# A tibble: 2 × 4
country code `2015` `2016`
<chr> <chr> <dbl> <dbl>
1 Aruba ABW 28419. 28450.
2 Albania ALB 3953. 4124.R programming with tidyverse
Session overview
What will be covered:
- Into the
tidyverse- What is
tidyverse? - Why should we use it?
- How do we use it?
- Piping with
%>%
- What is
- Vectorise R functions
- Vectors and vectorisation
- Vectorisation with
map() tidyversestyle vs. base R style
ggplot2for data visualisation- Grammar of graphics
Session overview
- Throughout this session:
- In-slide examples will be using the
mtcarsdataset (already in base R) - Walk-through exercises will be using the
covid_casesdataset
- In-slide examples will be using the
Into the tidyverse
What is tidyverse?
tidyverseis a collection of R packages built for data science- All packages shared the same principle of tidy data
- Consists of 8 core packages, and a dozen more packages for various specific purposes
What is tidyverse?
Tidy data
- A dataset with rows and columns
- Each column is a variable
- Each row is an observation
- Each cell contains 1 value only
What is tidyverse?
Tidy data
Which of these is a tidy data table
or
# A tibble: 4 × 4
country code year gdp
<chr> <chr> <dbl> <dbl>
1 Aruba ABW 2015 28419.
2 Aruba ABW 2016 28450.
3 Albania ALB 2015 3953.
4 Albania ALB 2016 4124.What is tidyverse?
Why tidyverse?
- Every function in
tidyverseshares a consistent structure and pattern. Making it very easy to implement and edit your R code - Outputs are also easier to read and consistent
- In general, your R code may look cleaner and easier to follow, compared to base R
- You just need to install everything once, instead of different packages for different things
Note
This is all subjective and your experience with tidyverse may varies
Why tidyverse?
Takeaway points
tidyverseis not a replacement of base R. It is just a collection of R packages- You don’t have to use
tidyversewhen using R tidyversehas most functions for data science needs. Though, there are times base R will be needed, and might be better/easier/faster- Understanding the principles and goals of
tidyversewill better your knowledge on good coding practices and R
Why tidyverse?
Commonly used tidyverse functions:
dplyr::select()to select columns from atibbledplyr::mutate()to change and create columns in atibbletidyr::pivot_longer()to transform atibblefrom wide to long format (less columns, more rows)tidyr::pivot_wider()to transform atibblefrom long to wide format (less rows, more columns)dplyr::group_by()anddplyr::summarise()to group data and summarise it using a function, e.g. mean, max, min, sum
Note
tibble is a type of dataframe that is used by all tidyverse functions
Why tidyverse?
- One of the main “unit” you will work with in R is a dataframe
- In base R, this object has type of
data.frame
class(mtcars)[1] "data.frame"- In the
tidyverse, we work with something called atibble(object type is calledtbl_df)
class(as_tibble(mtcars))[1] "tbl_df" "tbl" "data.frame"- You can see the differences more clearly in R
How to use tidyverse
Start by installing it!
install.packages("tidyverse")
Note
Most packages will be installed. You might need some more packages, but generally it should cover most of your needs.
How to use tidyverse
Then load it into your R environment!
library(tidyverse)Warning: package 'lubridate' was built under R version 4.4.2── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ purrr 1.0.2
✔ forcats 1.0.0 ✔ readr 2.1.5
✔ ggplot2 3.5.1 ✔ stringr 1.5.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Free R tip
You can load specific packages like library(dplyr)
How to use tidyverse
Run this example code below to make sure everything is running normally
ggplot(
data = ungroup(summarise(group_by(mtcars, gear), mean_mpg = mean(mpg))),
aes(x = gear, y = mean_mpg)
) +
geom_col()
Free R tips
- type
?function_namefor help on how to use a function and see which package is it coming from - add “tidyverse” into your Google searches when looking for R issues
Piping with %>%
- Look back at the example code
ggplot(
data = ungroup(summarise(group_by(mtcars, gear), mean_mpg = mean(mpg))),
aes(x = gear, y = mean_mpg)
) +
geom_col()- How easy is it to understand/follow this code?
- Can we improve readability?
Piping with %>%
- Typically in R, we perform a sequence of operations on a dataset, changing it as we go
- R has a functional style, which means the structure is typically:
new_data <- function(data, extra_arguments)functiondescribes your action, what you want to dodatais the data that you are execute the action onextra_argumentsare (optional) settings that changes how the action is performednew_datais the output, what you get after performing the action
Piping with %>%
- The example code can be rewritten as:
grouped_by_gear <- group_by(mtcars, gear)
mean_mpg_by_gear <- summarise(grouped_by_gear, mean_mpg = mean(mpg))
ungrouped_data <- ungroup(mean_mpg_by_gear)
ggplot(
data = ungrouped_data,
aes(x = gear, y = mean_mpg)
) +
geom_col()- Is this a better way to write it? Can we improve it even further?
- If we are performing a sequence of actions, each using the output of the previous action, we can use pipe
Piping with %>%
- Pipe is a powerful tool to express a sequence of actions (functions)
- It helps you write code that is easier to read and understand
- In R, you can pipe between functions using the
%>%or|>operators - Rstudio shortcut: Cmd+Shift+M or Ctrl+Shift+M
%>% vs. |>
%>%comes from themagrittrpackage which is used by all oftidyverse|>comes from R since version 4.1.0. It functions largely the same as%>%but not identical- R Café will use
%>%
Piping with %>%
- Pipes transfer the data from its left-hand side (LHS) to the function on its right-hand side (RHS) as the first argument of that function
- The structure:
new_data <- data %>% function(extra_arguments)
Piping with %>%
For example:
group_by(mtcars, gear)is exactly the same as
mtcars %>% group_by(gear)Piping with %>%
Another example:
summarise(group_by(mtcars, gear), mean_mpg = mean(mpg))is exactly the same as
mtcars %>% group_by(gear) %>% summarise(mean_mpg = mean(mpg))
Note
Every function under tidyverse will take the data as the first argument, so everything can be piped!
Piping with %>%
- Using pipe, we can now rewrite the code as:
mtcars %>%
group_by(gear) %>%
summarise(mean_mpg = mean(mpg)) %>%
ungroup() %>%
ggplot(aes(x = gear, y = mean_mpg)) +
geom_col()- For you, is it better/faster to understand what’s happening now?
Piping with %>%
Quick comparison
ggplot(
data = ungroup(summarise(group_by(mtcars, gear), mean_mpg = mean(mpg))),
aes(x = gear, y = mean_mpg)
) +
geom_col()vs.
mtcars %>%
group_by(gear) %>%
summarise(mean_mpg = mean(mpg)) %>%
ungroup() %>%
ggplot(aes(x = gear, y = mean_mpg)) +
geom_col()Exercise
Exercise
Checklist
- Load the
covid_casesdataset - Have a look at the dataset, do you understand how is it structured? Does it follow the tidy data standard?
- Use
tidyverseto transform the data into a tidytibble
Exercise
- Load the
covid_casesdataset
# read in data
covid_cases <- read_rds("../data/covid_cases.rds")
covid_cases date cases_chn cases_kor cases_aus cases_jpn cases_mys cases_phl
1 2020-01-20 0 0 0 0 0 0
2 2020-01-21 31 0 0 0 0 0
3 2020-01-23 262 0 0 0 0 0
4 2020-01-24 259 1 0 0 0 0
5 2020-01-25 467 0 3 2 0 0
6 2020-01-26 688 0 1 0 3 0
7 2020-01-27 776 2 0 1 1 0
8 2020-01-28 1776 0 1 2 0 0
9 2020-01-29 1460 0 2 1 0 0
10 2020-01-30 1739 0 0 4 3 1
11 2020-01-31 1984 7 2 3 1 0
12 2020-02-01 2101 1 3 3 0 0
13 2020-02-02 2590 3 0 3 0 1
14 2020-02-03 2827 0 0 0 0 0
15 2020-02-04 3233 1 0 0 2 0
16 2020-02-05 3892 2 1 13 0 1
17 2020-02-06 3697 5 1 -8 2 0
18 2020-02-07 3151 1 1 0 2 0
19 2020-02-08 3387 0 0 0 1 0
20 2020-02-09 2653 3 0 1 2 0
21 2020-02-10 2984 0 0 0 1 0
22 2020-02-11 2473 1 0 0 0 0
23 2020-02-12 2022 0 0 2 0 0
24 2020-02-13 1820 0 0 1 0 0
25 2020-02-14 1998 0 0 4 1 0
26 2020-02-15 1506 0 0 8 2 0
27 2020-02-16 1120 1 0 12 1 0
28 2020-02-17 19461 1 0 6 0 0
29 2020-02-18 1893 1 0 6 0 0
30 2020-02-19 1752 20 0 8 0 0
31 2020-02-20 395 53 0 12 0 0
32 2020-02-21 894 100 2 8 0 0
33 2020-02-22 823 142 4 12 0 0
34 2020-02-23 650 256 1 27 0 0
35 2020-02-24 220 161 0 12 0 0
36 2020-02-25 518 214 0 13 0 0
37 2020-02-26 411 284 1 7 0 0
38 2020-02-27 439 505 0 22 0 0
39 2020-02-28 331 571 0 24 2 0
40 2020-02-29 433 813 1 20 0 0
41 2020-03-01 574 586 1 9 0 0
42 2020-03-02 206 476 2 15 0 0
43 2020-03-03 130 600 6 14 5 0
44 2020-03-04 118 516 10 16 21 0
45 2020-03-05 143 438 23 33 0 0
46 2020-03-06 146 518 -9 32 5 2
47 2020-03-07 102 483 5 59 28 0
48 2020-03-08 46 367 12 47 10 1
49 2020-03-09 45 248 3 33 0 4
50 2020-03-10 20 131 15 26 24 23
51 2020-03-11 31 242 20 54 12 16
52 2020-03-12 26 114 10 52 0 3
53 2020-03-13 10 110 18 55 0 0
54 2020-03-14 30 107 57 41 68 12
55 2020-03-15 27 76 52 64 41 47
56 2020-03-16 29 74 49 34 315 29
57 2020-03-17 39 84 77 15 0 47
58 2020-03-18 0 0 39 0 0 0
59 2020-03-19 58 93 96 44 120 0
60 2020-03-20 126 239 199 77 227 30
61 2020-03-21 116 147 164 46 130 13
62 2020-03-22 82 98 208 50 153 77
63 2020-03-23 103 64 315 43 123 73
64 2020-03-24 146 76 313 39 212 82
65 2020-03-25 101 100 543 65 106 90
66 2020-03-26 113 104 547 98 172 84
67 2020-03-27 117 91 186 96 235 71
68 2020-03-28 152 146 650 112 130 96
69 2020-03-29 126 105 331 194 159 272
70 2020-03-30 91 78 0 173 150 343
71 2020-03-31 98 125 393 87 156 128
72 2020-04-01 86 101 348 225 140 538
73 2020-04-02 93 89 269 206 142 227
74 2020-04-03 78 86 248 233 208 322
75 2020-04-04 73 94 230 303 217 385
76 2020-04-05 55 81 181 351 150 76
77 2020-04-06 75 47 109 383 179 152
78 2020-04-07 66 47 100 252 131 414
79 2020-04-08 86 53 112 351 170 104
80 2020-04-09 92 39 96 511 156 106
81 2020-04-10 56 27 100 579 109 206
82 2020-04-11 64 30 86 658 118 119
83 2020-04-12 113 32 51 743 184 233
84 2020-04-13 115 25 33 507 153 220
85 2020-04-14 99 27 44 390 134 284
86 2020-04-15 49 27 50 455 170 291
87 2020-04-16 52 22 42 482 85 230
88 2020-04-17 352 22 10 585 110 207
89 2020-04-18 31 18 65 628 69 218
90 2020-04-19 21 8 53 566 54 209
91 2020-04-20 36 13 26 390 84 172
92 2020-04-21 13 9 13 367 35 200
cases_sgp cases_nzl cases_vnm cases_brn cases_khm cases_mng cases_fji
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cases_lao cases_png cases_gum cases_pyf cases_ncl cases_mnp cases_idn
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cases_mmr cases_tls cases_usa cases_can cases_bra cases_chl cases_ecu
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69 3 0 18093 739 502 299 228
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79 1 0 29510 1243 926 301 0
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cases_mex cases_dom cases_pan cases_per cases_arg cases_col cases_cri
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65 0 67 0 21 35 29 19
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cases_ury cases_cub cases_hnd cases_ven cases_bol cases_tto cases_pry
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cases_gtm cases_jam cases_slv cases_brb cases_bhm cases_guy cases_hti
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cases_lca cases_dma cases_grd cases_sur cases_kna cases_atg cases_nic
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cases_blz cases_vct cases_pri cases_mtq cases_glp cases_abw cases_guf
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cases_vir cases_bmu cases_cym cases_sxm cases_maf cases_cuw cases_blm
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cases_msr cases_tca cases_aia cases_vgb cases_bes cases_flk cases_spm
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cases_esp cases_ita cases_deu cases_fra cases_gbr cases_tur cases_bel
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cases_che cases_nld cases_prt cases_aut cases_rus cases_isr cases_swe
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63 894 573 320 607 132 188 160
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73 962 1019 808 529 440 462 512
74 1774 1083 783 418 771 620 519
75 862 1026 852 396 601 819 612
76 783 904 638 241 582 559 365
77 576 1224 754 217 658 429 387
78 509 952 452 314 954 593 376
79 590 777 712 343 1154 793 487
80 546 969 699 329 2634 0 726
81 785 1213 815 279 1786 351 722
82 733 1335 1516 312 1667 340 544
83 592 1316 515 247 2186 430 466
84 400 1174 598 130 2558 353 332
85 279 964 349 106 2774 357 465
86 254 868 514 191 3388 633 497
87 583 734 643 136 3448 332 482
88 315 1061 750 78 4070 391 613
89 346 1235 181 155 4785 264 676
90 325 1140 663 59 6060 252 606
91 336 1066 521 48 0 255 563
92 204 750 657 73 9910 521 392
cases_irl cases_nor cases_dnk cases_pol cases_cze cases_rou cases_lux
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0
38 0 1 1 0 0 1 0
39 0 3 0 0 0 0 0
40 0 2 1 0 0 2 0
41 1 9 1 0 0 0 0
42 0 4 1 0 3 0 1
43 0 6 1 0 0 0 0
44 1 7 3 1 2 1 0
45 0 24 2 0 0 0 0
46 12 30 8 0 7 2 0
47 4 27 5 4 0 1 1
48 1 34 8 1 14 6 0
49 2 22 5 5 6 2 0
50 3 23 54 5 6 0 3
51 10 85 172 6 23 10 0
52 9 0 353 22 33 23 12
53 27 212 59 5 22 0 0
54 20 261 127 15 34 16 21
55 39 157 26 47 64 59 0
56 40 170 71 39 84 35 0
57 54 92 62 0 85 0 43
58 69 139 17 96 51 26 59
59 0 115 67 41 88 62 70
60 265 129 88 38 172 14 135
61 126 190 123 100 210 48 139
62 102 184 71 111 91 59 186
63 121 206 69 98 170 66 128
64 219 239 65 115 71 143 77
65 204 195 131 152 158 186 224
66 235 350 133 150 260 144 234
67 255 240 153 170 408 123 120
68 302 425 169 168 217 263 152
69 294 264 155 249 384 160 226
70 200 257 194 224 166 308 119
71 295 124 182 193 173 192 38
72 325 221 283 256 306 293 190
73 212 218 247 243 281 215 141
74 402 270 279 392 269 278 168
75 424 273 371 437 332 445 125
76 331 302 320 244 282 430 117
77 507 130 292 475 115 251 75
78 253 115 312 311 235 193 39
79 345 108 390 435 195 360 127
80 515 147 331 357 295 344 64
81 1169 150 233 370 257 441 81
82 696 84 184 380 163 265 108
83 839 76 177 401 170 523 47
84 727 95 178 318 89 310 11
85 992 73 144 260 68 333 11
86 832 78 193 268 82 246 15
87 1068 111 170 380 162 337 66
88 724 114 198 336 130 491 71
89 709 0 194 461 116 360 36
90 778 193 169 363 105 351 57
91 493 84 142 545 133 328 13
92 401 45 131 306 127 190 8
cases_srb cases_fin cases_ukr cases_grc cases_isl cases_hrv cases_mda
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 1 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 0 0 0 0 0 2 0
38 0 1 0 1 0 1 0
39 0 0 0 2 0 0 0
40 0 0 0 0 0 2 0
41 0 0 0 0 0 2 0
42 0 4 0 4 2 0 0
43 0 1 0 0 7 2 0
44 0 0 1 0 7 0 0
45 0 0 0 2 10 0 0
46 1 5 0 23 0 1 0
47 0 7 0 0 19 1 0
48 0 0 0 34 0 0 1
49 0 11 0 7 10 0 0
50 0 10 0 0 0 1 0
51 11 0 0 16 6 4 2
52 7 0 0 9 0 0 1
53 0 69 2 0 0 0 0
54 12 0 0 0 0 11 4
55 10 101 0 130 77 10 4
56 0 57 0 103 0 12 11
57 29 5 4 0 61 7 6
58 15 47 7 56 26 9 1
59 11 40 2 31 25 16 6
60 27 10 0 0 80 0 13
61 12 81 10 77 79 45 17
62 38 71 21 35 64 80 14
63 15 105 0 94 95 29 14
64 61 74 37 71 20 71 15
65 54 92 29 48 60 76 16
66 81 88 43 78 89 36 24
67 73 78 62 71 65 77 28
68 71 67 93 74 88 91 22
69 131 193 107 95 73 71 32
70 82 0 62 95 57 56 32
71 44 95 69 56 66 77 35
72 115 71 120 102 49 77 55
73 160 62 135 61 85 96 70
74 111 72 183 139 99 48 168
75 305 97 109 99 45 68 0
76 148 267 155 60 53 47 161
77 284 45 68 62 69 56 112
78 292 249 143 20 76 40 101
79 247 132 206 77 24 60 91
80 219 179 224 52 30 61 118
81 201 118 311 71 32 64 115
82 238 164 308 56 27 88 149
83 275 136 266 70 14 39 122
84 250 69 325 33 12 66 102
85 424 90 270 31 10 50 50
86 411 97 392 25 9 54 222
87 408 76 398 22 7 37 115
88 445 132 500 15 12 50 105
89 372 120 444 0 15 23 110
90 304 192 343 0 6 18 87
91 324 102 261 28 11 39 121
92 312 85 415 10 2 10 76
cases_est cases_hun cases_svn cases_blr cases_ltu cases_arm cases_aze
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0
38 1 0 0 0 0 0 0
39 0 0 0 1 1 0 0
40 0 0 0 0 0 0 0
41 0 0 0 0 0 0 3
42 0 0 0 0 0 1 0
43 0 0 0 0 0 0 0
44 1 0 0 0 0 0 0
45 0 2 1 5 0 0 0
46 1 0 5 0 0 0 0
47 7 3 3 0 0 0 6
48 0 2 3 0 0 0 0
49 0 2 4 0 0 0 0
50 0 0 7 0 0 0 0
51 3 4 8 3 0 0 0
52 0 0 26 3 2 0 0
53 0 3 0 0 0 0 2
54 66 3 84 9 3 7 0
55 0 13 0 0 3 0 8
56 126 7 78 15 5 18 0
57 0 11 34 0 3 26 0
58 20 0 22 0 8 0 2
59 33 8 11 10 1 32 13
60 9 15 33 0 10 38 0
61 16 12 22 11 33 14 10
62 23 46 42 19 36 24 9
63 20 36 31 0 38 30 12
64 26 20 28 5 36 45 7
65 17 39 38 0 30 30 15
66 35 35 48 5 65 25 6
67 134 39 49 0 25 39 29
68 37 43 55 8 59 43 25
69 65 65 59 0 36 52 17
70 39 39 39 0 90 58 26
71 36 0 33 58 0 0 83
72 30 45 51 0 49 50 25
73 34 33 27 40 48 39 61
74 79 60 56 62 68 69 41
75 103 93 37 0 122 96 43
76 57 55 43 186 0 10 69
77 79 11 20 122 40 0 72
78 11 73 24 138 32 87 57
79 41 78 34 161 37 44 76
80 36 85 36 205 32 44 105
81 22 210 33 0 43 16 104
82 51 120 36 915 44 0 65
83 46 100 28 245 54 76 67
84 5 48 17 352 9 26 40
85 23 54 7 341 8 0 50
86 41 67 8 362 0 28 49
87 29 73 28 447 21 68 56
88 32 111 20 476 58 24 30
89 25 71 36 575 90 42 57
90 53 82 13 0 59 47 33
91 16 68 13 0 28 43 25
92 7 114 5 1485 24 48 38
cases_bih cases_kaz cases_svk cases_mkd cases_bgr cases_uzb cases_and
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0
38 0 0 0 1 0 0 0
39 0 0 0 0 0 0 0
40 0 0 0 0 0 0 0
41 0 0 0 0 0 0 0
42 0 0 0 0 0 0 0
43 0 0 0 0 0 0 1
44 0 0 0 0 0 0 0
45 2 0 0 0 0 0 0
46 0 0 0 0 0 0 0
47 0 0 1 2 0 0 0
48 0 0 2 0 2 0 0
49 0 0 2 0 2 0 0
50 0 0 2 4 0 0 0
51 2 0 0 0 6 0 0
52 0 0 3 0 -3 0 0
53 0 0 11 0 0 0 0
54 7 0 9 2 0 0 1
55 7 6 14 4 36 0 0
56 0 0 17 0 8 4 0
57 1 0 11 6 16 0 12
58 0 27 25 12 14 12 2
59 17 3 8 5 11 0 23
60 8 10 18 12 2 5 36
61 0 7 14 22 33 12 0
62 48 3 41 15 36 0 13
63 33 4 7 29 22 13 25
64 6 3 6 22 16 0 51
65 33 16 13 12 19 4 24
66 9 18 12 29 22 15 25
67 40 28 10 24 22 18 18
68 20 79 69 18 29 21 58
69 36 61 0 22 38 29 32
70 56 29 41 18 15 12 20
71 34 18 0 26 13 4 29
72 54 36 27 44 40 24 6
73 51 38 37 25 23 17 20
74 57 49 26 30 35 31 33
75 65 25 24 46 28 20 13
76 46 71 21 53 18 57 24
77 30 73 14 72 28 92 57
78 33 66 49 15 18 82 17
79 86 39 64 29 28 62 11
80 35 18 84 18 16 21 15
81 59 75 19 46 31 69 23
82 26 57 14 48 11 0 13
83 47 38 13 117 26 172 20
84 59 82 14 0 14 100 17
85 27 112 27 26 10 158 12
86 52 184 66 54 28 160 8
87 30 20 28 66 34 135 14
88 53 185 114 107 53 31 21
89 41 66 72 36 46 70 9
90 58 0 40 53 32 45 1
91 18 0 72 37 37 70 13
92 14 306 12 18 14 92 1
cases_lva cases_cyp cases_alb cases_smr cases_mlt cases_kgz cases_mne
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0
38 0 0 0 0 0 0 0
39 0 0 0 0 0 0 0
40 0 0 0 1 0 0 0
41 0 0 0 0 0 0 0
42 0 0 0 0 0 0 0
43 1 0 0 7 0 0 0
44 0 0 0 0 0 0 0
45 0 0 0 8 0 0 0
46 0 0 0 5 0 0 0
47 0 0 0 3 0 0 0
48 0 0 0 3 3 0 0
49 2 0 2 10 0 0 0
50 3 2 0 12 1 0 0
51 2 0 8 14 0 0 0
52 8 4 0 0 2 0 0
53 0 0 13 0 3 0 0
54 0 8 10 3 3 0 0
55 14 7 5 26 0 0 0
56 1 12 4 0 9 0 0
57 5 0 9 10 9 0 0
58 24 0 4 2 8 0 2
59 11 25 4 5 10 3 0
60 15 0 11 17 5 0 8
61 25 9 0 25 11 3 4
62 13 17 6 0 9 8 0
63 15 11 13 0 17 0 7
64 41 21 34 36 17 2 1
65 17 8 23 0 13 26 7
66 24 8 28 21 9 2 23
67 23 14 12 10 5 14 15
68 36 16 11 10 5 0 3
69 25 17 15 0 0 26 12
70 71 35 11 1 12 0 3
71 0 16 0 1 5 23 6
72 22 32 20 6 11 4 14
73 48 58 34 0 21 4 15
74 12 36 0 9 7 15 20
75 35 40 56 7 7 14 20
76 16 30 0 7 11 3 37
77 24 20 44 7 21 69 6
78 9 19 0 11 7 12 20
79 6 29 23 2 52 42 25
80 29 32 9 29 6 10 0
81 12 38 7 36 38 18 4
82 23 31 0 0 13 41 3
83 18 21 30 12 20 38 7
84 21 17 0 0 8 42 5
85 4 29 29 15 6 11 7
86 2 33 0 1 9 19 14
87 9 20 19 21 6 17 0
88 9 20 24 33 13 23 15
89 7 0 30 0 10 17 2
90 30 26 0 29 4 48 3
91 15 6 14 6 1 14 0
92 12 5 47 1 4 22 4
cases_geo cases_lie cases_mco cases_vat cases_fro cases_ggy cases_jey
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0
38 1 0 0 0 0 0 0
39 0 0 0 0 0 0 0
40 1 0 0 0 0 0 0
41 1 0 1 0 0 0 0
42 0 0 0 0 0 0 0
43 0 0 0 0 0 0 0
44 0 0 0 0 0 0 0
45 0 1 0 0 0 0 0
46 6 0 0 0 0 0 0
47 0 0 0 1 0 0 0
48 3 0 0 0 1 0 0
49 1 0 0 0 2 0 0
50 2 0 0 0 -1 1 0
51 8 0 0 0 0 0 0
52 0 0 0 0 0 0 0
53 2 3 0 0 0 0 2
54 0 0 1 0 1 0 0
55 5 0 0 0 6 0 0
56 3 3 7 0 2 0 0
57 0 0 0 0 36 0 0
58 1 0 0 0 0 0 3
59 4 18 0 0 11 0 0
60 0 0 0 0 14 0 0
61 5 9 3 0 8 0 7
62 6 2 6 0 12 0 0
63 5 10 5 0 23 16 3
64 13 0 0 0 3 3 3
65 6 1 0 0 4 3 -2
66 4 4 0 3 10 7 2
67 4 5 -4 0 8 4 14
68 4 4 0 0 4 2 20
69 5 1 0 0 11 3 9
70 8 1 27 2 4 0 2
71 5 2 3 0 9 6 0
72 12 4 3 0 1 15 0
73 6 4 -15 0 4 18 18
74 27 3 0 1 4 13 0
75 9 1 0 0 2 23 37
76 13 1 0 0 2 22 5
77 18 1 0 0 0 18 32
78 7 0 3 0 0 0 0
79 13 0 0 0 3 12 15
80 6 1 14 1 0 0 0
81 16 0 0 0 0 15 0
82 3 1 0 0 0 10 13
83 19 0 0 0 0 9 15
84 14 0 0 0 0 9 0
85 30 0 39 0 0 9 15
86 10 1 0 0 0 1 4
87 30 0 0 0 0 4 0
88 34 0 0 0 0 5 2
89 15 0 5 0 0 6 4
90 9 1 0 0 0 2 11
91 5 0 0 0 1 3 11
92 9 0 -30 1 0 0 4
cases_imn cases_gib cases_grl cases_irn cases_pak cases_sau cases_qat
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 2 0 0 0
32 0 0 0 3 0 0 0
33 0 0 0 13 0 0 0
34 0 0 0 10 0 0 0
35 0 0 0 15 0 0 0
36 0 0 0 18 0 0 0
37 0 0 0 34 0 0 0
38 0 0 0 46 2 0 0
39 0 0 0 104 0 0 0
40 0 0 0 143 0 0 0
41 0 0 0 205 2 0 1
42 0 0 0 385 0 0 2
43 0 0 0 523 1 1 4
44 0 0 0 835 0 0 1
45 0 1 0 586 0 1 0
46 0 0 0 591 0 6 0
47 0 0 0 1234 0 0 3
48 0 0 0 1076 0 -1 1
49 0 0 0 743 1 8 3
50 0 0 0 595 10 0 3
51 0 0 0 881 0 5 6
52 0 0 0 958 3 1 238
53 0 0 0 1075 1 0 0
54 0 0 0 1289 1 41 0
55 0 0 0 1365 7 41 75
56 0 0 0 2262 24 0 64
57 0 2 0 0 135 30 38
58 0 0 0 1178 0 38 3
59 0 5 2 1192 54 67 0
60 0 2 0 1046 61 0 10
61 1 0 0 1237 159 36 8
62 1 0 0 966 34 118 10
63 0 5 0 1028 289 119 24
64 11 0 2 1411 103 51 7
65 10 0 0 1762 104 205 25
66 0 11 1 2206 66 133 11
67 3 9 1 2389 0 112 12
68 3 20 3 2926 178 92 13
69 3 1 1 3076 291 99 28
70 10 9 0 2901 99 96 44
71 0 4 0 3186 240 154 59
72 10 0 0 3111 174 110 88
73 13 0 0 2987 252 157 54
74 6 12 0 2875 159 165 114
75 43 14 0 2715 0 154 126
76 12 3 0 2560 430 331 250
77 1 5 1 2483 397 93 279
78 0 0 0 2274 587 289 228
79 23 10 0 2089 208 43 225
80 0 0 0 1997 250 137 153
81 15 0 0 1634 279 355 166
82 25 0 0 1972 187 364 136
83 14 0 0 1837 250 382 216
84 22 16 0 1657 336 429 251
85 2 0 0 1617 342 472 252
86 14 0 0 1574 272 435 197
87 12 0 0 1512 517 493 283
88 4 2 0 1606 520 518 392
89 31 2 0 1499 456 762 560
90 2 0 0 1374 512 1132 345
91 6 0 0 1343 425 1088 440
92 0 0 0 1294 798 1122 567
cases_egy cases_irq cases_are cases_mar cases_bhr cases_lbn cases_tun
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 4 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 1 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 2 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 1 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 1 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 1 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 2 0 0 1 0
34 0 0 2 0 0 0 0
35 0 0 0 0 0 0 0
36 0 1 0 0 8 0 0
37 0 4 0 0 18 0 0
38 0 1 0 0 7 1 0
39 0 1 6 0 0 0 0
40 0 1 0 0 5 0 0
41 0 5 0 0 2 0 0
42 1 6 2 0 7 8 0
43 0 7 0 1 2 3 1
44 0 5 6 0 0 0 0
45 0 5 0 1 0 0 0
46 1 0 0 0 0 3 0
47 0 8 18 0 0 6 0
48 45 10 0 0 7 6 0
49 7 6 0 0 23 4 1
50 4 1 14 0 30 9 0
51 0 0 15 1 1 0 4
52 8 9 0 2 79 25 0
53 0 0 11 1 6 0 1
54 26 23 0 1 15 11 9
55 0 0 0 11 1 16 0
56 33 31 13 10 10 6 2
57 40 0 0 10 8 10 2
58 0 30 0 0 8 11 4
59 30 10 15 11 19 13 5
60 14 13 27 12 13 16 10
61 46 16 0 13 16 14 15
62 29 21 13 12 21 43 6
63 42 19 0 29 31 42 15
64 39 33 45 28 40 19 14
65 36 50 50 27 15 37 25
66 54 30 85 55 27 29 59
67 39 36 0 50 39 35 24
68 41 76 72 83 15 23 30
69 40 48 63 79 3 21 51
70 33 41 102 79 39 26 34
71 47 83 41 58 0 8 50
72 54 64 53 64 52 17 32
73 69 34 150 38 2 16 29
74 86 44 210 59 74 29 32
75 120 48 240 109 30 0 40
76 85 58 241 116 15 19 58
77 103 83 294 153 12 0 21
78 149 70 277 28 56 14 22
79 128 91 283 43 55 7 27
80 110 80 300 91 12 27 5
81 139 30 331 102 64 7 15
82 95 48 370 71 111 27 28
83 145 38 376 97 42 10 14
84 126 34 387 116 96 11 22
85 125 26 398 102 226 2 19
86 160 22 412 125 166 9 21
87 155 15 432 136 149 17 33
88 168 19 460 259 23 5 42
89 171 48 477 281 44 5 42
90 188 31 0 121 29 5 2
91 112 26 479 170 108 0 0
92 189 35 484 191 26 4 18
cases_jor cases_kwt cases_omn cases_afg cases_dji cases_syr cases_lby
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
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cases_sdn cases_som cases_yem cases_pse cases_zaf cases_dza cases_bfa
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cases_civ cases_sen cases_gha cases_cmr cases_nga cases_mus cases_cod
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cases_rwa cases_mdg cases_ken cases_zmb cases_tgo cases_uga cases_eth
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cases_ner cases_cog cases_tza cases_mli cases_gin cases_gnq cases_nam
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59 0 2 2 0 0 2 0
60 1 0 3 0 1 1 1
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63 0 0 6 0 0 0 0
64 1 0 0 0 2 0 0
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66 0 0 1 2 0 0 1
67 8 0 0 0 1 0 3
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71 0 0 5 0 0 1 0
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80 64 15 1 9 20 0 0
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cases_swz cases_moz cases_syc cases_gab cases_ben cases_caf cases_eri
1 0 0 0 0 0 0 0
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52 0 0 0 0 0 0 0
53 0 0 0 0 0 0 0
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55 1 0 0 0 0 1 0
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57 0 0 2 0 1 0 0
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60 0 0 0 0 1 0 0
61 0 0 0 0 0 0 0
62 0 0 1 0 0 2 1
63 3 1 0 3 0 1 0
64 0 0 0 0 3 0 0
65 0 2 0 0 0 0 0
66 0 2 0 0 0 1 3
67 2 0 0 0 1 0 2
68 3 2 0 1 0 1 0
69 0 1 0 0 0 0 0
70 0 0 1 0 0 0 0
71 0 0 0 0 0 0 0
72 0 0 0 0 3 0 0
73 0 2 2 0 4 2 9
74 0 0 0 11 0 0 5
75 0 0 0 3 0 0 0
76 0 0 0 0 0 1 0
77 0 0 0 0 9 0 9
78 0 0 1 0 1 0 0
79 1 0 0 3 3 0 2
80 2 7 0 0 0 1 2
81 0 0 0 20 4 0 0
82 0 3 0 0 0 1 0
83 0 0 0 5 5 0 0
84 2 0 0 8 0 0 1
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86 1 7 0 23 0 0 0
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cases_cpv cases_tcd cases_mrt cases_zwe cases_gmb cases_lbr cases_ago
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0
38 0 0 0 0 0 0 0
39 0 0 0 0 0 0 0
40 0 0 0 0 0 0 0
41 0 0 0 0 0 0 0
42 0 0 0 0 0 0 0
43 0 0 0 0 0 0 0
44 0 0 0 0 0 0 0
45 0 0 0 0 0 0 0
46 0 0 0 0 0 0 0
47 0 0 0 0 0 0 0
48 0 0 0 0 0 0 0
49 0 0 0 0 0 0 0
50 0 0 0 0 0 0 0
51 0 0 0 0 0 0 0
52 0 0 0 0 0 0 0
53 0 0 0 0 0 0 0
54 0 0 0 0 0 0 0
55 0 0 1 0 0 0 0
56 0 0 0 0 0 0 0
57 0 0 0 0 0 1 0
58 0 0 0 0 0 0 0
59 0 0 1 0 1 1 0
60 0 1 0 0 0 0 0
61 1 0 0 1 0 0 0
62 2 0 0 1 0 1 2
63 0 0 0 0 0 0 0
64 0 2 0 0 0 0 0
65 0 0 0 0 1 0 0
66 0 0 0 0 0 0 0
67 0 2 1 1 0 0 0
68 2 0 0 2 0 0 0
69 0 0 2 0 1 0 0
70 0 0 0 0 0 0 0
71 0 0 0 0 0 0 0
72 0 2 0 3 0 0 5
73 0 0 0 0 0 3 1
74 0 0 0 0 1 0 0
75 0 0 1 1 0 1 2
76 0 0 0 0 0 3 0
77 0 2 0 0 0 3 4
78 2 0 0 0 0 1 2
79 0 1 0 2 0 0 1
80 0 0 0 0 0 17 2
81 0 1 0 0 0 0 0
82 0 0 1 0 0 6 0
83 0 0 0 2 5 11 0
84 0 7 0 1 0 2 0
85 3 5 0 3 0 1 0
86 0 0 0 1 0 8 0
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cases_gnb cases_bwa cases_mwi cases_stp cases_bdi cases_sle cases_ssd
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
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10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
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20 0 0 0 0 0 0 0
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24 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
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30 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0
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35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
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40 0 0 0 0 0 0 0
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43 0 0 0 0 0 0 0
44 0 0 0 0 0 0 0
45 0 0 0 0 0 0 0
46 0 0 0 0 0 0 0
47 0 0 0 0 0 0 0
48 0 0 0 0 0 0 0
49 0 0 0 0 0 0 0
50 0 0 0 0 0 0 0
51 0 0 0 0 0 0 0
52 0 0 0 0 0 0 0
53 0 0 0 0 0 0 0
54 0 0 0 0 0 0 0
55 0 0 0 0 0 0 0
56 0 0 0 0 0 0 0
57 0 0 0 0 0 0 0
58 0 0 0 0 0 0 0
59 0 0 0 0 0 0 0
60 0 0 0 0 0 0 0
61 0 0 0 0 0 0 0
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63 0 0 0 0 0 0 0
64 0 0 0 0 0 0 0
65 0 0 0 0 0 0 0
66 2 0 0 0 0 0 0
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68 0 0 0 0 0 0 0
69 0 0 0 0 0 0 0
70 0 0 0 0 0 0 0
71 0 0 0 0 0 0 0
72 7 3 0 0 2 1 0
73 0 0 0 0 0 1 0
74 0 1 3 0 0 0 0
75 6 0 0 0 1 0 0
76 3 0 0 0 0 2 0
77 0 0 1 0 0 2 1
78 15 2 0 4 0 0 0
79 0 0 4 0 0 0 0
80 0 0 0 0 0 1 0
81 2 7 0 0 0 0 2
82 0 0 1 0 0 0 0
83 3 0 3 0 0 1 1
84 1 0 1 0 2 2 0
85 0 0 3 0 0 0 0
86 1 0 0 0 0 1 0
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88 3 2 0 0 0 2 0
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90 0 0 0 0 0 4 0
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cases_reu cases_myt
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 0 0
11 0 0
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 0 0
18 0 0
19 0 0
20 0 0
21 0 0
22 0 0
23 0 0
24 0 0
25 0 0
26 0 0
27 0 0
28 0 0
29 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 0 0
35 0 0
36 0 0
37 0 0
38 0 0
39 0 0
40 0 0
41 0 0
42 0 0
43 0 0
44 0 0
45 0 0
46 0 0
47 0 0
48 0 0
49 0 0
50 0 0
51 0 0
52 0 0
53 3 0
54 2 0
55 1 1
56 3 0
57 0 0
58 0 0
59 3 2
60 3 1
61 13 0
62 19 7
63 17 3
64 7 10
65 12 6
66 11 5
67 41 15
68 0 0
69 8 0
70 64 32
71 0 0
72 40 19
73 34 15
74 27 12
75 13 0
76 13 19
77 10 0
78 5 17
79 9 20
80 4 2
81 14 5
82 6 0
83 6 0
84 1 16
85 2 0
86 0 10
87 -1 4
88 4 12
89 8 12
90 5 0
91 1 39
92 0 0- Is this a tidy data table?
Exercise
- Recall what is the tidy data standard
- We want:
- Each column to represent a single variable. What are the variables here?
- Variables:
date,country,casesorcase_count - So, we want less columns more rows:
pivot_longer()
Exercise
- How to use:
?pivot_longer() - Make the data “longer”
covid_cases <- covid_cases %>%
pivot_longer(
cols = -date,
names_to = "country",
names_pattern = "cases_(.+)",
values_to = "cases"
)
covid_cases# A tibble: 19,412 × 3
date country cases
<date> <chr> <dbl>
1 2020-01-20 chn 0
2 2020-01-20 kor 0
3 2020-01-20 aus 0
4 2020-01-20 jpn 0
5 2020-01-20 mys 0
6 2020-01-20 phl 0
7 2020-01-20 sgp 0
8 2020-01-20 nzl 0
9 2020-01-20 vnm 0
10 2020-01-20 brn 0
# ℹ 19,402 more rowsVectorise R functions
What is a vector?
- A vector is a container of elements of similar classes
- In math, \([1\space 2\space 3]\) is a vector of 3 integers
- In R, you can define vectors by putting them inside
c()c(1, 2, 3)is a vector of 3numericelementsc("ab", "cd", "ef")is a vector of 3characterelementsc(c(1, 2), c(2, 3), c(3, 4))is a vector of 3vectorelements, each is a vector of 2numericelements
What is a vector?
- Question: is
12a vector?
class(12)[1] "numeric"class(c(12))[1] "numeric"class(c(12, 13, 14))[1] "numeric"- In R, most things are vectors. A single number by itself is also a vector
12itself is a vector that has one numeric element
What is a vector?
- Conceptually, a
tibbleis a list of named vectors! - You can see it using the
str()function
str(covid_cases)tibble [19,412 × 3] (S3: tbl_df/tbl/data.frame)
$ date : Date[1:19412], format: "2020-01-20" "2020-01-20" ...
$ country: chr [1:19412] "chn" "kor" "aus" "jpn" ...
$ cases : num [1:19412] 0 0 0 0 0 0 0 0 0 0 ...covid_caseshas 3 vectors:dateis a vector of datescountryis a vector of characterscasesis a vector of numbers
What is a vector?
- You can access vector elements with the
$operator
covid_cases$date[10000:10010] [1] "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08"
[6] "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08"
[11] "2020-03-08"covid_cases$country[10000:10010] [1] "spm" "esp" "ita" "deu" "fra" "gbr" "tur" "bel" "che" "nld" "prt"covid_cases$cases[10000:10010] [1] 0 56 1247 156 93 43 0 60 55 60 8What is a vector?
- Or the
tidyverseway withdplyr::slice()anddplyr::pull()
covid_cases %>% slice(10000:10010) %>% pull(date) [1] "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08"
[6] "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08" "2020-03-08"
[11] "2020-03-08"covid_cases %>% slice(10000:10010) %>% pull(country) [1] "spm" "esp" "ita" "deu" "fra" "gbr" "tur" "bel" "che" "nld" "prt"covid_cases %>% slice(10000:10010) %>% pull(cases) [1] 0 56 1247 156 93 43 0 60 55 60 8
Note
- one of the cases where using base R might be quicker and doesn’t sacrifice readability
- dplyr and tidyverse equivalents
For-loops
- In programming, we typically use for-loops to go through elements within a vector
- Example: We want to take the square of a vector
data <- covid_cases$cases[10000:10010]
# before
data [1] 0 56 1247 156 93 43 0 60 55 60 8squared_data <- numeric() # create new object to hold new data
idx <- seq(1, length(data)) # create index vector from 1 to the length of data
for (i in idx) {
squared_data[i] <- data[i]^2
}
# after
squared_data [1] 0 3136 1555009 24336 8649 1849 0 3600 3025
[10] 3600 64Vectorisation
- Instead of going through each element and perform an action, we can apply an action to all elements at once
- Example: Take the square root of a vector
# before
squared_data [1] 0 3136 1555009 24336 8649 1849 0 3600 3025
[10] 3600 64# after
sqrt_data <- sqrt(squared_data)
sqrt_data [1] 0 56 1247 156 93 43 0 60 55 60 8# alternative using `sapply()`
sqrt_data <- sapply(squared_data, sqrt)
sqrt_data [1] 0 56 1247 156 93 43 0 60 55 60 8
Note
Most functions math-related functions and operators in base R are already vectorised, e.g. sqrt(), log(), exp(), +, -, *, /
Vectorisation
- R has a functional style, which means vectorisation is more intuitive to write and read code
- On a technical level:
- R itself might be slightly faster when doing vectorisation, compared to for-loops
- for more complex and time-consuming functions, it is easier to paralellise with vectorisation
Vectorisation
- Vectorisation shines when there are more complex actions, and you have to write your own functions
- Example: Assume that
datais a vector of circle diameters, take its square roots and calculate the surface areas with vectorisation usingsapply()
area_from_diameter <- function(d) {
return(3.14 * (sqrt(d) / 2)^2)
}
sapply(data, area_from_diameter) [1] 0.000 43.960 978.895 122.460 73.005 33.755 0.000 47.100 43.175
[10] 47.100 6.280# alternatively
sapply(data, \(d) 3.14 * (sqrt(d) / 2)^2) [1] 0.000 43.960 978.895 122.460 73.005 33.755 0.000 47.100 43.175
[10] 47.100 6.280Vectorisation with map()
- In the
tidyverse, we can perform vectorisation with thepurrr::map()family - Let’s check how it works with
?purrr::map()
- Previous example using
map()
map(data, \(d) 3.14 * (sqrt(d) / 2)^2) %>% list_c() [1] 0.000 43.960 978.895 122.460 73.005 33.755 0.000 47.100 43.175
[10] 47.100 6.280
Note
We do list_c() after a map() because map() is designed to take in a list and return a list. list_c() helps combine a list into a vector
Vectorisation with map()
- If you have 2 vectors that you want to go through at the same time, you can use
map2() - Example:
data2is a vector of side lengths of squares, I want the sum of surface areas from the circles and the squares
data2 <- c(29, 37, 22, 35, 39, 29, 30, 33, 43, 36, 26)
map2(data, data2, \(d, l) (3.14 * (sqrt(d) / 2)^2) + (l^2)) %>% list_c() [1] 841.000 1412.960 1462.895 1347.460 1594.005 874.755 900.000 1136.100
[9] 1892.175 1343.100 682.280Vectorisation with map()
- You can create new columns, or edit current columns, with complex actions when using
map()withmutate() - Example: Using the
mtcarsdataset, create a new column calledhp_p_wt, which is the horsepower per weight of each car in kilograms
mtcars %>%
mutate(
hp_p_wt = map2(hp, wt, \(h, w) h / (w / 2.205)) %>% unlist(),
.before = mpg
) hp_p_wt mpg cyl disp hp drat wt qsec vs am gear
Mazda RX4 92.57634 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
Mazda RX4 Wag 84.36522 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
Datsun 710 88.39009 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
Hornet 4 Drive 75.44323 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
Hornet Sportabout 112.17297 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
Valiant 66.91474 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
Duster 360 151.32353 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
Merc 240D 42.85580 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
Merc 230 66.50000 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
Merc 280 78.84157 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
Merc 280C 78.84157 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
Merc 450SE 97.51843 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
Merc 450SL 106.40751 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
Merc 450SLC 105.00000 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
Cadillac Fleetwood 86.10000 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
Lincoln Continental 87.40321 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
Chrysler Imperial 94.88307 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
Fiat 128 66.15000 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
Honda Civic 70.99690 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
Toyota Corolla 78.10627 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
Toyota Corona 86.76876 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
Dodge Challenger 93.96307 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
AMC Javelin 96.28821 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
Camaro Z28 140.68359 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
Pontiac Firebird 100.35761 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
Fiat X1-9 75.20930 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
Porsche 914-2 93.76402 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
Lotus Europa 164.68275 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
Ford Pantera L 183.63407 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
Ferrari Dino 139.30505 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
Maserati Bora 206.91176 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
Volvo 142E 86.45504 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4
carb
Mazda RX4 4
Mazda RX4 Wag 4
Datsun 710 1
Hornet 4 Drive 1
Hornet Sportabout 2
Valiant 1
Duster 360 4
Merc 240D 2
Merc 230 2
Merc 280 4
Merc 280C 4
Merc 450SE 3
Merc 450SL 3
Merc 450SLC 3
Cadillac Fleetwood 4
Lincoln Continental 4
Chrysler Imperial 4
Fiat 128 1
Honda Civic 2
Toyota Corolla 1
Toyota Corona 1
Dodge Challenger 2
AMC Javelin 2
Camaro Z28 4
Pontiac Firebird 2
Fiat X1-9 1
Porsche 914-2 2
Lotus Europa 2
Ford Pantera L 4
Ferrari Dino 6
Maserati Bora 8
Volvo 142E 2ggplot2 for data visualisation
Data visualisation
- Now that we have the results of our study, we should visualise it to disseminate the information
- In base R, we use
plot()(and other functions) to draw figures - In
tidyverse, we use theggplot2package
An example
- Base R:
plot(
x = mtcars$wt,
y = mtcars$mpg,
xlab = "Weight (1000 lbs)",
ylab = "Miles Per Gallon",
main = "MPG vs. Weight",
pch = 19, # Solid circle points
col = "blue"
)
An example
ggplot2:
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "blue") +
scale_x_continuous("Weight (1000 lbs)") +
scale_y_continuous("Miles Per Gallon") +
ggtitle("MPG vs. Weight") +
theme_bw()
ggplot2
ggplot2works directly with tidy data (same as everything undertidyverse)- It is based on a concept called “grammar of graphics”
- Has intuitive and comprehensive documentation on how to use it effectively
- Provide enough flexibility for customisation
Grammar of graphics
- A framework to describe and construct graphics in a structured manner
- Has a layered approach, i.e. each component of a figure is a separate layer
Grammar of graphics
Grammar of graphics
- Look back at the example code:
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "blue") +
scale_x_continuous("Weight (1000 lbs)") +
scale_y_continuous("Miles Per Gallon") +
ggtitle("MPG vs. Weight") +
theme_bw()- Can you identify the different layers of this plot?
Grammar of graphics
- Checkout the reference tab and cheatsheet for ggplot2 here
Exercise
Checklist
- Have a look at the
covid_casesdataset: What would be a good visualisation?