library(tidyverse)AFI 2.4
Begin by loading the tidyverse package in the code chunk above and adding your name as the author.
The readr package includes functions for reading tabular data into R. Each code chuck below should read in the specified data file, storing it in an appropriately named object, and then print the data. The data are stored in the data folder.
Importing Data
Let’s start by reading in the AllCountries.csv data. Modify this code by filling in the ______ to do so:
all_countries <- read_csv("data/AllCountries.csv")
all_countries# A tibble: 217 × 25
Country LandArea Population Density GDP Rural CO2 PumpPrice Military
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 653. 30.6 46.8 665 74.1 35.3 1.28 8.65
2 Albania 27.4 2.90 106. 4460 44.6 12.8 1.81 NA
3 Algeria 2382. 39.2 16.5 5361 30.5 24.6 0.29 NA
4 American Sa… 0.2 0.055 275 NA 12.7 NA NA NA
5 Andorra 0.47 0.079 168. NA 13.8 9.5 1.67 NA
6 Angola 1247. 21.5 17.2 5783 57.5 44.8 0.63 13.8
7 Antigua and… 0.44 0.09 204. 13342 75.4 16.6 NA NA
8 Argentina 2737. 41.4 15.1 14715 8.5 16.9 1.46 NA
9 Armenia 28.5 2.98 105. 3505 37 13.9 1.25 16.8
10 Aruba 0.18 0.103 572. NA 57.9 10.4 NA NA
# ℹ 207 more rows
# ℹ 16 more variables: Health <dbl>, ArmedForces <dbl>, Internet <dbl>,
# Cell <dbl>, HIV <dbl>, Hunger <dbl>, Diabetes <dbl>, BirthRate <dbl>,
# DeathRate <dbl>, ElderlyPop <dbl>, LifeExpectancy <dbl>, FemaleLabor <dbl>,
# Unemployment <dbl>, EnergyUse <dbl>, Electricity <dbl>, Developed <dbl>That was easy, right? Note that including the #| message: false at the beginning of the code chunk suppresses unneeded messages and cleans up your output for whoever reads it (me!).
Now try the minn_stp_weather.csv data:
minn_stp_weather <- read_csv("data/minn_stp_weather.csv")Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
dat <- vroom(...)
problems(dat)minn_stp_weather# A tibble: 1,403 × 2
`Description: Monthly weather data for Minneapolis-St. Paul` Minnesota
<chr> <chr>
1 for 1/1900-12/2014. <NA>
2 Variables: <NA>
3 MonthY (Month of the year 1-12) <NA>
4 MonthS (Month of the Series 1-1380) <NA>
5 Year <NA>
6 LowTemp <NA>
7 HighTemp <NA>
8 WarmestMin (Warmest daily low temp) <NA>
9 ColdestHigh (Coldest daily high temp) <NA>
10 AveMin <NA>
# ℹ 1,393 more rowsDid you look at the data file before trying?
Now try the white_nonhisp_death_rates_from_1999_to_2013.txt data:
white_data <- read_tsv("data/white_nonhisp_death_rates_from_1999_to_2013.txt")
white_data# A tibble: 150 × 5
Age Year Deaths Population Rate
<dbl> <dbl> <dbl> <dbl> <dbl>
1 45 1999 8304 3166393 262.
2 45 2000 8604 3207271 268.
3 45 2001 8836 3152637 280.
4 45 2002 9217 3256317 283
5 45 2003 9287 3260376 285.
6 45 2004 9210 3211340 287.
7 45 2005 9352 3279109 285.
8 45 2006 9100 3222835 282.
9 45 2007 8805 3137876 281.
10 45 2008 8751 3074171 285.
# ℹ 140 more rowsHere’s a trickier one. Read in the deaton.txt data:
deaton <- read_table("data/deaton.txt")
deaton# A tibble: 10 × 4
age death_rate_1989 death_rate_2013 change
<dbl> <dbl> <dbl> <dbl>
1 45 262. 261. -1.6
2 46 293. 290. -3.1
3 47 306. 324. 17.6
4 48 337. 343. 5.7
5 49 359 384. 25.5
6 50 377. 422. 45.5
7 51 429 466. 37.1
8 52 445. 481. 36.4
9 53 545. 527. -18.4
10 54 555. 573. 17.4You might need to search for a function that wasn’t discussed.
Now read in the pga2004.csv data (the help page for read_csv might be useful):
pga2004 <- read_csv(
"data/pga2004.csv",
col_names = c(
"player", "age", "drive_dist", "fairway_pct",
"gir_pct", "putts_per_gir", "scrambling_pct",
"scoring_avg", "events", "earnings", "avg_earnings"
)
)Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
dat <- vroom(...)
problems(dat)pga2004# A tibble: 210 × 11
player age drive_dist fairway_pct gir_pct putts_per_gir scrambling_pct
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Aaron Badd… 23 288 53.1 58.2 1.77 50.9
2 Adam Scott 24 295. 57.7 65.6 1.76 59.3
3 Alex Cejka 34 286. 64.2 63.8 1.80 50.7
4 Andre Stolz 34 298. 59 63 1.79 47.7
5 Arjun Atwal 31 289. 60.5 62.5 1.77 43.5
6 Arron Ober… 29 285. 68.8 67 1.78 50.9
7 Bart Bryant 42 282. 74.2 68.9 1.78 40.4
8 Ben Crane 28 284. 64.4 64.2 1.74 53.8
9 Ben Curtis 27 282. 64.3 63.4 1.81 42.2
10 Bernhard L… 47 282. 62.6 65.3 1.78 47.7
# ℹ 200 more rows
# ℹ 4 more variables: scoring_avg <dbl>, events <dbl>, earnings <dbl>,
# avg_earnings <dbl>Lastly, read in the noise.txt data:
noise <- read_table("data/noise.txt")Warning: 3020 parsing failures.
row col expected actual file
1 -- 5 columns 6 columns 'data/noise.txt'
2 -- 5 columns 6 columns 'data/noise.txt'
3 -- 5 columns 6 columns 'data/noise.txt'
4 -- 5 columns 6 columns 'data/noise.txt'
5 -- 5 columns 6 columns 'data/noise.txt'
... ... ......... ......... ................
See problems(...) for more details.noise# A tibble: 3,020 × 5
`"V1"` `"V2"` `"V3"` `"V4"` `"V5"`
<chr> <dbl> <dbl> <dbl> <dbl>
1 "\"1\"" -0.675 0.831 -1.24 0.207
2 "\"2\"" 0.974 -0.0118 -0.415 0.192
3 "\"3\"" -0.745 -1.03 1.79 0.464
4 "\"4\"" 1.06 1.01 -0.203 -0.550
5 "\"5\"" 0.493 -0.215 -0.192 -1.84
6 "\"6\"" -1.21 -0.873 0.582 -0.190
7 "\"7\"" 2.00 0.622 -0.967 -1.71
8 "\"8\"" -0.0600 0.920 -0.431 0.350
9 "\"9\"" -1.13 -0.0190 -0.430 -1.12
10 "\"10\"" 0.988 -0.322 -0.00720 -1.36
# ℹ 3,010 more rows