Tidyverse Examples
Psych 251 Staff
10/2/2019
Manipulating data with dplyr
Let’s use mtcars, a built in dataset of cars and their horsepowers, mileage, etc.
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## 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
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 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
## 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
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Exercise: Summarise the overall MPG across the entire dataset.
mtcars %>%
summarise(mean = mean(mpg))## mean
## 1 20.09062Exercise: Summarise the overall MPG, broken down by the number of cylinders.
mtcars %>%
group_by(cyl) %>%
summarise(mean = mean(mpg))## # A tibble: 3 x 2
## cyl mean
## <dbl> <dbl>
## 1 4 26.7
## 2 6 19.7
## 3 8 15.1Exercise: Add standard deviations to this summary.
mtcars %>%
group_by(cyl) %>%
summarise(mean = mean(mpg),
sd = sd(mpg))## # A tibble: 3 x 3
## cyl mean sd
## <dbl> <dbl> <dbl>
## 1 4 26.7 4.51
## 2 6 19.7 1.45
## 3 8 15.1 2.56BONUS: Use ggplot to make a scatter plot of mpg by horsepower and add a smoothing line.
ggplot(mtcars,
aes(x = hp, y = mpg)) +
geom_point() +
geom_smooth() ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Reshaping with tidyr
From long to wide and back again
Use data from informative communication experiment.
http://langcog.stanford.edu/papers/FG-cogpsych2014.pdf
info <- read_csv("https://raw.githubusercontent.com/mcfrank/info_kid/master/data/info_e2_data.csv")## Parsed with column specification:
## cols(
## subid = col_double(),
## gender = col_character(),
## age_months = col_double(),
## `age group` = col_double(),
## order = col_double(),
## set = col_double(),
## item = col_character(),
## type = col_character(),
## target = col_character(),
## `novel word` = col_character(),
## comment = col_character(),
## `target train side` = col_character(),
## `target test side` = col_character(),
## correct = col_double()
## )info is in tidy format. Make this into wide data.
info_wide <- info %>%
spread(item, correct) Now make it back into tidy data.
info_long <- info_wide %>%
gather(item, correct, bear:rocket)From wide to long without seeing the tidy version
From wide to long without seeing the tidy version
These are pre-post data on children’s arithemtic scores from an RCT in which they were assigned either to CNTL (control) or MA (mental abacus math intervention). They were tested twice, once in 2015 and once in 2016.
https://jnc.psychopen.eu/article/view/106
majic <- read_csv("data/majic.csv")## Parsed with column specification:
## cols(
## subid = col_character(),
## grade = col_character(),
## group = col_character(),
## `2015` = col_double(),
## `2016` = col_double()
## )Make these tidy.
majic %>%
gather(year, score, `2015`, `2016`)## # A tibble: 328 x 5
## subid grade group year score
## <chr> <chr> <chr> <chr> <dbl>
## 1 S1-02-03 first grade CNTL 2015 0
## 2 S1-02-08 first grade CNTL 2015 0
## 3 S1-02-17 first grade CNTL 2015 8
## 4 S1-03-05 first grade MA 2015 4
## 5 S1-03-14 first grade MA 2015 3
## 6 S1-03-15 first grade MA 2015 0
## 7 S1-04-01 first grade MA 2015 0
## 8 S1-04-03 first grade MA 2015 3
## 9 S1-04-04 first grade MA 2015 3
## 10 S1-04-07 first grade MA 2015 1
## # … with 318 more rows