1. ConsumerGood.csv: A weekly multiple time series of the distribution, market share, and price of a fast-moving consumer good.
read_csv("https://web.stcloudstate.edu/mdernst/325/ConsumerGood.csv",
skip = 1, col_names = c("Week","Distribution","Share","Price"),
col_types = cols(
Week = col_double(),
Distribution = col_double(),
Share = col_double(),
Price = col_double()
))
## # A tibble: 108 x 4
## Week Distribution Share Price
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0.905 2.74 106.
## 2 2 0.9 3.01 106.
## 3 3 0.988 2.20 107.
## 4 4 0.96 2.67 106.
## 5 5 0.954 2.87 106.
## 6 6 0.988 2.77 106.
## 7 7 0.976 2.65 107.
## 8 8 0.93 3.11 105.
## 9 9 0.982 4.28 109.
## 10 10 0.88 2.36 110.
## # … with 98 more rows
3. minn_stp_weather.csv: Monthly weather data for Minneapolis-St. Paul, Minnesota for 1/1900-12/2014.
read_csv("https://web.stcloudstate.edu/mdernst/325/minn_stp_weather.csv",
skip = 17, na = c("", "NA", "T"),
col_types = cols(
MonthY = col_double(),
MonthS = col_double(),
Year = col_double(),
LowTemp = col_double(),
HighTemp = col_double(),
WarmestMin = col_double(),
ColdestHigh = col_double(),
AveMin = col_double(),
AveMax = col_double(),
meanTemp = col_double(),
TotPrecip = col_double(),
Max24hrPrecip = col_double()
))
## # A tibble: 1,388 x 12
## MonthY MonthS Year LowTemp HighTemp WarmestMin ColdestHigh AveMin AveMax
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1900 -15 51 36 -4 12.6 30
## 2 2 2 1900 -17 37 20 1 -1.3 18.5
## 3 3 3 1900 -10 54 31 8 17.2 33.9
## 4 4 4 1900 26 81 60 34 42.5 63
## 5 5 5 1900 33 90 68 50 49.9 74.1
## 6 6 6 1900 46 94 69 67 57.4 80.1
## 7 7 7 1900 51 95 69 66 60.7 81.2
## 8 8 8 1900 58 94 75 74 67 86.3
## 9 9 9 1900 36 92 70 54 52.5 70.5
## 10 10 10 1900 34 79 60 48 48.3 67.2
## # … with 1,378 more rows, and 3 more variables: meanTemp <dbl>,
## # TotPrecip <dbl>, Max24hrPrecip <dbl>
4. evap.txt: Daily Amounts of Water Evaporated, Temperature, and Barometric Pressure.
read_tsv("https://web.stcloudstate.edu/mdernst/325/evap.txt",
col_types = cols(
Day = col_double(),
Evap = col_double(),
Temp = col_double(),
mmHg = col_double(),
Date = col_date(format = "%d%b%Y")
))
## # A tibble: 365 x 5
## Day Evap Temp mmHg Date
## <dbl> <dbl> <dbl> <dbl> <date>
## 1 1 21 30 29.7 1692-11-10
## 2 2 32 27 29.7 1692-11-11
## 3 3 22.5 34 29.5 1692-11-12
## 4 4 31 23 29.4 1692-11-13
## 5 5 25.2 15 28.9 1692-11-14
## 6 6 27 10 29.4 1692-11-15
## 7 7 25.5 3 29.6 1692-11-16
## 8 8 26 -7 29.5 1692-11-17
## 9 9 21 -1 29.5 1692-11-18
## 10 10 21 -2 29.5 1692-11-19
## # … with 355 more rows
5. italy_ag.txt: Agricultural Production, population, and various measures for Central/Northern Italy Agricultural production.
read_fwf("https://web.stcloudstate.edu/mdernst/325/italy_ag.txt",
skip = 5, n_max = 70, col_types = cols(
Year = col_double(),
`price index of ag goods` = col_double(),
`price index of non-ag goods` = col_double(),
`price index` = col_double(),
`urban wages` = col_double(),
`ag wages` = col_double(),
`ag product per capita` = col_double(),
`population (1000s)` = col_number(),
`gross ag product` = col_double(),
`ag workforce as % of pop` = col_double(),
`ag workforce` = col_number(),
`output per worker` = col_double()
),
col_positions = fwf_positions(start = c(4,10,18,26,34,42,50,58,66,74,82,90),
end = c(8,16,24,32,40,48,56,64,72,80,88,NA),
col_names = c("Year","price index of ag goods","price index of non-ag goods","price index","urban wages","ag wages","ag product per capita","population (1000s)","gross ag product","ag workforce as % of pop","ag workforce","output per worker")))
## # A tibble: 70 x 12
## Year `price index of… `price index of… `price index` `urban wages`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1300 NA NA NA NA
## 2 1310 5.5 12.6 6.2 187.
## 3 1320 6.4 12.6 7.2 164.
## 4 1330 7.3 12.6 8.2 151.
## 5 1340 8.3 12.6 9.3 187.
## 6 1350 11.4 34.2 12.4 231.
## 7 1360 9.8 34.2 11 218.
## 8 1370 12.6 36.8 10.1 272.
## 9 1380 15.1 38.1 16.3 167.
## 10 1390 14.9 39.4 16.2 172.
## # … with 60 more rows, and 7 more variables: `ag wages` <dbl>, `ag product per
## # capita` <dbl>, `population (1000s)` <dbl>, `gross ag product` <dbl>, `ag
## # workforce as % of pop` <dbl>, `ag workforce` <dbl>, `output per
## # worker` <dbl>
6. donner.txt: Statistics regarding the 89 members of the Donner party 1846-1847. Data file description
read_fwf("https://web.stcloudstate.edu/mdernst/325/donner.txt",
col_types = cols(
Name = col_character(),
Age = col_double(),
Gender = col_character(),
Survive = col_double(),
`Date of death` = col_date(format = "%m/%d/%Y"),
`rescue party` = col_double(),
`date joined party` = col_date(format = "%m/%d/%Y"),
`trapped in mountains` = col_double(),
camp = col_character()
),
col_positions = fwf_positions(start = c(1,29,31,46,49,67,72,90,91),
end = c(27,30,31,46,58,67,81,90,92),
col_names = c("Name","Age","Gender","Survive","Date of death","rescue party","date joined party","trapped in mountains","camp")))
## # A tibble: 89 x 9
## Name Age Gender Survive `Date of death` `rescue party` `date joined pa…
## <chr> <dbl> <chr> <dbl> <date> <dbl> <date>
## 1 Geor… 60 M 0 1847-03-27 NA 1846-07-19
## 2 Tams… 45 F 0 1847-03-28 NA 1846-07-19
## 3 Fran… 6 F 1 NA 3 1846-07-19
## 4 Geor… 4 F 1 NA 3 1846-07-19
## 5 Eliz… 3 F 1 NA 3 1846-07-19
## 6 Elit… 13 F 1 NA 1 1846-07-19
## 7 Lean… 11 F 1 NA 1 1846-07-19
## 8 Jaco… 56 M 0 1846-12-15 NA 1846-07-19
## 9 Eliz… 45 F 0 1847-04-01 NA 1846-07-19
## 10 Geor… 9 M 1 NA 1 1846-07-19
## # … with 79 more rows, and 2 more variables: `trapped in mountains` <dbl>,
## # camp <chr>