3.4 Data manipulation (optional)
Now that we have the Meta-Analysis data in RStudio, let’s do a few manipulations with the data. These functions might come in handy when were conducting analyses later on.
Going back to the output of the str() function, we see that this also gives us details on the type of column data we have stored in our data. There a different abbreviations signifying different types of data.
| Abbreviation | Type | Description |
|---|---|---|
| num | Numerical | This is all data stored as numbers (e.g. 1.02) |
| chr | Character | This is all data stored as words |
| log | Logical | These are variables which are binary, meaning that they signify that a condition is either TRUE or FALSE |
| factor | Factor | Factors are stored as numbers, with each number signifying a different level of a variable. A possible factor of a variable might be 1 = low, 2 = medium, 3 = high |
3.4.1 Converting to factors
Let’s look at the different kinds of interventions, df$interventioncode. We can have a look at this variable by typing the name of our dataset, then adding the selector $ and then adding the variable we want to have a look at.
This variable is currently a character vector (text). We want it to be a factor: That’s a categorical variable.
To convert this to a factor variable now, we use the factor() function.
df$interventioncode <- factor(df$interventioncode)
df$interventioncodeWe now see that the variable has been converted to a factor with the levels “Acts of Kindness,”Other“, and”Prosocial Spending".
3.4.2 Selecting specific studies
It may often come in handy to select certain studies for further analyses, or to exclude some studies in further analyses (e.g., if they are outliers).
To do this, we can use the filter function in the dplyr package, which is part of the tidyverse package we installed before.
So, let’s load the package first.
library(dplyr)Let’s say we want to do a Meta-Analysis with studies conducted in the USA, or partly conducted in the USA, only. To do this, we need to create a new dataset containing only these studies using the dplyr::filter() function. The dplyr:: part is necessary as there is more than one `filter function in R, and we want to use to use the one of the dplyrpackage.
The R code to store these studies in a new dataset called df_usa looks like this:
df_usa <- dplyr::filter(df, location %in% c("USA", "USA/Korea"))Note that the %in%-Command tells the filter function to search for cases whose location is included in the vector c("USA", "USA/Korea").
Now, let’s have a look at the new data df_usa we just created.
| study_id | effect_id | d | vi | n1i | n2c | sex | age | location | donor | donorcode | interventioniv | interventioncode | control | controlcode | recipients | outcomedv | outcomecode |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Aknin, Fleerackers, et al. (2014) | 6 | 0.38 | 0.0342225 | 60 | 59 | 41 | 19.90 | USA | Typical | Typical | Prosocial Purchase | Prosocial Spending | Personal Purchase | Self Help | Anonymous Sick Children | PANAS | PN Affect |
| Aknin, Fleerackers, et al. (2014) | 7 | 0.44 | 0.0344293 | 60 | 59 | 41 | 19.90 | USA | Typical | Typical | Prosocial Purchase | Prosocial Spending | Personal Purchase | Self Help | Anonymous Sick Children | ORH | Other |
| Donnelly, Grant, et al. (2017) Study 1 | 21 | 0.77 | 0.0373841 | 59 | 56 | 52 | 22.57 | USA | Typical | Typical | Social recycling | Other | Trash/Recycling | Neutral Activity | Unknown lab workers | H | Happiness |
| Donnelly, Grant, et al. (2017) Study 1 | 22 | 0.85 | 0.0369597 | 59 | 59 | 52 | 22.57 | USA | Typical | Typical | Social recycling | Other | Take item | Self Help | Unknown lab workers | H | Happiness |
| Donnelly, Grant, et al. (2017) Study 2b | 23 | 1.25 | 0.0222388 | 107 | 108 | 50 | 37.77 | USA | Typical | Typical | Social recycling | Other | Trash | Neutral Activity | Unknown lab workers | PA | PN Affect |
| Layous, Kurtz, J, et al. (under review) Study 1 | 27 | 0.08 | 0.0288015 | 70 | 69 | 16 | 18.55 | USA | Typical | Typical | AK | Acts of Kindness | Track daily activity | Neutral Activity | Someone known | SHS | Happiness |
Note that the function can also be used for any other type of data and variable. We can also use it to e.g., only select studies where the donors were “typical”:
df_typical <- dplyr::filter(df, donorcode == "Typical")3.4.3 Changing cell values
Sometimes, even when preparing your data in EXCEL, you might want to change values in RStudio once you have imported your data.
To do this, we have to select a cell in our data frame in RStudio. This can be done by adding [x,y] to our dataset name, where x signifies the number of the row we want to select, and y signifies the number of the column.
To see how this works, let’s select a variable using this command first:
df[8,1]## [1] "Aknin, Hamlin, et al. (2012) "We now see the 6th study in our dataframe, and the value of this study for Column 1 (the author name) is displayed. Let’s say we had a typo in this name and want to have it changed. In this case, we have to give this exact cell a new value.
df[8,1] <- "Aknin, et al. (2012)"Let’s check if the name has changed.
df[8,1]## [1] "Aknin, et al. (2012)"You can also use this function to change any other type of data, including numericals and logicals. Only for characters, you have to put the values you want to insert in "".