a_thing <- 4
another_thing <- 1
another_Thing <- 7
both_things <- a_thing + another_thingThen we created a tiny data set.
a_data_thing <- data.frame(x = 2, y = 8)
a_data_thing$x## [1] 2How would we print the variable y? Type your answer in the chunk below
Write notes for yourself in the white space. Maybe explain to your future self what dollar signs do.
ERASE THIS AND TYPE SOME NOTES HERE
Enough playing around, let’s load some data!
acitelli <- read.csv("/Users/randigarcia/Desktop/Data/acitelli.csv", header=TRUE)Next, you want to look at your data.
head(acitelli)## cuplid Yearsmar gender self_pos other_pos satisfaction tension simhob
## 1 3 8.202667 -1 4.8 4.6 4.000000 1.5 0
## 2 3 8.202667 1 3.8 4.0 3.666667 2.5 1
## 3 10 10.452667 -1 4.6 3.8 3.166667 4.0 0
## 4 10 10.452667 1 4.2 4.0 3.666667 2.0 0
## 5 11 -8.297333 -1 5.0 4.4 3.833333 2.5 0
## 6 11 -8.297333 1 4.2 4.8 3.833333 2.5 0str(acitelli)## 'data.frame': 296 obs. of 8 variables:
## $ cuplid : int 3 3 10 10 11 11 17 17 21 21 ...
## $ Yearsmar : num 8.2 8.2 10.5 10.5 -8.3 ...
## $ gender : int -1 1 -1 1 -1 1 -1 1 -1 1 ...
## $ self_pos : num 4.8 3.8 4.6 4.2 5 4.2 4 4 4.2 4.4 ...
## $ other_pos : num 4.6 4 3.8 4 4.4 4.8 3.6 4.4 3.8 4.8 ...
## $ satisfaction: num 4 3.67 3.17 3.67 3.83 ...
## $ tension : num 1.5 2.5 4 2 2.5 2.5 3 2 3.5 2.5 ...
## $ simhob : int 0 1 0 0 0 0 -1 0 0 0 ...names(acitelli)## [1] "cuplid" "Yearsmar" "gender" "self_pos"
## [5] "other_pos" "satisfaction" "tension" "simhob"There is also documentation about functions.
?headYou probably also want descriptive statistics.
summary(acitelli)## cuplid Yearsmar gender self_pos
## Min. : 3.0 Min. :-11.214000 Min. :-1 Min. :2.600
## 1st Qu.:165.2 1st Qu.: -7.089000 1st Qu.:-1 1st Qu.:4.000
## Median :313.5 Median : -1.089000 Median : 0 Median :4.200
## Mean :282.6 Mean : -0.000036 Mean : 0 Mean :4.186
## 3rd Qu.:401.2 3rd Qu.: 6.077667 3rd Qu.: 1 3rd Qu.:4.400
## Max. :485.0 Max. : 15.036000 Max. : 1 Max. :5.000
## other_pos satisfaction tension simhob
## Min. :2.600 Min. :1.167 Min. :1.000 Min. :-1.0000
## 1st Qu.:4.000 1st Qu.:3.333 1st Qu.:2.000 1st Qu.: 0.0000
## Median :4.200 Median :3.833 Median :2.500 Median : 0.0000
## Mean :4.264 Mean :3.605 Mean :2.431 Mean : 0.0777
## 3rd Qu.:4.600 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.: 0.2500
## Max. :5.000 Max. :4.000 Max. :4.000 Max. : 1.0000We can also select pieces of a data frame. That first number is the row, the second is the column.
acitelli[2, 6]## [1] 3.666667#You try it! Find a numder you want to pull from the dataset.
#riggsi[ ?, ?]If it is instead a single variable, you can also select a piece.
acitelli$satisfaction[2]## [1] 3.666667In the chunk below, pick out the gender of the person in the 50th case.
#try it by referring to the row and column of the data frame.
#try it by referring to the variable, using the dollar sign notation.You might want to get descriptive stats or frequencies for specific variables. There are base R functions, but I like to use the package mosaic. You can find more information and a cheat sheet for mosaic at this website.
First we need to install the mosaic package using the install.packages() function. The package name goes inside of the paratheses in double quotes: ‘“mosaic”’. This is something we do only once in the console, you wouldn’t want to save it in your .Rmd file.
#install.packages("mosaic")Once a package is installed, any time we start a new R session and we want to use functions inside of that package, we will need to load the package with the library() function.
library(mosaic)mosaicThe function favstats() will give descriptive statistics for a numerical variable, and the function tally() will give you frequencies for a categorical variable (or a numerical variable…if you want it). Functions in mosaic use the formula syntax, where y ~ x, or for a single variable, ~x. The ~ key can be found just below your esc key. The first argument is the formula, and the second argument is the data frame, e.g., data = acitelli.
favstats(~satisfaction, data = acitelli)## min Q1 median Q3 max mean sd n missing
## 1.166667 3.333333 3.833333 4 4 3.60473 0.4964205 296 0tally(~gender, data = acitelli)## gender
## -1 1
## 148 148#tally() can also give you percentages
tally(~gender, data = acitelli, format = "percent")## gender
## -1 1
## 50 50Descriptives split by gender.
favstats(satisfaction ~ gender, data = acitelli)## gender min Q1 median Q3 max mean sd n missing
## 1 -1 1.500000 3.333333 3.833333 4 4 3.591216 0.5300260 148 0
## 2 1 1.166667 3.500000 3.833333 4 4 3.618243 0.4617875 148 0What are the standard deviations of perceived tension by gender?
What is(are) the mode(s) of the self_pos variable?
The mosaic package also has a function for getting the correlation coefficient, it’s called cor(). Using the same format (i.e., formula then data), how would you get the correlation of satisfaction and tension?