# Contents

1. How to load and view data from a CSV file
2. Working with data.frames
3. Measures of central tendency
4. Normal distribution
5. Dispersion measures
6. What to do when your data is not distributed normally

# 1. How to load data from a CSV file (1/2)

In this class, we will be working with data by Balota et al. (2007), provided by Levshina (2015):

• Data in CSV (comma-separated values)
• Experiment in which subjects had to decide whether or not a word is an existing word.
• Columns: Word, Word length, Word frequency, and Mean reaction time

# 1. How to load data from a CSV file (2/2)

To load data, we use the package readr and we load the data straight from the course website. This works the same for local data sets.

library(readr)
dataSet <- read_csv("http://www.jeroenclaes.be/statistics_for_linguistics/datasets/class2_balota_et_al_2007.csv")

# 1. How to create a CSV file

In Excel:

• Save as ‘CSV’ (Comma-separated values)
• Make sure you choose UTF-8 encoding (on Excel 2016 for Mac: CSV UTF-8 Encoding)
• If your Excel is set to Belgian locale, the separator will not be the comma ,, but rather the semicolon ;.
• In that case, you have to use the read_csv2 function to load your data.

# 2.1 Structure of data.frames (1/3)

Now let’s check out how the object dataSet looks like. Recall that we can use str() for this purpose.

str(dataSet)

# 2.1 Structure of data.frames (2/3)

We can also get a nice table-like view of it

View(dataSet)

# 2.1 Structure of data.frames (3/3)

With dim we compute the number of dimensions (columns and rows), with nrow the number of rows, and with ncol the number of columns.

dim(dataSet)
## [1] 100   4
nrow(dataSet)
## [1] 100
ncol(dataSet)
## [1] 4

# 2.3 tail

Print the last N lines from the data.frame

#last 10 lines
tail(dataSet, 10)
## # A tibble: 10 x 4
##    Length  Freq Mean_RT        Word
##     <int> <int>   <dbl>       <chr>
##  1      6   757  641.06      club's
##  2      6   775  775.59      minced
##  3      5 14860  602.09       drain
##  4      7 31679  576.58     justice
##  5     10   181  792.93  numerology
##  6      6   158  876.56      powwow
##  7      8   429  710.63    backdoor
##  8     11    93  875.27 maladjusted
##  9      6  2434  627.11      Elaine
## 10     11   162 1458.75 diacritical

# 2.4 glimpse

For data with many columns, RStudio will omit the columns that do not fit the console window. There’s a function glimpse in the dplyr package that prints the first couple of lines of your data frame as a list.

library(dplyr)
glimpse(dataSet)
## Observations: 100
## Variables: 4
## $Length <int> 8, 10, 7, 6, 12, 12, 3, 11, 11, 5, 6, 6, 11, 4, 11, 8,... ##$ Freq    <int> 131, 82, 0, 592, 2, 9, 14013, 15, 48, 290, 3264, 3523,...
## $Mean_RT <dbl> 819.19, 977.63, 908.22, 766.30, 1125.42, 948.33, 641.6... ##$ Word    <chr> "marveled", "persuaders", "midmost", "crutch", "resusp...

# 2.5 summary

The function summary prints a general overview of the values (minimum, maximum, median,mean,…) in a data.frame.

summary(dataSet)
##      Length           Freq            Mean_RT           Word
##  Min.   : 3.00   Min.   :    0.0   Min.   : 564.2   Length:100
##  1st Qu.: 6.00   1st Qu.:   53.5   1st Qu.: 713.1   Class :character
##  Median : 8.00   Median :  310.5   Median : 784.9   Mode  :character
##  Mean   : 8.23   Mean   : 3350.3   Mean   : 808.3
##  3rd Qu.:10.00   3rd Qu.: 2103.2   3rd Qu.: 905.2
##  Max.   :15.00   Max.   :75075.0   Max.   :1458.8

# 2.6 Dollar-sign operator: extracting a single column

To extract a column (a single vector) from the data.frame, we use the dollar sign operator e.g., dataSet$Word #first 6 lines of just the column Word head(dataSet$Word)
## [1] "marveled"     "persuaders"   "midmost"      "crutch"
## [5] "resuspension" "efflorescent"
#last 10 lines
tail(dataSet$Word, 10) ## [1] "club's" "minced" "drain" "justice" "numerology" ## [6] "powwow" "backdoor" "maladjusted" "Elaine" "diacritical" # 2.7 Extracting multiple columns To extract multiple columns from a data.frame, we simply put a character vector of the column names we want to extract between square brackets, preceded by a comma: #first 6 lines of Word and Freq head(dataSet[, c("Word", "Freq")]) ## # A tibble: 6 x 2 ## Word Freq ## <chr> <int> ## 1 marveled 131 ## 2 persuaders 82 ## 3 midmost 0 ## 4 crutch 592 ## 5 resuspension 2 ## 6 efflorescent 9 # 2.8 Subsetting by rows To extract rows from a data.frame we use a numeric vector of row numbers between square brackets, followed by a comma dataSet[c(1, 2, 5), ] ## # A tibble: 3 x 4 ## Length Freq Mean_RT Word ## <int> <int> <dbl> <chr> ## 1 8 131 819.19 marveled ## 2 10 82 977.63 persuaders ## 3 12 2 1125.42 resuspension # 2.9 Subsetting: the system • You will have noticed that the general pattern for subsetting dataframes is the following: • dataframeName[rows, columns] # 2.10 Conditional selection by row (1/2) • Instead of writing the row numbers ourselves, we can also ask R to compute the indices of rows that fulfill some condition. • The following statement will return the first six rows of all rows for which Freq > 100 head(dataSet[dataSet$Freq > 100, ])
## # A tibble: 6 x 4
##   Length  Freq Mean_RT     Word
##    <int> <int>   <dbl>    <chr>
## 1      8   131  819.19 marveled
## 2      6   592  766.30   crutch
## 3      3 14013  641.67      row
## 4      5   290  654.12    jumpy
## 5      6  3264  583.81   enjoys
## 6      6  3523  667.18   screws

# 2.10 Conditional selection by row (2/2)

• Other useful conditions are:
• is.na() (e.g., dataSet[is.na(dataSet$Freq), ]) - “is empty value” • !is.na() - “is NOT is empty value” • < - “smaller than” • <= - “smaller than or equal to” • > - “larger than” • >= - “larger than or equal to” • %in% (e.g., dataSet[dataSet$Freq %in% c(1,2,4)]) - “one of list”
• %in% with NOT operator (e.g., dataSet[!dataSet$Freq %in% c(1,2,4)]) - “not one of list” # 2.11 Adding columns • Adding columns is just as easy as selecting columns • We simply type the dataframe name, followed by the dollar-sign operator and a name for the column. Then we can assign whatever value we want to the new column dataSet$LogFreq <- log(dataSet$Freq) glimpse(dataSet) ## Observations: 100 ## Variables: 5 ##$ Length  <int> 8, 10, 7, 6, 12, 12, 3, 11, 11, 5, 6, 6, 11, 4, 11, 8,...
## $Freq <int> 131, 82, 0, 592, 2, 9, 14013, 15, 48, 290, 3264, 3523,... ##$ Mean_RT <dbl> 819.19, 977.63, 908.22, 766.30, 1125.42, 948.33, 641.6...
## $Word <chr> "marveled", "persuaders", "midmost", "crutch", "resusp... ##$ LogFreq <dbl> 4.8751973, 4.4067192, -Inf, 6.3835066, 0.6931472, 2.19...

# 3. Distributions and measures of central tendency (1/2)

• In statistics a set of scores/values/numbers is called a distribution.
• The values in the columns Length, Freq, and Mean_RT each constitute a distribution.
• The typical values of a distribution are called its central tendency
• Before we can establish relationships between variables, we have to understand their distributions first!

# 3. Distributions and measures of central tendency (2/2)

• We can describe distributions by calculating the following statistics:
• Mean (average)
• Median
• Quantiles/quartiles
• Mode
• Minimum
• Maximum

Each of these statistics will allow us to say something about the typical values (central tendency) of the distribution.

# 3.1 Mean

• The mean of a distribution equals to the sum of the values of a distribution divided by the number of values in the distribution.
• Summarizes the typical values of the distribution in a single number
#Mean word length
mean(dataSet$Length, na.rm=TRUE) ## [1] 8.23 # 3.2 Median • The median of a distribution equals to the value that marks the middle of the distribution. • If a distribution has 100 values and we rank them from small to large, the median will be the 50th value. median(dataSet$Length, na.rm=TRUE)
## [1] 8

# 3.3 Median

When combined with the mean, the median provides a good idea of the central tendency of a distribution:

• The mean is prone to shift upwards or downwards if there is a single atypical value (called outlier in statistics)
• The median remains identical
#Compare
median(c(1, 2, 3, 4, 5, 6, 7, 8000, 190, 1000000)) # mean = 100821.8
## [1] 5.5
median(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) # mean = 5.5
## [1] 5.5

# 3.4 Quantiles

• A related concept are quantiles (median = 0.5 quantile)
• 0.25 quantile is also called the first quartile, median is second quartile etc.
• Quantiles express that a percentage of the values of the distribution is lower than a particular value
• Useful to study the spread of the values in the distribution
# One quantile
quantile(dataSet$Length, 0.25) ## 25% ## 6 # Multiple quantiles quantile(dataSet$Length, c(0.25, 0.5, 0.75))
## 25% 50% 75%
##   6   8  10

# 3.5 Mode

• Most frequent value in a distribution
• Not widely used
# table() counts the number of times a value occurs in a vector
tab <- table(dataSet$Length) # We sort the table by its values, in decreasing order sorted <- sort(tab, decreasing = TRUE) # We extract the first value, which is the highest. mode <- sorted[1] # The number 8 is the mode. It occurs 16 time mode ## 8 ## 16 # In one line: sort(table(dataSet$Length), decreasing = TRUE)[1]
##  8
## 16

# 3.6 Minimum

• Minimum: lowest value
min(dataSet$Length, na.rm=TRUE) ## [1] 3 # 3.7 Maximum • Maximum: highest value max(dataSet$Length, na.rm=TRUE)
## [1] 15

# 3.8 Calculating most of these at once

By calling summary on a single column, you can calculate many of the statistics discussed here with one command

# 4.2.1.2 Density plots (2/2)

library(ggplot2)
ggplot(dataSet, aes(x=Length))  +
geom_line(stat="density") +

# 4.2.1.4 QQ-plots (1/2)

• Another way to identify if your data are normally distributed are QQ-plots
• If the dots follow the line closely, the data are normally distributed
qqnorm(dataSet$Length) qqline(dataSet$Length)

# 4.2.1.4 QQ-plots (2/2)

qqnorm(dataSet$Length) qqline(dataSet$Length)

# 4.2.2 Shapiro-Wilk test of normality

• If p < 0.05 (don’t worry about what that means for now), the data is not normally distributed
• Plotting is preferred (especially QQ-plots), because this test depends on sample size
shapiro.test(dataSet$Length) ## ## Shapiro-Wilk normality test ## ## data: dataSet$Length
## W = 0.97756, p-value = 0.08559

# 5. Measures of dispersion

• If you data are normally distributed, the mean and the median are good representatives of the typical values of a distribution
• Our analysis of the Freq and Mean_RTvariables have shown that this is not the case for non-normal distributions
• Measures of dispersion express how much variation there is between values of a distribution. They include:
• Range
• Variance
• Standard deviation
• Interquartile Range
• Median absolute deviation
• A boxplot allows you to display dispersion graphically

# 5.1 Range

The range is the difference between the maximum and the minimum value of a distribution

max(dataSet$Length) - min(dataSet$Length)
## [1] 12

# 5.2 Variance

• Sum of the squared deviations from the mean, divided by the sample size minus one.
• The larger the variance, the larger the differences between the individual values and the mean
• Not on the same scale as the data: squared (the sum of the deviations from the mean would be zero without squaring it)
• If the distribution is non-normal, this will lead us to overestimate the variation between the mean and an average score
# Same as:
# sum((dataSet$Length-mean(dataSet$Length))^2 )/(length(dataSet$Length)-1) var(dataSet$Length)
## [1] 6.259697

# 5.3 Standard deviation

• Then standard deviation is the square root of the variance
• By taking the square root of the variance (which are the squared deviations from the mean) we get back to the original units of our data
• Expresses the average deviation from the mean
• If the distribution is non-normal, the standard deviation will lead us to overestimate the variation between the mean and an average score
sd(dataSet$Length) ## [1] 2.501939 # 5.4 Interquartile Range • The interquartile range expresses the difference between the first quartile (the 25% lowest values) and the third quartile (the 75% lowest values) • More robust for non-normal distributions than the variance and standard deviations IQR(dataSet$Length)
## [1] 4

# 5.5 Median absolute deviation

• The median of the deviations between the values in the distribution and the mean
• Based on the median, so it is not sensitive to outliers
mad(dataSet$Length) ## [1] 2.9652 # 5.6 Displaying dispersion graphically (1/2) • A box-and-whisker plot displays the mean and the min and max individual values • If there are outliers, the lines will be longer on one end of the graph library(ggplot2) ggplot(dataSet, aes(x=1, y=Length)) + geom_boxplot() # 5.6 Displaying dispersion graphically (2/2) library(ggplot2) ggplot(dataSet, aes(x=1, y=Length)) + geom_boxplot() # 5.7 How to report it all? When you describe a distribution, you report: • Mean • Median • AND: • If the data are normally distributed, you report the standard deviation • If the data are not normally distributed, you report IQR or mad scores • Ideally you also provide a box-and-whisker plot # 5.8 Excercises # 6. What to do when your data is not distributed normally # 6. What to do when your data is not distributed normally • We have seen that Freq and Mean_RT are not normally distributed. • This does not mean that we cannot analyse them, but we will have to be careful. Most tests do not behave well when the data is not normally distributed or contains outliers (e.g., standard deviation and variance) • In general when you encounter a non-normal distribution you have two options: • Remove outliers • Apply a transformation # 6.1 Remove outliers (1/6) • If the deviation from the normal distribution is caused by a few atypical values, you may exclude these from your analysis • Let’s consider Mean_RT summary(dataSet$Mean_RT)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   564.2   713.1   784.9   808.3   905.2  1458.8

# 6.1 Remove outliers (2/6)

• The summary shows that the interquartile range is 192.1
• The difference between the 3rd quartile and the maximum value is 553.6.
• This suggest that there may be a few exceptionally large values in the right tail of the distribution

# 6.1 Remove outliers (3/6)

• Subjects appear to have had much difficulty with recognizing the top-4 words diacritical, dessertspoon, acquisitiveness, and resuspension
• Maybe these were not the best test items?
Length Freq Mean_RT Word LogFreq
11 162 1459 diacritical 5.088
12 11 1314 dessertspoon 2.398
15 4 1217 acquisitiveness 1.386
12 2 1125 resuspension 0.6931
11 185 1089 denominated 5.22
11 46 1055 euphemistic 3.829

# 6.1 Remove outliers (4/6)

• It is usually a good idea to select outliers based on z-scores
• z-scores express how far the value is removed from the mean in standard deviation units
• Positive z-scores are values that are larger than the mean, negative z-scores are values that are smaller
#(dataSet$Mean_RT - mean(dataSet$Mean_RT))/sd(dataSet$Mean_RT) dataSet$zscores <- scale(dataSet$Mean_RT) summary(dataSet$zscores)
##        V1
##  Min.   :-1.5931
##  1st Qu.:-0.6210
##  Median :-0.1522
##  Mean   : 0.0000
##  3rd Qu.: 0.6328
##  Max.   : 4.2459

# 6.1 Remove outliers (5/6)

• With this score we remove all values that are more than two standard deviations removed from the mean (absolute value of z >= 2) (Urdan, 2010: 18)
• We need the absolute values (we have to ignore their +/- sign), so we wrap the scores with abs

# 6.2 Apply a transformation (1/6)

• The variable Freq cannot be brought to a normal distribution by removing a few outliers
• It has the typical power-law distribution of word frequencies (Zipf’s law):
• A few words occur very often
• The rest of the words have a very low frequency
• Distribution with a very broad range
• Can only be corrected with a transformation

# 6.2 Apply a transformation (2/6)

• Popular transformations are:
• Logarithm:
• Expresses to which power we have to raise the mathematical constant e = 2.718281828459 (the base) to obtain the input number
• log(1000) = 6.907755, because 2.718281828459 ^ 6.907755 = 1000
• Other bases are:
• 10 (log10(1000) = 3; 10 ^ 3 = 1000)
• 2 (log2(1000) = 9.965784; 2 ^ 9.965784 = 1000)
• Fixes power-law distributions
• Exponentiate the base with the logarithm to undo

# 6.2 Apply a transformation (3/6)

• Popular transformations are:
• Logarithm (fixes power-law distributions)
• Square root (fixes positively skewed distributions)
• Square transformation (fixes negatively skewed distributions)
• Reciprocal transformation (1/(vector + 1)), for distributions that look like a j
• Best practice is to check which transformation works best for your data

# 6.2 Apply a transformation (4/6)

• Let’s apply the logarithm to the Freq variable
• The log of 0 is -Inf, which would render our data useless.
• We add 1 to the values of Freq and then we apply the transformation (the log of 1 is 0)

# 6.2 Apply a transformation (5/6)

dataSet$transformedFreq <- log(1+dataSet$Freq)
qqnorm(dataSet$transformedFreq) qqline(dataSet$transformedFreq)

# 6.2 Apply a transformation (6/6)

• Transforming your data makes it harder to interpret the values. E.g., log(dataSet$Freq) is less intuitively understandable than dataSet$Freq