Basic Descriptive Stats Exercise
Prof. Corker
June 5, 2014
Galton Exercises
Read in the Data
The data we will use are called ‘galton’, contained in the package ‘mosaic’. These data consist of children’s and their parents’ heights.
library(mosaic)data(Galton)
str(Galton)## 'data.frame': 898 obs. of 6 variables:
## $ family: Factor w/ 197 levels "1","10","100",..: 1 1 1 1 108 108 108 108 123 123 ...
## $ father: num 78.5 78.5 78.5 78.5 75.5 75.5 75.5 75.5 75 75 ...
## $ mother: num 67 67 67 67 66.5 66.5 66.5 66.5 64 64 ...
## $ sex : Factor w/ 2 levels "F","M": 2 1 1 1 2 2 1 1 2 1 ...
## $ height: num 73.2 69.2 69 69 73.5 72.5 65.5 65.5 71 68 ...
## $ nkids : int 4 4 4 4 4 4 4 4 2 2 ...#The str command tells us what the basics of our data are.Calculate a mean for children’s heights
mean(height, data=Galton)## [1] 66.76Calculate a standard deviation for children’s heights
sd(height, data=Galton)## [1] 3.583Make a plot of children’s heights
hist(Galton$height, main='', xlab='Height (Inches)', ylab='Frequency',xlim=range(55:80))
New Data Exercises
Get the data from Google Drive
library(mosaic)
library(RCurl)These data are a somewhat close replication of Baumeister et al. (1998). We induce ego depletion by asking participants to count the letter e’s in a passage either using no rules (control) or many rules (depletion condition).
ego_data<-fetchGoogle("https://docs.google.com/spreadsheet/pub?key=0Ampua78j1HupdHpBakRhRVZfQ1RQRVpvbm1ycVpqSlE&output=csv")
str(ego_data)## 'data.frame': 46 obs. of 28 variables:
## $ start_time : Factor w/ 45 levels "1/27/2014 10:10:00",..: 17 18 19 19 20 21 22 23 24 25 ...
## $ stop_time : Factor w/ 46 levels "1/27/2014 10:17:00",..: 17 19 18 21 20 22 35 23 24 25 ...
## $ min_to_complete: num 7.32 7.62 5.25 14.52 8.43 ...
## $ condition : int 1 1 2 2 1 1 2 2 1 1 ...
## $ count : int 54 55 17 21 48 53 36 16 57 50 ...
## $ e_timer : num 114.3 114.8 160.7 174.4 68.8 ...
## $ lett_diff : int 4 3 5 6 2 5 3 5 3 5 ...
## $ lett_conc : int 6 5 6 7 6 6 6 6 6 7 ...
## $ ana1_NIEDM : Factor w/ 19 levels "","DEMIN","denim",..: 5 9 1 11 7 3 1 11 1 11 ...
## $ ana2_LEESTC : Factor w/ 19 levels "","celest","celest ",..: 1 1 1 1 7 13 1 18 1 14 ...
## $ ana3_SDETRE : Factor w/ 20 levels "","desert","Desert",..: 1 5 1 20 12 5 1 7 1 7 ...
## $ ana4_HRBOT : Factor w/ 16 levels "","broth","Broth",..: 1 11 12 11 10 11 7 15 15 11 ...
## $ ana5_NELMO : Factor w/ 19 levels "","lemon","Lemon",..: 4 10 3 18 11 11 11 1 1 2 ...
## $ ana6_MILES : Factor w/ 30 levels "","limes, miles",..: 16 3 1 30 5 26 9 20 14 18 ...
## $ ana_time : num 214 190.9 67.7 316.9 244.2 ...
## $ ana1_score : int 1 2 0 1 1 1 0 1 0 1 ...
## $ ana2_score : int 0 0 0 0 0 2 0 2 0 1 ...
## $ ana3_score : int 0 2 0 0 1 2 0 1 0 1 ...
## $ ana4_score : int 0 1 1 1 1 1 2 2 2 1 ...
## $ ana5_score : int 1 2 1 2 1 1 1 0 0 1 ...
## $ ana6_score : int 2 3 0 3 1 2 2 4 2 3 ...
## $ anagram_sum : int 4 10 2 7 5 9 5 10 4 8 ...
## $ anagram_avg : num 0.667 1.667 0.333 1.167 0.833 ...
## $ ana_clicks : int 12 9 4 13 20 8 8 13 6 17 ...
## $ gender : int 2 2 2 2 2 2 1 2 1 2 ...
## $ age : int 19 22 19 19 21 21 20 21 21 19 ...
## $ race : int 4 6 4 4 5 NA 6 6 4 6 ...
## $ suspic : Factor w/ 42 levels "","all of the anagrams had 'e's in them",..: 42 16 18 6 36 22 1 21 5 37 ...Descriptive Statistics
Let’s use the psych package to get some basic descriptives. I’ll load library(psych) behind the scenes.
describe(ego_data)## vars n mean sd median trimmed mad min
## start_time* 1 46 22.91 13.00 22.50 22.89 16.31 1.00
## stop_time* 2 46 23.50 13.42 23.50 23.50 17.05 1.00
## min_to_complete 3 46 99.40 339.75 9.02 10.59 4.46 2.22
## condition 4 46 1.48 0.51 1.00 1.47 0.00 1.00
## count 5 46 36.07 18.79 42.00 36.47 23.72 9.00
## e_timer 6 46 166.99 84.67 148.25 158.45 68.29 24.50
## lett_diff 7 46 4.22 1.47 5.00 4.32 1.48 1.00
## lett_conc 8 46 5.85 1.07 6.00 6.00 0.00 2.00
## ana1_NIEDM* 9 46 7.57 5.52 6.50 7.21 6.67 1.00
## ana2_LEESTC* 10 46 6.52 6.09 4.50 5.95 5.19 1.00
## ana3_SDETRE* 11 46 6.85 6.14 5.00 6.24 5.93 1.00
## ana4_HRBOT* 12 46 9.65 4.72 11.00 9.95 3.71 1.00
## ana5_NELMO* 13 46 7.74 5.79 6.50 7.37 6.67 1.00
## ana6_MILES* 14 46 13.13 8.62 11.50 12.71 9.64 1.00
## ana_time 15 46 1627.23 9308.00 217.27 236.00 104.52 36.31
## ana1_score 16 46 0.89 0.53 1.00 0.87 0.00 0.00
## ana2_score 17 46 0.57 0.75 0.00 0.47 0.00 0.00
## ana3_score 18 46 0.87 0.72 1.00 0.82 0.00 0.00
## ana4_score 19 46 1.11 0.60 1.00 1.13 0.00 0.00
## ana5_score 20 46 1.13 0.62 1.00 1.16 0.00 0.00
## ana6_score 21 46 1.96 1.11 2.00 1.89 1.48 0.00
## anagram_sum 22 46 6.52 3.13 6.00 6.47 2.97 0.00
## anagram_avg 23 46 1.09 0.52 1.00 1.08 0.49 0.00
## ana_clicks 24 46 11.83 6.09 11.00 11.42 5.19 1.00
## gender 25 46 1.72 0.46 2.00 1.76 0.00 1.00
## age 26 46 19.54 1.19 19.00 19.50 1.48 18.00
## race 27 45 4.16 1.00 4.00 4.16 0.00 1.00
## suspic* 28 46 20.85 12.11 19.50 20.79 14.83 1.00
## max range skew kurtosis se
## start_time* 45.00 44.00 0.02 -1.25 1.92
## stop_time* 46.00 45.00 0.00 -1.28 1.98
## min_to_complete 1662.60 1660.38 3.57 11.54 50.09
## condition 2.00 1.00 0.08 -2.04 0.07
## count 59.00 50.00 -0.14 -1.79 2.77
## e_timer 413.01 388.52 1.01 0.71 12.48
## lett_diff 6.00 5.00 -0.61 -0.87 0.22
## lett_conc 7.00 5.00 -1.91 4.69 0.16
## ana1_NIEDM* 19.00 18.00 0.32 -1.30 0.81
## ana2_LEESTC* 19.00 18.00 0.58 -1.24 0.90
## ana3_SDETRE* 20.00 19.00 0.67 -1.02 0.90
## ana4_HRBOT* 16.00 15.00 -0.68 -0.87 0.70
## ana5_NELMO* 19.00 18.00 0.37 -1.31 0.85
## ana6_MILES* 30.00 29.00 0.35 -1.22 1.27
## ana_time 63373.51 63337.20 6.34 39.10 1372.39
## ana1_score 2.00 2.00 -0.13 0.33 0.08
## ana2_score 2.00 2.00 0.87 -0.74 0.11
## ana3_score 3.00 3.00 0.54 0.14 0.11
## ana4_score 2.00 2.00 -0.04 -0.40 0.09
## ana5_score 2.00 2.00 -0.08 -0.52 0.09
## ana6_score 4.00 4.00 0.46 -0.80 0.16
## anagram_sum 14.00 14.00 0.33 -0.62 0.46
## anagram_avg 2.33 2.33 0.33 -0.62 0.08
## ana_clicks 26.00 25.00 0.62 -0.39 0.90
## gender 2.00 1.00 -0.93 -1.15 0.07
## age 22.00 4.00 0.37 -1.08 0.18
## race 6.00 5.00 -0.57 3.04 0.15
## suspic* 42.00 41.00 0.05 -1.18 1.79Paul’s Stats Data
textbook_data<-fetchGoogle("https://docs.google.com/spreadsheet/pub?key=0Ampua78j1HupdE5OaDFOZjlNbWxwdXRYeHJJWUR0SkE&output=csv")
str(textbook_data)## 'data.frame': 30 obs. of 3 variables:
## $ Price: num 4.25 5.95 7 6.5 7 ...
## $ Pages: int 57 194 51 104 294 140 336 150 600 91 ...
## $ Year : int 2006 1972 2004 2005 2002 1991 1973 2003 2004 1997 ...