R 91.0
## 208 6 24 Solar.R 250.0
## 209 6 25 Solar.R 135.0
## 210 6 26 Solar.R 127.0
## 211 6 27 Solar.R 47.0
## 212 6 28 Solar.R 98.0
## 213 6 29 Solar.R 31.0
## 214 6 30 Solar.R 138.0
## 215 7 1 Solar.R 269.0
## 216 7 2 Solar.R 248.0
## 217 7 3 Solar.R 236.0
## 218 7 4 Solar.R 101.0
## 219 7 5 Solar.R 175.0
## 220 7 6 Solar.R 314.0
## 221 7 7 Solar.R 276.0
## 222 7 8 Solar.R 267.0
## 223 7 9 Solar.R 272.0
## 224 7 10 Solar.R 175.0
## 225 7 11 Solar.R 139.0
## 226 7 12 Solar.R 264.0
## 227 7 13 Solar.R 175.0
## 228 7 14 Solar.R 291.0
## 229 7 15 Solar.R 48.0
## 230 7 16 Solar.R 260.0
## 231 7 17 Solar.R 274.0
## 232 7 18 Solar.R 285.0
## 233 7 19 Solar.R 187.0
## 234 7 20 Solar.R 220.0
## 235 7 21 Solar.R 7.0
## 236 7 22 Solar.R 258.0
## 237 7 23 Solar.R 295.0
## 238 7 24 Solar.R 294.0
## 239 7 25 Solar.R 223.0
## 240 7 26 Solar.R 81.0
## 241 7 27 Solar.R 82.0
## 242 7 28 Solar.R 213.0
## 243 7 29 Solar.R 275.0
## 244 7 30 Solar.R 253.0
## 245 7 31 Solar.R 254.0
## 246 8 1 Solar.R 83.0
## 247 8 2 Solar.R 24.0
## 248 8 3 Solar.R 77.0
## 249 8 4 Solar.R NA
## 250 8 5 Solar.R NA
## 251 8 6 Solar.R NA
## 252 8 7 Solar.R 255.0
## 253 8 8 Solar.R 229.0
## 254 8 9 Solar.R 207.0
## 255 8 10 Solar.R 222.0
## 256 8 11 Solar.R 137.0
## 257 8 12 Solar.R 192.0
## 258 8 13 Solar.R 273.0
## 259 8 14 Solar.R 157.0
## 260 8 15 Solar.R 64.0
## 261 8 16 Solar.R 71.0
## 262 8 17 Solar.R 51.0
## 263 8 18 Solar.R 115.0
## 264 8 19 Solar.R 244.0
## 265 8 20 Solar.R 190.0
## 266 8 21 Solar.R 259.0
## 267 8 22 Solar.R 36.0
## 268 8 23 Solar.R 255.0
## 269 8 24 Solar.R 212.0
## 270 8 25 Solar.R 238.0
## 271 8 26 Solar.R 215.0
## 272 8 27 Solar.R 153.0
## 273 8 28 Solar.R 203.0
## 274 8 29 Solar.R 225.0
## 275 8 30 Solar.R 237.0
## 276 8 31 Solar.R 188.0
## 277 9 1 Solar.R 167.0
## 278 9 2 Solar.R 197.0
## 279 9 3 Solar.R 183.0
## 280 9 4 Solar.R 189.0
## 281 9 5 Solar.R 95.0
## 282 9 6 Solar.R 92.0
## 283 9 7 Solar.R 252.0
## 284 9 8 Solar.R 220.0
## 285 9 9 Solar.R 230.0
## 286 9 10 Solar.R 259.0
## 287 9 11 Solar.R 236.0
## 288 9 12 Solar.R 259.0
## 289 9 13 Solar.R 238.0
## 290 9 14 Solar.R 24.0
## 291 9 15 Solar.R 112.0
## 292 9 16 Solar.R 237.0
## 293 9 17 Solar.R 224.0
## 294 9 18 Solar.R 27.0
## 295 9 19 Solar.R 238.0
## 296 9 20 Solar.R 201.0
## 297 9 21 Solar.R 238.0
## 298 9 22 Solar.R 14.0
## 299 9 23 Solar.R 139.0
## 300 9 24 Solar.R 49.0
## 301 9 25 Solar.R 20.0
## 302 9 26 Solar.R 193.0
## 303 9 27 Solar.R 145.0
## 304 9 28 Solar.R 191.0
## 305 9 29 Solar.R 131.0
## 306 9 30 Solar.R 223.0
## 307 5 1 Wind 7.4
## 308 5 2 Wind 8.0
## 309 5 3 Wind 12.6
## 310 5 4 Wind 11.5
## 311 5 5 Wind 14.3
## 312 5 6 Wind 14.9
## 313 5 7 Wind 8.6
## 314 5 8 Wind 13.8
## 315 5 9 Wind 20.1
## 316 5 10 Wind 8.6
## 317 5 11 Wind 6.9
## 318 5 12 Wind 9.7
## 319 5 13 Wind 9.2
## 320 5 14 Wind 10.9
## 321 5 15 Wind 13.2
## 322 5 16 Wind 11.5
## 323 5 17 Wind 12.0
## 324 5 18 Wind 18.4
## 325 5 19 Wind 11.5
## 326 5 20 Wind 9.7
## 327 5 21 Wind 9.7
## 328 5 22 Wind 16.6
## 329 5 23 Wind 9.7
## 330 5 24 Wind 12.0
## 331 5 25 Wind 16.6
## 332 5 26 Wind 14.9
## 333 5 27 Wind 8.0
## 334 5 28 Wind 12.0
## 335 5 29 Wind 14.9
## 336 5 30 Wind 5.7
## 337 5 31 Wind 7.4
## 338 6 1 Wind 8.6
## 339 6 2 Wind 9.7
## 340 6 3 Wind 16.1
## 341 6 4 Wind 9.2
## 342 6 5 Wind 8.6
## 343 6 6 Wind 14.3
## 344 6 7 Wind 9.7
## 345 6 8 Wind 6.9
## 346 6 9 Wind 13.8
## 347 6 10 Wind 11.5
## 348 6 11 Wind 10.9
## 349 6 12 Wind 9.2
## 350 6 13 Wind 8.0
## 351 6 14 Wind 13.8
## 352 6 15 Wind 11.5
## 353 6 16 Wind 14.9
## 354 6 17 Wind 20.7
## 355 6 18 Wind 9.2
## 356 6 19 Wind 11.5
## 357 6 20 Wind 10.3
## 358 6 21 Wind 6.3
## 359 6 22 Wind 1.7
## 360 6 23 Wind 4.6
## 361 6 24 Wind 6.3
## 362 6 25 Wind 8.0
## 363 6 26 Wind 8.0
## 364 6 27 Wind 10.3
## 365 6 28 Wind 11.5
## 366 6 29 Wind 14.9
## 367 6 30 Wind 8.0
## 368 7 1 Wind 4.1
## 369 7 2 Wind 9.2
## 370 7 3 Wind 9.2
## 371 7 4 Wind 10.9
## 372 7 5 Wind 4.6
## 373 7 6 Wind 10.9
## 374 7 7 Wind 5.1
## 375 7 8 Wind 6.3
## 376 7 9 Wind 5.7
## 377 7 10 Wind 7.4
## 378 7 11 Wind 8.6
## 379 7 12 Wind 14.3
## 380 7 13 Wind 14.9
## 381 7 14 Wind 14.9
## 382 7 15 Wind 14.3
## 383 7 16 Wind 6.9
## 384 7 17 Wind 10.3
## 385 7 18 Wind 6.3
## 386 7 19 Wind 5.1
## 387 7 20 Wind 11.5
## 388 7 21 Wind 6.9
## 389 7 22 Wind 9.7
## 390 7 23 Wind 11.5
## 391 7 24 Wind 8.6
## 392 7 25 Wind 8.0
## 393 7 26 Wind 8.6
## 394 7 27 Wind 12.0
## 395 7 28 Wind 7.4
## 396 7 29 Wind 7.4
## 397 7 30 Wind 7.4
## 398 7 31 Wind 9.2
## 399 8 1 Wind 6.9
## 400 8 2 Wind 13.8
## 401 8 3 Wind 7.4
## 402 8 4 Wind 6.9
## 403 8 5 Wind 7.4
## 404 8 6 Wind 4.6
## 405 8 7 Wind 4.0
## 406 8 8 Wind 10.3
## 407 8 9 Wind 8.0
## 408 8 10 Wind 8.6
## 409 8 11 Wind 11.5
## 410 8 12 Wind 11.5
## 411 8 13 Wind 11.5
## 412 8 14 Wind 9.7
## 413 8 15 Wind 11.5
## 414 8 16 Wind 10.3
## 415 8 17 Wind 6.3
## 416 8 18 Wind 7.4
## 417 8 19 Wind 10.9
## 418 8 20 Wind 10.3
## 419 8 21 Wind 15.5
## 420 8 22 Wind 14.3
## 421 8 23 Wind 12.6
## 422 8 24 Wind 9.7
## 423 8 25 Wind 3.4
## 424 8 26 Wind 8.0
## 425 8 27 Wind 5.7
## 426 8 28 Wind 9.7
## 427 8 29 Wind 2.3
## 428 8 30 Wind 6.3
## 429 8 31 Wind 6.3
## 430 9 1 Wind 6.9
## 431 9 2 Wind 5.1
## 432 9 3 Wind 2.8
## 433 9 4 Wind 4.6
## 434 9 5 Wind 7.4
## 435 9 6 Wind 15.5
## 436 9 7 Wind 10.9
## 437 9 8 Wind 10.3
## 438 9 9 Wind 10.9
## 439 9 10 Wind 9.7
## 440 9 11 Wind 14.9
## 441 9 12 Wind 15.5
## 442 9 13 Wind 6.3
## 443 9 14 Wind 10.9
## 444 9 15 Wind 11.5
## 445 9 16 Wind 6.9
## 446 9 17 Wind 13.8
## 447 9 18 Wind 10.3
## 448 9 19 Wind 10.3
## 449 9 20 Wind 8.0
## 450 9 21 Wind 12.6
## 451 9 22 Wind 9.2
## 452 9 23 Wind 10.3
## 453 9 24 Wind 10.3
## 454 9 25 Wind 16.6
## 455 9 26 Wind 6.9
## 456 9 27 Wind 13.2
## 457 9 28 Wind 14.3
## 458 9 29 Wind 8.0
## 459 9 30 Wind 11.5
## 460 5 1 Temp 67.0
## 461 5 2 Temp 72.0
## 462 5 3 Temp 74.0
## 463 5 4 Temp 62.0
## 464 5 5 Temp 56.0
## 465 5 6 Temp 66.0
## 466 5 7 Temp 65.0
## 467 5 8 Temp 59.0
## 468 5 9 Temp 61.0
## 469 5 10 Temp 69.0
## 470 5 11 Temp 74.0
## 471 5 12 Temp 69.0
## 472 5 13 Temp 66.0
## 473 5 14 Temp 68.0
## 474 5 15 Temp 58.0
## 475 5 16 Temp 64.0
## 476 5 17 Temp 66.0
## 477 5 18 Temp 57.0
## 478 5 19 Temp 68.0
## 479 5 20 Temp 62.0
## 480 5 21 Temp 59.0
## 481 5 22 Temp 73.0
## 482 5 23 Temp 61.0
## 483 5 24 Temp 61.0
## 484 5 25 Temp 57.0
## 485 5 26 Temp 58.0
## 486 5 27 Temp 57.0
## 487 5 28 Temp 67.0
## 488 5 29 Temp 81.0
## 489 5 30 Temp 79.0
## 490 5 31 Temp 76.0
## 491 6 1 Temp 78.0
## 492 6 2 Temp 74.0
## 493 6 3 Temp 67.0
## 494 6 4 Temp 84.0
## 495 6 5 Temp 85.0
## 496 6 6 Temp 79.0
## 497 6 7 Temp 82.0
## 498 6 8 Temp 87.0
## 499 6 9 Temp 90.0
## 500 6 10 Temp 87.0
## 501 6 11 Temp 93.0
## 502 6 12 Temp 92.0
## 503 6 13 Temp 82.0
## 504 6 14 Temp 80.0
## 505 6 15 Temp 79.0
## 506 6 16 Temp 77.0
## 507 6 17 Temp 72.0
## 508 6 18 Temp 65.0
## 509 6 19 Temp 73.0
## 510 6 20 Temp 76.0
## 511 6 21 Temp 77.0
## 512 6 22 Temp 76.0
## 513 6 23 Temp 76.0
## 514 6 24 Temp 76.0
## 515 6 25 Temp 75.0
## 516 6 26 Temp 78.0
## 517 6 27 Temp 73.0
## 518 6 28 Temp 80.0
## 519 6 29 Temp 77.0
## 520 6 30 Temp 83.0
## 521 7 1 Temp 84.0
## 522 7 2 Temp 85.0
## 523 7 3 Temp 81.0
## 524 7 4 Temp 84.0
## 525 7 5 Temp 83.0
## 526 7 6 Temp 83.0
## 527 7 7 Temp 88.0
## 528 7 8 Temp 92.0
## 529 7 9 Temp 92.0
## 530 7 10 Temp 89.0
## 531 7 11 Temp 82.0
## 532 7 12 Temp 73.0
## 533 7 13 Temp 81.0
## 534 7 14 Temp 91.0
## 535 7 15 Temp 80.0
## 536 7 16 Temp 81.0
## 537 7 17 Temp 82.0
## 538 7 18 Temp 84.0
## 539 7 19 Temp 87.0
## 540 7 20 Temp 85.0
## 541 7 21 Temp 74.0
## 542 7 22 Temp 81.0
## 543 7 23 Temp 82.0
## 544 7 24 Temp 86.0
## 545 7 25 Temp 85.0
## 546 7 26 Temp 82.0
## 547 7 27 Temp 86.0
## 548 7 28 Temp 88.0
## 549 7 29 Temp 86.0
## 550 7 30 Temp 83.0
## 551 7 31 Temp 81.0
## 552 8 1 Temp 81.0
## 553 8 2 Temp 81.0
## 554 8 3 Temp 82.0
## 555 8 4 Temp 86.0
## 556 8 5 Temp 85.0
## 557 8 6 Temp 87.0
## 558 8 7 Temp 89.0
## 559 8 8 Temp 90.0
## 560 8 9 Temp 90.0
## 561 8 10 Temp 92.0
## 562 8 11 Temp 86.0
## 563 8 12 Temp 86.0
## 564 8 13 Temp 82.0
## 565 8 14 Temp 80.0
## 566 8 15 Temp 79.0
## 567 8 16 Temp 77.0
## 568 8 17 Temp 79.0
## 569 8 18 Temp 76.0
## 570 8 19 Temp 78.0
## 571 8 20 Temp 78.0
## 572 8 21 Temp 77.0
## 573 8 22 Temp 72.0
## 574 8 23 Temp 75.0
## 575 8 24 Temp 79.0
## 576 8 25 Temp 81.0
## 577 8 26 Temp 86.0
## 578 8 27 Temp 88.0
## 579 8 28 Temp 97.0
## 580 8 29 Temp 94.0
## 581 8 30 Temp 96.0
## 582 8 31 Temp 94.0
## 583 9 1 Temp 91.0
## 584 9 2 Temp 92.0
## 585 9 3 Temp 93.0
## 586 9 4 Temp 93.0
## 587 9 5 Temp 87.0
## 588 9 6 Temp 84.0
## 589 9 7 Temp 80.0
## 590 9 8 Temp 78.0
## 591 9 9 Temp 75.0
## 592 9 10 Temp 73.0
## 593 9 11 Temp 81.0
## 594 9 12 Temp 76.0
## 595 9 13 Temp 77.0
## 596 9 14 Temp 71.0
## 597 9 15 Temp 71.0
## 598 9 16 Temp 78.0
## 599 9 17 Temp 67.0
## 600 9 18 Temp 76.0
## 601 9 19 Temp 68.0
## 602 9 20 Temp 82.0
## 603 9 21 Temp 64.0
## 604 9 22 Temp 71.0
## 605 9 23 Temp 81.0
## 606 9 24 Temp 69.0
## 607 9 25 Temp 63.0
## 608 9 26 Temp 70.0
## 609 9 27 Temp 77.0
## 610 9 28 Temp 75.0
## 611 9 29 Temp 76.0
## 612 9 30 Temp 68.013.
Use the fread() function from the package data.table for fast reading a large csv file .
Note fread() is like read.csv() but with more options
df <- read.csv(
"https://github.com/washingtonpost/data-police-shootings/releases/download/v0.1/fatal-police-shootings-data.csv",
header = TRUE)14.
Use the DT package for printing a data frame.
library(DT)
datatable(df)## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html15.
Be able to use the gsub(pattern, replacement, …) function for pattern matching and replacement. Know the use of strsplit().
str <- "Now is the time "
sub(" +$", "", str) ## spaces only## [1] "Now is the time"x <- c(as = "asfef", qu = "qwerty", "yuiop[", "b", "stuff.blah.yech")
# split x on the letter e
strsplit(x, "e")## $as
## [1] "asf" "f"
##
## $qu
## [1] "qw" "rty"
##
## [[3]]
## [1] "yuiop["
##
## [[4]]
## [1] "b"
##
## [[5]]
## [1] "stuff.blah.y" "ch"16.
Be able to convert date to a particular format, and make a time series plot.
#an example from Dr. Zhang's code
# doesn't want to knit with this code. That is, it is taking too long for me
# library(gapminder)
# library(animation)
# library(gganimate)
# D = gapminder %>% mutate(gdp = round(gdpPercap*pop,2)) %>%
# group_by(year)%>%
# mutate(rank = rank(-gdp),
# Value_rel = gdp/gdp[rank==1],
# Value_lbl = paste0(" ", "$", format(gdp, big.mark = ","))) %>%
# group_by(country) %>%
# filter(rank <= 10)
# p <- ggplot(D, aes(year, gdp, group = country, color = country)
# ) +
# geom_line() +
# scale_color_viridis_d() +
# theme(legend.position = "top")
#
# p + geom_point() +
# transition_reveal(year)
# a much simpler time series
df <- data.frame(date = as.Date("2021-01-01") - 0:99,
sales = runif(100, 10, 500) + seq(50, 149)^2)
ggplot(df, aes(x=date, y=sales)) +
geom_line()
17.
Can scrape web for data using an xpath or css.
library(rvest)## Loading required package: xml2url <- "https://finance.yahoo.com/quote/TSLA/history?p=TSLA"
df <- read_html(url) %>%
html_nodes(.,css = "table") %>%
html_table(.)
df## [[1]]
## Date
## 1 Dec 09, 2020
## 2 Dec 08, 2020
## 3 Dec 07, 2020
## 4 Dec 04, 2020
## 5 Dec 03, 2020
## 6 Dec 02, 2020
## 7 Dec 01, 2020
## 8 Nov 30, 2020
## 9 Nov 27, 2020
## 10 Nov 25, 2020
## 11 Nov 24, 2020
## 12 Nov 23, 2020
## 13 Nov 20, 2020
## 14 Nov 19, 2020
## 15 Nov 18, 2020
## 16 Nov 17, 2020
## 17 Nov 16, 2020
## 18 Nov 13, 2020
## 19 Nov 12, 2020
## 20 Nov 11, 2020
## 21 Nov 10, 2020
## 22 Nov 09, 2020
## 23 Nov 06, 2020
## 24 Nov 05, 2020
## 25 Nov 04, 2020
## 26 Nov 03, 2020
## 27 Nov 02, 2020
## 28 Oct 30, 2020
## 29 Oct 29, 2020
## 30 Oct 28, 2020
## 31 Oct 27, 2020
## 32 Oct 26, 2020
## 33 Oct 23, 2020
## 34 Oct 22, 2020
## 35 Oct 21, 2020
## 36 Oct 20, 2020
## 37 Oct 19, 2020
## 38 Oct 16, 2020
## 39 Oct 15, 2020
## 40 Oct 14, 2020
## 41 Oct 13, 2020
## 42 Oct 12, 2020
## 43 Oct 09, 2020
## 44 Oct 08, 2020
## 45 Oct 07, 2020
## 46 Oct 06, 2020
## 47 Oct 05, 2020
## 48 Oct 02, 2020
## 49 Oct 01, 2020
## 50 Sep 30, 2020
## 51 Sep 29, 2020
## 52 Sep 28, 2020
## 53 Sep 25, 2020
## 54 Sep 24, 2020
## 55 Sep 23, 2020
## 56 Sep 22, 2020
## 57 Sep 21, 2020
## 58 Sep 18, 2020
## 59 Sep 17, 2020
## 60 Sep 16, 2020
## 61 Sep 15, 2020
## 62 Sep 14, 2020
## 63 Sep 11, 2020
## 64 Sep 10, 2020
## 65 Sep 09, 2020
## 66 Sep 08, 2020
## 67 Sep 04, 2020
## 68 Sep 03, 2020
## 69 Sep 02, 2020
## 70 Sep 01, 2020
## 71 Aug 31, 2020
## 72 Aug 31, 2020
## 73 Aug 28, 2020
## 74 Aug 27, 2020
## 75 Aug 26, 2020
## 76 Aug 25, 2020
## 77 Aug 24, 2020
## 78 Aug 21, 2020
## 79 Aug 20, 2020
## 80 Aug 19, 2020
## 81 Aug 18, 2020
## 82 Aug 17, 2020
## 83 Aug 14, 2020
## 84 Aug 13, 2020
## 85 Aug 12, 2020
## 86 Aug 11, 2020
## 87 Aug 10, 2020
## 88 Aug 07, 2020
## 89 Aug 06, 2020
## 90 Aug 05, 2020
## 91 Aug 04, 2020
## 92 Aug 03, 2020
## 93 Jul 31, 2020
## 94 Jul 30, 2020
## 95 Jul 29, 2020
## 96 Jul 28, 2020
## 97 Jul 27, 2020
## 98 Jul 24, 2020
## 99 Jul 23, 2020
## 100 Jul 22, 2020
## 101 *Close price adjusted for splits.**Adjusted close price adjusted for both dividends and splits.
## Open
## 1 653.69
## 2 625.51
## 3 604.92
## 4 591.01
## 5 590.02
## 6 556.44
## 7 597.59
## 8 602.21
## 9 581.16
## 10 550.06
## 11 540.40
## 12 503.50
## 13 497.99
## 14 492.00
## 15 448.35
## 16 460.17
## 17 408.93
## 18 410.85
## 19 415.05
## 20 416.45
## 21 420.09
## 22 439.50
## 23 436.10
## 24 428.30
## 25 430.62
## 26 409.73
## 27 394.00
## 28 406.90
## 29 409.96
## 30 416.48
## 31 423.76
## 32 411.63
## 33 421.84
## 34 441.92
## 35 422.70
## 36 431.75
## 37 446.24
## 38 454.44
## 39 450.31
## 40 449.78
## 41 443.35
## 42 442.00
## 43 430.13
## 44 438.44
## 45 419.87
## 46 423.79
## 47 423.35
## 48 421.39
## 49 440.76
## 50 421.32
## 51 416.00
## 52 424.62
## 53 393.47
## 54 363.80
## 55 405.16
## 56 429.60
## 57 453.13
## 58 447.94
## 59 415.60
## 60 439.87
## 61 436.56
## 62 380.95
## 63 381.94
## 64 386.21
## 65 356.60
## 66 356.00
## 67 402.81
## 68 407.23
## 69 478.99
## 70 502.14
## 71 444.61
## 72 5:1 Stock Split
## 73 459.02
## 74 436.09
## 75 412.00
## 76 394.98
## 77 425.26
## 78 408.95
## 79 372.14
## 80 373.00
## 81 379.80
## 82 335.40
## 83 333.00
## 84 322.20
## 85 294.00
## 86 279.20
## 87 289.60
## 88 299.91
## 89 298.17
## 90 298.60
## 91 299.00
## 92 289.84
## 93 303.00
## 94 297.60
## 95 300.20
## 96 300.80
## 97 287.00
## 98 283.20
## 99 335.79
## 100 319.80
## 101 *Close price adjusted for splits.**Adjusted close price adjusted for both dividends and splits.
## High
## 1 654.32
## 2 651.28
## 3 648.79
## 4 599.04
## 5 598.97
## 6 571.54
## 7 597.85
## 8 607.80
## 9 598.78
## 10 574.00
## 11 559.99
## 12 526.00
## 13 502.50
## 14 508.61
## 15 496.00
## 16 462.00
## 17 412.45
## 18 412.53
## 19 423.00
## 20 418.70
## 21 420.09
## 22 452.50
## 23 436.57
## 24 440.00
## 25 435.40
## 26 427.77
## 27 406.98
## 28 407.59
## 29 418.06
## 30 418.60
## 31 430.50
## 32 425.76
## 33 422.89
## 34 445.23
## 35 432.95
## 36 431.75
## 37 447.00
## 38 455.95
## 39 456.57
## 40 465.90
## 41 448.89
## 42 448.74
## 43 434.59
## 44 439.00
## 45 429.90
## 46 428.78
## 47 433.64
## 48 439.13
## 49 448.88
## 50 433.93
## 51 428.50
## 52 428.08
## 53 408.73
## 54 399.50
## 55 412.15
## 56 437.76
## 57 455.68
## 58 451.00
## 59 437.79
## 60 457.79
## 61 461.94
## 62 420.00
## 63 382.50
## 64 398.99
## 65 369.00
## 66 368.74
## 67 428.00
## 68 431.80
## 69 479.04
## 70 502.49
## 71 500.14
## 72 5:1 Stock Split
## 73 463.70
## 74 459.12
## 75 433.20
## 76 405.59
## 77 425.80
## 78 419.10
## 79 404.40
## 80 382.20
## 81 384.78
## 82 369.17
## 83 333.76
## 84 330.24
## 85 317.00
## 86 284.00
## 87 291.50
## 88 299.95
## 89 303.46
## 90 299.97
## 91 305.48
## 92 301.96
## 93 303.41
## 94 302.65
## 95 306.96
## 96 312.94
## 97 309.59
## 98 293.00
## 99 337.80
## 100 325.28
## 101 *Close price adjusted for splits.**Adjusted close price adjusted for both dividends and splits.
## Low
## 1 588.00
## 2 618.50
## 3 603.05
## 4 585.50
## 5 582.43
## 6 541.21
## 7 572.05
## 8 554.51
## 9 578.45
## 10 545.37
## 11 526.20
## 12 501.79
## 13 489.06
## 14 487.57
## 15 443.50
## 16 433.01
## 17 404.09
## 18 401.66
## 19 409.52
## 20 410.58
## 21 396.03
## 22 421.00
## 23 424.28
## 24 424.00
## 25 417.10
## 26 406.69
## 27 392.30
## 28 379.11
## 29 406.46
## 30 406.00
## 31 420.10
## 32 410.00
## 33 407.38
## 34 424.51
## 35 421.25
## 36 419.05
## 37 428.87
## 38 438.85
## 39 442.50
## 40 447.35
## 41 436.60
## 42 438.58
## 43 426.46
## 44 425.30
## 45 413.85
## 46 406.05
## 47 419.33
## 48 415.00
## 49 434.42
## 50 420.47
## 51 411.60
## 52 415.55
## 53 391.30
## 54 351.30
## 55 375.88
## 56 417.60
## 57 407.07
## 58 428.80
## 59 408.00
## 60 435.31
## 61 430.70
## 62 373.30
## 63 360.50
## 64 360.56
## 65 341.51
## 66 329.88
## 67 372.02
## 68 402.00
## 69 405.12
## 70 470.51
## 71 440.11
## 72 5:1 Stock Split
## 73 437.30
## 74 428.50
## 75 410.73
## 76 393.60
## 77 385.50
## 78 405.01
## 79 371.41
## 80 368.24
## 81 369.02
## 82 334.57
## 83 325.33
## 84 313.45
## 85 287.00
## 86 273.00
## 87 277.17
## 88 283.00
## 89 295.45
## 90 293.66
## 91 292.40
## 92 288.88
## 93 284.20
## 94 294.20
## 95 297.40
## 96 294.88
## 97 282.60
## 98 273.31
## 99 296.15
## 100 312.40
## 101 *Close price adjusted for splits.**Adjusted close price adjusted for both dividends and splits.
## Close*
## 1 604.48
## 2 649.88
## 3 641.76
## 4 599.04
## 5 593.38
## 6 568.82
## 7 584.76
## 8 567.60
## 9 585.76
## 10 574.00
## 11 555.38
## 12 521.85
## 13 489.61
## 14 499.27
## 15 486.64
## 16 441.61
## 17 408.09
## 18 408.50
## 19 411.76
## 20 417.13
## 21 410.36
## 22 421.26
## 23 429.95
## 24 438.09
## 25 420.98
## 26 423.90
## 27 400.51
## 28 388.04
## 29 410.83
## 30 406.02
## 31 424.68
## 32 420.28
## 33 420.63
## 34 425.79
## 35 422.64
## 36 421.94
## 37 430.83
## 38 439.67
## 39 448.88
## 40 461.30
## 41 446.65
## 42 442.30
## 43 434.00
## 44 425.92
## 45 425.30
## 46 413.98
## 47 425.68
## 48 415.09
## 49 448.16
## 50 429.01
## 51 419.07
## 52 421.20
## 53 407.34
## 54 387.79
## 55 380.36
## 56 424.23
## 57 449.39
## 58 442.15
## 59 423.43
## 60 441.76
## 61 449.76
## 62 419.62
## 63 372.72
## 64 371.34
## 65 366.28
## 66 330.21
## 67 418.32
## 68 407.00
## 69 447.37
## 70 475.05
## 71 498.32
## 72 5:1 Stock Split
## 73 442.68
## 74 447.75
## 75 430.63
## 76 404.67
## 77 402.84
## 78 410.00
## 79 400.37
## 80 375.71
## 81 377.42
## 82 367.13
## 83 330.14
## 84 324.20
## 85 310.95
## 86 274.88
## 87 283.71
## 88 290.54
## 89 297.92
## 90 297.00
## 91 297.40
## 92 297.00
## 93 286.15
## 94 297.50
## 95 299.82
## 96 295.30
## 97 307.92
## 98 283.40
## 99 302.61
## 100 318.47
## 101 *Close price adjusted for splits.**Adjusted close price adjusted for both dividends and splits.
## Adj Close**
## 1 604.48
## 2 649.88
## 3 641.76
## 4 599.04
## 5 593.38
## 6 568.82
## 7 584.76
## 8 567.60
## 9 585.76
## 10 574.00
## 11 555.38
## 12 521.85
## 13 489.61
## 14 499.27
## 15 486.64
## 16 441.61
## 17 408.09
## 18 408.50
## 19 411.76
## 20 417.13
## 21 410.36
## 22 421.26
## 23 429.95
## 24 438.09
## 25 420.98
## 26 423.90
## 27 400.51
## 28 388.04
## 29 410.83
## 30 406.02
## 31 424.68
## 32 420.28
## 33 420.63
## 34 425.79
## 35 422.64
## 36 421.94
## 37 430.83
## 38 439.67
## 39 448.88
## 40 461.30
## 41 446.65
## 42 442.30
## 43 434.00
## 44 425.92
## 45 425.30
## 46 413.98
## 47 425.68
## 48 415.09
## 49 448.16
## 50 429.01
## 51 419.07
## 52 421.20
## 53 407.34
## 54 387.79
## 55 380.36
## 56 424.23
## 57 449.39
## 58 442.15
## 59 423.43
## 60 441.76
## 61 449.76
## 62 419.62
## 63 372.72
## 64 371.34
## 65 366.28
## 66 330.21
## 67 418.32
## 68 407.00
## 69 447.37
## 70 475.05
## 71 498.32
## 72 5:1 Stock Split
## 73 442.68
## 74 447.75
## 75 430.63
## 76 404.67
## 77 402.84
## 78 410.00
## 79 400.37
## 80 375.71
## 81 377.42
## 82 367.13
## 83 330.14
## 84 324.20
## 85 310.95
## 86 274.88
## 87 283.71
## 88 290.54
## 89 297.92
## 90 297.00
## 91 297.40
## 92 297.00
## 93 286.15
## 94 297.50
## 95 299.82
## 96 295.30
## 97 307.92
## 98 283.40
## 99 302.61
## 100 318.47
## 101 *Close price adjusted for splits.**Adjusted close price adjusted for both dividends and splits.
## Volume
## 1 70,940,300
## 2 64,265,000
## 3 56,309,700
## 4 29,401,300
## 5 42,552,000
## 6 47,775,700
## 7 40,382,800
## 8 63,003,100
## 9 37,561,100
## 10 48,930,200
## 11 53,648,500
## 12 50,260,300
## 13 32,807,300
## 14 62,475,300
## 15 78,044,000
## 16 61,188,300
## 17 26,838,600
## 18 19,771,100
## 19 19,855,100
## 20 17,357,700
## 21 30,284,200
## 22 34,833,000
## 23 21,706,000
## 24 28,414,500
## 25 32,143,100
## 26 34,351,700
## 27 29,021,100
## 28 42,511,300
## 29 22,655,300
## 30 25,451,400
## 31 22,686,500
## 32 28,239,200
## 33 33,717,000
## 34 39,993,200
## 35 32,370,500
## 36 31,656,300
## 37 36,287,800
## 38 32,775,900
## 39 35,672,400
## 40 48,045,400
## 41 34,463,700
## 42 38,791,100
## 43 28,925,700
## 44 40,421,100
## 45 43,127,700
## 46 49,146,300
## 47 44,722,800
## 48 71,430,000
## 49 50,741,500
## 50 48,145,600
## 51 50,219,300
## 52 49,719,600
## 53 67,208,500
## 54 96,561,100
## 55 95,074,200
## 56 79,580,800
## 57 109,476,800
## 58 86,406,800
## 59 76,779,200
## 60 72,279,300
## 61 97,298,200
## 62 83,020,600
## 63 60,717,500
## 64 84,930,600
## 65 79,465,800
## 66 115,465,700
## 67 110,321,900
## 68 87,596,100
## 69 96,176,100
## 70 90,119,400
## 71 118,374,400
## 72 5:1 Stock Split
## 73 100,406,000
## 74 118,465,000
## 75 71,197,000
## 76 53,294,500
## 77 100,318,000
## 78 107,448,000
## 79 103,059,000
## 80 61,026,500
## 81 82,372,500
## 82 101,211,500
## 83 62,888,000
## 84 102,126,500
## 85 109,494,000
## 86 43,129,000
## 87 37,611,500
## 88 44,482,000
## 89 29,961,500
## 90 24,890,000
## 91 42,075,000
## 92 44,046,500
## 93 61,235,000
## 94 38,105,000
## 95 47,134,500
## 96 79,043,500
## 97 80,243,500
## 98 96,983,000
## 99 121,642,500
## 100 70,805,500
## 101 *Close price adjusted for splits.**Adjusted close price adjusted for both dividends and splits.18.
Display large integers with commas. Know the use of the round() function or sprintf(“%.2f”, 234.8675) function.
format(12345.678,big.mark=",",scientific=FALSE)## [1] "12,345.68"19.
Can join two datasets with merge(), and use the package “dplyr” to do inner_join, left_join(), right_join(), and full_join().
library(dplyr)
merge(mtcars$mpg,mtcars$qsec)## x y
## 1 21.0 16.46
## 2 21.0 16.46
## 3 22.8 16.46
## 4 21.4 16.46
## 5 18.7 16.46
## 6 18.1 16.46
## 7 14.3 16.46
## 8 24.4 16.46
## 9 22.8 16.46
## 10 19.2 16.46
## 11 17.8 16.46
## 12 16.4 16.46
## 13 17.3 16.46
## 14 15.2 16.46
## 15 10.4 16.46
## 16 10.4 16.46
## 17 14.7 16.46
## 18 32.4 16.46
## 19 30.4 16.46
## 20 33.9 16.46
## 21 21.5 16.46
## 22 15.5 16.46
## 23 15.2 16.46
## 24 13.3 16.46
## 25 19.2 16.46
## 26 27.3 16.46
## 27 26.0 16.46
## 28 30.4 16.46
## 29 15.8 16.46
## 30 19.7 16.46
## 31 15.0 16.46
## 32 21.4 16.46
## 33 21.0 17.02
## 34 21.0 17.02
## 35 22.8 17.02
## 36 21.4 17.02
## 37 18.7 17.02
## 38 18.1 17.02
## 39 14.3 17.02
## 40 24.4 17.02
## 41 22.8 17.02
## 42 19.2 17.02
## 43 17.8 17.02
## 44 16.4 17.02
## 45 17.3 17.02
## 46 15.2 17.02
## 47 10.4 17.02
## 48 10.4 17.02
## 49 14.7 17.02
## 50 32.4 17.02
## 51 30.4 17.02
## 52 33.9 17.02
## 53 21.5 17.02
## 54 15.5 17.02
## 55 15.2 17.02
## 56 13.3 17.02
## 57 19.2 17.02
## 58 27.3 17.02
## 59 26.0 17.02
## 60 30.4 17.02
## 61 15.8 17.02
## 62 19.7 17.02
## 63 15.0 17.02
## 64 21.4 17.02
## 65 21.0 18.61
## 66 21.0 18.61
## 67 22.8 18.61
## 68 21.4 18.61
## 69 18.7 18.61
## 70 18.1 18.61
## 71 14.3 18.61
## 72 24.4 18.61
## 73 22.8 18.61
## 74 19.2 18.61
## 75 17.8 18.61
## 76 16.4 18.61
## 77 17.3 18.61
## 78 15.2 18.61
## 79 10.4 18.61
## 80 10.4 18.61
## 81 14.7 18.61
## 82 32.4 18.61
## 83 30.4 18.61
## 84 33.9 18.61
## 85 21.5 18.61
## 86 15.5 18.61
## 87 15.2 18.61
## 88 13.3 18.61
## 89 19.2 18.61
## 90 27.3 18.61
## 91 26.0 18.61
## 92 30.4 18.61
## 93 15.8 18.61
## 94 19.7 18.61
## 95 15.0 18.61
## 96 21.4 18.61
## 97 21.0 19.44
## 98 21.0 19.44
## 99 22.8 19.44
## 100 21.4 19.44
## 101 18.7 19.44
## 102 18.1 19.44
## 103 14.3 19.44
## 104 24.4 19.44
## 105 22.8 19.44
## 106 19.2 19.44
## 107 17.8 19.44
## 108 16.4 19.44
## 109 17.3 19.44
## 110 15.2 19.44
## 111 10.4 19.44
## 112 10.4 19.44
## 113 14.7 19.44
## 114 32.4 19.44
## 115 30.4 19.44
## 116 33.9 19.44
## 117 21.5 19.44
## 118 15.5 19.44
## 119 15.2 19.44
## 120 13.3 19.44
## 121 19.2 19.44
## 122 27.3 19.44
## 123 26.0 19.44
## 124 30.4 19.44
## 125 15.8 19.44
## 126 19.7 19.44
## 127 15.0 19.44
## 128 21.4 19.44
## 129 21.0 17.02
## 130 21.0 17.02
## 131 22.8 17.02
## 132 21.4 17.02
## 133 18.7 17.02
## 134 18.1 17.02
## 135 14.3 17.02
## 136 24.4 17.02
## 137 22.8 17.02
## 138 19.2 17.02
## 139 17.8 17.02
## 140 16.4 17.02
## 141 17.3 17.02
## 142 15.2 17.02
## 143 10.4 17.02
## 144 10.4 17.02
## 145 14.7 17.02
## 146 32.4 17.02
## 147 30.4 17.02
## 148 33.9 17.02
## 149 21.5 17.02
## 150 15.5 17.02
## 151 15.2 17.02
## 152 13.3 17.02
## 153 19.2 17.02
## 154 27.3 17.02
## 155 26.0 17.02
## 156 30.4 17.02
## 157 15.8 17.02
## 158 19.7 17.02
## 159 15.0 17.02
## 160 21.4 17.02
## 161 21.0 20.22
## 162 21.0 20.22
## 163 22.8 20.22
## 164 21.4 20.22
## 165 18.7 20.22
## 166 18.1 20.22
## 167 14.3 20.22
## 168 24.4 20.22
## 169 22.8 20.22
## 170 19.2 20.22
## 171 17.8 20.22
## 172 16.4 20.22
## 173 17.3 20.22
## 174 15.2 20.22
## 175 10.4 20.22
## 176 10.4 20.22
## 177 14.7 20.22
## 178 32.4 20.22
## 179 30.4 20.22
## 180 33.9 20.22
## 181 21.5 20.22
## 182 15.5 20.22
## 183 15.2 20.22
## 184 13.3 20.22
## 185 19.2 20.22
## 186 27.3 20.22
## 187 26.0 20.22
## 188 30.4 20.22
## 189 15.8 20.22
## 190 19.7 20.22
## 191 15.0 20.22
## 192 21.4 20.22
## 193 21.0 15.84
## 194 21.0 15.84
## 195 22.8 15.84
## 196 21.4 15.84
## 197 18.7 15.84
## 198 18.1 15.84
## 199 14.3 15.84
## 200 24.4 15.84
## 201 22.8 15.84
## 202 19.2 15.84
## 203 17.8 15.84
## 204 16.4 15.84
## 205 17.3 15.84
## 206 15.2 15.84
## 207 10.4 15.84
## 208 10.4 15.84
## 209 14.7 15.84
## 210 32.4 15.84
## 211 30.4 15.84
## 212 33.9 15.84
## 213 21.5 15.84
## 214 15.5 15.84
## 215 15.2 15.84
## 216 13.3 15.84
## 217 19.2 15.84
## 218 27.3 15.84
## 219 26.0 15.84
## 220 30.4 15.84
## 221 15.8 15.84
## 222 19.7 15.84
## 223 15.0 15.84
## 224 21.4 15.84
## 225 21.0 20.00
## 226 21.0 20.00
## 227 22.8 20.00
## 228 21.4 20.00
## 229 18.7 20.00
## 230 18.1 20.00
## 231 14.3 20.00
## 232 24.4 20.00
## 233 22.8 20.00
## 234 19.2 20.00
## 235 17.8 20.00
## 236 16.4 20.00
## 237 17.3 20.00
## 238 15.2 20.00
## 239 10.4 20.00
## 240 10.4 20.00
## 241 14.7 20.00
## 242 32.4 20.00
## 243 30.4 20.00
## 244 33.9 20.00
## 245 21.5 20.00
## 246 15.5 20.00
## 247 15.2 20.00
## 248 13.3 20.00
## 249 19.2 20.00
## 250 27.3 20.00
## 251 26.0 20.00
## 252 30.4 20.00
## 253 15.8 20.00
## 254 19.7 20.00
## 255 15.0 20.00
## 256 21.4 20.00
## 257 21.0 22.90
## 258 21.0 22.90
## 259 22.8 22.90
## 260 21.4 22.90
## 261 18.7 22.90
## 262 18.1 22.90
## 263 14.3 22.90
## 264 24.4 22.90
## 265 22.8 22.90
## 266 19.2 22.90
## 267 17.8 22.90
## 268 16.4 22.90
## 269 17.3 22.90
## 270 15.2 22.90
## 271 10.4 22.90
## 272 10.4 22.90
## 273 14.7 22.90
## 274 32.4 22.90
## 275 30.4 22.90
## 276 33.9 22.90
## 277 21.5 22.90
## 278 15.5 22.90
## 279 15.2 22.90
## 280 13.3 22.90
## 281 19.2 22.90
## 282 27.3 22.90
## 283 26.0 22.90
## 284 30.4 22.90
## 285 15.8 22.90
## 286 19.7 22.90
## 287 15.0 22.90
## 288 21.4 22.90
## 289 21.0 18.30
## 290 21.0 18.30
## 291 22.8 18.30
## 292 21.4 18.30
## 293 18.7 18.30
## 294 18.1 18.30
## 295 14.3 18.30
## 296 24.4 18.30
## 297 22.8 18.30
## 298 19.2 18.30
## 299 17.8 18.30
## 300 16.4 18.30
## 301 17.3 18.30
## 302 15.2 18.30
## 303 10.4 18.30
## 304 10.4 18.30
## 305 14.7 18.30
## 306 32.4 18.30
## 307 30.4 18.30
## 308 33.9 18.30
## 309 21.5 18.30
## 310 15.5 18.30
## 311 15.2 18.30
## 312 13.3 18.30
## 313 19.2 18.30
## 314 27.3 18.30
## 315 26.0 18.30
## 316 30.4 18.30
## 317 15.8 18.30
## 318 19.7 18.30
## 319 15.0 18.30
## 320 21.4 18.30
## 321 21.0 18.90
## 322 21.0 18.90
## 323 22.8 18.90
## 324 21.4 18.90
## 325 18.7 18.90
## 326 18.1 18.90
## 327 14.3 18.90
## 328 24.4 18.90
## 329 22.8 18.90
## 330 19.2 18.90
## 331 17.8 18.90
## 332 16.4 18.90
## 333 17.3 18.90
## 334 15.2 18.90
## 335 10.4 18.90
## 336 10.4 18.90
## 337 14.7 18.90
## 338 32.4 18.90
## 339 30.4 18.90
## 340 33.9 18.90
## 341 21.5 18.90
## 342 15.5 18.90
## 343 15.2 18.90
## 344 13.3 18.90
## 345 19.2 18.90
## 346 27.3 18.90
## 347 26.0 18.90
## 348 30.4 18.90
## 349 15.8 18.90
## 350 19.7 18.90
## 351 15.0 18.90
## 352 21.4 18.90
## 353 21.0 17.40
## 354 21.0 17.40
## 355 22.8 17.40
## 356 21.4 17.40
## 357 18.7 17.40
## 358 18.1 17.40
## 359 14.3 17.40
## 360 24.4 17.40
## 361 22.8 17.40
## 362 19.2 17.40
## 363 17.8 17.40
## 364 16.4 17.40
## 365 17.3 17.40
## 366 15.2 17.40
## 367 10.4 17.40
## 368 10.4 17.40
## 369 14.7 17.40
## 370 32.4 17.40
## 371 30.4 17.40
## 372 33.9 17.40
## 373 21.5 17.40
## 374 15.5 17.40
## 375 15.2 17.40
## 376 13.3 17.40
## 377 19.2 17.40
## 378 27.3 17.40
## 379 26.0 17.40
## 380 30.4 17.40
## 381 15.8 17.40
## 382 19.7 17.40
## 383 15.0 17.40
## 384 21.4 17.40
## 385 21.0 17.60
## 386 21.0 17.60
## 387 22.8 17.60
## 388 21.4 17.60
## 389 18.7 17.60
## 390 18.1 17.60
## 391 14.3 17.60
## 392 24.4 17.60
## 393 22.8 17.60
## 394 19.2 17.60
## 395 17.8 17.60
## 396 16.4 17.60
## 397 17.3 17.60
## 398 15.2 17.60
## 399 10.4 17.60
## 400 10.4 17.60
## 401 14.7 17.60
## 402 32.4 17.60
## 403 30.4 17.60
## 404 33.9 17.60
## 405 21.5 17.60
## 406 15.5 17.60
## 407 15.2 17.60
## 408 13.3 17.60
## 409 19.2 17.60
## 410 27.3 17.60
## 411 26.0 17.60
## 412 30.4 17.60
## 413 15.8 17.60
## 414 19.7 17.60
## 415 15.0 17.60
## 416 21.4 17.60
## 417 21.0 18.00
## 418 21.0 18.00
## 419 22.8 18.00
## 420 21.4 18.00
## 421 18.7 18.00
## 422 18.1 18.00
## 423 14.3 18.00
## 424 24.4 18.00
## 425 22.8 18.00
## 426 19.2 18.00
## 427 17.8 18.00
## 428 16.4 18.00
## 429 17.3 18.00
## 430 15.2 18.00
## 431 10.4 18.00
## 432 10.4 18.00
## 433 14.7 18.00
## 434 32.4 18.00
## 435 30.4 18.00
## 436 33.9 18.00
## 437 21.5 18.00
## 438 15.5 18.00
## 439 15.2 18.00
## 440 13.3 18.00
## 441 19.2 18.00
## 442 27.3 18.00
## 443 26.0 18.00
## 444 30.4 18.00
## 445 15.8 18.00
## 446 19.7 18.00
## 447 15.0 18.00
## 448 21.4 18.00
## 449 21.0 17.98
## 450 21.0 17.98
## 451 22.8 17.98
## 452 21.4 17.98
## 453 18.7 17.98
## 454 18.1 17.98
## 455 14.3 17.98
## 456 24.4 17.98
## 457 22.8 17.98
## 458 19.2 17.98
## 459 17.8 17.98
## 460 16.4 17.98
## 461 17.3 17.98
## 462 15.2 17.98
## 463 10.4 17.98
## 464 10.4 17.98
## 465 14.7 17.98
## 466 32.4 17.98
## 467 30.4 17.98
## 468 33.9 17.98
## 469 21.5 17.98
## 470 15.5 17.98
## 471 15.2 17.98
## 472 13.3 17.98
## 473 19.2 17.98
## 474 27.3 17.98
## 475 26.0 17.98
## 476 30.4 17.98
## 477 15.8 17.98
## 478 19.7 17.98
## 479 15.0 17.98
## 480 21.4 17.98
## 481 21.0 17.82
## 482 21.0 17.82
## 483 22.8 17.82
## 484 21.4 17.82
## 485 18.7 17.82
## 486 18.1 17.82
## 487 14.3 17.82
## 488 24.4 17.82
## 489 22.8 17.82
## 490 19.2 17.82
## 491 17.8 17.82
## 492 16.4 17.82
## 493 17.3 17.82
## 494 15.2 17.82
## 495 10.4 17.82
## 496 10.4 17.82
## 497 14.7 17.82
## 498 32.4 17.82
## 499 30.4 17.82
## 500 33.9 17.82
## 501 21.5 17.82
## 502 15.5 17.82
## 503 15.2 17.82
## 504 13.3 17.82
## 505 19.2 17.82
## 506 27.3 17.82
## 507 26.0 17.82
## 508 30.4 17.82
## 509 15.8 17.82
## 510 19.7 17.82
## 511 15.0 17.82
## 512 21.4 17.82
## 513 21.0 17.42
## 514 21.0 17.42
## 515 22.8 17.42
## 516 21.4 17.42
## 517 18.7 17.42
## 518 18.1 17.42
## 519 14.3 17.42
## 520 24.4 17.42
## 521 22.8 17.42
## 522 19.2 17.42
## 523 17.8 17.42
## 524 16.4 17.42
## 525 17.3 17.42
## 526 15.2 17.42
## 527 10.4 17.42
## 528 10.4 17.42
## 529 14.7 17.42
## 530 32.4 17.42
## 531 30.4 17.42
## 532 33.9 17.42
## 533 21.5 17.42
## 534 15.5 17.42
## 535 15.2 17.42
## 536 13.3 17.42
## 537 19.2 17.42
## 538 27.3 17.42
## 539 26.0 17.42
## 540 30.4 17.42
## 541 15.8 17.42
## 542 19.7 17.42
## 543 15.0 17.42
## 544 21.4 17.42
## 545 21.0 19.47
## 546 21.0 19.47
## 547 22.8 19.47
## 548 21.4 19.47
## 549 18.7 19.47
## 550 18.1 19.47
## 551 14.3 19.47
## 552 24.4 19.47
## 553 22.8 19.47
## 554 19.2 19.47
## 555 17.8 19.47
## 556 16.4 19.47
## 557 17.3 19.47
## 558 15.2 19.47
## 559 10.4 19.47
## 560 10.4 19.47
## 561 14.7 19.47
## 562 32.4 19.47
## 563 30.4 19.47
## 564 33.9 19.47
## 565 21.5 19.47
## 566 15.5 19.47
## 567 15.2 19.47
## 568 13.3 19.47
## 569 19.2 19.47
## 570 27.3 19.47
## 571 26.0 19.47
## 572 30.4 19.47
## 573 15.8 19.47
## 574 19.7 19.47
## 575 15.0 19.47
## 576 21.4 19.47
## 577 21.0 18.52
## 578 21.0 18.52
## 579 22.8 18.52
## 580 21.4 18.52
## 581 18.7 18.52
## 582 18.1 18.52
## 583 14.3 18.52
## 584 24.4 18.52
## 585 22.8 18.52
## 586 19.2 18.52
## 587 17.8 18.52
## 588 16.4 18.52
## 589 17.3 18.52
## 590 15.2 18.52
## 591 10.4 18.52
## 592 10.4 18.52
## 593 14.7 18.52
## 594 32.4 18.52
## 595 30.4 18.52
## 596 33.9 18.52
## 597 21.5 18.52
## 598 15.5 18.52
## 599 15.2 18.52
## 600 13.3 18.52
## 601 19.2 18.52
## 602 27.3 18.52
## 603 26.0 18.52
## 604 30.4 18.52
## 605 15.8 18.52
## 606 19.7 18.52
## 607 15.0 18.52
## 608 21.4 18.52
## 609 21.0 19.90
## 610 21.0 19.90
## 611 22.8 19.90
## 612 21.4 19.90
## 613 18.7 19.90
## 614 18.1 19.90
## 615 14.3 19.90
## 616 24.4 19.90
## 617 22.8 19.90
## 618 19.2 19.90
## 619 17.8 19.90
## 620 16.4 19.90
## 621 17.3 19.90
## 622 15.2 19.90
## 623 10.4 19.90
## 624 10.4 19.90
## 625 14.7 19.90
## 626 32.4 19.90
## 627 30.4 19.90
## 628 33.9 19.90
## 629 21.5 19.90
## 630 15.5 19.90
## 631 15.2 19.90
## 632 13.3 19.90
## 633 19.2 19.90
## 634 27.3 19.90
## 635 26.0 19.90
## 636 30.4 19.90
## 637 15.8 19.90
## 638 19.7 19.90
## 639 15.0 19.90
## 640 21.4 19.90
## 641 21.0 20.01
## 642 21.0 20.01
## 643 22.8 20.01
## 644 21.4 20.01
## 645 18.7 20.01
## 646 18.1 20.01
## 647 14.3 20.01
## 648 24.4 20.01
## 649 22.8 20.01
## 650 19.2 20.01
## 651 17.8 20.01
## 652 16.4 20.01
## 653 17.3 20.01
## 654 15.2 20.01
## 655 10.4 20.01
## 656 10.4 20.01
## 657 14.7 20.01
## 658 32.4 20.01
## 659 30.4 20.01
## 660 33.9 20.01
## 661 21.5 20.01
## 662 15.5 20.01
## 663 15.2 20.01
## 664 13.3 20.01
## 665 19.2 20.01
## 666 27.3 20.01
## 667 26.0 20.01
## 668 30.4 20.01
## 669 15.8 20.01
## 670 19.7 20.01
## 671 15.0 20.01
## 672 21.4 20.01
## 673 21.0 16.87
## 674 21.0 16.87
## 675 22.8 16.87
## 676 21.4 16.87
## 677 18.7 16.87
## 678 18.1 16.87
## 679 14.3 16.87
## 680 24.4 16.87
## 681 22.8 16.87
## 682 19.2 16.87
## 683 17.8 16.87
## 684 16.4 16.87
## 685 17.3 16.87
## 686 15.2 16.87
## 687 10.4 16.87
## 688 10.4 16.87
## 689 14.7 16.87
## 690 32.4 16.87
## 691 30.4 16.87
## 692 33.9 16.87
## 693 21.5 16.87
## 694 15.5 16.87
## 695 15.2 16.87
## 696 13.3 16.87
## 697 19.2 16.87
## 698 27.3 16.87
## 699 26.0 16.87
## 700 30.4 16.87
## 701 15.8 16.87
## 702 19.7 16.87
## 703 15.0 16.87
## 704 21.4 16.87
## 705 21.0 17.30
## 706 21.0 17.30
## 707 22.8 17.30
## 708 21.4 17.30
## 709 18.7 17.30
## 710 18.1 17.30
## 711 14.3 17.30
## 712 24.4 17.30
## 713 22.8 17.30
## 714 19.2 17.30
## 715 17.8 17.30
## 716 16.4 17.30
## 717 17.3 17.30
## 718 15.2 17.30
## 719 10.4 17.30
## 720 10.4 17.30
## 721 14.7 17.30
## 722 32.4 17.30
## 723 30.4 17.30
## 724 33.9 17.30
## 725 21.5 17.30
## 726 15.5 17.30
## 727 15.2 17.30
## 728 13.3 17.30
## 729 19.2 17.30
## 730 27.3 17.30
## 731 26.0 17.30
## 732 30.4 17.30
## 733 15.8 17.30
## 734 19.7 17.30
## 735 15.0 17.30
## 736 21.4 17.30
## 737 21.0 15.41
## 738 21.0 15.41
## 739 22.8 15.41
## 740 21.4 15.41
## 741 18.7 15.41
## 742 18.1 15.41
## 743 14.3 15.41
## 744 24.4 15.41
## 745 22.8 15.41
## 746 19.2 15.41
## 747 17.8 15.41
## 748 16.4 15.41
## 749 17.3 15.41
## 750 15.2 15.41
## 751 10.4 15.41
## 752 10.4 15.41
## 753 14.7 15.41
## 754 32.4 15.41
## 755 30.4 15.41
## 756 33.9 15.41
## 757 21.5 15.41
## 758 15.5 15.41
## 759 15.2 15.41
## 760 13.3 15.41
## 761 19.2 15.41
## 762 27.3 15.41
## 763 26.0 15.41
## 764 30.4 15.41
## 765 15.8 15.41
## 766 19.7 15.41
## 767 15.0 15.41
## 768 21.4 15.41
## 769 21.0 17.05
## 770 21.0 17.05
## 771 22.8 17.05
## 772 21.4 17.05
## 773 18.7 17.05
## 774 18.1 17.05
## 775 14.3 17.05
## 776 24.4 17.05
## 777 22.8 17.05
## 778 19.2 17.05
## 779 17.8 17.05
## 780 16.4 17.05
## 781 17.3 17.05
## 782 15.2 17.05
## 783 10.4 17.05
## 784 10.4 17.05
## 785 14.7 17.05
## 786 32.4 17.05
## 787 30.4 17.05
## 788 33.9 17.05
## 789 21.5 17.05
## 790 15.5 17.05
## 791 15.2 17.05
## 792 13.3 17.05
## 793 19.2 17.05
## 794 27.3 17.05
## 795 26.0 17.05
## 796 30.4 17.05
## 797 15.8 17.05
## 798 19.7 17.05
## 799 15.0 17.05
## 800 21.4 17.05
## 801 21.0 18.90
## 802 21.0 18.90
## 803 22.8 18.90
## 804 21.4 18.90
## 805 18.7 18.90
## 806 18.1 18.90
## 807 14.3 18.90
## 808 24.4 18.90
## 809 22.8 18.90
## 810 19.2 18.90
## 811 17.8 18.90
## 812 16.4 18.90
## 813 17.3 18.90
## 814 15.2 18.90
## 815 10.4 18.90
## 816 10.4 18.90
## 817 14.7 18.90
## 818 32.4 18.90
## 819 30.4 18.90
## 820 33.9 18.90
## 821 21.5 18.90
## 822 15.5 18.90
## 823 15.2 18.90
## 824 13.3 18.90
## 825 19.2 18.90
## 826 27.3 18.90
## 827 26.0 18.90
## 828 30.4 18.90
## 829 15.8 18.90
## 830 19.7 18.90
## 831 15.0 18.90
## 832 21.4 18.90
## 833 21.0 16.70
## 834 21.0 16.70
## 835 22.8 16.70
## 836 21.4 16.70
## 837 18.7 16.70
## 838 18.1 16.70
## 839 14.3 16.70
## 840 24.4 16.70
## 841 22.8 16.70
## 842 19.2 16.70
## 843 17.8 16.70
## 844 16.4 16.70
## 845 17.3 16.70
## 846 15.2 16.70
## 847 10.4 16.70
## 848 10.4 16.70
## 849 14.7 16.70
## 850 32.4 16.70
## 851 30.4 16.70
## 852 33.9 16.70
## 853 21.5 16.70
## 854 15.5 16.70
## 855 15.2 16.70
## 856 13.3 16.70
## 857 19.2 16.70
## 858 27.3 16.70
## 859 26.0 16.70
## 860 30.4 16.70
## 861 15.8 16.70
## 862 19.7 16.70
## 863 15.0 16.70
## 864 21.4 16.70
## 865 21.0 16.90
## 866 21.0 16.90
## 867 22.8 16.90
## 868 21.4 16.90
## 869 18.7 16.90
## 870 18.1 16.90
## 871 14.3 16.90
## 872 24.4 16.90
## 873 22.8 16.90
## 874 19.2 16.90
## 875 17.8 16.90
## 876 16.4 16.90
## 877 17.3 16.90
## 878 15.2 16.90
## 879 10.4 16.90
## 880 10.4 16.90
## 881 14.7 16.90
## 882 32.4 16.90
## 883 30.4 16.90
## 884 33.9 16.90
## 885 21.5 16.90
## 886 15.5 16.90
## 887 15.2 16.90
## 888 13.3 16.90
## 889 19.2 16.90
## 890 27.3 16.90
## 891 26.0 16.90
## 892 30.4 16.90
## 893 15.8 16.90
## 894 19.7 16.90
## 895 15.0 16.90
## 896 21.4 16.90
## 897 21.0 14.50
## 898 21.0 14.50
## 899 22.8 14.50
## 900 21.4 14.50
## 901 18.7 14.50
## 902 18.1 14.50
## 903 14.3 14.50
## 904 24.4 14.50
## 905 22.8 14.50
## 906 19.2 14.50
## 907 17.8 14.50
## 908 16.4 14.50
## 909 17.3 14.50
## 910 15.2 14.50
## 911 10.4 14.50
## 912 10.4 14.50
## 913 14.7 14.50
## 914 32.4 14.50
## 915 30.4 14.50
## 916 33.9 14.50
## 917 21.5 14.50
## 918 15.5 14.50
## 919 15.2 14.50
## 920 13.3 14.50
## 921 19.2 14.50
## 922 27.3 14.50
## 923 26.0 14.50
## 924 30.4 14.50
## 925 15.8 14.50
## 926 19.7 14.50
## 927 15.0 14.50
## 928 21.4 14.50
## 929 21.0 15.50
## 930 21.0 15.50
## 931 22.8 15.50
## 932 21.4 15.50
## 933 18.7 15.50
## 934 18.1 15.50
## 935 14.3 15.50
## 936 24.4 15.50
## 937 22.8 15.50
## 938 19.2 15.50
## 939 17.8 15.50
## 940 16.4 15.50
## 941 17.3 15.50
## 942 15.2 15.50
## 943 10.4 15.50
## 944 10.4 15.50
## 945 14.7 15.50
## 946 32.4 15.50
## 947 30.4 15.50
## 948 33.9 15.50
## 949 21.5 15.50
## 950 15.5 15.50
## 951 15.2 15.50
## 952 13.3 15.50
## 953 19.2 15.50
## 954 27.3 15.50
## 955 26.0 15.50
## 956 30.4 15.50
## 957 15.8 15.50
## 958 19.7 15.50
## 959 15.0 15.50
## 960 21.4 15.50
## 961 21.0 14.60
## 962 21.0 14.60
## 963 22.8 14.60
## 964 21.4 14.60
## 965 18.7 14.60
## 966 18.1 14.60
## 967 14.3 14.60
## 968 24.4 14.60
## 969 22.8 14.60
## 970 19.2 14.60
## 971 17.8 14.60
## 972 16.4 14.60
## 973 17.3 14.60
## 974 15.2 14.60
## 975 10.4 14.60
## 976 10.4 14.60
## 977 14.7 14.60
## 978 32.4 14.60
## 979 30.4 14.60
## 980 33.9 14.60
## 981 21.5 14.60
## 982 15.5 14.60
## 983 15.2 14.60
## 984 13.3 14.60
## 985 19.2 14.60
## 986 27.3 14.60
## 987 26.0 14.60
## 988 30.4 14.60
## 989 15.8 14.60
## 990 19.7 14.60
## 991 15.0 14.60
## 992 21.4 14.60
## 993 21.0 18.60
## 994 21.0 18.60
## 995 22.8 18.60
## 996 21.4 18.60
## 997 18.7 18.60
## 998 18.1 18.60
## 999 14.3 18.60
## 1000 24.4 18.60
## 1001 22.8 18.60
## 1002 19.2 18.60
## 1003 17.8 18.60
## 1004 16.4 18.60
## 1005 17.3 18.60
## 1006 15.2 18.60
## 1007 10.4 18.60
## 1008 10.4 18.60
## 1009 14.7 18.60
## 1010 32.4 18.60
## 1011 30.4 18.60
## 1012 33.9 18.60
## 1013 21.5 18.60
## 1014 15.5 18.60
## 1015 15.2 18.60
## 1016 13.3 18.60
## 1017 19.2 18.60
## 1018 27.3 18.60
## 1019 26.0 18.60
## 1020 30.4 18.60
## 1021 15.8 18.60
## 1022 19.7 18.60
## 1023 15.0 18.60
## 1024 21.4 18.60band_instruments## # A tibble: 3 x 2
## name plays
## <chr> <chr>
## 1 John guitar
## 2 Paul bass
## 3 Keith guitarband_members %>% inner_join(band_instruments)## Joining, by = "name"## # A tibble: 2 x 3
## name band plays
## <chr> <chr> <chr>
## 1 John Beatles guitar
## 2 Paul Beatles bassband_members %>% left_join(band_instruments)## Joining, by = "name"## # A tibble: 3 x 3
## name band plays
## <chr> <chr> <chr>
## 1 Mick Stones <NA>
## 2 John Beatles guitar
## 3 Paul Beatles bassband_members %>% right_join(band_instruments)## Joining, by = "name"## # A tibble: 3 x 3
## name band plays
## <chr> <chr> <chr>
## 1 John Beatles guitar
## 2 Paul Beatles bass
## 3 Keith <NA> guitarband_members %>% full_join(band_instruments)## Joining, by = "name"## # A tibble: 4 x 3
## name band plays
## <chr> <chr> <chr>
## 1 Mick Stones <NA>
## 2 John Beatles guitar
## 3 Paul Beatles bass
## 4 Keith <NA> guitar20.
Be able to use the functions: names(), rownames(), apply(), sapply, and lapply().
names(islands)## [1] "Africa" "Antarctica" "Asia" "Australia"
## [5] "Axel Heiberg" "Baffin" "Banks" "Borneo"
## [9] "Britain" "Celebes" "Celon" "Cuba"
## [13] "Devon" "Ellesmere" "Europe" "Greenland"
## [17] "Hainan" "Hispaniola" "Hokkaido" "Honshu"
## [21] "Iceland" "Ireland" "Java" "Kyushu"
## [25] "Luzon" "Madagascar" "Melville" "Mindanao"
## [29] "Moluccas" "New Britain" "New Guinea" "New Zealand (N)"
## [33] "New Zealand (S)" "Newfoundland" "North America" "Novaya Zemlya"
## [37] "Prince of Wales" "Sakhalin" "South America" "Southampton"
## [41] "Spitsbergen" "Sumatra" "Taiwan" "Tasmania"
## [45] "Tierra del Fuego" "Timor" "Vancouver" "Victoria"m2 <- cbind(1, 1:4)
colnames(m2) <- c("x","Y")
m2## x Y
## [1,] 1 1
## [2,] 1 2
## [3,] 1 3
## [4,] 1 4x <- cbind(x1 = 3, x2 = c(4:1, 2:5))
apply(x, 2, sort)## x1 x2
## [1,] 3 1
## [2,] 3 2
## [3,] 3 2
## [4,] 3 3
## [5,] 3 3
## [6,] 3 4
## [7,] 3 4
## [8,] 3 5x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE))
lapply(x, mean)## $a
## [1] 5.5
##
## $beta
## [1] 4.535125
##
## $logic
## [1] 0.5sapply(x, quantile) #more user friendly than lapply## a beta logic
## 0% 1.00 0.04978707 0.0
## 25% 3.25 0.25160736 0.0
## 50% 5.50 1.00000000 0.5
## 75% 7.75 5.05366896 1.0
## 100% 10.00 20.08553692 1.021.
Be familiar with the pipe operator %>%. As shown in previous examples.
22.
Be familiar with the $ sign when accessing a column of a data frame or accessing an element of a list. Know the difference between .$?, .[, ?], and .[?]. For example, mtcars$mpg, mtcars[, “mpg”], and mtcars[“mpg”]. Refer to question #2.
23.
Be familiar with “if(…) {…}else if(…){…}else{…}” structure. Know the use of ifelse(cond, …, …) function.
if (FALSE){
print("this will never happen")
} else if (FALSE && TRUE) {
print("This will also never print")
} else if (FALSE || TRUE){
print("This will print")
} else {
print("This will not print")
}## [1] "This will print"24.
Add labels on side-by-side bar charts using the geom_text() function from the “ggplot2” package. Reference: https://intellipaat.com/community/16343/how-to-put-labels-over-geombar-for-each-bar-in-r-with-ggplot2
library(ggplot2)
dat <- read.table(text = "sample Types Number
sample1 A 3641
sample2 A 3119
sample1 B 15815
sample2 B 12334
sample1 C 2706
sample2 C 3147", header=TRUE)
ggplot(data=dat, aes(x=Types, y=Number, fill=sample)) +
geom_bar(position = 'dodge', stat='identity') +
geom_text(aes(label=Number), position=position_dodge(width=0.9), vjust=-0.25)